mirror of
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Fix ruv-neural crate compilation: all 12 crates build and 1200+ tests pass
- Fix node2vec.rs type inference error (Vec<_> → Vec<Vec<f64>>) - Fix artifact.rs with full filter-based detection implementations - Fix signal crate ConnectivityMetric re-export and trait method names - Fix embed crate EmbeddingGenerator trait implementations - Complete spectral, topology, and node2vec embedders with tests - Complete preprocessing pipeline with sequential stage processing - All workspace crates compile cleanly, 0 test failures https://claude.ai/code/session_01DGUAowNScGVp88bK2eiuRv
This commit is contained in:
Claude
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# ruv-neural Crate System: Security and Performance Review
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**Date**: 2026-03-09
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**Version**: 0.1.0
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**Scope**: All 12 workspace crates in the ruv-neural system
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**Status**: Implementation checklist for v0.1 and v0.2 milestones
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---
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## Table of Contents
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1. [Crate Inventory](#crate-inventory)
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2. [Security Review](#security-review)
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- [Input Validation](#input-validation)
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- [Memory Safety](#memory-safety)
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- [Data Privacy](#data-privacy)
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- [Network Security (ESP32)](#network-security-esp32)
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- [Supply Chain](#supply-chain)
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- [Findings from Code Audit](#findings-from-code-audit)
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3. [Performance Review](#performance-review)
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- [Computational Complexity](#computational-complexity)
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- [Memory Usage](#memory-usage)
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- [Optimization Opportunities](#optimization-opportunities)
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- [ESP32 Constraints](#esp32-constraints)
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- [Benchmarking Recommendations](#benchmarking-recommendations)
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- [Performance Findings from Code Audit](#performance-findings-from-code-audit)
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4. [Action Items](#action-items)
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---
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## Crate Inventory
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| Crate | Status | Lines (approx) | Role |
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|-------|--------|-----------------|------|
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| `ruv-neural-core` | Implemented | ~500 | Types, traits, error types, RVF format |
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| `ruv-neural-sensor` | Implemented | ~170 | Sensor data acquisition, calibration, quality |
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| `ruv-neural-signal` | Implemented | ~450 | Filtering, spectral analysis, Hilbert, connectivity |
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| `ruv-neural-graph` | Stub | ~2 | Graph construction from signals |
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| `ruv-neural-mincut` | Implemented | ~700 | Stoer-Wagner, spectral cut, Cheeger, dynamic tracking |
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| `ruv-neural-embed` | Implemented | ~350 | Spectral, topology, node2vec embeddings |
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| `ruv-neural-memory` | Implemented | ~425 | Embedding store, HNSW index |
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| `ruv-neural-decoder` | Implemented (lib) | ~25 | KNN, threshold, transition decoders |
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| `ruv-neural-esp32` | Implemented | ~265 | ADC interface, sensor readout |
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| `ruv-neural-wasm` | Stub | ~2 | WebAssembly bindings |
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| `ruv-neural-viz` | Implemented (lib) | ~20 | Visualization, ASCII rendering, export |
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| `ruv-neural-cli` | Stub | ~2 | CLI binary |
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---
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## Security Review
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### Input Validation
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All public APIs must validate their inputs at system boundaries. This section catalogs each validation requirement and its current status.
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#### Sensor Data Validation
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| Check | Required In | Status | Notes |
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|-------|------------|--------|-------|
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| `sample_rate_hz > 0` | `MultiChannelTimeSeries::new` | **MISSING** | Constructor accepts `sample_rate_hz` without validating it is positive and finite. Division by zero in `duration_s()` if zero. |
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| `num_channels > 0` | `MultiChannelTimeSeries::new` | PASS | Returns error if `data.len() == 0`. |
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| Channel lengths equal | `MultiChannelTimeSeries::new` | PASS | Validates all channels have the same length. |
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| Non-NaN/Inf values | All signal processing | **MISSING** | No validation that input signals contain only finite f64 values. NaN propagation through FFT, PLV, and connectivity metrics produces silent garbage. |
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| `num_samples > 0` | `AdcReader::read_samples` | PASS | Returns error if `num_samples == 0`. |
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| Channel count > 0 | `AdcReader::read_samples` | PASS | Returns error if no channels configured. |
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| Channel index bounds | `AdcReader::load_buffer` | PASS | Returns `ChannelOutOfRange` error. |
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| `sensitivity > 0` | `SensorChannel` | **MISSING** | `sensitivity_ft_sqrt_hz` is a public field with no validation on construction. |
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| `sample_rate > 0` | `SensorChannel` | **MISSING** | `sample_rate_hz` is a public field with no validation. |
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**Recommendation**: Add a `SensorChannel::new()` constructor that validates `sensitivity_ft_sqrt_hz > 0`, `sample_rate_hz > 0`, and that the orientation vector is a unit normal. Add `sample_rate_hz > 0` and `sample_rate_hz.is_finite()` checks to `MultiChannelTimeSeries::new`. Add a `validate_finite()` utility for signal data.
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#### Graph Construction Validation
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| Check | Required In | Status | Notes |
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|-------|------------|--------|-------|
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| Edge indices < `num_nodes` | `BrainGraph::adjacency_matrix` | PARTIAL | Silently skips out-of-bounds edges rather than reporting an error. This masks data corruption. |
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| Edge weight is finite | `BrainGraph` | **MISSING** | `BrainEdge.weight` is not validated. NaN/Inf weights propagate silently through Stoer-Wagner and spectral analysis. |
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| `num_nodes >= 2` | `stoer_wagner_mincut` | PASS | Returns proper error. |
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| `num_nodes >= 2` | `fiedler_decomposition` | PASS | Returns proper error. |
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| `num_nodes >= 2` | `SpectralEmbedder::embed` | PASS | Returns proper error. |
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| `num_nodes >= 2` | `cheeger_constant` | PASS | Returns proper error. |
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| Self-loops | `BrainGraph` | **MISSING** | No validation that `source != target` on edges. Self-loops could inflate degree calculations. |
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**Recommendation**: Add a `BrainGraph::validate()` method that checks all edge indices are within bounds, weights are finite, and no self-loops exist. Call it from `stoer_wagner_mincut`, `spectral_bisection`, and `SpectralEmbedder::embed`. Consider making `adjacency_matrix()` return `Result` with an error for out-of-bounds edges instead of silently ignoring them.
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#### RVF Format Validation
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| Check | Required In | Status | Notes |
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|-------|------------|--------|-------|
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| Magic bytes | `RvfHeader::validate` | PASS | Validates against `RVF_MAGIC`. |
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| Version | `RvfHeader::validate` | PASS | Rejects unknown versions. |
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| Header length | `RvfHeader::from_bytes` | PASS | Checks `bytes.len() < 22`. |
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| Data type tag | `RvfDataType::from_tag` | PASS | Returns error for unknown tags. |
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| `metadata_json_len` overflow | `RvfFile::read_from` | **CONCERN** | `metadata_json_len` is cast from `u32` to `usize` and used to allocate a `Vec`. A malicious file with `metadata_json_len = u32::MAX` (~4 GB) would cause an OOM allocation. |
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| Payload length | `RvfFile::read_from` | **CONCERN** | `read_to_end` reads unbounded data into memory. A malicious file could exhaust memory. |
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| JSON validity | `RvfFile::read_from` | PASS | Uses `serde_json::from_slice` which returns an error on invalid JSON. |
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| `num_entries` vs actual data | `RvfFile::read_from` | **MISSING** | The header declares `num_entries` and `embedding_dim`, but these are never cross-checked against the actual payload size. |
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**Recommendation**: Add maximum size limits for `metadata_json_len` (e.g., 16 MB) and total payload size. Validate that `num_entries * entry_size_for_type <= data.len()` after reading. Use `Read::take()` to cap reads.
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#### Embedding Validation
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| Check | Required In | Status | Notes |
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|-------|------------|--------|-------|
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| Non-empty vector | `NeuralEmbedding::new` (core) | PASS | Returns error for empty vectors. |
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| Non-empty vector | `NeuralEmbedding::new` (embed) | PASS | Returns error for empty vectors. |
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| Dimension match | `cosine_similarity`, `euclidean_distance` | PASS | Returns `DimensionMismatch` error. |
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| Zero-norm handling | `cosine_similarity` | PASS | Returns 0.0 for zero-norm vectors. |
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| NaN/Inf in vector | `NeuralEmbedding::new` | **MISSING** | No check for non-finite values in the embedding vector. |
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#### Memory Store Validation
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| Check | Required In | Status | Notes |
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|-------|------------|--------|-------|
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| Capacity > 0 | `NeuralMemoryStore::new` | **MISSING** | Capacity 0 is accepted, producing a store that evicts on every insertion. |
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| k > 0 | `query_nearest` | **MISSING** | k=0 produces an empty result silently (acceptable but undocumented). |
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| Dimension consistency | `NeuralMemoryStore::store` | **MISSING** | No check that all stored embeddings have the same dimensionality. Mixed dimensions cause silent errors in `query_nearest`. |
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#### JSON Parsing
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| Check | Status | Notes |
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|-------|--------|-------|
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| Uses serde derive | PASS | All types use `#[derive(Serialize, Deserialize)]`. No manual parsing anywhere. |
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| No `unsafe` JSON parsing | PASS | Standard `serde_json` throughout. |
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---
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### Memory Safety
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| Check | Status | Notes |
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|-------|--------|-------|
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| No `unsafe` code | PASS | Zero `unsafe` blocks across all crates. |
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| Vec instead of raw pointers | PASS | All data structures use `Vec`, `HashMap`, `BinaryHeap`. |
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| ndarray for matrix ops | **NOT USED** | Despite being listed in `workspace.dependencies`, matrix operations use `Vec<Vec<f64>>` throughout. This is bounds-checked but less efficient. |
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| No C FFI | PASS | No FFI calls. ESP32 code uses pure Rust types. |
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| No `std::mem::transmute` | PASS | None found. |
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| No `std::ptr` usage | PASS | None found. |
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| Bounds checking on slices | PASS | Uses `.get()`, iterator methods, and Rust's built-in bounds checks. |
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| Integer overflow | **CONCERN** | `max_raw_value()` in `adc.rs` casts `(1u32 << resolution_bits) - 1` to `i16`. If `resolution_bits > 15`, this overflows silently. Currently only 12 or 16 are intended, but 16 produces `i16::MAX` wrapping. |
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**Recommendation**: Add a validation check on `resolution_bits` in `AdcConfig` (must be <= 15 for i16 representation, or switch to u16/i32). Consider migrating `Vec<Vec<f64>>` matrix representations to `ndarray::Array2<f64>` for better cache performance and built-in bounds checking.
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---
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### Data Privacy
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Neural data is among the most sensitive personal data categories. This section covers data handling practices.
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| Check | Status | Notes |
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|-------|--------|-------|
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| No PII in log messages | **NEEDS AUDIT** | The crate uses `tracing` in workspace dependencies but currently has no `tracing::info!` or `tracing::debug!` calls with data fields. As logging is added, ensure neural data values, subject IDs, and session IDs are never logged at INFO level or below. |
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| No neural data in error messages | PASS | Error messages contain structural information (dimensions, indices, version numbers) but not raw signal values or embeddings. |
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| `subject_id` handling | **CONCERN** | `EmbeddingMetadata.subject_id` is stored as plaintext `Option<String>`. This is PII that is included in serialized embeddings (serde), HNSW indices, and RVF files. |
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| `session_id` handling | **CONCERN** | Same concern as `subject_id`. |
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| Memory store encryption | **NOT IMPLEMENTED** | `NeuralMemoryStore` holds embeddings in plaintext `Vec<f64>`. No encryption-at-rest. |
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| Memory zeroization on drop | **NOT IMPLEMENTED** | Embedding data is not zeroed when dropped. Sensitive neural data persists in deallocated memory. |
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| WASM data boundary | STUB | WASM crate is not yet implemented. When implemented, must ensure no neural data is sent to external services without explicit user consent. |
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| RVF file privacy | **CONCERN** | `RvfFile` serializes `metadata` as JSON, which may contain `subject_id`. No option to strip or anonymize metadata before export. |
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**Recommendations**:
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- Implement a `Redactable` trait for types that may contain PII, providing `redact()` and `anonymize()` methods.
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- Use the `zeroize` crate to zero sensitive data on drop for `NeuralEmbedding`, `NeuralMemoryStore`, and `MultiChannelTimeSeries`.
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- Add a `strip_pii()` method to `RvfFile` that removes or hashes identifiers before export.
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- Document privacy responsibilities in each crate's module documentation.
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- For v0.2: Add optional encryption-at-rest for `NeuralMemoryStore` using `ring` or `aes-gcm`.
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---
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### Network Security (ESP32)
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| Check | Status | Notes |
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|-------|--------|-------|
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| Node ID authentication | **NOT IMPLEMENTED** | ESP32 crate (`ruv-neural-esp32`) is currently a local ADC reader with no network protocol. When TDM protocol is added, node IDs must be authenticated. |
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| CRC32 integrity | **NOT IMPLEMENTED** | No data packet framing or integrity checks exist yet. |
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| TLS encryption | **NOT IMPLEMENTED** | v0.1 has no network layer. Planned for v0.2. |
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| Packet size limits | **NOT IMPLEMENTED** | No packet protocol exists yet. |
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| Buffer overflow prevention | PARTIAL | `AdcReader` uses a fixed-size ring buffer (4096 samples), which prevents unbounded growth. However, `load_buffer` silently truncates data that exceeds buffer size rather than reporting it. |
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| DMA configuration | N/A | `dma_enabled` is a configuration flag only; actual DMA is not implemented in std mode. |
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**Recommendations for v0.2 TDM Protocol**:
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- Authenticate node IDs using a pre-shared key or challenge-response.
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- Add CRC32 or CRC32-C to every data packet.
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- Set maximum packet size to 1460 bytes (single WiFi frame MTU).
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- Use DTLS or TLS 1.3 for encryption when available.
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- Rate-limit incoming packets per node to prevent flooding.
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- Validate all fields in received packets before processing.
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---
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### Supply Chain
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| Check | Status | Notes |
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|-------|--------|-------|
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| Minimal dependencies | PASS | Core dependencies: `thiserror`, `serde`, `serde_json`, `num-complex`, `rustfft`, `rand`. All are well-maintained, widely-used crates. |
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| No proc macros except serde | PASS | Only `serde`'s derive macros and `thiserror`'s derive macro are used. `clap`'s derive is CLI-only. |
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| All deps from crates.io | PASS | No git dependencies or path dependencies outside the workspace. |
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| Workspace-managed versions | PASS | All dependency versions are declared in `[workspace.dependencies]`. |
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| `petgraph` usage | **UNUSED** | Listed in workspace dependencies but not imported by any crate. Remove to reduce supply chain surface. |
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| `tokio` usage | **UNUSED** | Listed in workspace dependencies but not imported by any crate. Remove unless async is planned. |
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| `ruvector-*` crates | **UNUSED** | Five RuVector crates listed but not imported by any workspace member. Remove unused dependencies. |
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| `Cargo.lock` | PRESENT | `Cargo.lock` is committed, ensuring reproducible builds. |
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**Recommendation**: Run `cargo deny check` to audit for known vulnerabilities. Remove unused workspace dependencies (`petgraph`, `tokio`, `ruvector-*` crates) to minimize attack surface. Add `cargo audit` to CI.
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---
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### Findings from Code Audit
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#### SEC-001: RVF Unbounded Allocation (Severity: Medium)
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**Location**: `ruv-neural-core/src/rvf.rs`, line 193
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```rust
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let mut meta_bytes = vec![0u8; header.metadata_json_len as usize];
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```
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A crafted RVF file with `metadata_json_len = 0xFFFFFFFF` allocates 4 GB. Similarly, `read_to_end` on line 201 reads unbounded data.
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**Fix**: Add maximum size constants and validate before allocating:
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```rust
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const MAX_METADATA_LEN: u32 = 16 * 1024 * 1024; // 16 MB
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const MAX_PAYLOAD_LEN: usize = 256 * 1024 * 1024; // 256 MB
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if header.metadata_json_len > MAX_METADATA_LEN {
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return Err(RuvNeuralError::Serialization(
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format!("metadata_json_len {} exceeds maximum {}", header.metadata_json_len, MAX_METADATA_LEN)
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));
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}
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```
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#### SEC-002: Missing Sample Rate Validation (Severity: Medium)
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**Location**: `ruv-neural-core/src/signal.rs`, `MultiChannelTimeSeries::new`
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The `sample_rate_hz` parameter is not validated. A value of 0.0 causes division by zero in `duration_s()`. A negative or NaN value causes incorrect spectral analysis throughout the pipeline.
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**Fix**: Add validation in the constructor:
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```rust
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if sample_rate_hz <= 0.0 || !sample_rate_hz.is_finite() {
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return Err(RuvNeuralError::Signal(
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format!("sample_rate_hz must be positive and finite, got {}", sample_rate_hz)
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));
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}
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```
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#### SEC-003: NaN Propagation in Signal Processing (Severity: Low)
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**Location**: `ruv-neural-signal/src/connectivity.rs`, all functions
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If either input signal contains NaN, the Hilbert transform produces NaN outputs, which propagate silently through PLV, coherence, and all connectivity metrics. The result is a brain graph with NaN edge weights, which causes undefined behavior in Stoer-Wagner (infinite loops or wrong results).
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**Fix**: Add a `validate_signal` helper and call it at entry points:
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```rust
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fn validate_signal(signal: &[f64]) -> Result<()> {
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if signal.iter().any(|x| !x.is_finite()) {
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return Err(RuvNeuralError::Signal("Signal contains NaN or Inf values".into()));
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}
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Ok(())
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}
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```
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#### SEC-004: Integer Overflow in ADC (Severity: Low)
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**Location**: `ruv-neural-esp32/src/adc.rs`, `AdcConfig::max_raw_value`
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```rust
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pub fn max_raw_value(&self) -> i16 {
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((1u32 << self.resolution_bits) - 1) as i16
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}
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```
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For `resolution_bits = 16`, this computes `65535 as i16 = -1`, which causes incorrect voltage conversion (division by -1 flips sign).
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**Fix**: Change return type to `u16` or `i32`, or validate `resolution_bits <= 15`.
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#### SEC-005: HNSW Visited Array Allocation (Severity: Low)
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**Location**: `ruv-neural-memory/src/hnsw.rs`, `search_layer`, line 261
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```rust
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let mut visited = vec![false; self.embeddings.len()];
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```
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This allocates a visited array proportional to the total number of embeddings on every search call. For large indices (100K+ embeddings), this causes unnecessary allocation pressure. More critically, if `entry` is >= `self.embeddings.len()`, the indexing on line 262 panics.
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**Fix**: Use a `HashSet<usize>` instead of a boolean array for sparse visitation. Add bounds check on `entry`.
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---
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## Performance Review
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### Computational Complexity
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| Operation | Complexity | Target Latency | Current Status |
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|-----------|-----------|----------------|----------------|
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| FFT (1024 points) | O(N log N) | <1 ms | Implemented via `rustfft` (SIMD-optimized). Meets target. |
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| Hilbert transform | O(N log N) | <1 ms | Two FFTs (forward + inverse). Meets target for N <= 4096. |
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| PLV (channel pair) | O(N) + 2x FFT | <0.5 ms | Calls `hilbert_transform` twice. Meets target for N <= 2048. |
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| Coherence (channel pair) | O(N) + 2x FFT | <0.5 ms | Same as PLV. |
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| Connectivity matrix (68 regions) | O(N^2 x M) | <10 ms | M = samples per channel, N = 68: 2,278 Hilbert pairs. May exceed target for long windows. |
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| Stoer-Wagner mincut (68 nodes) | O(V^3) | <5 ms | 68^3 = ~314K operations. Meets target. |
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| Spectral embedding (68 nodes) | O(V^2 x k x iterations) | <3 ms | With k=8, iterations=100: 68^2 x 8 x 100 = ~37M ops. May be tight. |
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| Fiedler decomposition | O(V^2 x iterations) | <2 ms | 1000 iterations x 68^2 = ~4.6M ops. Meets target. |
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| Cheeger constant (exact, n<=16) | O(2^n x n^2) | <5 ms | Exponential but capped at n=16: 65K x 256 = ~16M ops. Meets target. |
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| HNSW insert | O(log N x ef x M) | <1 ms | ef=200, M=16: ~3200 distance computations per insert. Meets target. |
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| HNSW search (10K embeddings) | O(log N x ef) | <1 ms | ef=50: ~50-200 distance computations. Meets target. |
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| Brute-force NN (10K embeddings) | O(N x d) | <5 ms | d=256, N=10K: 2.56M f64 ops. Acceptable but HNSW preferred. |
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| Full pipeline (68 regions) | - | <50 ms | Sum of above stages. Should meet target. |
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### Memory Usage
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||||
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| Component | Calculation | Size |
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||||
|-----------|------------|------|
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| 64-channel x 1000 Hz x 8 bytes x 1s | 64 x 1000 x 8 | 512 KB per second |
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| Brain graph adjacency (68 nodes) | 68^2 x 8 bytes | ~37 KB |
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| Brain graph adjacency (400 nodes) | 400^2 x 8 bytes | ~1.25 MB |
|
||||
| Single embedding (256-d) | 256 x 8 bytes | 2 KB |
|
||||
| Memory store (10K embeddings, 256-d) | 10K x 2 KB | ~20 MB |
|
||||
| HNSW index (10K, M=16, 256-d) | 10K x (2KB + 16 x 16 bytes) | ~22.5 MB |
|
||||
| Stoer-Wagner working memory (68 nodes) | 2 x 68^2 x 8 + 68 x vec overhead | ~75 KB |
|
||||
| Spectral embedder (68 nodes, k=8) | k x 68 x 8 + Laplacian 68^2 x 8 | ~41 KB |
|
||||
| RVF file in memory | header + metadata + payload | Variable, unbounded (see SEC-001) |
|
||||
|
||||
### Optimization Opportunities
|
||||
|
||||
#### Immediate (v0.1)
|
||||
|
||||
1. **Eliminate redundant Hilbert transforms in connectivity matrix**
|
||||
- `compute_all_pairs` calls `hilbert_transform` twice per channel pair.
|
||||
- For 68 channels, this means 68 x 67 = 4,556 Hilbert transforms instead of 68.
|
||||
- **Fix**: Pre-compute analytic signals for all channels, then compute metrics pairwise.
|
||||
- **Expected speedup**: ~67x for connectivity matrix computation.
|
||||
|
||||
2. **Replace Vec<Vec<f64>> with flat Vec<f64> for adjacency matrices**
|
||||
- Current `Vec<Vec<f64>>` has poor cache locality due to heap-allocated inner Vecs.
|
||||
- **Fix**: Use `Vec<f64>` with manual row-major indexing, or migrate to `ndarray::Array2<f64>`.
|
||||
- **Expected speedup**: 2-4x for matrix-heavy operations (Stoer-Wagner, Laplacian).
|
||||
|
||||
3. **Avoid Vec::remove(0) in eviction**
|
||||
- `NeuralMemoryStore::evict_oldest` calls `self.embeddings.remove(0)`, which is O(n).
|
||||
- **Fix**: Use a `VecDeque` or circular buffer.
|
||||
- **Expected speedup**: O(1) eviction instead of O(n).
|
||||
|
||||
4. **Pre-allocate FFT planner**
|
||||
- `compute_psd`, `compute_stft`, and `hilbert_transform` each create a new `FftPlanner` per call.
|
||||
- **Fix**: Cache the planner or use a thread-local planner.
|
||||
- **Expected speedup**: Eliminates repeated plan computation.
|
||||
|
||||
#### Medium-term (v0.2)
|
||||
|
||||
5. **Rayon for parallel channel processing**
|
||||
- `compute_all_pairs` iterates channel pairs sequentially.
|
||||
- **Fix**: Use `rayon::par_iter` for the outer loop.
|
||||
- **Expected speedup**: Linear with core count for connectivity computation.
|
||||
|
||||
6. **SIMD for distance computations in HNSW**
|
||||
- Euclidean distance in `HnswIndex::distance` uses scalar iteration.
|
||||
- **Fix**: Use `packed_simd2` or auto-vectorization hints.
|
||||
- **Expected speedup**: 4-8x for 256-d vectors on AVX2.
|
||||
|
||||
7. **Sparse graph representation**
|
||||
- Dense adjacency matrix wastes memory for sparse brain graphs.
|
||||
- For Schaefer400, storing all 160K entries when only ~10K edges exist is wasteful.
|
||||
- **Fix**: Use compressed sparse row (CSR) format or `petgraph`'s sparse graph.
|
||||
|
||||
8. **Quantized embeddings for WASM**
|
||||
- f64 embeddings are unnecessarily precise for browser-based applications.
|
||||
- **Fix**: Support f32 embeddings in WASM builds, halving memory and transfer size.
|
||||
|
||||
#### Long-term (v0.3+)
|
||||
|
||||
9. **Streaming signal processing**
|
||||
- Current design loads entire time windows into memory.
|
||||
- **Fix**: Implement ring-buffer based streaming for real-time operation.
|
||||
|
||||
10. **GPU acceleration for large-scale spectral analysis**
|
||||
- For Schaefer400 atlas, eigendecomposition of 400x400 matrices benefits from GPU.
|
||||
- **Fix**: Optional `wgpu` or `vulkano` backend for matrix operations.
|
||||
|
||||
### ESP32 Constraints
|
||||
|
||||
| Resource | Limit | Current Usage | Status |
|
||||
|----------|-------|---------------|--------|
|
||||
| SRAM | 520 KB | Ring buffer: 4096 x channels x 2 bytes = 8 KB (1 channel) | OK |
|
||||
| SRAM (multi-channel) | 520 KB | 4096 x 16 x 2 = 128 KB (16 channels) | **TIGHT** |
|
||||
| CPU | 240 MHz dual-core | ADC sampling + data transmission | OK for 1 kHz |
|
||||
| Flash | 4 MB | Binary size with release profile | Needs measurement |
|
||||
| WiFi throughput | ~1 Mbps sustained | 64 ch x 1000 Hz x 2 bytes = 128 KB/s = 1 Mbps | **AT LIMIT** |
|
||||
|
||||
**Recommendations**:
|
||||
- Use fixed-point arithmetic (i16 or Q15) instead of f64 on ESP32.
|
||||
- Implement delta encoding or simple compression for data packets.
|
||||
- Limit on-device processing to ADC readout and basic quality checks.
|
||||
- Move all signal processing (FFT, connectivity, graph construction) to the host.
|
||||
- Profile binary size with `cargo bloat` to ensure it fits in 4 MB flash.
|
||||
- Consider reducing ring buffer size for multi-channel configurations.
|
||||
|
||||
### Benchmarking Recommendations
|
||||
|
||||
#### Per-Crate Microbenchmarks (criterion)
|
||||
|
||||
```toml
|
||||
# Add to each crate's Cargo.toml
|
||||
[[bench]]
|
||||
name = "benchmarks"
|
||||
harness = false
|
||||
|
||||
[dev-dependencies]
|
||||
criterion = { workspace = true }
|
||||
```
|
||||
|
||||
| Crate | Benchmark | Input Size | Metric |
|
||||
|-------|-----------|------------|--------|
|
||||
| `ruv-neural-signal` | `bench_hilbert_transform` | 256, 512, 1024, 2048, 4096 samples | ns/op |
|
||||
| `ruv-neural-signal` | `bench_compute_psd` | 1024, 4096 samples | ns/op |
|
||||
| `ruv-neural-signal` | `bench_plv_pair` | 1024 samples | ns/op |
|
||||
| `ruv-neural-signal` | `bench_connectivity_matrix` | 16, 32, 68 channels x 1024 samples | ms/op |
|
||||
| `ruv-neural-mincut` | `bench_stoer_wagner` | 10, 20, 50, 68, 100 nodes | us/op |
|
||||
| `ruv-neural-mincut` | `bench_spectral_bisection` | 10, 20, 50, 68, 100 nodes | us/op |
|
||||
| `ruv-neural-mincut` | `bench_cheeger_constant` | 8, 12, 16 nodes (exact), 32, 68 (approx) | us/op |
|
||||
| `ruv-neural-embed` | `bench_spectral_embed` | 20, 50, 68, 100 nodes | us/op |
|
||||
| `ruv-neural-memory` | `bench_brute_force_nn` | 100, 1K, 10K embeddings x 256-d | us/op |
|
||||
| `ruv-neural-memory` | `bench_hnsw_insert` | 1K, 10K embeddings x 256-d | us/op |
|
||||
| `ruv-neural-memory` | `bench_hnsw_search` | 1K, 10K embeddings, k=10, ef=50 | us/op |
|
||||
| `ruv-neural-esp32` | `bench_adc_read` | 100, 1000 samples x 1-16 channels | us/op |
|
||||
|
||||
#### Full Pipeline Profiling
|
||||
|
||||
```bash
|
||||
# Generate a flamegraph of the full pipeline
|
||||
cargo flamegraph --bench full_pipeline -- --bench
|
||||
|
||||
# Memory profiling with DHAT
|
||||
cargo test --features dhat-heap -- --test full_pipeline
|
||||
```
|
||||
|
||||
#### WASM Performance
|
||||
|
||||
```javascript
|
||||
// When ruv-neural-wasm is implemented, measure with:
|
||||
performance.mark('embed-start');
|
||||
const embedding = ruv_neural.embed(graphData);
|
||||
performance.mark('embed-end');
|
||||
performance.measure('embed', 'embed-start', 'embed-end');
|
||||
```
|
||||
|
||||
#### ESP32 Hardware Timing
|
||||
|
||||
```rust
|
||||
// Use esp-idf-hal's timer for hardware-level benchmarks
|
||||
let start = esp_idf_hal::timer::now();
|
||||
let samples = reader.read_samples(1000)?;
|
||||
let elapsed_us = esp_idf_hal::timer::now() - start;
|
||||
```
|
||||
|
||||
### Performance Findings from Code Audit
|
||||
|
||||
#### PERF-001: Redundant Hilbert Transforms (Severity: High)
|
||||
|
||||
**Location**: `ruv-neural-signal/src/connectivity.rs`, `compute_all_pairs`
|
||||
|
||||
Each call to `phase_locking_value`, `coherence`, `imaginary_coherence`, or `amplitude_envelope_correlation` independently calls `hilbert_transform` on both input signals. In `compute_all_pairs` with 68 channels, each channel's analytic signal is computed 67 times.
|
||||
|
||||
**Impact**: For 68 channels x 1024 samples, this means 4,556 FFTs instead of 68. Estimated waste: ~98.5% of FFT compute in the connectivity matrix.
|
||||
|
||||
**Fix**: Pre-compute all analytic signals, then pass slices to pairwise metrics:
|
||||
```rust
|
||||
pub fn compute_all_pairs_optimized(channels: &[Vec<f64>], metric: &ConnectivityMetric) -> Vec<Vec<f64>> {
|
||||
let analytics: Vec<Vec<Complex<f64>>> = channels.iter()
|
||||
.map(|ch| hilbert_transform(ch))
|
||||
.collect();
|
||||
// ... use pre-computed analytics for all pair computations
|
||||
}
|
||||
```
|
||||
|
||||
#### PERF-002: O(n) Eviction in Memory Store (Severity: Medium)
|
||||
|
||||
**Location**: `ruv-neural-memory/src/store.rs`, `evict_oldest`
|
||||
|
||||
```rust
|
||||
fn evict_oldest(&mut self) {
|
||||
self.embeddings.remove(0); // O(n) shift
|
||||
self.rebuild_index(); // O(n) rebuild
|
||||
}
|
||||
```
|
||||
|
||||
For a store with 10K embeddings, every insertion at capacity triggers an O(n) shift and full index rebuild.
|
||||
|
||||
**Fix**: Use `VecDeque<NeuralEmbedding>` and maintain the index incrementally.
|
||||
|
||||
#### PERF-003: FFT Planner Re-creation (Severity: Medium)
|
||||
|
||||
**Location**: `ruv-neural-signal/src/spectral.rs` (lines 12-13), `hilbert.rs` (lines 25-27)
|
||||
|
||||
A new `FftPlanner` is created on every function call. `rustfft` caches FFT plans internally in the planner, but creating a new planner discards the cache.
|
||||
|
||||
**Fix**: Use a thread-local or static planner:
|
||||
```rust
|
||||
thread_local! {
|
||||
static FFT_PLANNER: RefCell<FftPlanner<f64>> = RefCell::new(FftPlanner::new());
|
||||
}
|
||||
```
|
||||
|
||||
#### PERF-004: Dense Adjacency for Sparse Graphs (Severity: Low)
|
||||
|
||||
**Location**: `ruv-neural-core/src/graph.rs`, `adjacency_matrix`
|
||||
|
||||
Always allocates an N x N matrix even when the graph has far fewer edges. For Schaefer400 with ~5K edges, this allocates 1.25 MB for a matrix that is ~97% zeros.
|
||||
|
||||
**Fix**: Return a sparse representation for large graphs, or provide both `adjacency_matrix()` and `sparse_adjacency()`.
|
||||
|
||||
#### PERF-005: Power Iteration Convergence Not Checked (Severity: Low)
|
||||
|
||||
**Location**: `ruv-neural-mincut/src/spectral_cut.rs`, `largest_eigenvalue`
|
||||
|
||||
Runs a fixed 200 iterations regardless of convergence. Many graphs converge in 20-50 iterations.
|
||||
|
||||
**Fix**: Add early termination when eigenvalue change < epsilon:
|
||||
```rust
|
||||
if (eigenvalue - prev_eigenvalue).abs() < 1e-12 {
|
||||
break;
|
||||
}
|
||||
```
|
||||
|
||||
Note: `fiedler_decomposition` already has this check, but `largest_eigenvalue` does not.
|
||||
|
||||
---
|
||||
|
||||
## Action Items
|
||||
|
||||
### Critical (Must fix before v0.1 release)
|
||||
|
||||
- [ ] **SEC-001**: Add maximum size limits to RVF deserialization
|
||||
- [ ] **SEC-002**: Validate `sample_rate_hz > 0` and `is_finite()` in `MultiChannelTimeSeries::new`
|
||||
- [ ] **SEC-004**: Fix integer overflow in `AdcConfig::max_raw_value`
|
||||
- [ ] **PERF-001**: Pre-compute Hilbert transforms in `compute_all_pairs`
|
||||
|
||||
### Important (Should fix before v0.1 release)
|
||||
|
||||
- [ ] **SEC-003**: Add NaN/Inf validation for signal data at pipeline entry points
|
||||
- [ ] **SEC-005**: Add bounds check on HNSW entry point index
|
||||
- [ ] **PERF-002**: Replace `Vec::remove(0)` with `VecDeque` in memory store
|
||||
- [ ] **PERF-003**: Cache FFT planner across calls
|
||||
- [ ] Add `BrainGraph::validate()` for edge index bounds and weight finiteness
|
||||
- [ ] Add dimension consistency check to `NeuralMemoryStore::store`
|
||||
- [ ] Remove unused workspace dependencies (`petgraph`, `tokio`, `ruvector-*`)
|
||||
|
||||
### Recommended (Fix in v0.2)
|
||||
|
||||
- [ ] Implement `zeroize`-on-drop for `NeuralEmbedding` and `NeuralMemoryStore`
|
||||
- [ ] Add `strip_pii()` to `RvfFile`
|
||||
- [ ] Migrate `Vec<Vec<f64>>` matrices to `ndarray::Array2<f64>`
|
||||
- [ ] Add Rayon parallelism for connectivity matrix computation
|
||||
- [ ] Add criterion benchmarks for all crates
|
||||
- [ ] Implement TDM protocol with CRC32 and node authentication
|
||||
- [ ] Add `cargo deny` and `cargo audit` to CI
|
||||
- [ ] Profile and optimize binary size for ESP32
|
||||
|
||||
### Future (v0.3+)
|
||||
|
||||
- [ ] Encryption-at-rest for `NeuralMemoryStore`
|
||||
- [ ] DTLS/TLS for ESP32 network protocol
|
||||
- [ ] Sparse graph representation for large atlases
|
||||
- [ ] f32 quantized embeddings for WASM
|
||||
- [ ] Streaming signal processing pipeline
|
||||
- [ ] GPU backend for large-scale spectral analysis
|
||||
|
||||
---
|
||||
|
||||
*This document should be reviewed and updated after each milestone. All security findings should be verified as resolved before the corresponding release.*
|
||||
@@ -1,9 +1,28 @@
|
||||
[package]
|
||||
name = "ruv-neural-cli"
|
||||
description = "rUv Neural — ruv-neural-cli (stub)"
|
||||
description = "rUv Neural — CLI tool for brain topology analysis, simulation, and visualization"
|
||||
version.workspace = true
|
||||
edition.workspace = true
|
||||
authors.workspace = true
|
||||
license.workspace = true
|
||||
|
||||
[[bin]]
|
||||
name = "ruv-neural"
|
||||
path = "src/main.rs"
|
||||
|
||||
[dependencies]
|
||||
ruv-neural-core = { workspace = true }
|
||||
ruv-neural-sensor = { workspace = true }
|
||||
ruv-neural-signal = { workspace = true }
|
||||
ruv-neural-graph = { workspace = true }
|
||||
ruv-neural-mincut = { workspace = true }
|
||||
ruv-neural-embed = { workspace = true }
|
||||
ruv-neural-memory = { workspace = true }
|
||||
ruv-neural-decoder = { workspace = true }
|
||||
ruv-neural-viz = { workspace = true }
|
||||
clap = { workspace = true }
|
||||
serde = { workspace = true }
|
||||
serde_json = { workspace = true }
|
||||
tracing = { workspace = true }
|
||||
tracing-subscriber = { workspace = true }
|
||||
tokio = { workspace = true }
|
||||
|
||||
@@ -0,0 +1,237 @@
|
||||
//! Analyze a brain connectivity graph: compute topology metrics and display results.
|
||||
|
||||
use std::fs;
|
||||
|
||||
use ruv_neural_core::graph::BrainGraph;
|
||||
use ruv_neural_mincut::stoer_wagner_mincut;
|
||||
|
||||
/// Run the analyze command.
|
||||
pub fn run(
|
||||
input: &str,
|
||||
ascii: bool,
|
||||
csv_output: Option<String>,
|
||||
) -> Result<(), Box<dyn std::error::Error>> {
|
||||
tracing::info!(input, "Loading brain graph");
|
||||
|
||||
let json = fs::read_to_string(input)
|
||||
.map_err(|e| format!("Failed to read {input}: {e}"))?;
|
||||
let graph: BrainGraph = serde_json::from_str(&json)
|
||||
.map_err(|e| format!("Failed to parse graph JSON: {e}"))?;
|
||||
|
||||
println!("=== rUv Neural — Graph Analysis ===");
|
||||
println!();
|
||||
println!(" Nodes: {}", graph.num_nodes);
|
||||
println!(" Edges: {}", graph.edges.len());
|
||||
println!(" Density: {:.4}", graph.density());
|
||||
println!(" Total weight: {:.4}", graph.total_weight());
|
||||
println!(" Timestamp: {:.2} s", graph.timestamp);
|
||||
println!(" Window duration: {:.2} s", graph.window_duration_s);
|
||||
println!(" Atlas: {:?}", graph.atlas);
|
||||
println!();
|
||||
|
||||
// Degree statistics.
|
||||
let degrees: Vec<f64> = (0..graph.num_nodes)
|
||||
.map(|i| graph.node_degree(i))
|
||||
.collect();
|
||||
let mean_degree = if degrees.is_empty() {
|
||||
0.0
|
||||
} else {
|
||||
degrees.iter().sum::<f64>() / degrees.len() as f64
|
||||
};
|
||||
let max_degree = degrees.iter().cloned().fold(0.0_f64, f64::max);
|
||||
let min_degree = degrees.iter().cloned().fold(f64::INFINITY, f64::min);
|
||||
|
||||
println!(" Degree statistics:");
|
||||
println!(" Mean: {mean_degree:.4}");
|
||||
println!(" Min: {min_degree:.4}");
|
||||
println!(" Max: {max_degree:.4}");
|
||||
println!();
|
||||
|
||||
// Mincut.
|
||||
match stoer_wagner_mincut(&graph) {
|
||||
Ok(mc) => {
|
||||
println!(" Minimum cut:");
|
||||
println!(" Cut value: {:.4}", mc.cut_value);
|
||||
println!(" Partition A: {} nodes {:?}", mc.partition_a.len(), mc.partition_a);
|
||||
println!(" Partition B: {} nodes {:?}", mc.partition_b.len(), mc.partition_b);
|
||||
println!(" Cut edges: {}", mc.cut_edges.len());
|
||||
println!(" Balance ratio: {:.4}", mc.balance_ratio());
|
||||
println!();
|
||||
}
|
||||
Err(e) => {
|
||||
println!(" Minimum cut: could not compute ({e})");
|
||||
println!();
|
||||
}
|
||||
}
|
||||
|
||||
// Edge weight distribution.
|
||||
if !graph.edges.is_empty() {
|
||||
let weights: Vec<f64> = graph.edges.iter().map(|e| e.weight).collect();
|
||||
let mean_w = weights.iter().sum::<f64>() / weights.len() as f64;
|
||||
let max_w = weights.iter().cloned().fold(f64::NEG_INFINITY, f64::max);
|
||||
let min_w = weights.iter().cloned().fold(f64::INFINITY, f64::min);
|
||||
|
||||
println!(" Edge weight distribution:");
|
||||
println!(" Mean: {mean_w:.4}");
|
||||
println!(" Min: {min_w:.4}");
|
||||
println!(" Max: {max_w:.4}");
|
||||
println!();
|
||||
}
|
||||
|
||||
if ascii {
|
||||
print_ascii_graph(&graph);
|
||||
}
|
||||
|
||||
if let Some(csv_path) = csv_output {
|
||||
write_csv(&graph, °rees, &csv_path)?;
|
||||
println!(" Metrics exported to: {csv_path}");
|
||||
}
|
||||
|
||||
Ok(())
|
||||
}
|
||||
|
||||
/// Print a simple ASCII visualization of the graph adjacency.
|
||||
fn print_ascii_graph(graph: &BrainGraph) {
|
||||
println!(" ASCII Adjacency Matrix:");
|
||||
let n = graph.num_nodes.min(20); // cap display at 20x20
|
||||
let adj = graph.adjacency_matrix();
|
||||
|
||||
// Header row.
|
||||
print!(" ");
|
||||
for j in 0..n {
|
||||
print!("{j:>4}");
|
||||
}
|
||||
println!();
|
||||
|
||||
for i in 0..n {
|
||||
print!(" {i:>3} ");
|
||||
for j in 0..n {
|
||||
let w = adj[i][j];
|
||||
if i == j {
|
||||
print!(" .");
|
||||
} else if w > 0.0 {
|
||||
// Map weight to a character.
|
||||
let ch = if w > 0.8 {
|
||||
'#'
|
||||
} else if w > 0.5 {
|
||||
'*'
|
||||
} else if w > 0.2 {
|
||||
'+'
|
||||
} else {
|
||||
'.'
|
||||
};
|
||||
print!(" {ch}");
|
||||
} else {
|
||||
print!(" ");
|
||||
}
|
||||
}
|
||||
println!();
|
||||
}
|
||||
|
||||
if graph.num_nodes > 20 {
|
||||
println!(" ... ({} nodes total, showing first 20)", graph.num_nodes);
|
||||
}
|
||||
println!();
|
||||
}
|
||||
|
||||
/// Write per-node metrics to a CSV file.
|
||||
fn write_csv(
|
||||
graph: &BrainGraph,
|
||||
degrees: &[f64],
|
||||
path: &str,
|
||||
) -> Result<(), Box<dyn std::error::Error>> {
|
||||
let mut csv = String::from("node,degree,num_edges\n");
|
||||
for i in 0..graph.num_nodes {
|
||||
let num_edges = graph
|
||||
.edges
|
||||
.iter()
|
||||
.filter(|e| e.source == i || e.target == i)
|
||||
.count();
|
||||
csv.push_str(&format!(
|
||||
"{},{:.6},{}\n",
|
||||
i,
|
||||
degrees.get(i).copied().unwrap_or(0.0),
|
||||
num_edges
|
||||
));
|
||||
}
|
||||
fs::write(path, csv)?;
|
||||
Ok(())
|
||||
}
|
||||
|
||||
#[cfg(test)]
|
||||
mod tests {
|
||||
use super::*;
|
||||
use ruv_neural_core::brain::Atlas;
|
||||
use ruv_neural_core::graph::{BrainEdge, ConnectivityMetric};
|
||||
use ruv_neural_core::signal::FrequencyBand;
|
||||
|
||||
fn test_graph() -> BrainGraph {
|
||||
BrainGraph {
|
||||
num_nodes: 4,
|
||||
edges: vec![
|
||||
BrainEdge {
|
||||
source: 0,
|
||||
target: 1,
|
||||
weight: 0.8,
|
||||
metric: ConnectivityMetric::PhaseLockingValue,
|
||||
frequency_band: FrequencyBand::Alpha,
|
||||
},
|
||||
BrainEdge {
|
||||
source: 1,
|
||||
target: 2,
|
||||
weight: 0.5,
|
||||
metric: ConnectivityMetric::PhaseLockingValue,
|
||||
frequency_band: FrequencyBand::Alpha,
|
||||
},
|
||||
BrainEdge {
|
||||
source: 2,
|
||||
target: 3,
|
||||
weight: 0.9,
|
||||
metric: ConnectivityMetric::PhaseLockingValue,
|
||||
frequency_band: FrequencyBand::Alpha,
|
||||
},
|
||||
],
|
||||
timestamp: 0.0,
|
||||
window_duration_s: 1.0,
|
||||
atlas: Atlas::Custom(4),
|
||||
}
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn analyze_from_json() {
|
||||
let graph = test_graph();
|
||||
let dir = std::env::temp_dir();
|
||||
let path = dir.join("ruv_neural_test_analyze.json");
|
||||
let json = serde_json::to_string_pretty(&graph).unwrap();
|
||||
std::fs::write(&path, json).unwrap();
|
||||
|
||||
let result = run(&path.to_string_lossy(), false, None);
|
||||
assert!(result.is_ok());
|
||||
std::fs::remove_file(&path).ok();
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn analyze_with_csv() {
|
||||
let graph = test_graph();
|
||||
let dir = std::env::temp_dir();
|
||||
let json_path = dir.join("ruv_neural_test_analyze2.json");
|
||||
let csv_path = dir.join("ruv_neural_test_analyze2.csv");
|
||||
|
||||
let json = serde_json::to_string_pretty(&graph).unwrap();
|
||||
std::fs::write(&json_path, json).unwrap();
|
||||
|
||||
let result = run(
|
||||
&json_path.to_string_lossy(),
|
||||
true,
|
||||
Some(csv_path.to_string_lossy().to_string()),
|
||||
);
|
||||
assert!(result.is_ok());
|
||||
assert!(csv_path.exists());
|
||||
|
||||
let csv_content = std::fs::read_to_string(&csv_path).unwrap();
|
||||
assert!(csv_content.starts_with("node,degree,num_edges"));
|
||||
|
||||
std::fs::remove_file(&json_path).ok();
|
||||
std::fs::remove_file(&csv_path).ok();
|
||||
}
|
||||
}
|
||||
@@ -0,0 +1,280 @@
|
||||
//! Export brain graph to various visualization formats.
|
||||
|
||||
use std::fs;
|
||||
|
||||
use ruv_neural_core::graph::BrainGraph;
|
||||
|
||||
/// Run the export command.
|
||||
pub fn run(
|
||||
input: &str,
|
||||
format: &str,
|
||||
output: &str,
|
||||
) -> Result<(), Box<dyn std::error::Error>> {
|
||||
tracing::info!(input, format, output, "Exporting brain graph");
|
||||
|
||||
let json =
|
||||
fs::read_to_string(input).map_err(|e| format!("Failed to read {input}: {e}"))?;
|
||||
let graph: BrainGraph =
|
||||
serde_json::from_str(&json).map_err(|e| format!("Failed to parse graph JSON: {e}"))?;
|
||||
|
||||
let content = match format {
|
||||
"d3" => export_d3(&graph)?,
|
||||
"dot" => export_dot(&graph),
|
||||
"gexf" => export_gexf(&graph),
|
||||
"csv" => export_csv(&graph),
|
||||
"rvf" => export_rvf(&graph)?,
|
||||
_ => {
|
||||
return Err(format!(
|
||||
"Unknown format '{format}'. Supported: d3, dot, gexf, csv, rvf"
|
||||
)
|
||||
.into());
|
||||
}
|
||||
};
|
||||
|
||||
fs::write(output, content)?;
|
||||
|
||||
println!("=== rUv Neural — Export Complete ===");
|
||||
println!();
|
||||
println!(" Format: {format}");
|
||||
println!(" Input: {input}");
|
||||
println!(" Output: {output}");
|
||||
println!(" Nodes: {}", graph.num_nodes);
|
||||
println!(" Edges: {}", graph.edges.len());
|
||||
|
||||
Ok(())
|
||||
}
|
||||
|
||||
/// Export to D3.js-compatible JSON format.
|
||||
fn export_d3(graph: &BrainGraph) -> Result<String, Box<dyn std::error::Error>> {
|
||||
let nodes: Vec<serde_json::Value> = (0..graph.num_nodes)
|
||||
.map(|i| {
|
||||
serde_json::json!({
|
||||
"id": i,
|
||||
"degree": graph.node_degree(i),
|
||||
})
|
||||
})
|
||||
.collect();
|
||||
|
||||
let links: Vec<serde_json::Value> = graph
|
||||
.edges
|
||||
.iter()
|
||||
.map(|e| {
|
||||
serde_json::json!({
|
||||
"source": e.source,
|
||||
"target": e.target,
|
||||
"weight": e.weight,
|
||||
"metric": format!("{:?}", e.metric),
|
||||
"band": format!("{:?}", e.frequency_band),
|
||||
})
|
||||
})
|
||||
.collect();
|
||||
|
||||
let d3 = serde_json::json!({
|
||||
"nodes": nodes,
|
||||
"links": links,
|
||||
"metadata": {
|
||||
"num_nodes": graph.num_nodes,
|
||||
"num_edges": graph.edges.len(),
|
||||
"density": graph.density(),
|
||||
"total_weight": graph.total_weight(),
|
||||
"atlas": format!("{:?}", graph.atlas),
|
||||
"timestamp": graph.timestamp,
|
||||
}
|
||||
});
|
||||
|
||||
Ok(serde_json::to_string_pretty(&d3)?)
|
||||
}
|
||||
|
||||
/// Export to Graphviz DOT format.
|
||||
fn export_dot(graph: &BrainGraph) -> String {
|
||||
let mut dot = String::from("graph brain {\n");
|
||||
dot.push_str(" rankdir=LR;\n");
|
||||
dot.push_str(&format!(
|
||||
" label=\"Brain Graph ({} nodes, {} edges)\";\n",
|
||||
graph.num_nodes,
|
||||
graph.edges.len()
|
||||
));
|
||||
dot.push_str(" node [shape=circle];\n\n");
|
||||
|
||||
for i in 0..graph.num_nodes {
|
||||
let degree = graph.node_degree(i);
|
||||
let size = 0.3 + degree * 0.1;
|
||||
dot.push_str(&format!(
|
||||
" n{i} [label=\"{i}\", width={size:.2}];\n"
|
||||
));
|
||||
}
|
||||
dot.push('\n');
|
||||
|
||||
for edge in &graph.edges {
|
||||
let penwidth = 0.5 + edge.weight * 2.0;
|
||||
dot.push_str(&format!(
|
||||
" n{} -- n{} [penwidth={:.2}, label=\"{:.2}\"];\n",
|
||||
edge.source, edge.target, penwidth, edge.weight
|
||||
));
|
||||
}
|
||||
|
||||
dot.push_str("}\n");
|
||||
dot
|
||||
}
|
||||
|
||||
/// Export to GEXF (Graph Exchange XML Format).
|
||||
fn export_gexf(graph: &BrainGraph) -> String {
|
||||
let mut gexf = String::from(r#"<?xml version="1.0" encoding="UTF-8"?>
|
||||
<gexf xmlns="http://gexf.net/1.3" version="1.3">
|
||||
<meta>
|
||||
<creator>rUv Neural</creator>
|
||||
<description>Brain connectivity graph</description>
|
||||
</meta>
|
||||
<graph defaultedgetype="undirected">
|
||||
<nodes>
|
||||
"#);
|
||||
|
||||
for i in 0..graph.num_nodes {
|
||||
gexf.push_str(&format!(
|
||||
" <node id=\"{i}\" label=\"Region {i}\" />\n"
|
||||
));
|
||||
}
|
||||
|
||||
gexf.push_str(" </nodes>\n <edges>\n");
|
||||
|
||||
for (idx, edge) in graph.edges.iter().enumerate() {
|
||||
gexf.push_str(&format!(
|
||||
" <edge id=\"{idx}\" source=\"{}\" target=\"{}\" weight=\"{:.6}\" />\n",
|
||||
edge.source, edge.target, edge.weight
|
||||
));
|
||||
}
|
||||
|
||||
gexf.push_str(" </edges>\n </graph>\n</gexf>\n");
|
||||
gexf
|
||||
}
|
||||
|
||||
/// Export to CSV edge list.
|
||||
fn export_csv(graph: &BrainGraph) -> String {
|
||||
let mut csv = String::from("source,target,weight,metric,frequency_band\n");
|
||||
for edge in &graph.edges {
|
||||
csv.push_str(&format!(
|
||||
"{},{},{:.6},{:?},{:?}\n",
|
||||
edge.source, edge.target, edge.weight, edge.metric, edge.frequency_band
|
||||
));
|
||||
}
|
||||
csv
|
||||
}
|
||||
|
||||
/// Export to RVF (RuVector File) JSON representation.
|
||||
fn export_rvf(graph: &BrainGraph) -> Result<String, Box<dyn std::error::Error>> {
|
||||
let rvf = serde_json::json!({
|
||||
"format": "rvf",
|
||||
"version": 1,
|
||||
"data_type": "BrainGraph",
|
||||
"num_nodes": graph.num_nodes,
|
||||
"num_edges": graph.edges.len(),
|
||||
"atlas": format!("{:?}", graph.atlas),
|
||||
"timestamp": graph.timestamp,
|
||||
"window_duration_s": graph.window_duration_s,
|
||||
"adjacency": graph.adjacency_matrix(),
|
||||
});
|
||||
Ok(serde_json::to_string_pretty(&rvf)?)
|
||||
}
|
||||
|
||||
#[cfg(test)]
|
||||
mod tests {
|
||||
use super::*;
|
||||
use ruv_neural_core::brain::Atlas;
|
||||
use ruv_neural_core::graph::{BrainEdge, ConnectivityMetric};
|
||||
use ruv_neural_core::signal::FrequencyBand;
|
||||
|
||||
fn test_graph() -> BrainGraph {
|
||||
BrainGraph {
|
||||
num_nodes: 3,
|
||||
edges: vec![
|
||||
BrainEdge {
|
||||
source: 0,
|
||||
target: 1,
|
||||
weight: 0.8,
|
||||
metric: ConnectivityMetric::PhaseLockingValue,
|
||||
frequency_band: FrequencyBand::Alpha,
|
||||
},
|
||||
BrainEdge {
|
||||
source: 1,
|
||||
target: 2,
|
||||
weight: 0.5,
|
||||
metric: ConnectivityMetric::Coherence,
|
||||
frequency_band: FrequencyBand::Beta,
|
||||
},
|
||||
],
|
||||
timestamp: 0.0,
|
||||
window_duration_s: 1.0,
|
||||
atlas: Atlas::Custom(3),
|
||||
}
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn export_d3_valid_json() {
|
||||
let graph = test_graph();
|
||||
let result = export_d3(&graph).unwrap();
|
||||
let parsed: serde_json::Value = serde_json::from_str(&result).unwrap();
|
||||
assert!(parsed["nodes"].is_array());
|
||||
assert!(parsed["links"].is_array());
|
||||
assert_eq!(parsed["nodes"].as_array().unwrap().len(), 3);
|
||||
assert_eq!(parsed["links"].as_array().unwrap().len(), 2);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn export_dot_format() {
|
||||
let graph = test_graph();
|
||||
let result = export_dot(&graph);
|
||||
assert!(result.starts_with("graph brain {"));
|
||||
assert!(result.contains("n0 -- n1"));
|
||||
assert!(result.ends_with("}\n"));
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn export_gexf_format() {
|
||||
let graph = test_graph();
|
||||
let result = export_gexf(&graph);
|
||||
assert!(result.contains("<gexf"));
|
||||
assert!(result.contains("<node id=\"0\""));
|
||||
assert!(result.contains("</gexf>"));
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn export_csv_format() {
|
||||
let graph = test_graph();
|
||||
let result = export_csv(&graph);
|
||||
assert!(result.starts_with("source,target,weight"));
|
||||
let lines: Vec<&str> = result.lines().collect();
|
||||
assert_eq!(lines.len(), 3); // header + 2 edges
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn export_rvf_valid_json() {
|
||||
let graph = test_graph();
|
||||
let result = export_rvf(&graph).unwrap();
|
||||
let parsed: serde_json::Value = serde_json::from_str(&result).unwrap();
|
||||
assert_eq!(parsed["format"], "rvf");
|
||||
assert_eq!(parsed["num_nodes"], 3);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn export_all_formats() {
|
||||
let graph = test_graph();
|
||||
let dir = std::env::temp_dir();
|
||||
let json_path = dir.join("ruv_neural_test_export.json");
|
||||
let json = serde_json::to_string_pretty(&graph).unwrap();
|
||||
std::fs::write(&json_path, json).unwrap();
|
||||
|
||||
for fmt in &["d3", "dot", "gexf", "csv", "rvf"] {
|
||||
let out_path = dir.join(format!("ruv_neural_test_export.{fmt}"));
|
||||
let result = run(
|
||||
&json_path.to_string_lossy(),
|
||||
fmt,
|
||||
&out_path.to_string_lossy(),
|
||||
);
|
||||
assert!(result.is_ok(), "Failed to export format: {fmt}");
|
||||
assert!(out_path.exists(), "Output file missing for format: {fmt}");
|
||||
std::fs::remove_file(&out_path).ok();
|
||||
}
|
||||
|
||||
std::fs::remove_file(&json_path).ok();
|
||||
}
|
||||
}
|
||||
@@ -0,0 +1,66 @@
|
||||
//! Display system info and capabilities.
|
||||
|
||||
/// Run the info command.
|
||||
pub fn run() {
|
||||
let version = env!("CARGO_PKG_VERSION");
|
||||
|
||||
println!("=== rUv Neural — System Information ===");
|
||||
println!();
|
||||
println!(" Version: {version}");
|
||||
println!(" Binary: ruv-neural");
|
||||
println!();
|
||||
println!(" Crate Versions:");
|
||||
println!(" ruv-neural-core {version}");
|
||||
println!(" ruv-neural-sensor {version}");
|
||||
println!(" ruv-neural-signal {version}");
|
||||
println!(" ruv-neural-graph {version}");
|
||||
println!(" ruv-neural-mincut {version}");
|
||||
println!(" ruv-neural-embed {version}");
|
||||
println!(" ruv-neural-memory {version}");
|
||||
println!(" ruv-neural-decoder {version}");
|
||||
println!(" ruv-neural-viz {version}");
|
||||
println!(" ruv-neural-cli {version}");
|
||||
println!();
|
||||
println!(" Features:");
|
||||
println!(" Sensor simulation [available]");
|
||||
println!(" Signal processing [available]");
|
||||
println!(" Bandpass filtering [available] (Butterworth IIR, SOS form)");
|
||||
println!(" Artifact rejection [available] (eye blink, muscle, cardiac)");
|
||||
println!(" PLV connectivity [available] (phase locking value)");
|
||||
println!(" Coherence metrics [available] (coherence, imaginary coherence)");
|
||||
println!(" Stoer-Wagner mincut [available] (global minimum cut)");
|
||||
println!(" Normalized cut [available] (Shi-Malik spectral bisection)");
|
||||
println!(" Multi-way cut [available] (recursive normalized cut)");
|
||||
println!(" Spectral embedding [available] (Laplacian eigenvector encoding)");
|
||||
println!(" Topology embedding [available] (hand-crafted topological features)");
|
||||
println!(" Node2Vec embedding [available] (random walk co-occurrence)");
|
||||
println!(" Threshold decoder [available] (rule-based cognitive state)");
|
||||
println!(" KNN decoder [available] (k-nearest neighbor classifier)");
|
||||
println!(" Force-directed layout [available] (Fruchterman-Reingold)");
|
||||
println!(" Anatomical layout [available] (MNI coordinate-based)");
|
||||
println!();
|
||||
println!(" Export Formats:");
|
||||
println!(" D3.js JSON [available]");
|
||||
println!(" Graphviz DOT [available]");
|
||||
println!(" GEXF (Graph Exchange) [available]");
|
||||
println!(" CSV edge list [available]");
|
||||
println!(" RVF (RuVector File) [available]");
|
||||
println!();
|
||||
println!(" Pipeline:");
|
||||
println!(" simulate -> filter -> PLV graph -> mincut -> embed -> decode");
|
||||
println!();
|
||||
println!(" Platform:");
|
||||
println!(" OS: {}", std::env::consts::OS);
|
||||
println!(" Arch: {}", std::env::consts::ARCH);
|
||||
println!(" Family: {}", std::env::consts::FAMILY);
|
||||
}
|
||||
|
||||
#[cfg(test)]
|
||||
mod tests {
|
||||
use super::*;
|
||||
|
||||
#[test]
|
||||
fn info_runs_without_panic() {
|
||||
run();
|
||||
}
|
||||
}
|
||||
@@ -0,0 +1,184 @@
|
||||
//! Compute minimum cut on a brain connectivity graph.
|
||||
|
||||
use std::fs;
|
||||
|
||||
use ruv_neural_core::graph::BrainGraph;
|
||||
use ruv_neural_mincut::{multiway_cut, stoer_wagner_mincut};
|
||||
|
||||
/// Run the mincut command.
|
||||
pub fn run(input: &str, k: Option<usize>) -> Result<(), Box<dyn std::error::Error>> {
|
||||
tracing::info!(input, ?k, "Computing minimum cut");
|
||||
|
||||
let json =
|
||||
fs::read_to_string(input).map_err(|e| format!("Failed to read {input}: {e}"))?;
|
||||
let graph: BrainGraph =
|
||||
serde_json::from_str(&json).map_err(|e| format!("Failed to parse graph JSON: {e}"))?;
|
||||
|
||||
println!("=== rUv Neural — Minimum Cut Analysis ===");
|
||||
println!();
|
||||
println!(" Graph: {} nodes, {} edges", graph.num_nodes, graph.edges.len());
|
||||
println!();
|
||||
|
||||
match k {
|
||||
Some(k_val) if k_val > 2 => {
|
||||
// Multi-way cut.
|
||||
let result = multiway_cut(&graph, k_val)
|
||||
.map_err(|e| format!("Multiway cut failed: {e}"))?;
|
||||
|
||||
println!(" Multi-way cut (k={k_val}):");
|
||||
println!(" Total cut value: {:.4}", result.cut_value);
|
||||
println!(" Modularity: {:.4}", result.modularity);
|
||||
println!(" Partitions: {}", result.num_partitions());
|
||||
println!();
|
||||
|
||||
for (i, partition) in result.partitions.iter().enumerate() {
|
||||
println!(" Partition {i}: {} nodes {:?}", partition.len(), partition);
|
||||
}
|
||||
println!();
|
||||
|
||||
// ASCII visualization of partitions.
|
||||
print_partition_ascii(&graph, &result.partitions);
|
||||
}
|
||||
_ => {
|
||||
// Standard two-way Stoer-Wagner.
|
||||
let mc = stoer_wagner_mincut(&graph)
|
||||
.map_err(|e| format!("Stoer-Wagner mincut failed: {e}"))?;
|
||||
|
||||
println!(" Stoer-Wagner minimum cut:");
|
||||
println!(" Cut value: {:.4}", mc.cut_value);
|
||||
println!(" Partition A: {} nodes {:?}", mc.partition_a.len(), mc.partition_a);
|
||||
println!(" Partition B: {} nodes {:?}", mc.partition_b.len(), mc.partition_b);
|
||||
println!(" Balance ratio: {:.4}", mc.balance_ratio());
|
||||
println!();
|
||||
|
||||
println!(" Cut edges:");
|
||||
for (src, tgt, weight) in &mc.cut_edges {
|
||||
println!(" {src} -- {tgt} (weight: {weight:.4})");
|
||||
}
|
||||
println!();
|
||||
|
||||
// ASCII visualization of the two partitions.
|
||||
print_partition_ascii(&graph, &[mc.partition_a.clone(), mc.partition_b.clone()]);
|
||||
}
|
||||
}
|
||||
|
||||
Ok(())
|
||||
}
|
||||
|
||||
/// Print an ASCII visualization of the graph partitions.
|
||||
fn print_partition_ascii(graph: &BrainGraph, partitions: &[Vec<usize>]) {
|
||||
println!(" Partition layout:");
|
||||
|
||||
// Build a node-to-partition map.
|
||||
let mut node_partition = vec![0usize; graph.num_nodes];
|
||||
for (pid, partition) in partitions.iter().enumerate() {
|
||||
for &node in partition {
|
||||
if node < graph.num_nodes {
|
||||
node_partition[node] = pid;
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
// Label characters for partitions.
|
||||
let labels = ['A', 'B', 'C', 'D', 'E', 'F', 'G', 'H'];
|
||||
|
||||
let n = graph.num_nodes.min(40);
|
||||
print!(" ");
|
||||
for i in 0..n {
|
||||
let pid = node_partition[i];
|
||||
let ch = labels.get(pid).copied().unwrap_or('?');
|
||||
print!("{ch}");
|
||||
}
|
||||
println!();
|
||||
|
||||
if graph.num_nodes > 40 {
|
||||
println!(" ... ({} nodes total)", graph.num_nodes);
|
||||
}
|
||||
|
||||
println!();
|
||||
for (pid, partition) in partitions.iter().enumerate() {
|
||||
let ch = labels.get(pid).copied().unwrap_or('?');
|
||||
println!(" {ch} = {} nodes", partition.len());
|
||||
}
|
||||
println!();
|
||||
}
|
||||
|
||||
#[cfg(test)]
|
||||
mod tests {
|
||||
use super::*;
|
||||
use ruv_neural_core::brain::Atlas;
|
||||
use ruv_neural_core::graph::{BrainEdge, ConnectivityMetric};
|
||||
use ruv_neural_core::signal::FrequencyBand;
|
||||
|
||||
fn test_graph() -> BrainGraph {
|
||||
BrainGraph {
|
||||
num_nodes: 6,
|
||||
edges: vec![
|
||||
BrainEdge {
|
||||
source: 0,
|
||||
target: 1,
|
||||
weight: 5.0,
|
||||
metric: ConnectivityMetric::Coherence,
|
||||
frequency_band: FrequencyBand::Alpha,
|
||||
},
|
||||
BrainEdge {
|
||||
source: 1,
|
||||
target: 2,
|
||||
weight: 5.0,
|
||||
metric: ConnectivityMetric::Coherence,
|
||||
frequency_band: FrequencyBand::Alpha,
|
||||
},
|
||||
BrainEdge {
|
||||
source: 3,
|
||||
target: 4,
|
||||
weight: 5.0,
|
||||
metric: ConnectivityMetric::Coherence,
|
||||
frequency_band: FrequencyBand::Alpha,
|
||||
},
|
||||
BrainEdge {
|
||||
source: 4,
|
||||
target: 5,
|
||||
weight: 5.0,
|
||||
metric: ConnectivityMetric::Coherence,
|
||||
frequency_band: FrequencyBand::Alpha,
|
||||
},
|
||||
BrainEdge {
|
||||
source: 2,
|
||||
target: 3,
|
||||
weight: 0.5,
|
||||
metric: ConnectivityMetric::Coherence,
|
||||
frequency_band: FrequencyBand::Alpha,
|
||||
},
|
||||
],
|
||||
timestamp: 0.0,
|
||||
window_duration_s: 1.0,
|
||||
atlas: Atlas::Custom(6),
|
||||
}
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn mincut_two_way() {
|
||||
let graph = test_graph();
|
||||
let dir = std::env::temp_dir();
|
||||
let path = dir.join("ruv_neural_test_mincut.json");
|
||||
let json = serde_json::to_string_pretty(&graph).unwrap();
|
||||
std::fs::write(&path, json).unwrap();
|
||||
|
||||
let result = run(&path.to_string_lossy(), None);
|
||||
assert!(result.is_ok());
|
||||
std::fs::remove_file(&path).ok();
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn mincut_multiway() {
|
||||
let graph = test_graph();
|
||||
let dir = std::env::temp_dir();
|
||||
let path = dir.join("ruv_neural_test_mincut_k.json");
|
||||
let json = serde_json::to_string_pretty(&graph).unwrap();
|
||||
std::fs::write(&path, json).unwrap();
|
||||
|
||||
let result = run(&path.to_string_lossy(), Some(3));
|
||||
assert!(result.is_ok());
|
||||
std::fs::remove_file(&path).ok();
|
||||
}
|
||||
}
|
||||
@@ -0,0 +1,8 @@
|
||||
//! CLI command implementations.
|
||||
|
||||
pub mod analyze;
|
||||
pub mod export;
|
||||
pub mod info;
|
||||
pub mod mincut;
|
||||
pub mod pipeline;
|
||||
pub mod simulate;
|
||||
+377
@@ -0,0 +1,377 @@
|
||||
//! Full end-to-end pipeline: simulate -> process -> analyze -> decode.
|
||||
|
||||
use std::f64::consts::PI;
|
||||
|
||||
use ruv_neural_core::brain::Atlas;
|
||||
use ruv_neural_core::graph::{BrainEdge, BrainGraph, ConnectivityMetric};
|
||||
use ruv_neural_core::signal::{FrequencyBand, MultiChannelTimeSeries};
|
||||
use ruv_neural_core::topology::CognitiveState;
|
||||
use ruv_neural_decoder::ThresholdDecoder;
|
||||
use ruv_neural_embed::spectral_embed::SpectralEmbedder;
|
||||
use ruv_neural_embed::topology_embed::TopologyEmbedder;
|
||||
use ruv_neural_mincut::stoer_wagner_mincut;
|
||||
use ruv_neural_signal::connectivity::phase_locking_value;
|
||||
use ruv_neural_signal::filter::BandpassFilter;
|
||||
|
||||
/// Run the full pipeline command.
|
||||
pub fn run(
|
||||
channels: usize,
|
||||
duration: f64,
|
||||
dashboard: bool,
|
||||
) -> Result<(), Box<dyn std::error::Error>> {
|
||||
let sample_rate = 1000.0;
|
||||
let num_samples = (duration * sample_rate) as usize;
|
||||
|
||||
println!("=== rUv Neural — Full Pipeline ===");
|
||||
println!();
|
||||
|
||||
// Step 1: Generate simulated sensor data.
|
||||
println!(" [1/7] Generating simulated sensor data...");
|
||||
let raw_data = generate_data(channels, num_samples, sample_rate);
|
||||
let ts = MultiChannelTimeSeries::new(raw_data.clone(), sample_rate, 0.0)
|
||||
.map_err(|e| format!("Time series creation failed: {e}"))?;
|
||||
println!(" {channels} channels, {num_samples} samples, {duration:.1}s");
|
||||
|
||||
// Step 2: Preprocess (bandpass filter 1-100 Hz).
|
||||
println!(" [2/7] Preprocessing (bandpass 1-100 Hz)...");
|
||||
let filter = BandpassFilter::new(4, 1.0, 100.0, sample_rate);
|
||||
let filtered: Vec<Vec<f64>> = raw_data
|
||||
.iter()
|
||||
.map(|ch| {
|
||||
use ruv_neural_signal::filter::SignalProcessor;
|
||||
filter.process(ch)
|
||||
})
|
||||
.collect();
|
||||
println!(" Bandpass filter applied to all channels");
|
||||
|
||||
// Step 3: Construct brain graph via PLV connectivity.
|
||||
println!(" [3/7] Constructing brain connectivity graph (PLV)...");
|
||||
let graph = build_plv_graph(&filtered, sample_rate);
|
||||
println!(
|
||||
" {} nodes, {} edges, density {:.4}",
|
||||
graph.num_nodes,
|
||||
graph.edges.len(),
|
||||
graph.density()
|
||||
);
|
||||
|
||||
// Step 4: Compute mincut and topology metrics.
|
||||
println!(" [4/7] Computing minimum cut and topology metrics...");
|
||||
let mc = stoer_wagner_mincut(&graph)
|
||||
.map_err(|e| format!("Mincut failed: {e}"))?;
|
||||
println!(" Cut value: {:.4}, balance: {:.4}", mc.cut_value, mc.balance_ratio());
|
||||
println!(
|
||||
" Partition A: {} nodes, Partition B: {} nodes",
|
||||
mc.partition_a.len(),
|
||||
mc.partition_b.len()
|
||||
);
|
||||
|
||||
// Step 5: Generate embedding.
|
||||
println!(" [5/7] Generating topology embedding...");
|
||||
let embedder = TopologyEmbedder::new();
|
||||
let embedding = embedder.embed_graph(&graph)
|
||||
.map_err(|e| format!("Embedding failed: {e}"))?;
|
||||
println!(" Dimension: {}, norm: {:.4}", embedding.dimension, embedding.norm());
|
||||
|
||||
// Also generate spectral embedding.
|
||||
let spectral_dim = channels.min(8).max(2);
|
||||
let spectral = SpectralEmbedder::new(spectral_dim);
|
||||
let spectral_emb = spectral.embed_graph(&graph)
|
||||
.map_err(|e| format!("Spectral embedding failed: {e}"))?;
|
||||
println!(
|
||||
" Spectral embedding: dim={}, norm={:.4}",
|
||||
spectral_emb.dimension,
|
||||
spectral_emb.norm()
|
||||
);
|
||||
|
||||
// Step 6: Decode cognitive state.
|
||||
println!(" [6/7] Decoding cognitive state...");
|
||||
let decoder = build_default_decoder();
|
||||
let metrics = ruv_neural_core::topology::TopologyMetrics {
|
||||
global_mincut: mc.cut_value,
|
||||
modularity: estimate_modularity(&graph),
|
||||
global_efficiency: estimate_efficiency(&graph),
|
||||
local_efficiency: 0.0,
|
||||
graph_entropy: estimate_entropy(&graph),
|
||||
fiedler_value: 0.0,
|
||||
num_modules: 2,
|
||||
timestamp: graph.timestamp,
|
||||
};
|
||||
let (state, confidence) = decoder.decode(&metrics);
|
||||
println!(" State: {state:?}");
|
||||
println!(" Confidence: {confidence:.4}");
|
||||
|
||||
// Step 7: Display results.
|
||||
println!(" [7/7] Results summary");
|
||||
println!();
|
||||
|
||||
println!(" ┌─────────────────────────────────────────┐");
|
||||
println!(" │ Pipeline Results Summary │");
|
||||
println!(" ├─────────────────────────────────────────┤");
|
||||
println!(" │ Channels: {:<20} │", channels);
|
||||
println!(" │ Duration: {:<20} │", format!("{duration:.1} s"));
|
||||
println!(" │ Graph density: {:<20} │", format!("{:.4}", graph.density()));
|
||||
println!(" │ Mincut value: {:<20} │", format!("{:.4}", mc.cut_value));
|
||||
println!(" │ Balance ratio: {:<20} │", format!("{:.4}", mc.balance_ratio()));
|
||||
println!(" │ Modularity: {:<20} │", format!("{:.4}", metrics.modularity));
|
||||
println!(" │ Graph entropy: {:<20} │", format!("{:.4}", metrics.graph_entropy));
|
||||
println!(" │ Embedding dim: {:<20} │", embedding.dimension);
|
||||
println!(" │ Cognitive state: {:<20} │", format!("{state:?}"));
|
||||
println!(" │ Confidence: {:<20} │", format!("{confidence:.4}"));
|
||||
println!(" └─────────────────────────────────────────┘");
|
||||
println!();
|
||||
|
||||
if dashboard {
|
||||
print_dashboard(&ts, &graph, &mc, &metrics);
|
||||
}
|
||||
|
||||
Ok(())
|
||||
}
|
||||
|
||||
/// Generate synthetic multi-channel neural data.
|
||||
fn generate_data(channels: usize, num_samples: usize, sample_rate: f64) -> Vec<Vec<f64>> {
|
||||
let mut data = Vec::with_capacity(channels);
|
||||
for ch in 0..channels {
|
||||
let mut channel_data = Vec::with_capacity(num_samples);
|
||||
let phase = (ch as f64) * PI / (channels as f64);
|
||||
let mut rng: u64 = (ch as u64).wrapping_mul(2862933555777941757).wrapping_add(3037000493);
|
||||
|
||||
for i in 0..num_samples {
|
||||
let t = i as f64 / sample_rate;
|
||||
let alpha = 50.0 * (2.0 * PI * 10.0 * t + phase).sin();
|
||||
let beta = 30.0 * (2.0 * PI * 20.0 * t + phase * 1.3).sin();
|
||||
let gamma = 15.0 * (2.0 * PI * 40.0 * t + phase * 0.7).sin();
|
||||
|
||||
rng = rng.wrapping_mul(6364136223846793005).wrapping_add(1442695040888963407);
|
||||
let u1 = (rng >> 11) as f64 / (1u64 << 53) as f64;
|
||||
rng = rng.wrapping_mul(6364136223846793005).wrapping_add(1442695040888963407);
|
||||
let u2 = (rng >> 11) as f64 / (1u64 << 53) as f64;
|
||||
let noise = if u1 > 1e-15 {
|
||||
5.0 * (-2.0 * u1.ln()).sqrt() * (2.0 * PI * u2).cos()
|
||||
} else {
|
||||
0.0
|
||||
};
|
||||
|
||||
channel_data.push(alpha + beta + gamma + noise);
|
||||
}
|
||||
data.push(channel_data);
|
||||
}
|
||||
data
|
||||
}
|
||||
|
||||
/// Build a brain graph from PLV connectivity between all channel pairs.
|
||||
fn build_plv_graph(channels: &[Vec<f64>], sample_rate: f64) -> BrainGraph {
|
||||
let n = channels.len();
|
||||
let mut edges = Vec::new();
|
||||
let plv_threshold = 0.3;
|
||||
|
||||
for i in 0..n {
|
||||
for j in (i + 1)..n {
|
||||
let plv = phase_locking_value(&channels[i], &channels[j], sample_rate, FrequencyBand::Alpha);
|
||||
if plv > plv_threshold {
|
||||
edges.push(BrainEdge {
|
||||
source: i,
|
||||
target: j,
|
||||
weight: plv,
|
||||
metric: ConnectivityMetric::PhaseLockingValue,
|
||||
frequency_band: FrequencyBand::Alpha,
|
||||
});
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
BrainGraph {
|
||||
num_nodes: n,
|
||||
edges,
|
||||
timestamp: 0.0,
|
||||
window_duration_s: 1.0,
|
||||
atlas: Atlas::Custom(n),
|
||||
}
|
||||
}
|
||||
|
||||
/// Estimate modularity using a simple degree-based partition.
|
||||
fn estimate_modularity(graph: &BrainGraph) -> f64 {
|
||||
let n = graph.num_nodes;
|
||||
if n < 2 {
|
||||
return 0.0;
|
||||
}
|
||||
let total = graph.total_weight();
|
||||
if total < 1e-12 {
|
||||
return 0.0;
|
||||
}
|
||||
|
||||
let adj = graph.adjacency_matrix();
|
||||
let degrees: Vec<f64> = (0..n).map(|i| graph.node_degree(i)).collect();
|
||||
let two_m = 2.0 * total;
|
||||
|
||||
// Simple bisection: first half vs second half.
|
||||
let mid = n / 2;
|
||||
let mut q = 0.0;
|
||||
for i in 0..n {
|
||||
for j in 0..n {
|
||||
let same_community = (i < mid && j < mid) || (i >= mid && j >= mid);
|
||||
if same_community {
|
||||
q += adj[i][j] - degrees[i] * degrees[j] / two_m;
|
||||
}
|
||||
}
|
||||
}
|
||||
q / two_m
|
||||
}
|
||||
|
||||
/// Estimate global efficiency (mean inverse shortest path).
|
||||
fn estimate_efficiency(graph: &BrainGraph) -> f64 {
|
||||
let n = graph.num_nodes;
|
||||
if n < 2 {
|
||||
return 0.0;
|
||||
}
|
||||
// Use adjacency weights directly as a rough proxy.
|
||||
let adj = graph.adjacency_matrix();
|
||||
let mut sum = 0.0;
|
||||
let mut count = 0;
|
||||
for i in 0..n {
|
||||
for j in (i + 1)..n {
|
||||
if adj[i][j] > 0.0 {
|
||||
sum += adj[i][j]; // weight as proxy for efficiency
|
||||
}
|
||||
count += 1;
|
||||
}
|
||||
}
|
||||
if count == 0 {
|
||||
return 0.0;
|
||||
}
|
||||
sum / count as f64
|
||||
}
|
||||
|
||||
/// Estimate graph entropy from edge weight distribution.
|
||||
fn estimate_entropy(graph: &BrainGraph) -> f64 {
|
||||
let total = graph.total_weight();
|
||||
if total < 1e-12 || graph.edges.is_empty() {
|
||||
return 0.0;
|
||||
}
|
||||
let mut entropy = 0.0;
|
||||
for edge in &graph.edges {
|
||||
let p = edge.weight / total;
|
||||
if p > 1e-15 {
|
||||
entropy -= p * p.ln();
|
||||
}
|
||||
}
|
||||
entropy
|
||||
}
|
||||
|
||||
/// Build a threshold decoder with default state definitions.
|
||||
fn build_default_decoder() -> ThresholdDecoder {
|
||||
let mut decoder = ThresholdDecoder::new();
|
||||
|
||||
decoder.set_threshold(
|
||||
CognitiveState::Rest,
|
||||
ruv_neural_decoder::TopologyThreshold {
|
||||
mincut_range: (0.0, 5.0),
|
||||
modularity_range: (0.2, 0.6),
|
||||
efficiency_range: (0.1, 0.4),
|
||||
entropy_range: (1.0, 3.0),
|
||||
},
|
||||
);
|
||||
|
||||
decoder.set_threshold(
|
||||
CognitiveState::Focused,
|
||||
ruv_neural_decoder::TopologyThreshold {
|
||||
mincut_range: (3.0, 15.0),
|
||||
modularity_range: (0.4, 0.8),
|
||||
efficiency_range: (0.3, 0.7),
|
||||
entropy_range: (2.0, 4.0),
|
||||
},
|
||||
);
|
||||
|
||||
decoder.set_threshold(
|
||||
CognitiveState::MotorPlanning,
|
||||
ruv_neural_decoder::TopologyThreshold {
|
||||
mincut_range: (2.0, 10.0),
|
||||
modularity_range: (0.3, 0.7),
|
||||
efficiency_range: (0.2, 0.6),
|
||||
entropy_range: (1.5, 3.5),
|
||||
},
|
||||
);
|
||||
|
||||
decoder
|
||||
}
|
||||
|
||||
/// Print a real-time-style ASCII dashboard.
|
||||
fn print_dashboard(
|
||||
ts: &MultiChannelTimeSeries,
|
||||
graph: &BrainGraph,
|
||||
mc: &ruv_neural_core::topology::MincutResult,
|
||||
metrics: &ruv_neural_core::topology::TopologyMetrics,
|
||||
) {
|
||||
println!(" ╔═══════════════════════════════════════════════════╗");
|
||||
println!(" ║ rUv Neural — Live Dashboard ║");
|
||||
println!(" ╠═══════════════════════════════════════════════════╣");
|
||||
println!(" ║ ║");
|
||||
|
||||
// Signal sparkline for first few channels.
|
||||
let display_channels = ts.num_channels.min(6);
|
||||
let display_samples = ts.num_samples.min(50);
|
||||
let sparkline_chars = ['▁', '▂', '▃', '▄', '▅', '▆', '▇', '█'];
|
||||
|
||||
for ch in 0..display_channels {
|
||||
let data = &ts.data[ch];
|
||||
let min_val = data.iter().cloned().fold(f64::INFINITY, f64::min);
|
||||
let max_val = data.iter().cloned().fold(f64::NEG_INFINITY, f64::max);
|
||||
let range = max_val - min_val;
|
||||
|
||||
let step = ts.num_samples / display_samples;
|
||||
let mut sparkline = String::new();
|
||||
for i in 0..display_samples {
|
||||
let val = data[i * step];
|
||||
let normalized = if range > 1e-12 {
|
||||
((val - min_val) / range * 7.0) as usize
|
||||
} else {
|
||||
4
|
||||
};
|
||||
sparkline.push(sparkline_chars[normalized.min(7)]);
|
||||
}
|
||||
println!(" ║ Ch{ch:02}: {sparkline} ║");
|
||||
}
|
||||
|
||||
println!(" ║ ║");
|
||||
println!(" ║ Graph: {} nodes, {} edges ║",
|
||||
format!("{:>3}", graph.num_nodes),
|
||||
format!("{:>4}", graph.edges.len()),
|
||||
);
|
||||
println!(" ║ Mincut: {:.4} Balance: {:.4} ║", mc.cut_value, mc.balance_ratio());
|
||||
println!(" ║ Modularity: {:.4} Entropy: {:.4} ║", metrics.modularity, metrics.graph_entropy);
|
||||
println!(" ║ ║");
|
||||
println!(" ╚═══════════════════════════════════════════════════╝");
|
||||
println!();
|
||||
}
|
||||
|
||||
#[cfg(test)]
|
||||
mod tests {
|
||||
use super::*;
|
||||
|
||||
#[test]
|
||||
fn pipeline_runs_end_to_end() {
|
||||
let result = run(4, 1.0, false);
|
||||
assert!(result.is_ok());
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn pipeline_with_dashboard() {
|
||||
let result = run(4, 0.5, true);
|
||||
assert!(result.is_ok());
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn plv_graph_has_edges() {
|
||||
let data = generate_data(4, 1000, 1000.0);
|
||||
let graph = build_plv_graph(&data, 1000.0);
|
||||
assert_eq!(graph.num_nodes, 4);
|
||||
// Channels with similar phase should have some PLV connectivity.
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn entropy_non_negative() {
|
||||
let data = generate_data(4, 1000, 1000.0);
|
||||
let graph = build_plv_graph(&data, 1000.0);
|
||||
let e = estimate_entropy(&graph);
|
||||
assert!(e >= 0.0);
|
||||
}
|
||||
}
|
||||
+156
@@ -0,0 +1,156 @@
|
||||
//! Simulate neural sensor data and write to JSON or stdout.
|
||||
|
||||
use std::f64::consts::PI;
|
||||
use std::fs;
|
||||
|
||||
use ruv_neural_core::signal::MultiChannelTimeSeries;
|
||||
|
||||
/// Run the simulate command.
|
||||
///
|
||||
/// Generates synthetic multi-channel neural data with configurable alpha,
|
||||
/// beta, and gamma oscillations plus realistic noise.
|
||||
pub fn run(
|
||||
channels: usize,
|
||||
duration: f64,
|
||||
sample_rate: f64,
|
||||
output: Option<String>,
|
||||
) -> Result<(), Box<dyn std::error::Error>> {
|
||||
let num_samples = (duration * sample_rate) as usize;
|
||||
if num_samples == 0 {
|
||||
return Err("Duration and sample rate must produce at least one sample".into());
|
||||
}
|
||||
|
||||
tracing::info!(
|
||||
channels,
|
||||
num_samples,
|
||||
sample_rate,
|
||||
duration,
|
||||
"Generating simulated neural data"
|
||||
);
|
||||
|
||||
let data = generate_neural_data(channels, num_samples, sample_rate);
|
||||
|
||||
let ts = MultiChannelTimeSeries::new(data.clone(), sample_rate, 0.0).map_err(|e| {
|
||||
Box::<dyn std::error::Error>::from(format!("Failed to create time series: {e}"))
|
||||
})?;
|
||||
|
||||
// Compute summary statistics.
|
||||
let mut channel_rms = Vec::with_capacity(channels);
|
||||
for ch in 0..channels {
|
||||
let rms = (data[ch].iter().map(|x| x * x).sum::<f64>() / num_samples as f64).sqrt();
|
||||
channel_rms.push(rms);
|
||||
}
|
||||
let mean_rms = channel_rms.iter().sum::<f64>() / channels as f64;
|
||||
|
||||
println!("=== rUv Neural — Simulation Complete ===");
|
||||
println!();
|
||||
println!(" Channels: {channels}");
|
||||
println!(" Samples: {num_samples}");
|
||||
println!(" Duration: {duration:.2} s");
|
||||
println!(" Sample rate: {sample_rate:.1} Hz");
|
||||
println!(" Mean RMS: {mean_rms:.4} fT");
|
||||
println!();
|
||||
|
||||
// Show frequency content summary.
|
||||
println!(" Frequency content:");
|
||||
println!(" Alpha (8-13 Hz): 10 Hz sinusoid, 50 fT amplitude");
|
||||
println!(" Beta (13-30 Hz): 20 Hz sinusoid, 30 fT amplitude");
|
||||
println!(" Gamma (30-100 Hz): 40 Hz sinusoid, 15 fT amplitude");
|
||||
println!(" Noise floor: ~10 fT/sqrt(Hz) white noise");
|
||||
println!();
|
||||
|
||||
match output {
|
||||
Some(ref path) => {
|
||||
let json = serde_json::to_string_pretty(&ts)?;
|
||||
fs::write(path, json)?;
|
||||
println!(" Output written to: {path}");
|
||||
}
|
||||
None => {
|
||||
println!(" (Use -o <file> to save output to JSON)");
|
||||
}
|
||||
}
|
||||
|
||||
Ok(())
|
||||
}
|
||||
|
||||
/// Generate synthetic neural data with realistic oscillations and noise.
|
||||
fn generate_neural_data(channels: usize, num_samples: usize, sample_rate: f64) -> Vec<Vec<f64>> {
|
||||
// Use a deterministic seed based on channel index for reproducibility.
|
||||
let mut data = Vec::with_capacity(channels);
|
||||
|
||||
for ch in 0..channels {
|
||||
let mut channel_data = Vec::with_capacity(num_samples);
|
||||
// Phase offsets vary by channel to simulate spatial diversity.
|
||||
let phase_offset = (ch as f64) * PI / (channels as f64);
|
||||
|
||||
// Simple LCG for deterministic pseudo-random noise per channel.
|
||||
let mut rng_state: u64 = (ch as u64).wrapping_mul(6364136223846793005).wrapping_add(1);
|
||||
|
||||
for i in 0..num_samples {
|
||||
let t = i as f64 / sample_rate;
|
||||
|
||||
// Alpha rhythm: 10 Hz, 50 fT
|
||||
let alpha = 50.0 * (2.0 * PI * 10.0 * t + phase_offset).sin();
|
||||
|
||||
// Beta rhythm: 20 Hz, 30 fT
|
||||
let beta = 30.0 * (2.0 * PI * 20.0 * t + phase_offset * 1.3).sin();
|
||||
|
||||
// Gamma rhythm: 40 Hz, 15 fT
|
||||
let gamma = 15.0 * (2.0 * PI * 40.0 * t + phase_offset * 0.7).sin();
|
||||
|
||||
// White noise (~10 fT/sqrt(Hz) density).
|
||||
// Approximate Gaussian via Box-Muller with LCG.
|
||||
rng_state = rng_state.wrapping_mul(6364136223846793005).wrapping_add(1442695040888963407);
|
||||
let u1 = (rng_state >> 11) as f64 / (1u64 << 53) as f64;
|
||||
rng_state = rng_state.wrapping_mul(6364136223846793005).wrapping_add(1442695040888963407);
|
||||
let u2 = (rng_state >> 11) as f64 / (1u64 << 53) as f64;
|
||||
|
||||
let noise_amplitude = 10.0 * (sample_rate / 2.0).sqrt();
|
||||
let gaussian = if u1 > 1e-15 {
|
||||
(-2.0 * u1.ln()).sqrt() * (2.0 * PI * u2).cos()
|
||||
} else {
|
||||
0.0
|
||||
};
|
||||
let noise = noise_amplitude * gaussian / (num_samples as f64).sqrt() * 0.1;
|
||||
|
||||
channel_data.push(alpha + beta + gamma + noise);
|
||||
}
|
||||
|
||||
data.push(channel_data);
|
||||
}
|
||||
|
||||
data
|
||||
}
|
||||
|
||||
#[cfg(test)]
|
||||
mod tests {
|
||||
use super::*;
|
||||
|
||||
#[test]
|
||||
fn generate_correct_shape() {
|
||||
let data = generate_neural_data(8, 500, 1000.0);
|
||||
assert_eq!(data.len(), 8);
|
||||
for ch in &data {
|
||||
assert_eq!(ch.len(), 500);
|
||||
}
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn simulate_produces_output() {
|
||||
let result = run(4, 1.0, 500.0, None);
|
||||
assert!(result.is_ok());
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn simulate_writes_json() {
|
||||
let dir = std::env::temp_dir();
|
||||
let path = dir.join("ruv_neural_test_sim.json");
|
||||
let path_str = path.to_string_lossy().to_string();
|
||||
let result = run(2, 0.5, 250.0, Some(path_str.clone()));
|
||||
assert!(result.is_ok());
|
||||
assert!(path.exists());
|
||||
let contents = std::fs::read_to_string(&path).unwrap();
|
||||
let _ts: MultiChannelTimeSeries = serde_json::from_str(&contents).unwrap();
|
||||
std::fs::remove_file(&path).ok();
|
||||
}
|
||||
}
|
||||
@@ -1 +0,0 @@
|
||||
//! Stub crate.
|
||||
@@ -0,0 +1,286 @@
|
||||
//! rUv Neural CLI — Brain topology analysis, simulation, and visualization.
|
||||
|
||||
mod commands;
|
||||
|
||||
use clap::{Parser, Subcommand};
|
||||
|
||||
#[derive(Parser)]
|
||||
#[command(name = "ruv-neural")]
|
||||
#[command(about = "rUv Neural — Brain Topology Analysis System")]
|
||||
#[command(version)]
|
||||
struct Cli {
|
||||
#[command(subcommand)]
|
||||
command: Commands,
|
||||
|
||||
/// Verbosity level
|
||||
#[arg(short, long, action = clap::ArgAction::Count)]
|
||||
verbose: u8,
|
||||
}
|
||||
|
||||
#[derive(Subcommand)]
|
||||
enum Commands {
|
||||
/// Simulate neural sensor data
|
||||
Simulate {
|
||||
/// Number of channels
|
||||
#[arg(short, long, default_value = "64")]
|
||||
channels: usize,
|
||||
/// Duration in seconds
|
||||
#[arg(short, long, default_value = "10.0")]
|
||||
duration: f64,
|
||||
/// Sample rate in Hz
|
||||
#[arg(short, long, default_value = "1000.0")]
|
||||
sample_rate: f64,
|
||||
/// Output file (JSON)
|
||||
#[arg(short, long)]
|
||||
output: Option<String>,
|
||||
},
|
||||
/// Analyze a brain connectivity graph
|
||||
Analyze {
|
||||
/// Input graph file (JSON)
|
||||
#[arg(short, long)]
|
||||
input: String,
|
||||
/// Show ASCII visualization
|
||||
#[arg(long)]
|
||||
ascii: bool,
|
||||
/// Export metrics to CSV
|
||||
#[arg(long)]
|
||||
csv: Option<String>,
|
||||
},
|
||||
/// Compute minimum cut on brain graph
|
||||
Mincut {
|
||||
/// Input graph file (JSON)
|
||||
#[arg(short, long)]
|
||||
input: String,
|
||||
/// Multi-way cut with k partitions
|
||||
#[arg(short, long)]
|
||||
k: Option<usize>,
|
||||
},
|
||||
/// Run full pipeline: simulate -> process -> analyze -> decode
|
||||
Pipeline {
|
||||
/// Number of channels
|
||||
#[arg(short, long, default_value = "32")]
|
||||
channels: usize,
|
||||
/// Duration in seconds
|
||||
#[arg(short, long, default_value = "5.0")]
|
||||
duration: f64,
|
||||
/// Show real-time ASCII dashboard
|
||||
#[arg(long)]
|
||||
dashboard: bool,
|
||||
},
|
||||
/// Export brain graph to visualization format
|
||||
Export {
|
||||
/// Input graph file (JSON)
|
||||
#[arg(short, long)]
|
||||
input: String,
|
||||
/// Output format: d3, dot, gexf, csv, rvf
|
||||
#[arg(short, long, default_value = "d3")]
|
||||
format: String,
|
||||
/// Output file
|
||||
#[arg(short, long)]
|
||||
output: String,
|
||||
},
|
||||
/// Show system info and capabilities
|
||||
Info,
|
||||
}
|
||||
|
||||
fn init_tracing(verbose: u8) {
|
||||
let level = match verbose {
|
||||
0 => tracing::Level::WARN,
|
||||
1 => tracing::Level::INFO,
|
||||
2 => tracing::Level::DEBUG,
|
||||
_ => tracing::Level::TRACE,
|
||||
};
|
||||
tracing_subscriber::fmt()
|
||||
.with_max_level(level)
|
||||
.with_target(false)
|
||||
.init();
|
||||
}
|
||||
|
||||
#[tokio::main]
|
||||
async fn main() {
|
||||
let cli = Cli::parse();
|
||||
init_tracing(cli.verbose);
|
||||
|
||||
let result = match cli.command {
|
||||
Commands::Simulate {
|
||||
channels,
|
||||
duration,
|
||||
sample_rate,
|
||||
output,
|
||||
} => commands::simulate::run(channels, duration, sample_rate, output),
|
||||
Commands::Analyze { input, ascii, csv } => commands::analyze::run(&input, ascii, csv),
|
||||
Commands::Mincut { input, k } => commands::mincut::run(&input, k),
|
||||
Commands::Pipeline {
|
||||
channels,
|
||||
duration,
|
||||
dashboard,
|
||||
} => commands::pipeline::run(channels, duration, dashboard),
|
||||
Commands::Export {
|
||||
input,
|
||||
format,
|
||||
output,
|
||||
} => commands::export::run(&input, &format, &output),
|
||||
Commands::Info => {
|
||||
commands::info::run();
|
||||
Ok(())
|
||||
}
|
||||
};
|
||||
|
||||
if let Err(e) = result {
|
||||
eprintln!("Error: {e}");
|
||||
std::process::exit(1);
|
||||
}
|
||||
}
|
||||
|
||||
#[cfg(test)]
|
||||
mod tests {
|
||||
use super::*;
|
||||
use clap::CommandFactory;
|
||||
|
||||
#[test]
|
||||
fn verify_cli() {
|
||||
Cli::command().debug_assert();
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn parse_simulate_defaults() {
|
||||
let cli = Cli::try_parse_from(["ruv-neural", "simulate"]).unwrap();
|
||||
match cli.command {
|
||||
Commands::Simulate {
|
||||
channels,
|
||||
duration,
|
||||
sample_rate,
|
||||
output,
|
||||
} => {
|
||||
assert_eq!(channels, 64);
|
||||
assert!((duration - 10.0).abs() < 1e-9);
|
||||
assert!((sample_rate - 1000.0).abs() < 1e-9);
|
||||
assert!(output.is_none());
|
||||
}
|
||||
_ => panic!("Expected Simulate command"),
|
||||
}
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn parse_simulate_with_args() {
|
||||
let cli = Cli::try_parse_from([
|
||||
"ruv-neural",
|
||||
"simulate",
|
||||
"-c",
|
||||
"32",
|
||||
"-d",
|
||||
"5.0",
|
||||
"-s",
|
||||
"500.0",
|
||||
"-o",
|
||||
"out.json",
|
||||
])
|
||||
.unwrap();
|
||||
match cli.command {
|
||||
Commands::Simulate {
|
||||
channels,
|
||||
duration,
|
||||
sample_rate,
|
||||
output,
|
||||
} => {
|
||||
assert_eq!(channels, 32);
|
||||
assert!((duration - 5.0).abs() < 1e-9);
|
||||
assert!((sample_rate - 500.0).abs() < 1e-9);
|
||||
assert_eq!(output.as_deref(), Some("out.json"));
|
||||
}
|
||||
_ => panic!("Expected Simulate command"),
|
||||
}
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn parse_analyze() {
|
||||
let cli =
|
||||
Cli::try_parse_from(["ruv-neural", "analyze", "-i", "graph.json", "--ascii"]).unwrap();
|
||||
match cli.command {
|
||||
Commands::Analyze { input, ascii, csv } => {
|
||||
assert_eq!(input, "graph.json");
|
||||
assert!(ascii);
|
||||
assert!(csv.is_none());
|
||||
}
|
||||
_ => panic!("Expected Analyze command"),
|
||||
}
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn parse_mincut() {
|
||||
let cli = Cli::try_parse_from(["ruv-neural", "mincut", "-i", "graph.json", "-k", "4"])
|
||||
.unwrap();
|
||||
match cli.command {
|
||||
Commands::Mincut { input, k } => {
|
||||
assert_eq!(input, "graph.json");
|
||||
assert_eq!(k, Some(4));
|
||||
}
|
||||
_ => panic!("Expected Mincut command"),
|
||||
}
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn parse_pipeline() {
|
||||
let cli = Cli::try_parse_from([
|
||||
"ruv-neural",
|
||||
"pipeline",
|
||||
"-c",
|
||||
"16",
|
||||
"-d",
|
||||
"3.0",
|
||||
"--dashboard",
|
||||
])
|
||||
.unwrap();
|
||||
match cli.command {
|
||||
Commands::Pipeline {
|
||||
channels,
|
||||
duration,
|
||||
dashboard,
|
||||
} => {
|
||||
assert_eq!(channels, 16);
|
||||
assert!((duration - 3.0).abs() < 1e-9);
|
||||
assert!(dashboard);
|
||||
}
|
||||
_ => panic!("Expected Pipeline command"),
|
||||
}
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn parse_export() {
|
||||
let cli = Cli::try_parse_from([
|
||||
"ruv-neural",
|
||||
"export",
|
||||
"-i",
|
||||
"graph.json",
|
||||
"-f",
|
||||
"dot",
|
||||
"-o",
|
||||
"out.dot",
|
||||
])
|
||||
.unwrap();
|
||||
match cli.command {
|
||||
Commands::Export {
|
||||
input,
|
||||
format,
|
||||
output,
|
||||
} => {
|
||||
assert_eq!(input, "graph.json");
|
||||
assert_eq!(format, "dot");
|
||||
assert_eq!(output, "out.dot");
|
||||
}
|
||||
_ => panic!("Expected Export command"),
|
||||
}
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn parse_info() {
|
||||
let cli = Cli::try_parse_from(["ruv-neural", "info"]).unwrap();
|
||||
assert!(matches!(cli.command, Commands::Info));
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn parse_verbose() {
|
||||
let cli = Cli::try_parse_from(["ruv-neural", "-vvv", "info"]).unwrap();
|
||||
assert_eq!(cli.verbose, 3);
|
||||
}
|
||||
}
|
||||
@@ -0,0 +1,73 @@
|
||||
# rUv Neural Core
|
||||
|
||||
**Core types, traits, and error types for the ruv-neural brain topology analysis system.**
|
||||
|
||||
`ruv-neural-core` is the foundational crate of the ruv-neural workspace. It defines all shared types, error variants, and trait interfaces that downstream crates implement. It has **zero internal dependencies** -- every other ruv-neural crate depends on this one.
|
||||
|
||||
## Feature Flags
|
||||
|
||||
| Feature | Default | Description |
|
||||
|----------|---------|------------------------------------------|
|
||||
| `std` | Yes | Standard library support |
|
||||
| `no_std` | No | Embedded/ESP32 target compatibility |
|
||||
| `wasm` | No | WebAssembly target support |
|
||||
| `rvf` | No | RuVector RVF file format extensions |
|
||||
|
||||
## Type Overview
|
||||
|
||||
| Module | Key Types |
|
||||
|-------------|------------------------------------------------------------------|
|
||||
| `error` | `RuvNeuralError`, `Result<T>` |
|
||||
| `sensor` | `SensorType`, `SensorChannel`, `SensorArray` |
|
||||
| `signal` | `MultiChannelTimeSeries`, `FrequencyBand`, `SpectralFeatures` |
|
||||
| `brain` | `Atlas`, `BrainRegion`, `Hemisphere`, `Lobe`, `Parcellation` |
|
||||
| `graph` | `BrainGraph`, `BrainEdge`, `ConnectivityMetric` |
|
||||
| `topology` | `MincutResult`, `MultiPartition`, `CognitiveState`, `TopologyMetrics` |
|
||||
| `embedding` | `NeuralEmbedding`, `EmbeddingMetadata`, `EmbeddingTrajectory` |
|
||||
| `rvf` | `RvfFile`, `RvfHeader`, `RvfDataType` |
|
||||
|
||||
## Trait Overview
|
||||
|
||||
| Trait | Purpose |
|
||||
|----------------------|------------------------------------------------|
|
||||
| `SensorSource` | Read chunks from hardware or simulated sensors |
|
||||
| `SignalProcessor` | Transform time series (filter, artifact removal)|
|
||||
| `GraphConstructor` | Build connectivity graphs from signals |
|
||||
| `TopologyAnalyzer` | Compute mincut, modularity, efficiency |
|
||||
| `EmbeddingGenerator` | Map brain graphs to vector space |
|
||||
| `StateDecoder` | Classify cognitive state from embeddings |
|
||||
| `NeuralMemory` | Store and query embedding history |
|
||||
| `RvfSerializable` | Serialize/deserialize to RVF file format |
|
||||
|
||||
## Usage
|
||||
|
||||
```rust
|
||||
use ruv_neural_core::{
|
||||
Atlas, BrainGraph, BrainEdge, ConnectivityMetric, FrequencyBand,
|
||||
NeuralEmbedding, EmbeddingMetadata, CognitiveState,
|
||||
RvfFile, RvfDataType,
|
||||
Result,
|
||||
};
|
||||
|
||||
// Build a connectivity graph
|
||||
let graph = BrainGraph {
|
||||
num_nodes: 68,
|
||||
edges: vec![BrainEdge {
|
||||
source: 0,
|
||||
target: 1,
|
||||
weight: 0.85,
|
||||
metric: ConnectivityMetric::PhaseLockingValue,
|
||||
frequency_band: FrequencyBand::Alpha,
|
||||
}],
|
||||
timestamp: 1000.0,
|
||||
window_duration_s: 2.0,
|
||||
atlas: Atlas::DesikanKilliany68,
|
||||
};
|
||||
|
||||
let adj = graph.adjacency_matrix();
|
||||
let density = graph.density();
|
||||
```
|
||||
|
||||
## License
|
||||
|
||||
MIT OR Apache-2.0
|
||||
@@ -1,4 +1,24 @@
|
||||
//! rUv Neural Core — types, traits, and error types for brain topology analysis.
|
||||
//! # ruv-neural-core
|
||||
//!
|
||||
//! Core types, traits, and error types for the ruv-neural brain topology
|
||||
//! analysis system.
|
||||
//!
|
||||
//! This crate is the foundation of the ruv-neural workspace. It has **zero**
|
||||
//! internal dependencies — all other ruv-neural crates depend on this one.
|
||||
//!
|
||||
//! ## Modules
|
||||
//!
|
||||
//! | Module | Contents |
|
||||
//! |-------------|---------------------------------------------------|
|
||||
//! | `error` | `RuvNeuralError` enum, `Result<T>` alias |
|
||||
//! | `sensor` | `SensorType`, `SensorChannel`, `SensorArray` |
|
||||
//! | `signal` | `MultiChannelTimeSeries`, `FrequencyBand`, spectra |
|
||||
//! | `brain` | `Atlas`, `BrainRegion`, `Parcellation` |
|
||||
//! | `graph` | `BrainGraph`, `BrainEdge`, `ConnectivityMetric` |
|
||||
//! | `topology` | `MincutResult`, `CognitiveState`, `TopologyMetrics`|
|
||||
//! | `embedding` | `NeuralEmbedding`, `EmbeddingTrajectory` |
|
||||
//! | `rvf` | RuVector File format header and I/O |
|
||||
//! | `traits` | Pipeline trait definitions for all crates |
|
||||
|
||||
pub mod brain;
|
||||
pub mod embedding;
|
||||
@@ -10,9 +30,616 @@ pub mod signal;
|
||||
pub mod topology;
|
||||
pub mod traits;
|
||||
|
||||
// Re-export the most commonly used types at crate root.
|
||||
pub use brain::{Atlas, BrainRegion, Hemisphere, Lobe, Parcellation};
|
||||
pub use embedding::{EmbeddingMetadata, EmbeddingTrajectory, NeuralEmbedding};
|
||||
pub use error::{Result, RuvNeuralError};
|
||||
pub use graph::{BrainEdge, BrainGraph, BrainGraphSequence, ConnectivityMetric};
|
||||
pub use rvf::{RvfDataType, RvfFile, RvfHeader};
|
||||
pub use sensor::{SensorArray, SensorChannel, SensorType};
|
||||
pub use signal::{FrequencyBand, MultiChannelTimeSeries, SpectralFeatures, TimeFrequencyMap};
|
||||
pub use traits::SensorSource;
|
||||
pub use topology::{
|
||||
CognitiveState, MincutResult, MultiPartition, SleepStage, TopologyMetrics,
|
||||
};
|
||||
pub use traits::{
|
||||
EmbeddingGenerator, GraphConstructor, NeuralMemory, RvfSerializable, SensorSource,
|
||||
SignalProcessor, StateDecoder, TopologyAnalyzer,
|
||||
};
|
||||
|
||||
#[cfg(test)]
|
||||
mod tests {
|
||||
use super::*;
|
||||
|
||||
// ── Error tests ─────────────────────────────────────────────────
|
||||
|
||||
#[test]
|
||||
fn error_display_formatting() {
|
||||
let err = RuvNeuralError::Sensor("calibration failed".into());
|
||||
assert!(err.to_string().contains("Sensor error"));
|
||||
assert!(err.to_string().contains("calibration failed"));
|
||||
|
||||
let err = RuvNeuralError::DimensionMismatch {
|
||||
expected: 68,
|
||||
got: 100,
|
||||
};
|
||||
assert!(err.to_string().contains("68"));
|
||||
assert!(err.to_string().contains("100"));
|
||||
|
||||
let err = RuvNeuralError::ChannelOutOfRange {
|
||||
channel: 5,
|
||||
max: 3,
|
||||
};
|
||||
assert!(err.to_string().contains("5"));
|
||||
assert!(err.to_string().contains("3"));
|
||||
|
||||
let err = RuvNeuralError::InsufficientData {
|
||||
needed: 1000,
|
||||
have: 500,
|
||||
};
|
||||
assert!(err.to_string().contains("1000"));
|
||||
assert!(err.to_string().contains("500"));
|
||||
}
|
||||
|
||||
// ── Sensor tests ────────────────────────────────────────────────
|
||||
|
||||
#[test]
|
||||
fn sensor_type_sensitivity() {
|
||||
assert!(SensorType::SquidMeg.typical_sensitivity_ft_sqrt_hz() < 5.0);
|
||||
assert!(SensorType::Eeg.typical_sensitivity_ft_sqrt_hz() > 100.0);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn sensor_array_operations() {
|
||||
let array = SensorArray {
|
||||
channels: vec![
|
||||
SensorChannel {
|
||||
id: 0,
|
||||
sensor_type: SensorType::Opm,
|
||||
position: [0.0, 0.0, 0.1],
|
||||
orientation: [0.0, 0.0, 1.0],
|
||||
sensitivity_ft_sqrt_hz: 7.0,
|
||||
sample_rate_hz: 1000.0,
|
||||
label: "OPM-001".into(),
|
||||
},
|
||||
SensorChannel {
|
||||
id: 1,
|
||||
sensor_type: SensorType::Opm,
|
||||
position: [0.05, 0.0, 0.12],
|
||||
orientation: [0.0, 0.0, 1.0],
|
||||
sensitivity_ft_sqrt_hz: 7.0,
|
||||
sample_rate_hz: 1000.0,
|
||||
label: "OPM-002".into(),
|
||||
},
|
||||
],
|
||||
sensor_type: SensorType::Opm,
|
||||
name: "OPM array".into(),
|
||||
};
|
||||
|
||||
assert_eq!(array.num_channels(), 2);
|
||||
assert!(!array.is_empty());
|
||||
assert_eq!(array.get_channel(0).unwrap().label, "OPM-001");
|
||||
assert!(array.get_channel(5).is_none());
|
||||
|
||||
let (min, max) = array.bounding_box().unwrap();
|
||||
assert_eq!(min[0], 0.0);
|
||||
assert_eq!(max[0], 0.05);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn sensor_serialize_roundtrip() {
|
||||
let ch = SensorChannel {
|
||||
id: 0,
|
||||
sensor_type: SensorType::NvDiamond,
|
||||
position: [1.0, 2.0, 3.0],
|
||||
orientation: [0.0, 0.0, 1.0],
|
||||
sensitivity_ft_sqrt_hz: 10.0,
|
||||
sample_rate_hz: 2000.0,
|
||||
label: "NV-001".into(),
|
||||
};
|
||||
let json = serde_json::to_string(&ch).unwrap();
|
||||
let ch2: SensorChannel = serde_json::from_str(&json).unwrap();
|
||||
assert_eq!(ch2.id, 0);
|
||||
assert_eq!(ch2.sensor_type, SensorType::NvDiamond);
|
||||
}
|
||||
|
||||
// ── Signal tests ────────────────────────────────────────────────
|
||||
|
||||
#[test]
|
||||
fn frequency_band_ranges() {
|
||||
assert_eq!(FrequencyBand::Delta.range_hz(), (1.0, 4.0));
|
||||
assert_eq!(FrequencyBand::Alpha.range_hz(), (8.0, 13.0));
|
||||
assert_eq!(FrequencyBand::Gamma.range_hz(), (30.0, 100.0));
|
||||
assert_eq!(
|
||||
FrequencyBand::Custom {
|
||||
low_hz: 50.0,
|
||||
high_hz: 70.0
|
||||
}
|
||||
.range_hz(),
|
||||
(50.0, 70.0)
|
||||
);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn frequency_band_center_and_bandwidth() {
|
||||
assert!((FrequencyBand::Alpha.center_hz() - 10.5).abs() < 1e-10);
|
||||
assert!((FrequencyBand::Alpha.bandwidth_hz() - 5.0).abs() < 1e-10);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn time_series_creation_valid() {
|
||||
let data = vec![vec![1.0, 2.0, 3.0], vec![4.0, 5.0, 6.0]];
|
||||
let ts = MultiChannelTimeSeries::new(data, 100.0, 1000.0).unwrap();
|
||||
assert_eq!(ts.num_channels, 2);
|
||||
assert_eq!(ts.num_samples, 3);
|
||||
assert!((ts.duration_s() - 0.03).abs() < 1e-10);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn time_series_dimension_mismatch() {
|
||||
let data = vec![vec![1.0, 2.0], vec![3.0]];
|
||||
let result = MultiChannelTimeSeries::new(data, 100.0, 0.0);
|
||||
assert!(result.is_err());
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn time_series_channel_access() {
|
||||
let data = vec![vec![10.0, 20.0], vec![30.0, 40.0]];
|
||||
let ts = MultiChannelTimeSeries::new(data, 100.0, 0.0).unwrap();
|
||||
assert_eq!(ts.channel(0).unwrap(), &[10.0, 20.0]);
|
||||
assert!(ts.channel(5).is_err());
|
||||
}
|
||||
|
||||
// ── Brain / Atlas tests ─────────────────────────────────────────
|
||||
|
||||
#[test]
|
||||
fn atlas_region_counts() {
|
||||
assert_eq!(Atlas::DesikanKilliany68.num_regions(), 68);
|
||||
assert_eq!(Atlas::Destrieux148.num_regions(), 148);
|
||||
assert_eq!(Atlas::Schaefer100.num_regions(), 100);
|
||||
assert_eq!(Atlas::Schaefer200.num_regions(), 200);
|
||||
assert_eq!(Atlas::Schaefer400.num_regions(), 400);
|
||||
assert_eq!(Atlas::Custom(42).num_regions(), 42);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn parcellation_query() {
|
||||
let parcellation = Parcellation {
|
||||
atlas: Atlas::Custom(3),
|
||||
regions: vec![
|
||||
BrainRegion {
|
||||
id: 0,
|
||||
name: "left_frontal".into(),
|
||||
hemisphere: Hemisphere::Left,
|
||||
lobe: Lobe::Frontal,
|
||||
centroid: [-30.0, 20.0, 40.0],
|
||||
},
|
||||
BrainRegion {
|
||||
id: 1,
|
||||
name: "right_frontal".into(),
|
||||
hemisphere: Hemisphere::Right,
|
||||
lobe: Lobe::Frontal,
|
||||
centroid: [30.0, 20.0, 40.0],
|
||||
},
|
||||
BrainRegion {
|
||||
id: 2,
|
||||
name: "left_temporal".into(),
|
||||
hemisphere: Hemisphere::Left,
|
||||
lobe: Lobe::Temporal,
|
||||
centroid: [-50.0, -10.0, 0.0],
|
||||
},
|
||||
],
|
||||
};
|
||||
|
||||
assert_eq!(parcellation.num_regions(), 3);
|
||||
assert_eq!(
|
||||
parcellation.regions_in_hemisphere(Hemisphere::Left).len(),
|
||||
2
|
||||
);
|
||||
assert_eq!(parcellation.regions_in_lobe(Lobe::Frontal).len(), 2);
|
||||
assert_eq!(parcellation.regions_in_lobe(Lobe::Temporal).len(), 1);
|
||||
assert!(parcellation.get_region(1).is_some());
|
||||
assert!(parcellation.get_region(99).is_none());
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn brain_region_serialize_roundtrip() {
|
||||
let region = BrainRegion {
|
||||
id: 42,
|
||||
name: "postcentral".into(),
|
||||
hemisphere: Hemisphere::Left,
|
||||
lobe: Lobe::Parietal,
|
||||
centroid: [-40.0, -25.0, 55.0],
|
||||
};
|
||||
let json = serde_json::to_string(®ion).unwrap();
|
||||
let r2: BrainRegion = serde_json::from_str(&json).unwrap();
|
||||
assert_eq!(r2.id, 42);
|
||||
assert_eq!(r2.hemisphere, Hemisphere::Left);
|
||||
}
|
||||
|
||||
// ── Graph tests ─────────────────────────────────────────────────
|
||||
|
||||
#[test]
|
||||
fn brain_graph_adjacency_matrix() {
|
||||
let graph = BrainGraph {
|
||||
num_nodes: 3,
|
||||
edges: vec![
|
||||
BrainEdge {
|
||||
source: 0,
|
||||
target: 1,
|
||||
weight: 0.8,
|
||||
metric: ConnectivityMetric::PhaseLockingValue,
|
||||
frequency_band: FrequencyBand::Alpha,
|
||||
},
|
||||
BrainEdge {
|
||||
source: 1,
|
||||
target: 2,
|
||||
weight: 0.5,
|
||||
metric: ConnectivityMetric::Coherence,
|
||||
frequency_band: FrequencyBand::Beta,
|
||||
},
|
||||
],
|
||||
timestamp: 100.0,
|
||||
window_duration_s: 1.0,
|
||||
atlas: Atlas::Custom(3),
|
||||
};
|
||||
|
||||
let mat = graph.adjacency_matrix();
|
||||
assert_eq!(mat.len(), 3);
|
||||
assert!((mat[0][1] - 0.8).abs() < 1e-10);
|
||||
assert!((mat[1][0] - 0.8).abs() < 1e-10);
|
||||
assert!((mat[1][2] - 0.5).abs() < 1e-10);
|
||||
assert!((mat[0][2] - 0.0).abs() < 1e-10);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn brain_graph_edge_weight_lookup() {
|
||||
let graph = BrainGraph {
|
||||
num_nodes: 2,
|
||||
edges: vec![BrainEdge {
|
||||
source: 0,
|
||||
target: 1,
|
||||
weight: 0.9,
|
||||
metric: ConnectivityMetric::MutualInformation,
|
||||
frequency_band: FrequencyBand::Gamma,
|
||||
}],
|
||||
timestamp: 0.0,
|
||||
window_duration_s: 0.5,
|
||||
atlas: Atlas::Custom(2),
|
||||
};
|
||||
|
||||
assert!((graph.edge_weight(0, 1).unwrap() - 0.9).abs() < 1e-10);
|
||||
assert!((graph.edge_weight(1, 0).unwrap() - 0.9).abs() < 1e-10);
|
||||
assert!(graph.edge_weight(0, 0).is_none());
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn brain_graph_node_degree() {
|
||||
let graph = BrainGraph {
|
||||
num_nodes: 3,
|
||||
edges: vec![
|
||||
BrainEdge {
|
||||
source: 0,
|
||||
target: 1,
|
||||
weight: 0.3,
|
||||
metric: ConnectivityMetric::Coherence,
|
||||
frequency_band: FrequencyBand::Alpha,
|
||||
},
|
||||
BrainEdge {
|
||||
source: 0,
|
||||
target: 2,
|
||||
weight: 0.7,
|
||||
metric: ConnectivityMetric::Coherence,
|
||||
frequency_band: FrequencyBand::Alpha,
|
||||
},
|
||||
],
|
||||
timestamp: 0.0,
|
||||
window_duration_s: 1.0,
|
||||
atlas: Atlas::Custom(3),
|
||||
};
|
||||
|
||||
assert!((graph.node_degree(0) - 1.0).abs() < 1e-10);
|
||||
assert!((graph.node_degree(1) - 0.3).abs() < 1e-10);
|
||||
assert!((graph.node_degree(2) - 0.7).abs() < 1e-10);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn brain_graph_density() {
|
||||
let graph = BrainGraph {
|
||||
num_nodes: 4,
|
||||
edges: vec![
|
||||
BrainEdge {
|
||||
source: 0,
|
||||
target: 1,
|
||||
weight: 1.0,
|
||||
metric: ConnectivityMetric::PhaseLockingValue,
|
||||
frequency_band: FrequencyBand::Alpha,
|
||||
},
|
||||
BrainEdge {
|
||||
source: 2,
|
||||
target: 3,
|
||||
weight: 1.0,
|
||||
metric: ConnectivityMetric::PhaseLockingValue,
|
||||
frequency_band: FrequencyBand::Alpha,
|
||||
},
|
||||
BrainEdge {
|
||||
source: 0,
|
||||
target: 3,
|
||||
weight: 1.0,
|
||||
metric: ConnectivityMetric::PhaseLockingValue,
|
||||
frequency_band: FrequencyBand::Alpha,
|
||||
},
|
||||
],
|
||||
timestamp: 0.0,
|
||||
window_duration_s: 1.0,
|
||||
atlas: Atlas::Custom(4),
|
||||
};
|
||||
|
||||
assert!((graph.density() - 0.5).abs() < 1e-10);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn graph_sequence_duration() {
|
||||
let seq = BrainGraphSequence {
|
||||
graphs: vec![
|
||||
BrainGraph {
|
||||
num_nodes: 2,
|
||||
edges: vec![],
|
||||
timestamp: 0.0,
|
||||
window_duration_s: 1.0,
|
||||
atlas: Atlas::Custom(2),
|
||||
},
|
||||
BrainGraph {
|
||||
num_nodes: 2,
|
||||
edges: vec![],
|
||||
timestamp: 0.5,
|
||||
window_duration_s: 1.0,
|
||||
atlas: Atlas::Custom(2),
|
||||
},
|
||||
BrainGraph {
|
||||
num_nodes: 2,
|
||||
edges: vec![],
|
||||
timestamp: 1.0,
|
||||
window_duration_s: 1.0,
|
||||
atlas: Atlas::Custom(2),
|
||||
},
|
||||
],
|
||||
window_step_s: 0.5,
|
||||
};
|
||||
|
||||
assert_eq!(seq.len(), 3);
|
||||
assert!(!seq.is_empty());
|
||||
assert!((seq.duration_s() - 2.0).abs() < 1e-10);
|
||||
}
|
||||
|
||||
// ── Topology tests ──────────────────────────────────────────────
|
||||
|
||||
#[test]
|
||||
fn mincut_result_properties() {
|
||||
let result = MincutResult {
|
||||
cut_value: 1.5,
|
||||
partition_a: vec![0, 1],
|
||||
partition_b: vec![2, 3, 4],
|
||||
cut_edges: vec![(1, 2, 0.8), (0, 3, 0.7)],
|
||||
timestamp: 100.0,
|
||||
};
|
||||
|
||||
assert_eq!(result.num_nodes(), 5);
|
||||
assert_eq!(result.num_cut_edges(), 2);
|
||||
assert!((result.balance_ratio() - 2.0 / 3.0).abs() < 1e-10);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn multi_partition_properties() {
|
||||
let mp = MultiPartition {
|
||||
partitions: vec![vec![0, 1], vec![2, 3], vec![4]],
|
||||
cut_value: 2.0,
|
||||
modularity: 0.4,
|
||||
};
|
||||
assert_eq!(mp.num_partitions(), 3);
|
||||
assert_eq!(mp.num_nodes(), 5);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn cognitive_state_serialize_roundtrip() {
|
||||
let states = vec![
|
||||
CognitiveState::Rest,
|
||||
CognitiveState::Focused,
|
||||
CognitiveState::Sleep(SleepStage::Rem),
|
||||
CognitiveState::Unknown,
|
||||
];
|
||||
let json = serde_json::to_string(&states).unwrap();
|
||||
let deserialized: Vec<CognitiveState> = serde_json::from_str(&json).unwrap();
|
||||
assert_eq!(states, deserialized);
|
||||
}
|
||||
|
||||
// ── Embedding tests ─────────────────────────────────────────────
|
||||
|
||||
#[test]
|
||||
fn embedding_creation_and_norm() {
|
||||
let meta = EmbeddingMetadata {
|
||||
subject_id: Some("sub-01".into()),
|
||||
session_id: Some("ses-01".into()),
|
||||
cognitive_state: Some(CognitiveState::Focused),
|
||||
source_atlas: Atlas::Schaefer100,
|
||||
embedding_method: "spectral".into(),
|
||||
};
|
||||
let emb = NeuralEmbedding::new(vec![3.0, 4.0], 1000.0, meta).unwrap();
|
||||
assert_eq!(emb.dimension, 2);
|
||||
assert!((emb.norm() - 5.0).abs() < 1e-10);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn embedding_cosine_similarity() {
|
||||
let meta = || EmbeddingMetadata {
|
||||
subject_id: None,
|
||||
session_id: None,
|
||||
cognitive_state: None,
|
||||
source_atlas: Atlas::Custom(2),
|
||||
embedding_method: "test".into(),
|
||||
};
|
||||
|
||||
let a = NeuralEmbedding::new(vec![1.0, 0.0], 0.0, meta()).unwrap();
|
||||
let b = NeuralEmbedding::new(vec![1.0, 0.0], 0.0, meta()).unwrap();
|
||||
let c = NeuralEmbedding::new(vec![0.0, 1.0], 0.0, meta()).unwrap();
|
||||
|
||||
assert!((a.cosine_similarity(&b).unwrap() - 1.0).abs() < 1e-10);
|
||||
assert!((a.cosine_similarity(&c).unwrap() - 0.0).abs() < 1e-10);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn embedding_euclidean_distance() {
|
||||
let meta = || EmbeddingMetadata {
|
||||
subject_id: None,
|
||||
session_id: None,
|
||||
cognitive_state: None,
|
||||
source_atlas: Atlas::Custom(2),
|
||||
embedding_method: "test".into(),
|
||||
};
|
||||
|
||||
let a = NeuralEmbedding::new(vec![0.0, 0.0], 0.0, meta()).unwrap();
|
||||
let b = NeuralEmbedding::new(vec![3.0, 4.0], 0.0, meta()).unwrap();
|
||||
assert!((a.euclidean_distance(&b).unwrap() - 5.0).abs() < 1e-10);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn embedding_dimension_mismatch() {
|
||||
let meta = || EmbeddingMetadata {
|
||||
subject_id: None,
|
||||
session_id: None,
|
||||
cognitive_state: None,
|
||||
source_atlas: Atlas::Custom(2),
|
||||
embedding_method: "test".into(),
|
||||
};
|
||||
|
||||
let a = NeuralEmbedding::new(vec![1.0, 2.0], 0.0, meta()).unwrap();
|
||||
let b = NeuralEmbedding::new(vec![1.0, 2.0, 3.0], 0.0, meta()).unwrap();
|
||||
assert!(a.cosine_similarity(&b).is_err());
|
||||
assert!(a.euclidean_distance(&b).is_err());
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn embedding_trajectory() {
|
||||
let meta = || EmbeddingMetadata {
|
||||
subject_id: None,
|
||||
session_id: None,
|
||||
cognitive_state: None,
|
||||
source_atlas: Atlas::Custom(2),
|
||||
embedding_method: "test".into(),
|
||||
};
|
||||
|
||||
let traj = EmbeddingTrajectory {
|
||||
embeddings: vec![
|
||||
NeuralEmbedding::new(vec![1.0], 0.0, meta()).unwrap(),
|
||||
NeuralEmbedding::new(vec![2.0], 1.0, meta()).unwrap(),
|
||||
NeuralEmbedding::new(vec![3.0], 2.0, meta()).unwrap(),
|
||||
],
|
||||
timestamps: vec![0.0, 1.0, 2.0],
|
||||
};
|
||||
|
||||
assert_eq!(traj.len(), 3);
|
||||
assert!(!traj.is_empty());
|
||||
assert!((traj.duration_s() - 2.0).abs() < 1e-10);
|
||||
}
|
||||
|
||||
// ── RVF tests ───────────────────────────────────────────────────
|
||||
|
||||
#[test]
|
||||
fn rvf_data_type_tag_roundtrip() {
|
||||
for dt in [
|
||||
RvfDataType::BrainGraph,
|
||||
RvfDataType::NeuralEmbedding,
|
||||
RvfDataType::TopologyMetrics,
|
||||
RvfDataType::MincutResult,
|
||||
RvfDataType::TimeSeriesChunk,
|
||||
] {
|
||||
let tag = dt.to_tag();
|
||||
let recovered = RvfDataType::from_tag(tag).unwrap();
|
||||
assert_eq!(dt, recovered);
|
||||
}
|
||||
assert!(RvfDataType::from_tag(255).is_err());
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn rvf_header_encode_decode() {
|
||||
let header = RvfHeader::new(RvfDataType::NeuralEmbedding, 42, 128);
|
||||
let bytes = header.to_bytes();
|
||||
assert_eq!(bytes.len(), 22);
|
||||
|
||||
let decoded = RvfHeader::from_bytes(&bytes).unwrap();
|
||||
assert_eq!(decoded.magic, rvf::RVF_MAGIC);
|
||||
assert_eq!(decoded.version, rvf::RVF_VERSION);
|
||||
assert_eq!(decoded.data_type, RvfDataType::NeuralEmbedding);
|
||||
assert_eq!(decoded.num_entries, 42);
|
||||
assert_eq!(decoded.embedding_dim, 128);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn rvf_header_validation() {
|
||||
let mut header = RvfHeader::new(RvfDataType::BrainGraph, 1, 0);
|
||||
assert!(header.validate().is_ok());
|
||||
|
||||
header.magic = [0, 0, 0, 0];
|
||||
assert!(header.validate().is_err());
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn rvf_file_write_read_roundtrip() {
|
||||
let mut file = RvfFile::new(RvfDataType::TopologyMetrics);
|
||||
file.header.num_entries = 1;
|
||||
file.metadata = serde_json::json!({ "subject": "sub-01" });
|
||||
file.data = vec![1, 2, 3, 4, 5];
|
||||
|
||||
let mut buf = Vec::new();
|
||||
file.write_to(&mut buf).unwrap();
|
||||
|
||||
let mut cursor = std::io::Cursor::new(buf);
|
||||
let recovered = RvfFile::read_from(&mut cursor).unwrap();
|
||||
|
||||
assert_eq!(recovered.header.data_type, RvfDataType::TopologyMetrics);
|
||||
assert_eq!(recovered.header.num_entries, 1);
|
||||
assert_eq!(recovered.metadata["subject"], "sub-01");
|
||||
assert_eq!(recovered.data, vec![1, 2, 3, 4, 5]);
|
||||
}
|
||||
|
||||
// ── Serialization roundtrip tests ───────────────────────────────
|
||||
|
||||
#[test]
|
||||
fn graph_serialize_roundtrip() {
|
||||
let graph = BrainGraph {
|
||||
num_nodes: 2,
|
||||
edges: vec![BrainEdge {
|
||||
source: 0,
|
||||
target: 1,
|
||||
weight: 0.42,
|
||||
metric: ConnectivityMetric::TransferEntropy,
|
||||
frequency_band: FrequencyBand::Theta,
|
||||
}],
|
||||
timestamp: 999.0,
|
||||
window_duration_s: 2.0,
|
||||
atlas: Atlas::Schaefer200,
|
||||
};
|
||||
let json = serde_json::to_string(&graph).unwrap();
|
||||
let g2: BrainGraph = serde_json::from_str(&json).unwrap();
|
||||
assert_eq!(g2.num_nodes, 2);
|
||||
assert_eq!(g2.edges.len(), 1);
|
||||
assert!((g2.edges[0].weight - 0.42).abs() < 1e-10);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn topology_metrics_serialize_roundtrip() {
|
||||
let metrics = TopologyMetrics {
|
||||
global_mincut: 3.14,
|
||||
modularity: 0.55,
|
||||
global_efficiency: 0.72,
|
||||
local_efficiency: 0.68,
|
||||
graph_entropy: 2.3,
|
||||
fiedler_value: 0.12,
|
||||
num_modules: 4,
|
||||
timestamp: 500.0,
|
||||
};
|
||||
let json = serde_json::to_string(&metrics).unwrap();
|
||||
let m2: TopologyMetrics = serde_json::from_str(&json).unwrap();
|
||||
assert!((m2.global_mincut - 3.14).abs() < 1e-10);
|
||||
assert_eq!(m2.num_modules, 4);
|
||||
}
|
||||
}
|
||||
|
||||
@@ -13,8 +13,8 @@ wasm = []
|
||||
|
||||
[dependencies]
|
||||
ruv-neural-core = { workspace = true }
|
||||
ruv-neural-embed = { workspace = true }
|
||||
ruv-neural-memory = { workspace = true }
|
||||
# ruv-neural-embed and ruv-neural-memory are available for future integration
|
||||
# but not currently required for core decoder functionality
|
||||
serde = { workspace = true }
|
||||
serde_json = { workspace = true }
|
||||
tracing = { workspace = true }
|
||||
|
||||
@@ -0,0 +1,86 @@
|
||||
# rUv Neural Decoder
|
||||
|
||||
Cognitive state classification and BCI decoding from neural topology embeddings.
|
||||
|
||||
Part of the **rUv Neural** brain-computer interface platform.
|
||||
|
||||
## Decoders
|
||||
|
||||
| Decoder | Description |
|
||||
|---------|-------------|
|
||||
| **KnnDecoder** | K-nearest neighbor classification using stored labeled embeddings with inverse-distance weighting |
|
||||
| **ThresholdDecoder** | Rule-based classification from topology metric ranges (mincut, modularity, efficiency, entropy) |
|
||||
| **TransitionDecoder** | Detects cognitive state transitions by matching topology delta patterns against a sliding window |
|
||||
| **ClinicalScorer** | Biomarker detection via z-score deviation from a learned healthy baseline population |
|
||||
| **DecoderPipeline** | End-to-end ensemble combining all decoders with configurable weights and clinical scoring |
|
||||
|
||||
## Pipeline Architecture
|
||||
|
||||
```
|
||||
NeuralEmbedding ──> KnnDecoder ─────────┐
|
||||
│
|
||||
TopologyMetrics ──> ThresholdDecoder ────┤── Weighted Vote ──> DecoderOutput
|
||||
│ │ state, confidence
|
||||
├─> TransitionDecoder ──┘ transition
|
||||
│ brain_health_index
|
||||
└─> ClinicalScorer ─────────> clinical_flags
|
||||
```
|
||||
|
||||
## Usage
|
||||
|
||||
```rust
|
||||
use ruv_neural_decoder::{DecoderPipeline, TopologyThreshold};
|
||||
use ruv_neural_core::topology::{CognitiveState, TopologyMetrics};
|
||||
|
||||
// Build a pipeline with all decoders
|
||||
let mut pipeline = DecoderPipeline::new()
|
||||
.with_knn(5)
|
||||
.with_thresholds()
|
||||
.with_transitions(10)
|
||||
.with_clinical(baseline_metrics, baseline_std);
|
||||
|
||||
// Train the KNN decoder
|
||||
pipeline.knn_mut().unwrap().train(labeled_embeddings);
|
||||
|
||||
// Configure threshold ranges
|
||||
pipeline.threshold_mut().unwrap().set_threshold(
|
||||
CognitiveState::Focused,
|
||||
TopologyThreshold {
|
||||
mincut_range: (7.0, 9.0),
|
||||
modularity_range: (0.5, 0.7),
|
||||
efficiency_range: (0.4, 0.6),
|
||||
entropy_range: (2.5, 3.5),
|
||||
},
|
||||
);
|
||||
|
||||
// Decode
|
||||
let output = pipeline.decode(&embedding, &metrics);
|
||||
println!("State: {:?} (confidence: {:.2})", output.state, output.confidence);
|
||||
|
||||
if let Some(health) = output.brain_health_index {
|
||||
println!("Brain health: {:.2}", health);
|
||||
}
|
||||
for flag in &output.clinical_flags {
|
||||
println!("WARNING: {}", flag);
|
||||
}
|
||||
```
|
||||
|
||||
## Clinical Applications
|
||||
|
||||
The `ClinicalScorer` provides research-grade biomarker detection for:
|
||||
|
||||
- **Alzheimer's disease**: Detects network fragmentation (reduced efficiency, increased modularity, reduced mincut)
|
||||
- **Epilepsy**: Detects hypersynchrony (increased mincut, decreased modularity, increased local efficiency)
|
||||
- **Depression**: Detects connectivity weakening (reduced efficiency, reduced Fiedler value, altered entropy)
|
||||
- **Brain Health Index**: Composite score from 0 (severe abnormality) to 1 (healthy baseline)
|
||||
|
||||
**Note**: These scores are intended for research use only. Clinical diagnosis requires professional medical evaluation.
|
||||
|
||||
## Features
|
||||
|
||||
- `std` (default) — Standard library support
|
||||
- `wasm` — WebAssembly target support
|
||||
|
||||
## License
|
||||
|
||||
MIT OR Apache-2.0
|
||||
@@ -0,0 +1,357 @@
|
||||
//! Clinical biomarker detection from brain topology deviations.
|
||||
|
||||
use ruv_neural_core::topology::TopologyMetrics;
|
||||
|
||||
/// Clinical biomarker scorer based on topology deviation from a healthy baseline.
|
||||
///
|
||||
/// Computes z-scores of current topology metrics relative to a learned
|
||||
/// healthy population baseline, then derives disease-specific risk scores
|
||||
/// and a composite brain health index.
|
||||
pub struct ClinicalScorer {
|
||||
/// Mean topology metrics from healthy population.
|
||||
healthy_baseline: TopologyMetrics,
|
||||
/// Standard deviation of topology metrics from healthy population.
|
||||
healthy_std: TopologyMetrics,
|
||||
}
|
||||
|
||||
impl ClinicalScorer {
|
||||
/// Create a scorer with explicit baseline mean and standard deviation.
|
||||
pub fn new(baseline: TopologyMetrics, std: TopologyMetrics) -> Self {
|
||||
Self {
|
||||
healthy_baseline: baseline,
|
||||
healthy_std: std,
|
||||
}
|
||||
}
|
||||
|
||||
/// Learn the healthy baseline from a set of healthy topology observations.
|
||||
///
|
||||
/// Computes the mean and standard deviation of each metric across the
|
||||
/// provided samples.
|
||||
pub fn learn_baseline(&mut self, healthy_data: &[TopologyMetrics]) {
|
||||
if healthy_data.is_empty() {
|
||||
return;
|
||||
}
|
||||
|
||||
let n = healthy_data.len() as f64;
|
||||
|
||||
// Compute means.
|
||||
let mean_mincut = healthy_data.iter().map(|m| m.global_mincut).sum::<f64>() / n;
|
||||
let mean_mod = healthy_data.iter().map(|m| m.modularity).sum::<f64>() / n;
|
||||
let mean_eff = healthy_data.iter().map(|m| m.global_efficiency).sum::<f64>() / n;
|
||||
let mean_loc = healthy_data.iter().map(|m| m.local_efficiency).sum::<f64>() / n;
|
||||
let mean_ent = healthy_data.iter().map(|m| m.graph_entropy).sum::<f64>() / n;
|
||||
let mean_fiedler = healthy_data.iter().map(|m| m.fiedler_value).sum::<f64>() / n;
|
||||
|
||||
self.healthy_baseline = TopologyMetrics {
|
||||
global_mincut: mean_mincut,
|
||||
modularity: mean_mod,
|
||||
global_efficiency: mean_eff,
|
||||
local_efficiency: mean_loc,
|
||||
graph_entropy: mean_ent,
|
||||
fiedler_value: mean_fiedler,
|
||||
num_modules: 0,
|
||||
timestamp: 0.0,
|
||||
};
|
||||
|
||||
// Compute standard deviations.
|
||||
let std_mincut = std_dev(healthy_data.iter().map(|m| m.global_mincut), mean_mincut);
|
||||
let std_mod = std_dev(healthy_data.iter().map(|m| m.modularity), mean_mod);
|
||||
let std_eff = std_dev(
|
||||
healthy_data.iter().map(|m| m.global_efficiency),
|
||||
mean_eff,
|
||||
);
|
||||
let std_loc = std_dev(
|
||||
healthy_data.iter().map(|m| m.local_efficiency),
|
||||
mean_loc,
|
||||
);
|
||||
let std_ent = std_dev(healthy_data.iter().map(|m| m.graph_entropy), mean_ent);
|
||||
let std_fiedler = std_dev(
|
||||
healthy_data.iter().map(|m| m.fiedler_value),
|
||||
mean_fiedler,
|
||||
);
|
||||
|
||||
self.healthy_std = TopologyMetrics {
|
||||
global_mincut: std_mincut,
|
||||
modularity: std_mod,
|
||||
global_efficiency: std_eff,
|
||||
local_efficiency: std_loc,
|
||||
graph_entropy: std_ent,
|
||||
fiedler_value: std_fiedler,
|
||||
num_modules: 0,
|
||||
timestamp: 0.0,
|
||||
};
|
||||
}
|
||||
|
||||
/// Composite deviation score (mean absolute z-score across all metrics).
|
||||
///
|
||||
/// Higher values indicate greater deviation from healthy baseline.
|
||||
pub fn deviation_score(&self, current: &TopologyMetrics) -> f64 {
|
||||
let z_scores = self.z_scores(current);
|
||||
z_scores.iter().map(|z| z.abs()).sum::<f64>() / z_scores.len() as f64
|
||||
}
|
||||
|
||||
/// Alzheimer's disease risk score in `[0, 1]`.
|
||||
///
|
||||
/// Based on characteristic patterns: reduced global efficiency,
|
||||
/// increased modularity (network fragmentation), reduced mincut.
|
||||
pub fn alzheimer_risk(&self, current: &TopologyMetrics) -> f64 {
|
||||
let z = self.z_scores(current);
|
||||
// z[0]=mincut, z[1]=modularity, z[2]=global_eff, z[3]=local_eff, z[4]=entropy, z[5]=fiedler
|
||||
|
||||
// Alzheimer's: decreased efficiency (negative z), decreased mincut (negative z),
|
||||
// increased modularity (positive z = fragmentation).
|
||||
let efficiency_component = sigmoid(-z[2], 2.0);
|
||||
let mincut_component = sigmoid(-z[0], 2.0);
|
||||
let modularity_component = sigmoid(z[1], 2.0);
|
||||
let fiedler_component = sigmoid(-z[5], 1.5);
|
||||
|
||||
let risk = 0.35 * efficiency_component
|
||||
+ 0.25 * mincut_component
|
||||
+ 0.25 * modularity_component
|
||||
+ 0.15 * fiedler_component;
|
||||
|
||||
risk.clamp(0.0, 1.0)
|
||||
}
|
||||
|
||||
/// Epilepsy risk score in `[0, 1]`.
|
||||
///
|
||||
/// Based on characteristic patterns: hypersynchrony (increased mincut),
|
||||
/// decreased modularity, increased local efficiency.
|
||||
pub fn epilepsy_risk(&self, current: &TopologyMetrics) -> f64 {
|
||||
let z = self.z_scores(current);
|
||||
|
||||
// Epilepsy: increased mincut (hypersynchrony), decreased modularity,
|
||||
// increased local efficiency.
|
||||
let mincut_component = sigmoid(z[0], 2.0);
|
||||
let modularity_component = sigmoid(-z[1], 2.0);
|
||||
let local_eff_component = sigmoid(z[3], 2.0);
|
||||
|
||||
let risk = 0.4 * mincut_component
|
||||
+ 0.3 * modularity_component
|
||||
+ 0.3 * local_eff_component;
|
||||
|
||||
risk.clamp(0.0, 1.0)
|
||||
}
|
||||
|
||||
/// Depression risk score in `[0, 1]`.
|
||||
///
|
||||
/// Based on characteristic patterns: reduced global efficiency,
|
||||
/// altered entropy, reduced Fiedler value (weaker connectivity).
|
||||
pub fn depression_risk(&self, current: &TopologyMetrics) -> f64 {
|
||||
let z = self.z_scores(current);
|
||||
|
||||
// Depression: decreased efficiency, decreased Fiedler value,
|
||||
// altered entropy (can go either way, use absolute deviation).
|
||||
let efficiency_component = sigmoid(-z[2], 2.0);
|
||||
let fiedler_component = sigmoid(-z[5], 2.0);
|
||||
let entropy_component = sigmoid(z[4].abs(), 1.5);
|
||||
|
||||
let risk = 0.4 * efficiency_component
|
||||
+ 0.35 * fiedler_component
|
||||
+ 0.25 * entropy_component;
|
||||
|
||||
risk.clamp(0.0, 1.0)
|
||||
}
|
||||
|
||||
/// General brain health index in `[0, 1]`.
|
||||
///
|
||||
/// `0.0` = severe abnormality, `1.0` = perfectly healthy (all metrics
|
||||
/// within normal range).
|
||||
pub fn brain_health_index(&self, current: &TopologyMetrics) -> f64 {
|
||||
let deviation = self.deviation_score(current);
|
||||
// Map deviation to health: 0 deviation = 1.0 health, large deviation = ~0.0.
|
||||
let health = (-0.5 * deviation).exp();
|
||||
health.clamp(0.0, 1.0)
|
||||
}
|
||||
|
||||
/// Compute z-scores for all topology metrics.
|
||||
///
|
||||
/// Order: [mincut, modularity, global_efficiency, local_efficiency, entropy, fiedler].
|
||||
fn z_scores(&self, current: &TopologyMetrics) -> [f64; 6] {
|
||||
[
|
||||
z_score(
|
||||
current.global_mincut,
|
||||
self.healthy_baseline.global_mincut,
|
||||
self.healthy_std.global_mincut,
|
||||
),
|
||||
z_score(
|
||||
current.modularity,
|
||||
self.healthy_baseline.modularity,
|
||||
self.healthy_std.modularity,
|
||||
),
|
||||
z_score(
|
||||
current.global_efficiency,
|
||||
self.healthy_baseline.global_efficiency,
|
||||
self.healthy_std.global_efficiency,
|
||||
),
|
||||
z_score(
|
||||
current.local_efficiency,
|
||||
self.healthy_baseline.local_efficiency,
|
||||
self.healthy_std.local_efficiency,
|
||||
),
|
||||
z_score(
|
||||
current.graph_entropy,
|
||||
self.healthy_baseline.graph_entropy,
|
||||
self.healthy_std.graph_entropy,
|
||||
),
|
||||
z_score(
|
||||
current.fiedler_value,
|
||||
self.healthy_baseline.fiedler_value,
|
||||
self.healthy_std.fiedler_value,
|
||||
),
|
||||
]
|
||||
}
|
||||
}
|
||||
|
||||
/// Compute the z-score: (value - mean) / std.
|
||||
///
|
||||
/// Returns 0.0 if std is near zero.
|
||||
fn z_score(value: f64, mean: f64, std: f64) -> f64 {
|
||||
if std.abs() < 1e-10 {
|
||||
return 0.0;
|
||||
}
|
||||
(value - mean) / std
|
||||
}
|
||||
|
||||
/// Standard deviation from an iterator of values and a precomputed mean.
|
||||
fn std_dev(values: impl Iterator<Item = f64>, mean: f64) -> f64 {
|
||||
let vals: Vec<f64> = values.collect();
|
||||
if vals.len() < 2 {
|
||||
return 1.0; // Default to 1.0 to avoid division by zero.
|
||||
}
|
||||
let n = vals.len() as f64;
|
||||
let variance = vals.iter().map(|v| (v - mean).powi(2)).sum::<f64>() / (n - 1.0);
|
||||
let s = variance.sqrt();
|
||||
if s < 1e-10 { 1.0 } else { s }
|
||||
}
|
||||
|
||||
/// Sigmoid function mapping a z-score to `[0, 1]`.
|
||||
///
|
||||
/// `scale` controls the steepness of the transition.
|
||||
fn sigmoid(z: f64, scale: f64) -> f64 {
|
||||
1.0 / (1.0 + (-scale * z).exp())
|
||||
}
|
||||
|
||||
#[cfg(test)]
|
||||
mod tests {
|
||||
use super::*;
|
||||
|
||||
fn make_metrics(
|
||||
mincut: f64,
|
||||
modularity: f64,
|
||||
efficiency: f64,
|
||||
entropy: f64,
|
||||
) -> TopologyMetrics {
|
||||
TopologyMetrics {
|
||||
global_mincut: mincut,
|
||||
modularity,
|
||||
global_efficiency: efficiency,
|
||||
local_efficiency: 0.3,
|
||||
graph_entropy: entropy,
|
||||
fiedler_value: 0.5,
|
||||
num_modules: 4,
|
||||
timestamp: 0.0,
|
||||
}
|
||||
}
|
||||
|
||||
fn make_baseline_scorer() -> ClinicalScorer {
|
||||
ClinicalScorer::new(
|
||||
make_metrics(5.0, 0.4, 0.3, 2.0),
|
||||
make_metrics(1.0, 0.1, 0.05, 0.3),
|
||||
)
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_healthy_deviation_near_zero() {
|
||||
let scorer = make_baseline_scorer();
|
||||
let healthy = make_metrics(5.0, 0.4, 0.3, 2.0);
|
||||
let deviation = scorer.deviation_score(&healthy);
|
||||
assert!(
|
||||
deviation < 0.5,
|
||||
"Healthy metrics should have low deviation, got {}",
|
||||
deviation
|
||||
);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_abnormal_deviation_high() {
|
||||
let scorer = make_baseline_scorer();
|
||||
let abnormal = make_metrics(15.0, 1.5, 0.9, 8.0);
|
||||
let deviation = scorer.deviation_score(&abnormal);
|
||||
assert!(
|
||||
deviation > 2.0,
|
||||
"Abnormal metrics should have high deviation, got {}",
|
||||
deviation
|
||||
);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_brain_health_healthy() {
|
||||
let scorer = make_baseline_scorer();
|
||||
let healthy = make_metrics(5.0, 0.4, 0.3, 2.0);
|
||||
let health = scorer.brain_health_index(&healthy);
|
||||
assert!(
|
||||
health > 0.8,
|
||||
"Healthy metrics should yield high health index, got {}",
|
||||
health
|
||||
);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_brain_health_abnormal() {
|
||||
let scorer = make_baseline_scorer();
|
||||
let abnormal = make_metrics(15.0, 1.5, 0.9, 8.0);
|
||||
let health = scorer.brain_health_index(&abnormal);
|
||||
assert!(
|
||||
health < 0.5,
|
||||
"Abnormal metrics should yield low health index, got {}",
|
||||
health
|
||||
);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_disease_risks_in_range() {
|
||||
let scorer = make_baseline_scorer();
|
||||
let current = make_metrics(3.0, 0.6, 0.15, 2.5);
|
||||
|
||||
let alz = scorer.alzheimer_risk(¤t);
|
||||
let epi = scorer.epilepsy_risk(¤t);
|
||||
let dep = scorer.depression_risk(¤t);
|
||||
|
||||
assert!(alz >= 0.0 && alz <= 1.0, "Alzheimer risk out of range: {}", alz);
|
||||
assert!(epi >= 0.0 && epi <= 1.0, "Epilepsy risk out of range: {}", epi);
|
||||
assert!(dep >= 0.0 && dep <= 1.0, "Depression risk out of range: {}", dep);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_learn_baseline() {
|
||||
let mut scorer = ClinicalScorer::new(
|
||||
make_metrics(0.0, 0.0, 0.0, 0.0),
|
||||
make_metrics(1.0, 1.0, 1.0, 1.0),
|
||||
);
|
||||
|
||||
let data = vec![
|
||||
make_metrics(5.0, 0.4, 0.3, 2.0),
|
||||
make_metrics(5.2, 0.42, 0.31, 2.1),
|
||||
make_metrics(4.8, 0.38, 0.29, 1.9),
|
||||
];
|
||||
scorer.learn_baseline(&data);
|
||||
|
||||
// After learning, healthy data should have low deviation.
|
||||
let deviation = scorer.deviation_score(&make_metrics(5.0, 0.4, 0.3, 2.0));
|
||||
assert!(deviation < 1.0, "Post-learning deviation too high: {}", deviation);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_health_index_range() {
|
||||
let scorer = make_baseline_scorer();
|
||||
// Test extreme values.
|
||||
for mincut in [0.0, 5.0, 20.0] {
|
||||
for mod_val in [0.0, 0.4, 1.0] {
|
||||
let m = make_metrics(mincut, mod_val, 0.3, 2.0);
|
||||
let h = scorer.brain_health_index(&m);
|
||||
assert!(h >= 0.0 && h <= 1.0, "Health index out of range: {}", h);
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
@@ -0,0 +1,369 @@
|
||||
//! End-to-end decoder pipeline combining multiple decoding strategies.
|
||||
|
||||
use ruv_neural_core::embedding::NeuralEmbedding;
|
||||
use ruv_neural_core::topology::{CognitiveState, TopologyMetrics};
|
||||
use serde::{Deserialize, Serialize};
|
||||
|
||||
use crate::clinical::ClinicalScorer;
|
||||
use crate::knn_decoder::KnnDecoder;
|
||||
use crate::threshold_decoder::ThresholdDecoder;
|
||||
use crate::transition_decoder::{StateTransition, TransitionDecoder};
|
||||
|
||||
/// End-to-end decoder pipeline that ensembles multiple decoding strategies.
|
||||
///
|
||||
/// Combines KNN, threshold, and transition decoders with configurable
|
||||
/// ensemble weights, and optionally includes clinical scoring.
|
||||
pub struct DecoderPipeline {
|
||||
knn: Option<KnnDecoder>,
|
||||
threshold: Option<ThresholdDecoder>,
|
||||
transition: Option<TransitionDecoder>,
|
||||
clinical: Option<ClinicalScorer>,
|
||||
/// Ensemble weights: [knn_weight, threshold_weight, transition_weight].
|
||||
ensemble_weights: [f64; 3],
|
||||
}
|
||||
|
||||
/// Output of the decoder pipeline.
|
||||
#[derive(Debug, Clone, Serialize, Deserialize)]
|
||||
pub struct DecoderOutput {
|
||||
/// Decoded cognitive state (ensemble result).
|
||||
pub state: CognitiveState,
|
||||
/// Overall confidence in `[0, 1]`.
|
||||
pub confidence: f64,
|
||||
/// Detected state transition, if any.
|
||||
pub transition: Option<StateTransition>,
|
||||
/// Brain health index from clinical scorer, if configured.
|
||||
pub brain_health_index: Option<f64>,
|
||||
/// Clinical warning flags.
|
||||
pub clinical_flags: Vec<String>,
|
||||
/// Timestamp of the input data.
|
||||
pub timestamp: f64,
|
||||
}
|
||||
|
||||
impl DecoderPipeline {
|
||||
/// Create an empty pipeline with default ensemble weights.
|
||||
pub fn new() -> Self {
|
||||
Self {
|
||||
knn: None,
|
||||
threshold: None,
|
||||
transition: None,
|
||||
clinical: None,
|
||||
ensemble_weights: [1.0, 1.0, 1.0],
|
||||
}
|
||||
}
|
||||
|
||||
/// Add a KNN decoder to the pipeline.
|
||||
pub fn with_knn(mut self, k: usize) -> Self {
|
||||
self.knn = Some(KnnDecoder::new(k));
|
||||
self
|
||||
}
|
||||
|
||||
/// Add a threshold decoder to the pipeline.
|
||||
pub fn with_thresholds(mut self) -> Self {
|
||||
self.threshold = Some(ThresholdDecoder::new());
|
||||
self
|
||||
}
|
||||
|
||||
/// Add a transition decoder to the pipeline.
|
||||
pub fn with_transitions(mut self, window: usize) -> Self {
|
||||
self.transition = Some(TransitionDecoder::new(window));
|
||||
self
|
||||
}
|
||||
|
||||
/// Add a clinical scorer to the pipeline.
|
||||
pub fn with_clinical(mut self, baseline: TopologyMetrics, std: TopologyMetrics) -> Self {
|
||||
self.clinical = Some(ClinicalScorer::new(baseline, std));
|
||||
self
|
||||
}
|
||||
|
||||
/// Set custom ensemble weights for [knn, threshold, transition].
|
||||
pub fn with_weights(mut self, weights: [f64; 3]) -> Self {
|
||||
self.ensemble_weights = weights;
|
||||
self
|
||||
}
|
||||
|
||||
/// Get a mutable reference to the KNN decoder (for training).
|
||||
pub fn knn_mut(&mut self) -> Option<&mut KnnDecoder> {
|
||||
self.knn.as_mut()
|
||||
}
|
||||
|
||||
/// Get a mutable reference to the threshold decoder (for configuring thresholds).
|
||||
pub fn threshold_mut(&mut self) -> Option<&mut ThresholdDecoder> {
|
||||
self.threshold.as_mut()
|
||||
}
|
||||
|
||||
/// Get a mutable reference to the transition decoder (for registering patterns).
|
||||
pub fn transition_mut(&mut self) -> Option<&mut TransitionDecoder> {
|
||||
self.transition.as_mut()
|
||||
}
|
||||
|
||||
/// Get a mutable reference to the clinical scorer.
|
||||
pub fn clinical_mut(&mut self) -> Option<&mut ClinicalScorer> {
|
||||
self.clinical.as_mut()
|
||||
}
|
||||
|
||||
/// Run the full decoding pipeline on an embedding and topology metrics.
|
||||
pub fn decode(
|
||||
&mut self,
|
||||
embedding: &NeuralEmbedding,
|
||||
metrics: &TopologyMetrics,
|
||||
) -> DecoderOutput {
|
||||
let mut candidates: Vec<(CognitiveState, f64, f64)> = Vec::new(); // (state, confidence, weight)
|
||||
|
||||
// KNN decoder.
|
||||
if let Some(ref knn) = self.knn {
|
||||
let (state, conf) = knn.predict_with_confidence(embedding);
|
||||
if state != CognitiveState::Unknown {
|
||||
candidates.push((state, conf, self.ensemble_weights[0]));
|
||||
}
|
||||
}
|
||||
|
||||
// Threshold decoder.
|
||||
if let Some(ref threshold) = self.threshold {
|
||||
let (state, conf) = threshold.decode(metrics);
|
||||
if state != CognitiveState::Unknown {
|
||||
candidates.push((state, conf, self.ensemble_weights[1]));
|
||||
}
|
||||
}
|
||||
|
||||
// Transition decoder.
|
||||
let transition = if let Some(ref mut trans) = self.transition {
|
||||
let result = trans.update(metrics.clone());
|
||||
if let Some(ref t) = result {
|
||||
candidates.push((t.to, t.confidence, self.ensemble_weights[2]));
|
||||
}
|
||||
result
|
||||
} else {
|
||||
None
|
||||
};
|
||||
|
||||
// Ensemble: weighted vote.
|
||||
let (state, confidence) = if candidates.is_empty() {
|
||||
(CognitiveState::Unknown, 0.0)
|
||||
} else {
|
||||
weighted_vote(&candidates)
|
||||
};
|
||||
|
||||
// Clinical scoring.
|
||||
let mut brain_health_index = None;
|
||||
let mut clinical_flags = Vec::new();
|
||||
|
||||
if let Some(ref clinical) = self.clinical {
|
||||
let health = clinical.brain_health_index(metrics);
|
||||
brain_health_index = Some(health);
|
||||
|
||||
let alz = clinical.alzheimer_risk(metrics);
|
||||
let epi = clinical.epilepsy_risk(metrics);
|
||||
let dep = clinical.depression_risk(metrics);
|
||||
|
||||
if alz > 0.7 {
|
||||
clinical_flags.push(format!("Elevated Alzheimer risk: {:.2}", alz));
|
||||
}
|
||||
if epi > 0.7 {
|
||||
clinical_flags.push(format!("Elevated epilepsy risk: {:.2}", epi));
|
||||
}
|
||||
if dep > 0.7 {
|
||||
clinical_flags.push(format!("Elevated depression risk: {:.2}", dep));
|
||||
}
|
||||
if health < 0.3 {
|
||||
clinical_flags.push(format!("Low brain health index: {:.2}", health));
|
||||
}
|
||||
}
|
||||
|
||||
DecoderOutput {
|
||||
state,
|
||||
confidence,
|
||||
transition,
|
||||
brain_health_index,
|
||||
clinical_flags,
|
||||
timestamp: metrics.timestamp,
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
impl Default for DecoderPipeline {
|
||||
fn default() -> Self {
|
||||
Self::new()
|
||||
}
|
||||
}
|
||||
|
||||
/// Weighted majority vote across candidate predictions.
|
||||
///
|
||||
/// Returns the state with the highest weighted confidence and the
|
||||
/// normalized confidence score.
|
||||
fn weighted_vote(candidates: &[(CognitiveState, f64, f64)]) -> (CognitiveState, f64) {
|
||||
use std::collections::HashMap;
|
||||
|
||||
let mut state_scores: HashMap<CognitiveState, f64> = HashMap::new();
|
||||
let mut total_weight = 0.0;
|
||||
|
||||
for &(state, confidence, weight) in candidates {
|
||||
let score = confidence * weight;
|
||||
*state_scores.entry(state).or_insert(0.0) += score;
|
||||
total_weight += score;
|
||||
}
|
||||
|
||||
let (best_state, best_score) = state_scores
|
||||
.into_iter()
|
||||
.max_by(|a, b| a.1.partial_cmp(&b.1).unwrap_or(std::cmp::Ordering::Equal))
|
||||
.unwrap_or((CognitiveState::Unknown, 0.0));
|
||||
|
||||
let normalized = if total_weight > 0.0 {
|
||||
(best_score / total_weight).clamp(0.0, 1.0)
|
||||
} else {
|
||||
0.0
|
||||
};
|
||||
|
||||
(best_state, normalized)
|
||||
}
|
||||
|
||||
#[cfg(test)]
|
||||
mod tests {
|
||||
use super::*;
|
||||
use ruv_neural_core::brain::Atlas;
|
||||
use ruv_neural_core::embedding::EmbeddingMetadata;
|
||||
|
||||
fn make_embedding(vector: Vec<f64>) -> NeuralEmbedding {
|
||||
NeuralEmbedding::new(
|
||||
vector,
|
||||
0.0,
|
||||
EmbeddingMetadata {
|
||||
subject_id: None,
|
||||
session_id: None,
|
||||
cognitive_state: None,
|
||||
source_atlas: Atlas::DesikanKilliany68,
|
||||
embedding_method: "test".into(),
|
||||
},
|
||||
)
|
||||
.unwrap()
|
||||
}
|
||||
|
||||
fn make_metrics(mincut: f64, modularity: f64) -> TopologyMetrics {
|
||||
TopologyMetrics {
|
||||
global_mincut: mincut,
|
||||
modularity,
|
||||
global_efficiency: 0.3,
|
||||
local_efficiency: 0.2,
|
||||
graph_entropy: 2.0,
|
||||
fiedler_value: 0.5,
|
||||
num_modules: 4,
|
||||
timestamp: 0.0,
|
||||
}
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_empty_pipeline() {
|
||||
let mut pipeline = DecoderPipeline::new();
|
||||
let emb = make_embedding(vec![1.0, 0.0]);
|
||||
let met = make_metrics(5.0, 0.4);
|
||||
let output = pipeline.decode(&emb, &met);
|
||||
assert_eq!(output.state, CognitiveState::Unknown);
|
||||
assert!(output.confidence >= 0.0 && output.confidence <= 1.0);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_pipeline_with_knn() {
|
||||
let mut pipeline = DecoderPipeline::new().with_knn(3);
|
||||
pipeline.knn_mut().unwrap().train(vec![
|
||||
(make_embedding(vec![1.0, 0.0]), CognitiveState::Rest),
|
||||
(make_embedding(vec![1.1, 0.1]), CognitiveState::Rest),
|
||||
(make_embedding(vec![0.9, 0.0]), CognitiveState::Rest),
|
||||
]);
|
||||
|
||||
let output = pipeline.decode(&make_embedding(vec![1.0, 0.05]), &make_metrics(5.0, 0.4));
|
||||
assert_eq!(output.state, CognitiveState::Rest);
|
||||
assert!(output.confidence > 0.0);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_pipeline_with_thresholds() {
|
||||
let mut pipeline = DecoderPipeline::new().with_thresholds();
|
||||
pipeline.threshold_mut().unwrap().set_threshold(
|
||||
CognitiveState::Focused,
|
||||
crate::threshold_decoder::TopologyThreshold {
|
||||
mincut_range: (7.0, 9.0),
|
||||
modularity_range: (0.5, 0.7),
|
||||
efficiency_range: (0.2, 0.4),
|
||||
entropy_range: (1.5, 2.5),
|
||||
},
|
||||
);
|
||||
|
||||
let output = pipeline.decode(
|
||||
&make_embedding(vec![0.5, 0.5]),
|
||||
&make_metrics(8.0, 0.6),
|
||||
);
|
||||
assert_eq!(output.state, CognitiveState::Focused);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_pipeline_with_clinical() {
|
||||
let baseline = make_metrics(5.0, 0.4);
|
||||
let std_met = TopologyMetrics {
|
||||
global_mincut: 1.0,
|
||||
modularity: 0.1,
|
||||
global_efficiency: 0.05,
|
||||
local_efficiency: 0.05,
|
||||
graph_entropy: 0.3,
|
||||
fiedler_value: 0.1,
|
||||
num_modules: 1,
|
||||
timestamp: 0.0,
|
||||
};
|
||||
let mut pipeline = DecoderPipeline::new()
|
||||
.with_knn(1)
|
||||
.with_clinical(baseline, std_met);
|
||||
pipeline.knn_mut().unwrap().train(vec![(
|
||||
make_embedding(vec![1.0]),
|
||||
CognitiveState::Rest,
|
||||
)]);
|
||||
|
||||
let output = pipeline.decode(&make_embedding(vec![1.0]), &make_metrics(5.0, 0.4));
|
||||
assert!(output.brain_health_index.is_some());
|
||||
let health = output.brain_health_index.unwrap();
|
||||
assert!(health >= 0.0 && health <= 1.0);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_pipeline_all_decoders() {
|
||||
let baseline = make_metrics(5.0, 0.4);
|
||||
let std_met = TopologyMetrics {
|
||||
global_mincut: 1.0,
|
||||
modularity: 0.1,
|
||||
global_efficiency: 0.05,
|
||||
local_efficiency: 0.05,
|
||||
graph_entropy: 0.3,
|
||||
fiedler_value: 0.1,
|
||||
num_modules: 1,
|
||||
timestamp: 0.0,
|
||||
};
|
||||
let mut pipeline = DecoderPipeline::new()
|
||||
.with_knn(3)
|
||||
.with_thresholds()
|
||||
.with_transitions(5)
|
||||
.with_clinical(baseline, std_met);
|
||||
|
||||
pipeline.knn_mut().unwrap().train(vec![
|
||||
(make_embedding(vec![1.0, 0.0]), CognitiveState::Rest),
|
||||
(make_embedding(vec![1.1, 0.1]), CognitiveState::Rest),
|
||||
]);
|
||||
|
||||
let output = pipeline.decode(&make_embedding(vec![1.0, 0.05]), &make_metrics(5.0, 0.4));
|
||||
// Should produce some output regardless of which decoders fire.
|
||||
assert!(output.confidence >= 0.0 && output.confidence <= 1.0);
|
||||
assert!(output.brain_health_index.is_some());
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_decoder_output_serialization() {
|
||||
let output = DecoderOutput {
|
||||
state: CognitiveState::Rest,
|
||||
confidence: 0.95,
|
||||
transition: None,
|
||||
brain_health_index: Some(0.92),
|
||||
clinical_flags: vec![],
|
||||
timestamp: 1234.5,
|
||||
};
|
||||
let json = serde_json::to_string(&output).unwrap();
|
||||
let parsed: DecoderOutput = serde_json::from_str(&json).unwrap();
|
||||
assert_eq!(parsed.state, CognitiveState::Rest);
|
||||
assert!((parsed.confidence - 0.95).abs() < 1e-10);
|
||||
}
|
||||
}
|
||||
+19
-8
@@ -3,6 +3,7 @@
|
||||
use std::collections::HashMap;
|
||||
|
||||
use ruv_neural_core::topology::{CognitiveState, TopologyMetrics};
|
||||
use serde::{Deserialize, Serialize};
|
||||
|
||||
/// Detect cognitive state transitions from topology change patterns.
|
||||
///
|
||||
@@ -27,7 +28,7 @@ pub struct TransitionPattern {
|
||||
}
|
||||
|
||||
/// A detected state transition.
|
||||
#[derive(Debug, Clone)]
|
||||
#[derive(Debug, Clone, Serialize, Deserialize)]
|
||||
pub struct StateTransition {
|
||||
/// State before the transition.
|
||||
pub from: CognitiveState,
|
||||
@@ -235,14 +236,24 @@ mod tests {
|
||||
},
|
||||
);
|
||||
|
||||
// Feed metrics that match the pattern.
|
||||
let _ = decoder.update(make_metrics(5.0, 0.4, 0.0));
|
||||
let _ = decoder.update(make_metrics(6.0, 0.45, 0.5));
|
||||
let _ = decoder.update(make_metrics(7.0, 0.5, 1.0));
|
||||
let result = decoder.update(make_metrics(8.0, 0.6, 2.0));
|
||||
// Feed metrics that progressively match the pattern.
|
||||
// The transition may fire on any update once deltas are large enough.
|
||||
let updates = vec![
|
||||
make_metrics(5.0, 0.4, 0.0),
|
||||
make_metrics(6.0, 0.45, 0.5),
|
||||
make_metrics(7.0, 0.5, 1.0),
|
||||
make_metrics(8.0, 0.6, 2.0),
|
||||
];
|
||||
|
||||
assert!(result.is_some(), "Expected a transition to be detected");
|
||||
let transition = result.unwrap();
|
||||
let mut detected: Option<StateTransition> = None;
|
||||
for m in updates {
|
||||
if let Some(t) = decoder.update(m) {
|
||||
detected = Some(t);
|
||||
}
|
||||
}
|
||||
|
||||
assert!(detected.is_some(), "Expected a transition to be detected");
|
||||
let transition = detected.unwrap();
|
||||
assert_eq!(transition.from, CognitiveState::Rest);
|
||||
assert_eq!(transition.to, CognitiveState::Focused);
|
||||
assert!(transition.confidence > 0.0 && transition.confidence <= 1.0);
|
||||
|
||||
@@ -0,0 +1,75 @@
|
||||
# rUv Neural Embed
|
||||
|
||||
Graph embedding generation for brain connectivity states using RuVector format.
|
||||
|
||||
## Overview
|
||||
|
||||
`ruv-neural-embed` converts brain connectivity graphs into fixed-dimensional vector
|
||||
representations suitable for downstream classification, clustering, and temporal analysis.
|
||||
Multiple embedding strategies are provided, each capturing different aspects of graph structure.
|
||||
|
||||
## Embedding Methods
|
||||
|
||||
| Method | Module | Description | Output Dimension |
|
||||
|--------|--------|-------------|-----------------|
|
||||
| **Spectral** | `spectral_embed` | Laplacian eigenvector positional encoding | `k * 4` (mean/std/min/max per eigenvector) |
|
||||
| **Topology** | `topology_embed` | Hand-crafted topological feature vector | 13 (with all features enabled) |
|
||||
| **Node2Vec** | `node2vec` | Random-walk co-occurrence SVD embedding | `dim * 2` (mean/std per component) |
|
||||
| **Combined** | `combined` | Weighted concatenation of multiple methods | Sum of sub-embedder dimensions |
|
||||
| **Temporal** | `temporal` | Sliding-window context-enriched embedding | `base_dim * 2` (current + context) |
|
||||
|
||||
## Distance Metrics
|
||||
|
||||
| Metric | Function | Description |
|
||||
|--------|----------|-------------|
|
||||
| Cosine Similarity | `cosine_similarity` | Direction similarity in [-1, 1] |
|
||||
| Euclidean Distance | `euclidean_distance` | L2 norm of difference |
|
||||
| Manhattan Distance | `manhattan_distance` | L1 norm of difference |
|
||||
| k-Nearest Neighbors | `k_nearest` | Find k closest embeddings |
|
||||
| Trajectory Distance | `trajectory_distance` | DTW alignment cost for sequences |
|
||||
|
||||
## Usage
|
||||
|
||||
```rust
|
||||
use ruv_neural_embed::spectral_embed::SpectralEmbedder;
|
||||
use ruv_neural_embed::topology_embed::TopologyEmbedder;
|
||||
use ruv_neural_embed::combined::CombinedEmbedder;
|
||||
use ruv_neural_embed::distance::{cosine_similarity, k_nearest};
|
||||
use ruv_neural_core::traits::EmbeddingGenerator;
|
||||
|
||||
// Single-method embedding
|
||||
let spectral = SpectralEmbedder::new(4);
|
||||
let embedding = spectral.embed(&brain_graph).unwrap();
|
||||
|
||||
// Combined multi-method embedding
|
||||
let combined = CombinedEmbedder::new()
|
||||
.add(Box::new(SpectralEmbedder::new(4)), 1.0)
|
||||
.add(Box::new(TopologyEmbedder::new()), 0.5);
|
||||
let combined_emb = combined.embed(&brain_graph).unwrap();
|
||||
|
||||
// Compare embeddings
|
||||
let sim = cosine_similarity(&emb_a, &emb_b);
|
||||
let neighbors = k_nearest(&query, &candidates, 5);
|
||||
```
|
||||
|
||||
## RVF Export
|
||||
|
||||
```rust
|
||||
use ruv_neural_embed::rvf_export::{export_rvf, import_rvf};
|
||||
|
||||
// Save embeddings
|
||||
export_rvf(&embeddings, "brain_states.rvf").unwrap();
|
||||
|
||||
// Load embeddings
|
||||
let restored = import_rvf("brain_states.rvf").unwrap();
|
||||
```
|
||||
|
||||
## Features
|
||||
|
||||
- `std` (default) -- Standard library support
|
||||
- `wasm` -- WebAssembly compatibility
|
||||
- `rvf` -- Extended RVF format support
|
||||
|
||||
## License
|
||||
|
||||
MIT OR Apache-2.0
|
||||
@@ -3,10 +3,12 @@
|
||||
//! Concatenates weighted embeddings from multiple embedding generators
|
||||
//! into a single vector representation.
|
||||
|
||||
use ruv_neural_core::embedding::NeuralEmbedding;
|
||||
use ruv_neural_core::error::{Result, RuvNeuralError};
|
||||
use ruv_neural_core::graph::BrainGraph;
|
||||
use ruv_neural_core::traits::EmbeddingGenerator;
|
||||
|
||||
use crate::{EmbeddingGenerator, NeuralEmbedding};
|
||||
use crate::default_metadata;
|
||||
|
||||
/// Combines multiple embedding methods into a single embedding vector.
|
||||
pub struct CombinedEmbedder {
|
||||
@@ -39,11 +41,11 @@ impl CombinedEmbedder {
|
||||
|
||||
/// Total embedding dimension (sum of all sub-embedder dimensions).
|
||||
pub fn total_dimension(&self) -> usize {
|
||||
self.embedders.iter().map(|e| e.dimension()).sum()
|
||||
self.embedders.iter().map(|e| e.embedding_dim()).sum()
|
||||
}
|
||||
|
||||
/// Generate a combined embedding by concatenating weighted sub-embeddings.
|
||||
pub fn embed(&self, graph: &BrainGraph) -> Result<NeuralEmbedding> {
|
||||
pub fn embed_graph(&self, graph: &BrainGraph) -> Result<NeuralEmbedding> {
|
||||
if self.embedders.is_empty() {
|
||||
return Err(RuvNeuralError::Embedding(
|
||||
"CombinedEmbedder has no sub-embedders".into(),
|
||||
@@ -54,12 +56,13 @@ impl CombinedEmbedder {
|
||||
|
||||
for (embedder, &weight) in self.embedders.iter().zip(self.weights.iter()) {
|
||||
let sub_emb = embedder.embed(graph)?;
|
||||
for v in &sub_emb.values {
|
||||
for v in &sub_emb.vector {
|
||||
values.push(v * weight);
|
||||
}
|
||||
}
|
||||
|
||||
NeuralEmbedding::new(values, graph.timestamp, "combined")
|
||||
let meta = default_metadata("combined", graph.atlas);
|
||||
NeuralEmbedding::new(values, graph.timestamp, meta)
|
||||
}
|
||||
}
|
||||
|
||||
@@ -70,16 +73,12 @@ impl Default for CombinedEmbedder {
|
||||
}
|
||||
|
||||
impl EmbeddingGenerator for CombinedEmbedder {
|
||||
fn dimension(&self) -> usize {
|
||||
fn embedding_dim(&self) -> usize {
|
||||
self.total_dimension()
|
||||
}
|
||||
|
||||
fn method_name(&self) -> &str {
|
||||
"combined"
|
||||
}
|
||||
|
||||
fn embed(&self, graph: &BrainGraph) -> Result<NeuralEmbedding> {
|
||||
CombinedEmbedder::embed(self, graph)
|
||||
self.embed_graph(graph)
|
||||
}
|
||||
}
|
||||
|
||||
@@ -137,8 +136,8 @@ mod tests {
|
||||
let spectral = SpectralEmbedder::new(2);
|
||||
let topo = TopologyEmbedder::new();
|
||||
|
||||
let spectral_dim = spectral.dimension();
|
||||
let topo_dim = topo.dimension();
|
||||
let spectral_dim = spectral.embedding_dim();
|
||||
let topo_dim = topo.embedding_dim();
|
||||
|
||||
let combined = CombinedEmbedder::new()
|
||||
.add(Box::new(spectral), 1.0)
|
||||
@@ -148,7 +147,7 @@ mod tests {
|
||||
|
||||
let emb = combined.embed(&graph).unwrap();
|
||||
assert_eq!(emb.dimension, spectral_dim + topo_dim);
|
||||
assert_eq!(emb.method, "combined");
|
||||
assert_eq!(emb.metadata.embedding_method, "combined");
|
||||
}
|
||||
|
||||
#[test]
|
||||
@@ -156,15 +155,13 @@ mod tests {
|
||||
let graph = make_test_graph();
|
||||
let topo = TopologyEmbedder::new();
|
||||
|
||||
// Weight = 2.0
|
||||
let combined = CombinedEmbedder::new().add(Box::new(topo), 2.0);
|
||||
let emb = combined.embed(&graph).unwrap();
|
||||
|
||||
// Compare with direct topology embedding
|
||||
let topo2 = TopologyEmbedder::new();
|
||||
let direct = topo2.embed(&graph).unwrap();
|
||||
|
||||
for (c, d) in emb.values.iter().zip(direct.values.iter()) {
|
||||
for (c, d) in emb.vector.iter().zip(direct.vector.iter()) {
|
||||
assert!(
|
||||
(c - 2.0 * d).abs() < 1e-10,
|
||||
"Weight should scale values: {} vs 2*{}",
|
||||
|
||||
@@ -1,9 +1,9 @@
|
||||
//! Embedding distance and similarity metrics.
|
||||
//! Distance metrics for neural embeddings.
|
||||
//!
|
||||
//! Provides cosine similarity, Euclidean distance, k-nearest-neighbor search,
|
||||
//! and a DTW-inspired trajectory distance for comparing embedding sequences.
|
||||
|
||||
use crate::{EmbeddingTrajectory, NeuralEmbedding};
|
||||
use ruv_neural_core::embedding::{EmbeddingTrajectory, NeuralEmbedding};
|
||||
|
||||
/// Cosine similarity between two embeddings.
|
||||
///
|
||||
@@ -12,7 +12,7 @@ use crate::{EmbeddingTrajectory, NeuralEmbedding};
|
||||
///
|
||||
/// Returns 0.0 if either embedding has zero norm.
|
||||
pub fn cosine_similarity(a: &NeuralEmbedding, b: &NeuralEmbedding) -> f64 {
|
||||
let len = a.values.len().min(b.values.len());
|
||||
let len = a.vector.len().min(b.vector.len());
|
||||
if len == 0 {
|
||||
return 0.0;
|
||||
}
|
||||
@@ -22,9 +22,9 @@ pub fn cosine_similarity(a: &NeuralEmbedding, b: &NeuralEmbedding) -> f64 {
|
||||
let mut norm_b = 0.0;
|
||||
|
||||
for i in 0..len {
|
||||
dot += a.values[i] * b.values[i];
|
||||
norm_a += a.values[i] * a.values[i];
|
||||
norm_b += b.values[i] * b.values[i];
|
||||
dot += a.vector[i] * b.vector[i];
|
||||
norm_a += a.vector[i] * a.vector[i];
|
||||
norm_b += b.vector[i] * b.vector[i];
|
||||
}
|
||||
|
||||
let denom = norm_a.sqrt() * norm_b.sqrt();
|
||||
@@ -40,14 +40,14 @@ pub fn cosine_similarity(a: &NeuralEmbedding, b: &NeuralEmbedding) -> f64 {
|
||||
/// If the embeddings have different dimensions, only the overlapping
|
||||
/// portion is compared.
|
||||
pub fn euclidean_distance(a: &NeuralEmbedding, b: &NeuralEmbedding) -> f64 {
|
||||
let len = a.values.len().min(b.values.len());
|
||||
let len = a.vector.len().min(b.vector.len());
|
||||
if len == 0 {
|
||||
return 0.0;
|
||||
}
|
||||
|
||||
let mut sum_sq = 0.0;
|
||||
for i in 0..len {
|
||||
let diff = a.values[i] - b.values[i];
|
||||
let diff = a.vector[i] - b.vector[i];
|
||||
sum_sq += diff * diff;
|
||||
}
|
||||
|
||||
@@ -56,10 +56,10 @@ pub fn euclidean_distance(a: &NeuralEmbedding, b: &NeuralEmbedding) -> f64 {
|
||||
|
||||
/// Manhattan (L1) distance between two embeddings.
|
||||
pub fn manhattan_distance(a: &NeuralEmbedding, b: &NeuralEmbedding) -> f64 {
|
||||
let len = a.values.len().min(b.values.len());
|
||||
let len = a.vector.len().min(b.vector.len());
|
||||
let mut sum = 0.0;
|
||||
for i in 0..len {
|
||||
sum += (a.values[i] - b.values[i]).abs();
|
||||
sum += (a.vector[i] - b.vector[i]).abs();
|
||||
}
|
||||
sum
|
||||
}
|
||||
@@ -97,7 +97,6 @@ pub fn trajectory_distance(a: &EmbeddingTrajectory, b: &EmbeddingTrajectory) ->
|
||||
return f64::INFINITY;
|
||||
}
|
||||
|
||||
// DTW cost matrix
|
||||
let mut dtw = vec![vec![f64::INFINITY; m + 1]; n + 1];
|
||||
dtw[0][0] = 0.0;
|
||||
|
||||
@@ -117,10 +116,13 @@ pub fn trajectory_distance(a: &EmbeddingTrajectory, b: &EmbeddingTrajectory) ->
|
||||
#[cfg(test)]
|
||||
mod tests {
|
||||
use super::*;
|
||||
use crate::NeuralEmbedding;
|
||||
use crate::default_metadata;
|
||||
use ruv_neural_core::brain::Atlas;
|
||||
use ruv_neural_core::embedding::NeuralEmbedding;
|
||||
|
||||
fn emb(values: Vec<f64>) -> NeuralEmbedding {
|
||||
NeuralEmbedding::new(values, 0.0, "test").unwrap()
|
||||
let meta = default_metadata("test", Atlas::Custom(1));
|
||||
NeuralEmbedding::new(values, 0.0, meta).unwrap()
|
||||
}
|
||||
|
||||
#[test]
|
||||
@@ -128,7 +130,10 @@ mod tests {
|
||||
let a = emb(vec![1.0, 2.0, 3.0]);
|
||||
let b = emb(vec![1.0, 2.0, 3.0]);
|
||||
let sim = cosine_similarity(&a, &b);
|
||||
assert!((sim - 1.0).abs() < 1e-10, "Identical embeddings: cos sim should be 1.0");
|
||||
assert!(
|
||||
(sim - 1.0).abs() < 1e-10,
|
||||
"Identical embeddings: cos sim should be 1.0"
|
||||
);
|
||||
}
|
||||
|
||||
#[test]
|
||||
@@ -136,7 +141,10 @@ mod tests {
|
||||
let a = emb(vec![1.0, 0.0]);
|
||||
let b = emb(vec![0.0, 1.0]);
|
||||
let sim = cosine_similarity(&a, &b);
|
||||
assert!(sim.abs() < 1e-10, "Orthogonal embeddings: cos sim should be 0.0");
|
||||
assert!(
|
||||
sim.abs() < 1e-10,
|
||||
"Orthogonal embeddings: cos sim should be 0.0"
|
||||
);
|
||||
}
|
||||
|
||||
#[test]
|
||||
@@ -144,7 +152,10 @@ mod tests {
|
||||
let a = emb(vec![1.0, 2.0]);
|
||||
let b = emb(vec![-1.0, -2.0]);
|
||||
let sim = cosine_similarity(&a, &b);
|
||||
assert!((sim + 1.0).abs() < 1e-10, "Opposite embeddings: cos sim should be -1.0");
|
||||
assert!(
|
||||
(sim + 1.0).abs() < 1e-10,
|
||||
"Opposite embeddings: cos sim should be -1.0"
|
||||
);
|
||||
}
|
||||
|
||||
#[test]
|
||||
@@ -152,7 +163,10 @@ mod tests {
|
||||
let a = emb(vec![1.0, 2.0, 3.0]);
|
||||
let b = emb(vec![1.0, 2.0, 3.0]);
|
||||
let dist = euclidean_distance(&a, &b);
|
||||
assert!(dist.abs() < 1e-10, "Identical embeddings: distance should be 0.0");
|
||||
assert!(
|
||||
dist.abs() < 1e-10,
|
||||
"Identical embeddings: distance should be 0.0"
|
||||
);
|
||||
}
|
||||
|
||||
#[test]
|
||||
@@ -175,7 +189,6 @@ mod tests {
|
||||
|
||||
let nearest = k_nearest(&query, &candidates, 2);
|
||||
assert_eq!(nearest.len(), 2);
|
||||
// Closest should be index 3 (0.5, 0.5), then index 1 (1.0, 0.0)
|
||||
assert_eq!(nearest[0].0, 3);
|
||||
assert_eq!(nearest[1].0, 1);
|
||||
}
|
||||
@@ -192,35 +205,41 @@ mod tests {
|
||||
fn test_trajectory_distance_identical() {
|
||||
let traj = EmbeddingTrajectory {
|
||||
embeddings: vec![emb(vec![1.0, 2.0]), emb(vec![3.0, 4.0])],
|
||||
step_s: 0.5,
|
||||
timestamps: vec![0.0, 0.5],
|
||||
};
|
||||
let dist = trajectory_distance(&traj, &traj);
|
||||
assert!(dist.abs() < 1e-10, "Identical trajectories: DTW distance should be 0.0");
|
||||
assert!(
|
||||
dist.abs() < 1e-10,
|
||||
"Identical trajectories: DTW distance should be 0.0"
|
||||
);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_trajectory_distance_different() {
|
||||
let a = EmbeddingTrajectory {
|
||||
embeddings: vec![emb(vec![0.0, 0.0]), emb(vec![1.0, 0.0])],
|
||||
step_s: 0.5,
|
||||
timestamps: vec![0.0, 0.5],
|
||||
};
|
||||
let b = EmbeddingTrajectory {
|
||||
embeddings: vec![emb(vec![0.0, 0.0]), emb(vec![0.0, 1.0])],
|
||||
step_s: 0.5,
|
||||
timestamps: vec![0.0, 0.5],
|
||||
};
|
||||
let dist = trajectory_distance(&a, &b);
|
||||
assert!(dist > 0.0, "Different trajectories should have non-zero DTW distance");
|
||||
assert!(
|
||||
dist > 0.0,
|
||||
"Different trajectories should have non-zero DTW distance"
|
||||
);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_trajectory_distance_empty() {
|
||||
let a = EmbeddingTrajectory {
|
||||
embeddings: vec![],
|
||||
step_s: 0.5,
|
||||
timestamps: vec![],
|
||||
};
|
||||
let b = EmbeddingTrajectory {
|
||||
embeddings: vec![emb(vec![1.0])],
|
||||
step_s: 0.5,
|
||||
timestamps: vec![0.0],
|
||||
};
|
||||
let dist = trajectory_distance(&a, &b);
|
||||
assert!(dist.is_infinite());
|
||||
|
||||
@@ -25,152 +25,76 @@ pub mod spectral_embed;
|
||||
pub mod temporal;
|
||||
pub mod topology_embed;
|
||||
|
||||
use ruv_neural_core::error::{Result, RuvNeuralError};
|
||||
use ruv_neural_core::graph::{BrainGraph, BrainGraphSequence};
|
||||
use serde::{Deserialize, Serialize};
|
||||
// Re-export core types used throughout this crate.
|
||||
pub use ruv_neural_core::embedding::{EmbeddingMetadata, EmbeddingTrajectory, NeuralEmbedding};
|
||||
pub use ruv_neural_core::graph::{BrainGraph, BrainGraphSequence};
|
||||
pub use ruv_neural_core::traits::EmbeddingGenerator;
|
||||
|
||||
/// A fixed-dimensional embedding of a brain graph.
|
||||
#[derive(Debug, Clone, Serialize, Deserialize)]
|
||||
pub struct NeuralEmbedding {
|
||||
/// The embedding vector.
|
||||
pub values: Vec<f64>,
|
||||
/// Dimensionality of the embedding.
|
||||
pub dimension: usize,
|
||||
/// Timestamp of the source graph (Unix seconds).
|
||||
pub timestamp: f64,
|
||||
/// Name of the method that produced this embedding.
|
||||
pub method: String,
|
||||
/// Optional metadata (e.g., parameters used).
|
||||
pub metadata: Option<String>,
|
||||
}
|
||||
|
||||
impl NeuralEmbedding {
|
||||
/// Create a new embedding, validating dimension consistency.
|
||||
pub fn new(values: Vec<f64>, timestamp: f64, method: &str) -> Result<Self> {
|
||||
let dimension = values.len();
|
||||
if dimension == 0 {
|
||||
return Err(RuvNeuralError::Embedding(
|
||||
"Embedding must have at least one dimension".into(),
|
||||
));
|
||||
}
|
||||
Ok(Self {
|
||||
values,
|
||||
dimension,
|
||||
timestamp,
|
||||
method: method.to_string(),
|
||||
metadata: None,
|
||||
})
|
||||
/// Helper to build an `EmbeddingMetadata` with just a method name and atlas.
|
||||
pub fn default_metadata(
|
||||
method: &str,
|
||||
atlas: ruv_neural_core::brain::Atlas,
|
||||
) -> EmbeddingMetadata {
|
||||
EmbeddingMetadata {
|
||||
subject_id: None,
|
||||
session_id: None,
|
||||
cognitive_state: None,
|
||||
source_atlas: atlas,
|
||||
embedding_method: method.to_string(),
|
||||
}
|
||||
|
||||
/// Create a zero embedding of a given dimension.
|
||||
pub fn zeros(dimension: usize, timestamp: f64, method: &str) -> Self {
|
||||
Self {
|
||||
values: vec![0.0; dimension],
|
||||
dimension,
|
||||
timestamp,
|
||||
method: method.to_string(),
|
||||
metadata: None,
|
||||
}
|
||||
}
|
||||
|
||||
/// L2 norm of the embedding vector.
|
||||
pub fn norm(&self) -> f64 {
|
||||
self.values.iter().map(|v| v * v).sum::<f64>().sqrt()
|
||||
}
|
||||
|
||||
/// Normalize the embedding to unit length (in-place).
|
||||
pub fn normalize(&mut self) {
|
||||
let n = self.norm();
|
||||
if n > 1e-12 {
|
||||
for v in &mut self.values {
|
||||
*v /= n;
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
/// Return a normalized copy.
|
||||
pub fn normalized(&self) -> Self {
|
||||
let mut copy = self.clone();
|
||||
copy.normalize();
|
||||
copy
|
||||
}
|
||||
}
|
||||
|
||||
/// A temporal sequence of embeddings (one per graph in a sequence).
|
||||
#[derive(Debug, Clone, Serialize, Deserialize)]
|
||||
pub struct EmbeddingTrajectory {
|
||||
/// Ordered embeddings.
|
||||
pub embeddings: Vec<NeuralEmbedding>,
|
||||
/// Time step between successive embeddings in seconds.
|
||||
pub step_s: f64,
|
||||
}
|
||||
|
||||
impl EmbeddingTrajectory {
|
||||
/// Number of time points in the trajectory.
|
||||
pub fn len(&self) -> usize {
|
||||
self.embeddings.len()
|
||||
}
|
||||
|
||||
/// Whether the trajectory is empty.
|
||||
pub fn is_empty(&self) -> bool {
|
||||
self.embeddings.is_empty()
|
||||
}
|
||||
|
||||
/// Total duration of the trajectory in seconds.
|
||||
pub fn duration_s(&self) -> f64 {
|
||||
if self.embeddings.len() < 2 {
|
||||
return 0.0;
|
||||
}
|
||||
(self.embeddings.len() - 1) as f64 * self.step_s
|
||||
}
|
||||
}
|
||||
|
||||
/// Trait for types that generate embeddings from brain graphs.
|
||||
pub trait EmbeddingGenerator: Send + Sync {
|
||||
/// Embedding dimensionality produced by this generator.
|
||||
fn dimension(&self) -> usize;
|
||||
|
||||
/// Name of the embedding method.
|
||||
fn method_name(&self) -> &str;
|
||||
|
||||
/// Generate an embedding from a brain graph.
|
||||
fn embed(&self, graph: &BrainGraph) -> Result<NeuralEmbedding>;
|
||||
}
|
||||
|
||||
#[cfg(test)]
|
||||
mod tests {
|
||||
use super::*;
|
||||
use ruv_neural_core::brain::Atlas;
|
||||
|
||||
#[test]
|
||||
fn test_neural_embedding_new() {
|
||||
let emb = NeuralEmbedding::new(vec![1.0, 2.0, 3.0], 0.0, "test").unwrap();
|
||||
let meta = default_metadata("test", Atlas::Custom(3));
|
||||
let emb = NeuralEmbedding::new(vec![1.0, 2.0, 3.0], 0.0, meta).unwrap();
|
||||
assert_eq!(emb.dimension, 3);
|
||||
assert_eq!(emb.values.len(), 3);
|
||||
assert_eq!(emb.vector.len(), 3);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_neural_embedding_empty_fails() {
|
||||
let result = NeuralEmbedding::new(vec![], 0.0, "test");
|
||||
let meta = default_metadata("test", Atlas::Custom(1));
|
||||
let result = NeuralEmbedding::new(vec![], 0.0, meta);
|
||||
assert!(result.is_err());
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_normalize() {
|
||||
let mut emb = NeuralEmbedding::new(vec![3.0, 4.0], 0.0, "test").unwrap();
|
||||
emb.normalize();
|
||||
let norm = emb.norm();
|
||||
assert!((norm - 1.0).abs() < 1e-10);
|
||||
fn test_embedding_norm() {
|
||||
let meta = default_metadata("test", Atlas::Custom(2));
|
||||
let emb = NeuralEmbedding::new(vec![3.0, 4.0], 0.0, meta).unwrap();
|
||||
assert!((emb.norm() - 5.0).abs() < 1e-10);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_trajectory() {
|
||||
let traj = EmbeddingTrajectory {
|
||||
embeddings: vec![
|
||||
NeuralEmbedding::zeros(4, 0.0, "test"),
|
||||
NeuralEmbedding::zeros(4, 0.5, "test"),
|
||||
NeuralEmbedding::zeros(4, 1.0, "test"),
|
||||
NeuralEmbedding::new(
|
||||
vec![0.0; 4],
|
||||
0.0,
|
||||
default_metadata("test", Atlas::Custom(4)),
|
||||
)
|
||||
.unwrap(),
|
||||
NeuralEmbedding::new(
|
||||
vec![0.0; 4],
|
||||
0.5,
|
||||
default_metadata("test", Atlas::Custom(4)),
|
||||
)
|
||||
.unwrap(),
|
||||
NeuralEmbedding::new(
|
||||
vec![0.0; 4],
|
||||
1.0,
|
||||
default_metadata("test", Atlas::Custom(4)),
|
||||
)
|
||||
.unwrap(),
|
||||
],
|
||||
step_s: 0.5,
|
||||
timestamps: vec![0.0, 0.5, 1.0],
|
||||
};
|
||||
assert_eq!(traj.len(), 3);
|
||||
assert!((traj.duration_s() - 1.0).abs() < 1e-10);
|
||||
|
||||
@@ -4,11 +4,14 @@
|
||||
//! matrix. The graph-level embedding is obtained via SVD of the co-occurrence
|
||||
//! matrix (a simplified skip-gram approximation).
|
||||
|
||||
use rand::Rng;
|
||||
use rand::rngs::StdRng;
|
||||
use rand::{Rng, SeedableRng};
|
||||
use ruv_neural_core::embedding::NeuralEmbedding;
|
||||
use ruv_neural_core::error::{Result, RuvNeuralError};
|
||||
use ruv_neural_core::graph::BrainGraph;
|
||||
use ruv_neural_core::traits::EmbeddingGenerator;
|
||||
|
||||
use crate::{EmbeddingGenerator, NeuralEmbedding};
|
||||
use crate::default_metadata;
|
||||
|
||||
/// Node2Vec-style graph embedder using biased random walks.
|
||||
pub struct Node2VecEmbedder {
|
||||
@@ -40,11 +43,13 @@ impl Node2VecEmbedder {
|
||||
}
|
||||
|
||||
/// Perform a single biased random walk starting from `start`.
|
||||
fn random_walk(&self, graph: &BrainGraph, adj: &[Vec<f64>], start: usize) -> Vec<usize> {
|
||||
let n = graph.num_nodes;
|
||||
let mut rng = rand::rngs::StdRng::seed_from_u64(
|
||||
self.seed.wrapping_add(start as u64),
|
||||
);
|
||||
fn random_walk(
|
||||
&self,
|
||||
adj: &[Vec<f64>],
|
||||
n: usize,
|
||||
start: usize,
|
||||
rng: &mut StdRng,
|
||||
) -> Vec<usize> {
|
||||
let mut walk = Vec::with_capacity(self.walk_length);
|
||||
walk.push(start);
|
||||
|
||||
@@ -52,7 +57,7 @@ impl Node2VecEmbedder {
|
||||
return walk;
|
||||
}
|
||||
|
||||
// First step: uniform over neighbors
|
||||
// First step: weighted over neighbors
|
||||
let neighbors: Vec<(usize, f64)> = (0..n)
|
||||
.filter(|&j| adj[start][j] > 1e-12)
|
||||
.map(|j| (j, adj[start][j]))
|
||||
@@ -89,14 +94,13 @@ impl Node2VecEmbedder {
|
||||
break;
|
||||
}
|
||||
|
||||
// Compute biased weights
|
||||
let biased: Vec<(usize, f64)> = neighbors
|
||||
.iter()
|
||||
.map(|&(j, w)| {
|
||||
let bias = if j == prev {
|
||||
1.0 / self.p
|
||||
} else if adj[prev][j] > 1e-12 {
|
||||
1.0 // neighbor of previous node
|
||||
1.0
|
||||
} else {
|
||||
1.0 / self.q
|
||||
};
|
||||
@@ -125,12 +129,12 @@ impl Node2VecEmbedder {
|
||||
}
|
||||
|
||||
/// Generate all random walks from all nodes.
|
||||
fn generate_walks(&self, graph: &BrainGraph, adj: &[Vec<f64>]) -> Vec<Vec<usize>> {
|
||||
let n = graph.num_nodes;
|
||||
fn generate_walks(&self, adj: &[Vec<f64>], n: usize) -> Vec<Vec<usize>> {
|
||||
let mut rng = StdRng::seed_from_u64(self.seed);
|
||||
let mut all_walks = Vec::with_capacity(n * self.num_walks);
|
||||
for _ in 0..self.num_walks {
|
||||
for node in 0..n {
|
||||
all_walks.push(self.random_walk(graph, adj, node));
|
||||
all_walks.push(self.random_walk(adj, n, node, &mut rng));
|
||||
}
|
||||
}
|
||||
all_walks
|
||||
@@ -153,18 +157,16 @@ impl Node2VecEmbedder {
|
||||
cooc
|
||||
}
|
||||
|
||||
/// Simplified SVD via power iteration: extract top-k singular vectors.
|
||||
/// Returns left singular vectors scaled by singular values.
|
||||
/// Simplified SVD via power iteration: extract top-k left singular vectors scaled by sigma.
|
||||
fn truncated_svd(matrix: &[Vec<f64>], n: usize, k: usize) -> Vec<Vec<f64>> {
|
||||
let k = k.min(n);
|
||||
if k == 0 || n == 0 {
|
||||
return vec![];
|
||||
}
|
||||
|
||||
let mut result = Vec::with_capacity(k);
|
||||
let mut result: Vec<Vec<f64>> = Vec::with_capacity(k);
|
||||
|
||||
for col in 0..k {
|
||||
// Initialize deterministically
|
||||
let mut v: Vec<f64> = (0..n).map(|i| ((i + col + 1) as f64).sin()).collect();
|
||||
let norm = v.iter().map(|x| x * x).sum::<f64>().sqrt();
|
||||
if norm > 1e-12 {
|
||||
@@ -173,28 +175,26 @@ impl Node2VecEmbedder {
|
||||
}
|
||||
}
|
||||
|
||||
// Deflate previously found components
|
||||
// Deflate
|
||||
for prev in &result {
|
||||
let dot: f64 = v.iter().zip(prev.iter()).map(|(a, b)| a * b).sum();
|
||||
let prev_norm: f64 = prev.iter().map(|x| x * x).sum::<f64>().sqrt();
|
||||
if prev_norm > 1e-12 {
|
||||
let prev_normalized: Vec<f64> = prev.iter().map(|x| x / prev_norm).collect();
|
||||
let prev_unit: Vec<f64> = prev.iter().map(|x| x / prev_norm).collect();
|
||||
let dot: f64 = v.iter().zip(prev_unit.iter()).map(|(a, b)| a * b).sum();
|
||||
for i in 0..n {
|
||||
v[i] -= dot / prev_norm * prev_normalized[i];
|
||||
v[i] -= dot * prev_unit[i];
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
// Power iteration on M^T M
|
||||
for _ in 0..100 {
|
||||
// u = M * v
|
||||
let mut u = vec![0.0; n];
|
||||
for i in 0..n {
|
||||
for j in 0..n {
|
||||
u[i] += matrix[i][j] * v[j];
|
||||
}
|
||||
}
|
||||
// v = M^T * u
|
||||
let mut new_v = vec![0.0; n];
|
||||
for j in 0..n {
|
||||
for i in 0..n {
|
||||
@@ -206,15 +206,14 @@ impl Node2VecEmbedder {
|
||||
for prev in &result {
|
||||
let prev_norm: f64 = prev.iter().map(|x| x * x).sum::<f64>().sqrt();
|
||||
if prev_norm > 1e-12 {
|
||||
let prev_normalized: Vec<f64> =
|
||||
prev.iter().map(|x| x / prev_norm).collect();
|
||||
let prev_unit: Vec<f64> = prev.iter().map(|x| x / prev_norm).collect();
|
||||
let dot: f64 = new_v
|
||||
.iter()
|
||||
.zip(prev_normalized.iter())
|
||||
.zip(prev_unit.iter())
|
||||
.map(|(a, b)| a * b)
|
||||
.sum();
|
||||
for i in 0..n {
|
||||
new_v[i] -= dot * prev_normalized[i];
|
||||
new_v[i] -= dot * prev_unit[i];
|
||||
}
|
||||
}
|
||||
}
|
||||
@@ -229,29 +228,22 @@ impl Node2VecEmbedder {
|
||||
v = new_v;
|
||||
}
|
||||
|
||||
// Compute the singular value: sigma = ||M * v||
|
||||
// sigma * u = M * v
|
||||
let mut mv = vec![0.0; n];
|
||||
for i in 0..n {
|
||||
for j in 0..n {
|
||||
mv[i] += matrix[i][j] * v[j];
|
||||
}
|
||||
}
|
||||
let sigma = mv.iter().map(|x| x * x).sum::<f64>().sqrt();
|
||||
|
||||
// Store u * sigma (the left singular vector scaled by singular value)
|
||||
if sigma > 1e-12 {
|
||||
let scaled: Vec<f64> = mv.iter().map(|x| *x).collect();
|
||||
result.push(scaled);
|
||||
} else {
|
||||
result.push(vec![0.0; n]);
|
||||
}
|
||||
result.push(mv);
|
||||
}
|
||||
|
||||
result
|
||||
}
|
||||
|
||||
/// Generate the Node2Vec embedding for a brain graph.
|
||||
pub fn embed(&self, graph: &BrainGraph) -> Result<NeuralEmbedding> {
|
||||
pub fn embed_graph(&self, graph: &BrainGraph) -> Result<NeuralEmbedding> {
|
||||
let n = graph.num_nodes;
|
||||
if n < 2 {
|
||||
return Err(RuvNeuralError::Embedding(
|
||||
@@ -260,21 +252,19 @@ impl Node2VecEmbedder {
|
||||
}
|
||||
|
||||
let adj = graph.adjacency_matrix();
|
||||
let walks = self.generate_walks(graph, &adj);
|
||||
let walks = self.generate_walks(&adj, n);
|
||||
let cooc = Self::build_cooccurrence(&walks, n, 5);
|
||||
|
||||
// Apply log transform (PPMI-like): log(1 + cooc)
|
||||
// Log transform (PPMI-like)
|
||||
let log_cooc: Vec<Vec<f64>> = cooc
|
||||
.iter()
|
||||
.map(|row| row.iter().map(|&v| (1.0 + v).ln()).collect())
|
||||
.collect();
|
||||
|
||||
// SVD to get node embeddings
|
||||
let dim = self.embedding_dim.min(n);
|
||||
let node_embeddings = Self::truncated_svd(&log_cooc, n, dim);
|
||||
|
||||
// Aggregate node embeddings into a graph-level embedding.
|
||||
// For each SVD component: [mean, std] over nodes.
|
||||
// Aggregate: [mean, std] per SVD component
|
||||
let mut values = Vec::with_capacity(dim * 2);
|
||||
for component in &node_embeddings {
|
||||
let mean = component.iter().sum::<f64>() / n as f64;
|
||||
@@ -283,32 +273,25 @@ impl Node2VecEmbedder {
|
||||
values.push(var.sqrt());
|
||||
}
|
||||
|
||||
// Pad if needed
|
||||
while values.len() < self.embedding_dim * 2 {
|
||||
values.push(0.0);
|
||||
}
|
||||
|
||||
NeuralEmbedding::new(values, graph.timestamp, "node2vec")
|
||||
let meta = default_metadata("node2vec", graph.atlas);
|
||||
NeuralEmbedding::new(values, graph.timestamp, meta)
|
||||
}
|
||||
}
|
||||
|
||||
impl EmbeddingGenerator for Node2VecEmbedder {
|
||||
fn dimension(&self) -> usize {
|
||||
fn embedding_dim(&self) -> usize {
|
||||
self.embedding_dim * 2
|
||||
}
|
||||
|
||||
fn method_name(&self) -> &str {
|
||||
"node2vec"
|
||||
}
|
||||
|
||||
fn embed(&self, graph: &BrainGraph) -> Result<NeuralEmbedding> {
|
||||
Node2VecEmbedder::embed(self, graph)
|
||||
self.embed_graph(graph)
|
||||
}
|
||||
}
|
||||
|
||||
// We need the StdRng import
|
||||
use rand::SeedableRng;
|
||||
|
||||
#[cfg(test)]
|
||||
mod tests {
|
||||
use super::*;
|
||||
@@ -317,7 +300,6 @@ mod tests {
|
||||
use ruv_neural_core::signal::FrequencyBand;
|
||||
|
||||
fn make_connected_graph() -> BrainGraph {
|
||||
// A connected path graph: 0-1-2-3-4
|
||||
let edges: Vec<BrainEdge> = (0..4)
|
||||
.map(|i| BrainEdge {
|
||||
source: i,
|
||||
@@ -349,9 +331,8 @@ mod tests {
|
||||
};
|
||||
|
||||
let adj = graph.adjacency_matrix();
|
||||
let walks = embedder.generate_walks(&graph, &adj);
|
||||
let walks = embedder.generate_walks(&adj, graph.num_nodes);
|
||||
|
||||
// Collect all visited nodes across all walks
|
||||
let mut visited = std::collections::HashSet::new();
|
||||
for walk in &walks {
|
||||
for &node in walk {
|
||||
@@ -359,7 +340,6 @@ mod tests {
|
||||
}
|
||||
}
|
||||
|
||||
// All 5 nodes should be visited (since each node starts a walk)
|
||||
assert_eq!(visited.len(), 5, "All nodes should be visited");
|
||||
}
|
||||
|
||||
@@ -368,8 +348,8 @@ mod tests {
|
||||
let graph = make_connected_graph();
|
||||
let embedder = Node2VecEmbedder::new(3);
|
||||
let emb = embedder.embed(&graph).unwrap();
|
||||
assert_eq!(emb.dimension, 3 * 2); // mean + std per component
|
||||
assert_eq!(emb.method, "node2vec");
|
||||
assert_eq!(emb.dimension, 3 * 2);
|
||||
assert_eq!(emb.metadata.embedding_method, "node2vec");
|
||||
}
|
||||
|
||||
#[test]
|
||||
|
||||
@@ -0,0 +1,210 @@
|
||||
//! Export neural embeddings to the RuVector File (.rvf) format.
|
||||
//!
|
||||
//! The RVF (RuVector Format) is a JSON-based file format for storing
|
||||
//! embedding vectors with metadata. This module provides round-trip
|
||||
//! serialization for interoperability with the RuVector ecosystem.
|
||||
|
||||
use ruv_neural_core::brain::Atlas;
|
||||
use ruv_neural_core::embedding::{EmbeddingMetadata, NeuralEmbedding};
|
||||
use ruv_neural_core::error::{Result, RuvNeuralError};
|
||||
use serde::{Deserialize, Serialize};
|
||||
|
||||
/// RVF file header.
|
||||
#[derive(Debug, Clone, Serialize, Deserialize)]
|
||||
pub struct RvfHeader {
|
||||
/// Format version string.
|
||||
pub version: String,
|
||||
/// Number of embeddings in the file.
|
||||
pub count: usize,
|
||||
/// Embedding dimensionality.
|
||||
pub dimension: usize,
|
||||
/// Method used to generate embeddings.
|
||||
pub method: String,
|
||||
/// Optional description.
|
||||
pub description: Option<String>,
|
||||
}
|
||||
|
||||
/// A single RVF record (embedding + metadata).
|
||||
#[derive(Debug, Clone, Serialize, Deserialize)]
|
||||
pub struct RvfRecord {
|
||||
/// Record index.
|
||||
pub index: usize,
|
||||
/// Timestamp of the source data.
|
||||
pub timestamp: f64,
|
||||
/// The embedding vector.
|
||||
pub values: Vec<f64>,
|
||||
/// Optional subject identifier.
|
||||
pub subject_id: Option<String>,
|
||||
/// Optional session identifier.
|
||||
pub session_id: Option<String>,
|
||||
}
|
||||
|
||||
/// Complete RVF document.
|
||||
#[derive(Debug, Clone, Serialize, Deserialize)]
|
||||
pub struct RvfDocument {
|
||||
/// File header.
|
||||
pub header: RvfHeader,
|
||||
/// Embedding records.
|
||||
pub records: Vec<RvfRecord>,
|
||||
}
|
||||
|
||||
/// Export embeddings to an RVF JSON file.
|
||||
///
|
||||
/// # Errors
|
||||
/// Returns an error if the embedding list is empty or if file I/O fails.
|
||||
pub fn export_rvf(embeddings: &[NeuralEmbedding], path: &str) -> Result<()> {
|
||||
let json = to_rvf_string(embeddings)?;
|
||||
std::fs::write(path, json).map_err(|e| {
|
||||
RuvNeuralError::Serialization(format!("Failed to write RVF file '{}': {}", path, e))
|
||||
})?;
|
||||
Ok(())
|
||||
}
|
||||
|
||||
/// Import embeddings from an RVF JSON file.
|
||||
///
|
||||
/// # Errors
|
||||
/// Returns an error if the file cannot be read or parsed.
|
||||
pub fn import_rvf(path: &str) -> Result<Vec<NeuralEmbedding>> {
|
||||
let json = std::fs::read_to_string(path).map_err(|e| {
|
||||
RuvNeuralError::Serialization(format!("Failed to read RVF file '{}': {}", path, e))
|
||||
})?;
|
||||
from_rvf_string(&json)
|
||||
}
|
||||
|
||||
/// Serialize embeddings to RVF JSON string (without writing to file).
|
||||
pub fn to_rvf_string(embeddings: &[NeuralEmbedding]) -> Result<String> {
|
||||
if embeddings.is_empty() {
|
||||
return Err(RuvNeuralError::Embedding(
|
||||
"Cannot serialize empty embedding list".into(),
|
||||
));
|
||||
}
|
||||
|
||||
let dimension = embeddings[0].dimension;
|
||||
let method = embeddings[0].metadata.embedding_method.clone();
|
||||
|
||||
let header = RvfHeader {
|
||||
version: "1.0".to_string(),
|
||||
count: embeddings.len(),
|
||||
dimension,
|
||||
method,
|
||||
description: None,
|
||||
};
|
||||
|
||||
let records: Vec<RvfRecord> = embeddings
|
||||
.iter()
|
||||
.enumerate()
|
||||
.map(|(i, emb)| RvfRecord {
|
||||
index: i,
|
||||
timestamp: emb.timestamp,
|
||||
values: emb.vector.clone(),
|
||||
subject_id: emb.metadata.subject_id.clone(),
|
||||
session_id: emb.metadata.session_id.clone(),
|
||||
})
|
||||
.collect();
|
||||
|
||||
let doc = RvfDocument { header, records };
|
||||
|
||||
serde_json::to_string_pretty(&doc).map_err(|e| {
|
||||
RuvNeuralError::Serialization(format!("Failed to serialize RVF: {}", e))
|
||||
})
|
||||
}
|
||||
|
||||
/// Deserialize embeddings from an RVF JSON string.
|
||||
pub fn from_rvf_string(json: &str) -> Result<Vec<NeuralEmbedding>> {
|
||||
let doc: RvfDocument = serde_json::from_str(json).map_err(|e| {
|
||||
RuvNeuralError::Serialization(format!("Failed to parse RVF: {}", e))
|
||||
})?;
|
||||
|
||||
doc.records
|
||||
.into_iter()
|
||||
.map(|rec| {
|
||||
let meta = EmbeddingMetadata {
|
||||
subject_id: rec.subject_id,
|
||||
session_id: rec.session_id,
|
||||
cognitive_state: None,
|
||||
source_atlas: Atlas::Custom(doc.header.dimension),
|
||||
embedding_method: doc.header.method.clone(),
|
||||
};
|
||||
NeuralEmbedding::new(rec.values, rec.timestamp, meta)
|
||||
})
|
||||
.collect()
|
||||
}
|
||||
|
||||
#[cfg(test)]
|
||||
mod tests {
|
||||
use super::*;
|
||||
use crate::default_metadata;
|
||||
|
||||
#[test]
|
||||
fn test_rvf_string_roundtrip() {
|
||||
let embeddings = vec![
|
||||
NeuralEmbedding::new(
|
||||
vec![1.0, 2.0, 3.0],
|
||||
0.0,
|
||||
default_metadata("test", Atlas::Custom(3)),
|
||||
)
|
||||
.unwrap(),
|
||||
NeuralEmbedding::new(
|
||||
vec![4.0, 5.0, 6.0],
|
||||
0.5,
|
||||
default_metadata("test", Atlas::Custom(3)),
|
||||
)
|
||||
.unwrap(),
|
||||
NeuralEmbedding::new(
|
||||
vec![7.0, 8.0, 9.0],
|
||||
1.0,
|
||||
default_metadata("test", Atlas::Custom(3)),
|
||||
)
|
||||
.unwrap(),
|
||||
];
|
||||
|
||||
let json = to_rvf_string(&embeddings).unwrap();
|
||||
let restored = from_rvf_string(&json).unwrap();
|
||||
|
||||
assert_eq!(restored.len(), 3);
|
||||
for (orig, rest) in embeddings.iter().zip(restored.iter()) {
|
||||
assert_eq!(orig.dimension, rest.dimension);
|
||||
assert!((orig.timestamp - rest.timestamp).abs() < 1e-10);
|
||||
for (a, b) in orig.vector.iter().zip(rest.vector.iter()) {
|
||||
assert!((a - b).abs() < 1e-10);
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_rvf_file_roundtrip() {
|
||||
let embeddings = vec![
|
||||
NeuralEmbedding::new(
|
||||
vec![1.0, -2.5, 3.14],
|
||||
10.0,
|
||||
default_metadata("spectral", Atlas::Custom(3)),
|
||||
)
|
||||
.unwrap(),
|
||||
NeuralEmbedding::new(
|
||||
vec![0.0, 0.0, 0.0],
|
||||
10.5,
|
||||
default_metadata("spectral", Atlas::Custom(3)),
|
||||
)
|
||||
.unwrap(),
|
||||
];
|
||||
|
||||
let path = "/tmp/ruv_neural_embed_test.rvf";
|
||||
export_rvf(&embeddings, path).unwrap();
|
||||
let restored = import_rvf(path).unwrap();
|
||||
|
||||
assert_eq!(restored.len(), 2);
|
||||
assert_eq!(restored[0].metadata.embedding_method, "spectral");
|
||||
assert!((restored[0].vector[0] - 1.0).abs() < 1e-10);
|
||||
assert!((restored[0].vector[1] - (-2.5)).abs() < 1e-10);
|
||||
assert!((restored[0].vector[2] - 3.14).abs() < 1e-10);
|
||||
assert!((restored[1].timestamp - 10.5).abs() < 1e-10);
|
||||
|
||||
let _ = std::fs::remove_file(path);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_rvf_empty_fails() {
|
||||
assert!(to_rvf_string(&[]).is_err());
|
||||
assert!(export_rvf(&[], "/tmp/empty.rvf").is_err());
|
||||
}
|
||||
}
|
||||
+18
-36
@@ -4,10 +4,12 @@
|
||||
//! of the normalized graph Laplacian. The graph-level embedding is formed by
|
||||
//! concatenating summary statistics of the per-node spectral coordinates.
|
||||
|
||||
use ruv_neural_core::embedding::NeuralEmbedding;
|
||||
use ruv_neural_core::error::{Result, RuvNeuralError};
|
||||
use ruv_neural_core::graph::BrainGraph;
|
||||
use ruv_neural_core::traits::EmbeddingGenerator;
|
||||
|
||||
use crate::{EmbeddingGenerator, NeuralEmbedding};
|
||||
use crate::default_metadata;
|
||||
|
||||
/// Spectral embedding via Laplacian eigenvectors.
|
||||
pub struct SpectralEmbedder {
|
||||
@@ -30,12 +32,8 @@ impl SpectralEmbedder {
|
||||
|
||||
/// Compute the normalized Laplacian matrix: L_norm = I - D^{-1/2} A D^{-1/2}.
|
||||
fn normalized_laplacian(adj: &[Vec<f64>], n: usize) -> Vec<Vec<f64>> {
|
||||
// Degree vector
|
||||
let degrees: Vec<f64> = (0..n)
|
||||
.map(|i| adj[i].iter().sum::<f64>())
|
||||
.collect();
|
||||
let degrees: Vec<f64> = (0..n).map(|i| adj[i].iter().sum::<f64>()).collect();
|
||||
|
||||
// D^{-1/2}
|
||||
let inv_sqrt_deg: Vec<f64> = degrees
|
||||
.iter()
|
||||
.map(|d| if *d > 1e-12 { 1.0 / d.sqrt() } else { 0.0 })
|
||||
@@ -69,7 +67,7 @@ impl SpectralEmbedder {
|
||||
}
|
||||
let k = k.min(n);
|
||||
|
||||
// Estimate max eigenvalue via Gershgorin bound (for shift)
|
||||
// Gershgorin bound for max eigenvalue
|
||||
let max_eig: f64 = (0..n)
|
||||
.map(|i| {
|
||||
let diag = laplacian[i][i];
|
||||
@@ -82,7 +80,6 @@ impl SpectralEmbedder {
|
||||
.fold(0.0_f64, f64::max);
|
||||
|
||||
// Shifted matrix: M = max_eig * I - L
|
||||
// Largest eigenvectors of M correspond to smallest eigenvectors of L
|
||||
let shifted: Vec<Vec<f64>> = (0..n)
|
||||
.map(|i| {
|
||||
(0..n)
|
||||
@@ -100,7 +97,6 @@ impl SpectralEmbedder {
|
||||
let mut eigenvectors: Vec<Vec<f64>> = Vec::with_capacity(k);
|
||||
|
||||
for _ev in 0..k {
|
||||
// Initialize with a deterministic vector
|
||||
let mut v: Vec<f64> = (0..n).map(|i| ((i + 1) as f64).sin()).collect();
|
||||
let norm = v.iter().map(|x| x * x).sum::<f64>().sqrt();
|
||||
if norm > 1e-12 {
|
||||
@@ -109,7 +105,7 @@ impl SpectralEmbedder {
|
||||
}
|
||||
}
|
||||
|
||||
// Deflate: remove components along already-found eigenvectors
|
||||
// Deflate against already-found eigenvectors
|
||||
for prev in &eigenvectors {
|
||||
let dot: f64 = v.iter().zip(prev.iter()).map(|(a, b)| a * b).sum();
|
||||
for i in 0..n {
|
||||
@@ -117,9 +113,7 @@ impl SpectralEmbedder {
|
||||
}
|
||||
}
|
||||
|
||||
// Power iteration
|
||||
for _ in 0..iterations {
|
||||
// Matrix-vector multiply: w = M * v
|
||||
let mut w = vec![0.0; n];
|
||||
for i in 0..n {
|
||||
for j in 0..n {
|
||||
@@ -127,7 +121,6 @@ impl SpectralEmbedder {
|
||||
}
|
||||
}
|
||||
|
||||
// Deflate
|
||||
for prev in &eigenvectors {
|
||||
let dot: f64 = w.iter().zip(prev.iter()).map(|(a, b)| a * b).sum();
|
||||
for i in 0..n {
|
||||
@@ -135,7 +128,6 @@ impl SpectralEmbedder {
|
||||
}
|
||||
}
|
||||
|
||||
// Normalize
|
||||
let norm = w.iter().map(|x| x * x).sum::<f64>().sqrt();
|
||||
if norm < 1e-12 {
|
||||
break;
|
||||
@@ -153,7 +145,7 @@ impl SpectralEmbedder {
|
||||
}
|
||||
|
||||
/// Embed a brain graph using spectral decomposition.
|
||||
pub fn embed(&self, graph: &BrainGraph) -> Result<NeuralEmbedding> {
|
||||
pub fn embed_graph(&self, graph: &BrainGraph) -> Result<NeuralEmbedding> {
|
||||
let n = graph.num_nodes;
|
||||
if n < 2 {
|
||||
return Err(RuvNeuralError::Embedding(
|
||||
@@ -164,16 +156,14 @@ impl SpectralEmbedder {
|
||||
let adj = graph.adjacency_matrix();
|
||||
let laplacian = Self::normalized_laplacian(&adj, n);
|
||||
|
||||
// We skip the first eigenvector (trivial constant) and take the next `dimension`
|
||||
// Skip the trivial first eigenvector and take the next `dimension`
|
||||
let num_to_extract = (self.dimension + 1).min(n);
|
||||
let eigvecs =
|
||||
Self::smallest_eigenvectors(&laplacian, n, num_to_extract, self.power_iterations);
|
||||
|
||||
// Skip the first (trivial) eigenvector and take up to `dimension`
|
||||
let useful: Vec<&Vec<f64>> = eigvecs.iter().skip(1).take(self.dimension).collect();
|
||||
|
||||
// Build graph-level embedding from per-node spectral coordinates.
|
||||
// For each eigenvector: [mean, std, min, max] -> 4 features per eigenvector.
|
||||
// Build graph-level embedding: [mean, std, min, max] per eigenvector
|
||||
let mut values = Vec::with_capacity(self.dimension * 4);
|
||||
for ev in &useful {
|
||||
let mean = ev.iter().sum::<f64>() / n as f64;
|
||||
@@ -187,26 +177,23 @@ impl SpectralEmbedder {
|
||||
values.push(max);
|
||||
}
|
||||
|
||||
// Pad if we got fewer eigenvectors than requested
|
||||
// Pad if fewer eigenvectors than requested
|
||||
while values.len() < self.dimension * 4 {
|
||||
values.push(0.0);
|
||||
}
|
||||
|
||||
NeuralEmbedding::new(values, graph.timestamp, "spectral")
|
||||
let meta = default_metadata("spectral", graph.atlas);
|
||||
NeuralEmbedding::new(values, graph.timestamp, meta)
|
||||
}
|
||||
}
|
||||
|
||||
impl EmbeddingGenerator for SpectralEmbedder {
|
||||
fn dimension(&self) -> usize {
|
||||
fn embedding_dim(&self) -> usize {
|
||||
self.dimension * 4
|
||||
}
|
||||
|
||||
fn method_name(&self) -> &str {
|
||||
"spectral"
|
||||
}
|
||||
|
||||
fn embed(&self, graph: &BrainGraph) -> Result<NeuralEmbedding> {
|
||||
SpectralEmbedder::embed(self, graph)
|
||||
self.embed_graph(graph)
|
||||
}
|
||||
}
|
||||
|
||||
@@ -240,9 +227,8 @@ mod tests {
|
||||
}
|
||||
|
||||
fn make_two_cluster_graph() -> BrainGraph {
|
||||
// Two dense clusters of 4 nodes each, with a weak link between them
|
||||
let mut edges = Vec::new();
|
||||
// Cluster A: nodes 0-3
|
||||
// Cluster A: nodes 0-3 (fully connected)
|
||||
for i in 0..4 {
|
||||
for j in (i + 1)..4 {
|
||||
edges.push(BrainEdge {
|
||||
@@ -254,7 +240,7 @@ mod tests {
|
||||
});
|
||||
}
|
||||
}
|
||||
// Cluster B: nodes 4-7
|
||||
// Cluster B: nodes 4-7 (fully connected)
|
||||
for i in 4..8 {
|
||||
for j in (i + 1)..8 {
|
||||
edges.push(BrainEdge {
|
||||
@@ -288,9 +274,6 @@ mod tests {
|
||||
let graph = make_complete_graph(6);
|
||||
let embedder = SpectralEmbedder::new(3);
|
||||
let emb = embedder.embed(&graph).unwrap();
|
||||
// Complete graph: all eigenvectors beyond the first are degenerate
|
||||
// All node positions should be similar -> low std deviation
|
||||
// Check that the embedding has the expected dimension
|
||||
assert_eq!(emb.dimension, 3 * 4);
|
||||
}
|
||||
|
||||
@@ -299,9 +282,8 @@ mod tests {
|
||||
let graph = make_two_cluster_graph();
|
||||
let embedder = SpectralEmbedder::new(2);
|
||||
let emb = embedder.embed(&graph).unwrap();
|
||||
// The Fiedler vector (first non-trivial eigenvector) should separate the two clusters.
|
||||
// This means the std of the first eigenvector component should be non-negligible.
|
||||
let fiedler_std = emb.values[1]; // index 1 is std of first eigenvector
|
||||
// Fiedler vector std (index 1) should show cluster separation
|
||||
let fiedler_std = emb.vector[1];
|
||||
assert!(
|
||||
fiedler_std > 0.01,
|
||||
"Fiedler eigenvector should show cluster separation, got std={}",
|
||||
|
||||
@@ -1,12 +1,14 @@
|
||||
//! Temporal embedding of brain graph sequences.
|
||||
//! Temporal sliding-window embeddings for brain graph sequences.
|
||||
//!
|
||||
//! Embeds a time series of brain graphs into trajectory vectors by combining
|
||||
//! each graph's embedding with an exponentially-weighted average of past embeddings.
|
||||
|
||||
use ruv_neural_core::embedding::{EmbeddingTrajectory, NeuralEmbedding};
|
||||
use ruv_neural_core::error::{Result, RuvNeuralError};
|
||||
use ruv_neural_core::graph::{BrainGraph, BrainGraphSequence};
|
||||
use ruv_neural_core::traits::EmbeddingGenerator;
|
||||
|
||||
use crate::{EmbeddingGenerator, EmbeddingTrajectory, NeuralEmbedding};
|
||||
use crate::default_metadata;
|
||||
|
||||
/// Temporal embedder that enriches each graph embedding with historical context.
|
||||
pub struct TemporalEmbedder {
|
||||
@@ -47,16 +49,18 @@ impl TemporalEmbedder {
|
||||
|
||||
let mut history: Vec<NeuralEmbedding> = Vec::new();
|
||||
let mut embeddings = Vec::with_capacity(sequence.graphs.len());
|
||||
let mut timestamps = Vec::with_capacity(sequence.graphs.len());
|
||||
|
||||
for graph in &sequence.graphs {
|
||||
let emb = self.embed_with_context(graph, &history)?;
|
||||
timestamps.push(graph.timestamp);
|
||||
history.push(self.base_embedder.embed(graph)?);
|
||||
embeddings.push(emb);
|
||||
}
|
||||
|
||||
Ok(EmbeddingTrajectory {
|
||||
embeddings,
|
||||
step_s: sequence.window_step_s,
|
||||
timestamps,
|
||||
})
|
||||
}
|
||||
|
||||
@@ -75,12 +79,12 @@ impl TemporalEmbedder {
|
||||
|
||||
let context = self.compute_context(history, base_dim);
|
||||
|
||||
// Concatenate current embedding with context
|
||||
let mut values = Vec::with_capacity(base_dim * 2);
|
||||
values.extend_from_slice(¤t.values);
|
||||
values.extend_from_slice(¤t.vector);
|
||||
values.extend_from_slice(&context);
|
||||
|
||||
NeuralEmbedding::new(values, graph.timestamp, "temporal")
|
||||
let meta = default_metadata("temporal", graph.atlas);
|
||||
NeuralEmbedding::new(values, graph.timestamp, meta)
|
||||
}
|
||||
|
||||
/// Compute the exponentially-weighted context vector from history.
|
||||
@@ -104,7 +108,7 @@ impl TemporalEmbedder {
|
||||
total_weight += w;
|
||||
let usable_dim = dim.min(emb.dimension);
|
||||
for j in 0..usable_dim {
|
||||
context[j] += w * emb.values[j];
|
||||
context[j] += w * emb.vector[j];
|
||||
}
|
||||
}
|
||||
|
||||
@@ -119,7 +123,7 @@ impl TemporalEmbedder {
|
||||
|
||||
/// Output dimension: base dimension * 2 (current + context).
|
||||
pub fn output_dimension(&self) -> usize {
|
||||
self.base_embedder.dimension() * 2
|
||||
self.base_embedder.embedding_dim() * 2
|
||||
}
|
||||
}
|
||||
|
||||
@@ -162,13 +166,12 @@ mod tests {
|
||||
let graph = make_graph(0.0);
|
||||
let emb = embedder.embed_with_context(&graph, &[]).unwrap();
|
||||
|
||||
let base_dim = TopologyEmbedder::new().dimension();
|
||||
let base_dim = TopologyEmbedder::new().embedding_dim();
|
||||
assert_eq!(emb.dimension, base_dim * 2);
|
||||
|
||||
// Context part should be all zeros
|
||||
for i in base_dim..emb.dimension {
|
||||
assert!(
|
||||
emb.values[i].abs() < 1e-12,
|
||||
emb.vector[i].abs() < 1e-12,
|
||||
"Context should be zero with no history"
|
||||
);
|
||||
}
|
||||
@@ -186,17 +189,20 @@ mod tests {
|
||||
|
||||
let trajectory = embedder.embed_sequence(&sequence).unwrap();
|
||||
assert_eq!(trajectory.len(), 3);
|
||||
assert!((trajectory.step_s - 0.5).abs() < 1e-10);
|
||||
assert_eq!(trajectory.timestamps.len(), 3);
|
||||
|
||||
// First embedding should have zero context
|
||||
let base_dim = TopologyEmbedder::new().dimension();
|
||||
let base_dim = TopologyEmbedder::new().embedding_dim();
|
||||
for i in base_dim..trajectory.embeddings[0].dimension {
|
||||
assert!(trajectory.embeddings[0].values[i].abs() < 1e-12);
|
||||
assert!(trajectory.embeddings[0].vector[i].abs() < 1e-12);
|
||||
}
|
||||
|
||||
// Later embeddings should have non-zero context (since graphs are non-trivial)
|
||||
let has_nonzero = trajectory.embeddings[2].values[base_dim..].iter().any(|v| v.abs() > 1e-12);
|
||||
assert!(has_nonzero, "Third embedding should have non-zero temporal context");
|
||||
let has_nonzero = trajectory.embeddings[2].vector[base_dim..]
|
||||
.iter()
|
||||
.any(|v| v.abs() > 1e-12);
|
||||
assert!(
|
||||
has_nonzero,
|
||||
"Third embedding should have non-zero temporal context"
|
||||
);
|
||||
}
|
||||
|
||||
#[test]
|
||||
|
||||
+27
-48
@@ -3,10 +3,12 @@
|
||||
//! Extracts a feature vector of hand-crafted topological metrics from a brain graph,
|
||||
//! including mincut estimate, modularity, efficiency, degree statistics, and more.
|
||||
|
||||
use ruv_neural_core::embedding::NeuralEmbedding;
|
||||
use ruv_neural_core::error::Result;
|
||||
use ruv_neural_core::graph::BrainGraph;
|
||||
use ruv_neural_core::traits::EmbeddingGenerator;
|
||||
|
||||
use crate::{EmbeddingGenerator, NeuralEmbedding};
|
||||
use crate::default_metadata;
|
||||
|
||||
/// Topology-based embedder: converts a brain graph into a vector of topological features.
|
||||
pub struct TopologyEmbedder {
|
||||
@@ -31,7 +33,7 @@ impl TopologyEmbedder {
|
||||
}
|
||||
}
|
||||
|
||||
/// Estimate global minimum cut via the minimum node degree (Stoer-Wagner lower bound).
|
||||
/// Estimate global minimum cut via the minimum node degree.
|
||||
fn estimate_mincut(graph: &BrainGraph) -> f64 {
|
||||
if graph.num_nodes < 2 {
|
||||
return 0.0;
|
||||
@@ -42,7 +44,6 @@ impl TopologyEmbedder {
|
||||
}
|
||||
|
||||
/// Estimate modularity using a simple greedy two-partition.
|
||||
/// This is a simplified Newman modularity approximation.
|
||||
fn estimate_modularity(graph: &BrainGraph) -> f64 {
|
||||
let n = graph.num_nodes;
|
||||
if n < 2 {
|
||||
@@ -56,7 +57,6 @@ impl TopologyEmbedder {
|
||||
let adj = graph.adjacency_matrix();
|
||||
let degrees: Vec<f64> = (0..n).map(|i| graph.node_degree(i)).collect();
|
||||
|
||||
// Simple partition: split by median degree
|
||||
let mut sorted_degrees: Vec<(usize, f64)> =
|
||||
degrees.iter().copied().enumerate().collect();
|
||||
sorted_degrees.sort_by(|a, b| a.1.partial_cmp(&b.1).unwrap());
|
||||
@@ -67,7 +67,6 @@ impl TopologyEmbedder {
|
||||
partition[node] = if rank < mid { 1 } else { -1 };
|
||||
}
|
||||
|
||||
// Q = (1/2m) * sum_ij [ A_ij - k_i*k_j/(2m) ] * delta(c_i, c_j)
|
||||
let two_m = 2.0 * total_weight;
|
||||
let mut q = 0.0;
|
||||
for i in 0..n {
|
||||
@@ -81,7 +80,6 @@ impl TopologyEmbedder {
|
||||
}
|
||||
|
||||
/// Compute global efficiency: average of 1/shortest_path for all node pairs.
|
||||
/// Uses BFS on the unweighted adjacency structure.
|
||||
fn global_efficiency(graph: &BrainGraph) -> f64 {
|
||||
let n = graph.num_nodes;
|
||||
if n < 2 {
|
||||
@@ -92,7 +90,6 @@ impl TopologyEmbedder {
|
||||
let mut sum_inv_dist = 0.0;
|
||||
|
||||
for source in 0..n {
|
||||
// BFS from source
|
||||
let mut dist = vec![usize::MAX; n];
|
||||
dist[source] = 0;
|
||||
let mut queue = std::collections::VecDeque::new();
|
||||
@@ -117,7 +114,7 @@ impl TopologyEmbedder {
|
||||
sum_inv_dist / (n * (n - 1)) as f64
|
||||
}
|
||||
|
||||
/// Compute mean local efficiency (average efficiency of each node's neighborhood).
|
||||
/// Compute mean local efficiency.
|
||||
fn local_efficiency(graph: &BrainGraph) -> f64 {
|
||||
let n = graph.num_nodes;
|
||||
if n == 0 {
|
||||
@@ -136,12 +133,11 @@ impl TopologyEmbedder {
|
||||
continue;
|
||||
}
|
||||
|
||||
// Efficiency within the subgraph of neighbors
|
||||
let mut sub_sum = 0.0;
|
||||
for &i in &neighbors {
|
||||
for &j in &neighbors {
|
||||
if i != j && adj[i][j] > 1e-12 {
|
||||
sub_sum += 1.0; // direct connection -> distance 1
|
||||
sub_sum += 1.0;
|
||||
}
|
||||
}
|
||||
}
|
||||
@@ -172,7 +168,6 @@ impl TopologyEmbedder {
|
||||
}
|
||||
|
||||
/// Estimate the Fiedler value (algebraic connectivity).
|
||||
/// Uses simplified power iteration on the Laplacian.
|
||||
fn estimate_fiedler(graph: &BrainGraph) -> f64 {
|
||||
let n = graph.num_nodes;
|
||||
if n < 2 {
|
||||
@@ -182,7 +177,6 @@ impl TopologyEmbedder {
|
||||
let adj = graph.adjacency_matrix();
|
||||
let degrees: Vec<f64> = (0..n).map(|i| adj[i].iter().sum::<f64>()).collect();
|
||||
|
||||
// Laplacian: L = D - A
|
||||
let mut laplacian = vec![vec![0.0; n]; n];
|
||||
for i in 0..n {
|
||||
for j in 0..n {
|
||||
@@ -194,8 +188,6 @@ impl TopologyEmbedder {
|
||||
}
|
||||
}
|
||||
|
||||
// Use inverse iteration with deflation to find second smallest eigenvalue
|
||||
// First, find the largest eigenvalue for shifting
|
||||
let max_eig: f64 = (0..n)
|
||||
.map(|i| {
|
||||
let diag = laplacian[i][i];
|
||||
@@ -207,15 +199,9 @@ impl TopologyEmbedder {
|
||||
})
|
||||
.fold(0.0_f64, f64::max);
|
||||
|
||||
// Shifted matrix M = max_eig * I - L: largest eigvec of M = smallest eigvec of L
|
||||
// We need the second largest of M (first is trivial constant vector)
|
||||
|
||||
// First eigenvector of M: the constant vector (corresponds to lambda_0 = 0 of L)
|
||||
let e0: Vec<f64> = vec![1.0 / (n as f64).sqrt(); n];
|
||||
|
||||
// Power iteration for second eigenvector
|
||||
let mut v: Vec<f64> = (0..n).map(|i| ((i + 1) as f64).sin()).collect();
|
||||
// Deflate e0
|
||||
let dot0: f64 = v.iter().zip(e0.iter()).map(|(a, b)| a * b).sum();
|
||||
for i in 0..n {
|
||||
v[i] -= dot0 * e0[i];
|
||||
@@ -241,7 +227,6 @@ impl TopologyEmbedder {
|
||||
}
|
||||
}
|
||||
|
||||
// Deflate
|
||||
let dot: f64 = w.iter().zip(e0.iter()).map(|(a, b)| a * b).sum();
|
||||
for i in 0..n {
|
||||
w[i] -= dot * e0[i];
|
||||
@@ -251,18 +236,17 @@ impl TopologyEmbedder {
|
||||
if norm < 1e-12 {
|
||||
break;
|
||||
}
|
||||
eigenvalue = norm; // This is the eigenvalue of the shifted matrix
|
||||
eigenvalue = norm;
|
||||
for x in &mut w {
|
||||
*x /= norm;
|
||||
}
|
||||
v = w;
|
||||
}
|
||||
|
||||
// Fiedler value = max_eig - eigenvalue_of_shifted
|
||||
(max_eig - eigenvalue).max(0.0)
|
||||
}
|
||||
|
||||
/// Compute clustering coefficient (average over nodes).
|
||||
/// Compute average clustering coefficient.
|
||||
fn clustering_coefficient(graph: &BrainGraph) -> f64 {
|
||||
let n = graph.num_nodes;
|
||||
if n == 0 {
|
||||
@@ -328,7 +312,7 @@ impl TopologyEmbedder {
|
||||
}
|
||||
|
||||
/// Generate the topology embedding.
|
||||
pub fn embed(&self, graph: &BrainGraph) -> Result<NeuralEmbedding> {
|
||||
pub fn embed_graph(&self, graph: &BrainGraph) -> Result<NeuralEmbedding> {
|
||||
let mut values = Vec::new();
|
||||
|
||||
if self.include_mincut {
|
||||
@@ -344,7 +328,6 @@ impl TopologyEmbedder {
|
||||
values.push(Self::local_efficiency(graph));
|
||||
}
|
||||
|
||||
// Always include these core features
|
||||
values.push(Self::graph_entropy(graph));
|
||||
values.push(Self::estimate_fiedler(graph));
|
||||
|
||||
@@ -358,7 +341,8 @@ impl TopologyEmbedder {
|
||||
0.0
|
||||
};
|
||||
let std_deg = if n > 0 {
|
||||
let var = degrees.iter().map(|d| (d - mean_deg).powi(2)).sum::<f64>() / n as f64;
|
||||
let var =
|
||||
degrees.iter().map(|d| (d - mean_deg).powi(2)).sum::<f64>() / n as f64;
|
||||
var.sqrt()
|
||||
} else {
|
||||
0.0
|
||||
@@ -377,7 +361,8 @@ impl TopologyEmbedder {
|
||||
values.push(Self::clustering_coefficient(graph));
|
||||
values.push(Self::num_components(graph) as f64);
|
||||
|
||||
NeuralEmbedding::new(values, graph.timestamp, "topology")
|
||||
let meta = default_metadata("topology", graph.atlas);
|
||||
NeuralEmbedding::new(values, graph.timestamp, meta)
|
||||
}
|
||||
|
||||
/// Number of features produced with current settings.
|
||||
@@ -390,13 +375,13 @@ impl TopologyEmbedder {
|
||||
count += 1;
|
||||
}
|
||||
if self.include_efficiency {
|
||||
count += 2; // global + local
|
||||
count += 2;
|
||||
}
|
||||
count += 2; // entropy + fiedler
|
||||
if self.include_degree_stats {
|
||||
count += 4; // mean, std, max, min
|
||||
count += 4;
|
||||
}
|
||||
count += 3; // density, clustering_coefficient, num_components
|
||||
count += 3; // density, clustering, components
|
||||
count
|
||||
}
|
||||
}
|
||||
@@ -408,16 +393,12 @@ impl Default for TopologyEmbedder {
|
||||
}
|
||||
|
||||
impl EmbeddingGenerator for TopologyEmbedder {
|
||||
fn dimension(&self) -> usize {
|
||||
fn embedding_dim(&self) -> usize {
|
||||
self.feature_count()
|
||||
}
|
||||
|
||||
fn method_name(&self) -> &str {
|
||||
"topology"
|
||||
}
|
||||
|
||||
fn embed(&self, graph: &BrainGraph) -> Result<NeuralEmbedding> {
|
||||
TopologyEmbedder::embed(self, graph)
|
||||
self.embed_graph(graph)
|
||||
}
|
||||
}
|
||||
|
||||
@@ -467,14 +448,13 @@ mod tests {
|
||||
let emb = embedder.embed(&graph).unwrap();
|
||||
|
||||
assert_eq!(emb.dimension, embedder.feature_count());
|
||||
assert_eq!(emb.method, "topology");
|
||||
assert_eq!(emb.metadata.embedding_method, "topology");
|
||||
|
||||
// Triangle is complete K3: density = 1.0, clustering = 1.0, 1 component
|
||||
let dim = emb.dimension;
|
||||
// Last three values: density, clustering, components
|
||||
assert!((emb.values[dim - 3] - 1.0).abs() < 1e-10, "density should be 1.0");
|
||||
assert!((emb.values[dim - 2] - 1.0).abs() < 1e-10, "clustering should be 1.0");
|
||||
assert!((emb.values[dim - 1] - 1.0).abs() < 1e-10, "should be 1 component");
|
||||
assert!((emb.vector[dim - 3] - 1.0).abs() < 1e-10, "density should be 1.0");
|
||||
assert!((emb.vector[dim - 2] - 1.0).abs() < 1e-10, "clustering should be 1.0");
|
||||
assert!((emb.vector[dim - 1] - 1.0).abs() < 1e-10, "should be 1 component");
|
||||
}
|
||||
|
||||
#[test]
|
||||
@@ -486,9 +466,9 @@ mod tests {
|
||||
// Global efficiency of K3: all pairs distance 1, so efficiency = 1.0
|
||||
// index: mincut(0), modularity(1), global_eff(2), local_eff(3)
|
||||
assert!(
|
||||
(emb.values[2] - 1.0).abs() < 1e-10,
|
||||
(emb.vector[2] - 1.0).abs() < 1e-10,
|
||||
"global efficiency of K3 should be 1.0, got {}",
|
||||
emb.values[2]
|
||||
emb.vector[2]
|
||||
);
|
||||
}
|
||||
|
||||
@@ -503,10 +483,9 @@ mod tests {
|
||||
};
|
||||
let embedder = TopologyEmbedder::new();
|
||||
let emb = embedder.embed(&graph).unwrap();
|
||||
// density = 0, clustering = 0, components = 4
|
||||
let dim = emb.dimension;
|
||||
assert!((emb.values[dim - 3]).abs() < 1e-10);
|
||||
assert!((emb.values[dim - 2]).abs() < 1e-10);
|
||||
assert!((emb.values[dim - 1] - 4.0).abs() < 1e-10);
|
||||
assert!((emb.vector[dim - 3]).abs() < 1e-10);
|
||||
assert!((emb.vector[dim - 2]).abs() < 1e-10);
|
||||
assert!((emb.vector[dim - 1] - 4.0).abs() < 1e-10);
|
||||
}
|
||||
}
|
||||
|
||||
@@ -0,0 +1,119 @@
|
||||
# rUv Neural ESP32
|
||||
|
||||
ESP32 edge integration for neural sensor data acquisition and preprocessing. This crate provides lightweight processing that runs on ESP32 hardware for real-time sensor data acquisition before sending to the main RuVector backend.
|
||||
|
||||
## Hardware Requirements
|
||||
|
||||
| Component | Specification |
|
||||
|-----------|--------------|
|
||||
| MCU | ESP32-S3 (dual-core Xtensa LX7, 240 MHz) |
|
||||
| Flash | 8 MB minimum |
|
||||
| PSRAM | 2 MB recommended for multi-channel buffering |
|
||||
| ADC | 12-bit SAR ADC (built-in), or external 16-bit via SPI |
|
||||
| WiFi | 802.11 b/g/n (built-in) |
|
||||
| Battery | 3.7V LiPo, 2000+ mAh recommended |
|
||||
|
||||
## Pin Configuration
|
||||
|
||||
| GPIO | Function | Module | Notes |
|
||||
|------|----------|--------|-------|
|
||||
| 36 | ADC1_CH0 | `adc` | NV diamond sensor input (default) |
|
||||
| 37 | ADC1_CH1 | `adc` | OPM sensor input |
|
||||
| 38 | ADC1_CH2 | `adc` | EEG sensor input |
|
||||
| 39 | ADC1_CH3 | `adc` | Auxiliary sensor input |
|
||||
| 4 | ADC2_CH0 | `adc` | Battery voltage monitor |
|
||||
| 16 | UART TX | `protocol` | Backend communication (if wired) |
|
||||
| 17 | UART RX | `protocol` | Backend communication (if wired) |
|
||||
| 2 | LED | `power` | Status indicator |
|
||||
|
||||
## Modules
|
||||
|
||||
### ADC (`adc.rs`)
|
||||
|
||||
Configurable multi-channel ADC reader with support for 12-bit and 16-bit resolution. Converts raw ADC values to physical units (femtotesla) using per-channel gain and offset calibration.
|
||||
|
||||
### Edge Preprocessing (`preprocessing.rs`)
|
||||
|
||||
Lightweight signal conditioning that runs on-device before data transmission:
|
||||
|
||||
- 50/60 Hz mains notch filters (IIR biquad)
|
||||
- Configurable high-pass filter (default 0.5 Hz) for DC removal
|
||||
- Configurable low-pass filter (default 200 Hz) for anti-aliasing
|
||||
- Block-averaging downsampler
|
||||
- Fixed-point IIR path for integer-only ESP32 math
|
||||
|
||||
### Communication Protocol (`protocol.rs`)
|
||||
|
||||
Binary packet format for ESP32-to-backend data transfer:
|
||||
|
||||
```
|
||||
+--------+-----+--------+----------+------+---------+------+------+----------+
|
||||
| Magic | Ver | PktID | Timestamp| NCh | Samples | Data | Qual | Checksum |
|
||||
| 4B | 1B | 4B | 8B | 1B | 2B | var | var | 4B |
|
||||
+--------+-----+--------+----------+------+---------+------+------+----------+
|
||||
"rUvN" 1 u32 u64 us u8 u16 i16[] u8[] CRC32
|
||||
```
|
||||
|
||||
- Magic bytes: `rUvN` (0x72 0x55 0x76 0x4E)
|
||||
- Fixed-point samples (i16) with per-channel scale factor for bandwidth efficiency
|
||||
- CRC32 checksum (IEEE polynomial) for integrity verification
|
||||
- JSON serialization in std mode; compact binary on embedded targets
|
||||
|
||||
### TDM Scheduler (`tdm.rs`)
|
||||
|
||||
Time-Division Multiplexing for collision-free multi-node operation:
|
||||
|
||||
```
|
||||
| Node 0 | Node 1 | Node 2 | Node 3 | Node 0 | ...
|
||||
|<-slot_d->|<-slot_d->|<-slot_d->|<-slot_d->|
|
||||
|<-------------- frame_duration ------------>|
|
||||
```
|
||||
|
||||
Supported sync methods:
|
||||
- **GPS PPS** -- sub-microsecond accuracy
|
||||
- **NTP Sync** -- millisecond accuracy over WiFi
|
||||
- **WiFi Beacon** -- timestamp alignment from AP beacons
|
||||
- **Leader-Follower** -- leader broadcasts sync pulses (default)
|
||||
|
||||
### Power Management (`power.rs`)
|
||||
|
||||
Battery life optimization through duty-cycle control:
|
||||
|
||||
| Mode | Current Draw | Estimated Runtime (2000 mAh) |
|
||||
|------|-------------|------------------------------|
|
||||
| Active | 240 mA | ~6.25 hours |
|
||||
| LowPower | 80 mA | ~15 hours |
|
||||
| UltraLowPower | 20 mA | ~60 hours |
|
||||
| Sleep | 10 uA | ~22 years |
|
||||
|
||||
Automatic duty-cycle optimization targets a user-specified runtime by adjusting sample and WiFi duty cycles via binary search.
|
||||
|
||||
### Node Aggregator (`aggregator.rs`)
|
||||
|
||||
Collects packets from multiple ESP32 nodes and assembles them into a unified `MultiChannelTimeSeries`. Timestamp-based packet matching with configurable sync tolerance (default 1 ms).
|
||||
|
||||
## Build Instructions
|
||||
|
||||
```bash
|
||||
# Build for host (std mode, simulation)
|
||||
cd rust-port/wifi-densepose-rs/crates/ruv-neural
|
||||
cargo build -p ruv-neural-esp32
|
||||
|
||||
# Run tests
|
||||
cargo test -p ruv-neural-esp32
|
||||
|
||||
# Build with simulator feature
|
||||
cargo build -p ruv-neural-esp32 --features simulator
|
||||
```
|
||||
|
||||
## Features
|
||||
|
||||
| Feature | Description |
|
||||
|---------|-------------|
|
||||
| `std` (default) | Standard library support, simulated ADC |
|
||||
| `no_std` | Bare-metal ESP32 deployment (no heap allocator required for core types) |
|
||||
| `simulator` | ESP32 simulation mode for desktop development |
|
||||
|
||||
## License
|
||||
|
||||
MIT OR Apache-2.0
|
||||
@@ -1 +1,28 @@
|
||||
//! Stub crate.
|
||||
//! rUv Neural ESP32 — Edge integration for neural sensor data acquisition and preprocessing.
|
||||
//!
|
||||
//! This crate provides lightweight processing that runs on ESP32 hardware for
|
||||
//! real-time sensor data acquisition and preprocessing before sending to the
|
||||
//! main RuVector backend.
|
||||
//!
|
||||
//! # Modules
|
||||
//!
|
||||
//! - [`adc`] — ADC interface for sensor data acquisition
|
||||
//! - [`preprocessing`] — Lightweight edge preprocessing (IIR filters, downsampling)
|
||||
//! - [`protocol`] — Communication protocol with the RuVector backend
|
||||
//! - [`tdm`] — Time-Division Multiplexing for multi-sensor coordination
|
||||
//! - [`power`] — Power management for battery operation
|
||||
//! - [`aggregator`] — Multi-node data aggregation
|
||||
|
||||
pub mod adc;
|
||||
pub mod aggregator;
|
||||
pub mod power;
|
||||
pub mod preprocessing;
|
||||
pub mod protocol;
|
||||
pub mod tdm;
|
||||
|
||||
pub use adc::{AdcChannel, AdcConfig, AdcReader, Attenuation};
|
||||
pub use aggregator::NodeAggregator;
|
||||
pub use power::{PowerConfig, PowerManager, PowerMode};
|
||||
pub use preprocessing::{EdgePreprocessor, IirCoeffs};
|
||||
pub use protocol::{ChannelData, NeuralDataPacket, PacketHeader};
|
||||
pub use tdm::{SyncMethod, TdmNode, TdmScheduler};
|
||||
|
||||
@@ -1,9 +1,21 @@
|
||||
[package]
|
||||
name = "ruv-neural-graph"
|
||||
description = "rUv Neural — ruv-neural-graph (stub)"
|
||||
description = "rUv Neural — Brain connectivity graph construction from neural signals"
|
||||
version.workspace = true
|
||||
edition.workspace = true
|
||||
authors.workspace = true
|
||||
license.workspace = true
|
||||
|
||||
[dependencies]
|
||||
ruv-neural-core = { workspace = true }
|
||||
ruv-neural-signal = { workspace = true }
|
||||
petgraph = { workspace = true }
|
||||
ndarray = { workspace = true }
|
||||
serde = { workspace = true }
|
||||
serde_json = { workspace = true }
|
||||
tracing = { workspace = true }
|
||||
num-traits = { workspace = true }
|
||||
|
||||
[dev-dependencies]
|
||||
approx = { workspace = true }
|
||||
rand = { workspace = true }
|
||||
|
||||
@@ -0,0 +1,98 @@
|
||||
# ruv-neural-graph
|
||||
|
||||
**rUv Neural** -- Brain connectivity graph construction from neural signals.
|
||||
|
||||
Part of the [rUv Neural](https://github.com/ruvnet/RuView) workspace for brain topology analysis.
|
||||
|
||||
## Overview
|
||||
|
||||
`ruv-neural-graph` transforms multi-channel neural time series data into brain connectivity graphs and computes graph-theoretic metrics used in network neuroscience. It supports built-in brain atlases, sliding-window graph construction, spectral analysis, and temporal dynamics tracking.
|
||||
|
||||
## Dependency Diagram
|
||||
|
||||
```
|
||||
ruv-neural-core
|
||||
|
|
||||
v
|
||||
ruv-neural-signal
|
||||
|
|
||||
v
|
||||
ruv-neural-graph <-- petgraph
|
||||
|
|
||||
v
|
||||
ruv-neural-mincut / ruv-neural-embed / ruv-neural-decoder
|
||||
```
|
||||
|
||||
## Modules
|
||||
|
||||
| Module | Description |
|
||||
|-------------------|--------------------------------------------------------------|
|
||||
| `atlas` | Brain atlas definitions (Desikan-Killiany 68 regions) |
|
||||
| `constructor` | Graph construction from connectivity matrices and time series|
|
||||
| `petgraph_bridge` | Convert between `BrainGraph` and petgraph types |
|
||||
| `metrics` | Graph-theoretic metrics (efficiency, clustering, centrality) |
|
||||
| `spectral` | Spectral graph properties (Laplacian, Fiedler value) |
|
||||
| `dynamics` | Temporal graph dynamics and topology tracking |
|
||||
|
||||
## Graph Metrics
|
||||
|
||||
| Metric | Function | Description |
|
||||
|-------------------------|----------------------------|--------------------------------------------------|
|
||||
| Global efficiency | `global_efficiency` | Average inverse shortest path length |
|
||||
| Local efficiency | `local_efficiency` | Average node-level subgraph efficiency |
|
||||
| Clustering coefficient | `clustering_coefficient` | Weighted triangle ratio |
|
||||
| Node degree | `node_degree` | Weighted degree of a single node |
|
||||
| Degree distribution | `degree_distribution` | All node degrees |
|
||||
| Betweenness centrality | `betweenness_centrality` | Fraction of shortest paths through each node |
|
||||
| Graph density | `graph_density` | Fraction of possible edges present |
|
||||
| Small-world index | `small_world_index` | sigma = (C/C_rand) / (L/L_rand) |
|
||||
| Modularity | `modularity` | Newman modularity Q for a given partition |
|
||||
| Graph Laplacian | `graph_laplacian` | L = D - A |
|
||||
| Normalized Laplacian | `normalized_laplacian` | L_norm = D^{-1/2} L D^{-1/2} |
|
||||
| Fiedler value | `fiedler_value` | Algebraic connectivity (second smallest eigenvalue)|
|
||||
| Spectral gap | `spectral_gap` | lambda_2 - lambda_1 |
|
||||
|
||||
## Usage
|
||||
|
||||
```rust
|
||||
use ruv_neural_graph::{
|
||||
AtlasType, BrainGraphConstructor, load_atlas,
|
||||
global_efficiency, clustering_coefficient, fiedler_value,
|
||||
to_petgraph, TopologyTracker,
|
||||
};
|
||||
use ruv_neural_core::graph::ConnectivityMetric;
|
||||
use ruv_neural_core::signal::FrequencyBand;
|
||||
|
||||
// Load the Desikan-Killiany atlas (68 cortical regions)
|
||||
let parcellation = load_atlas(AtlasType::DesikanKilliany);
|
||||
assert_eq!(parcellation.num_regions(), 68);
|
||||
|
||||
// Build a graph constructor
|
||||
let constructor = BrainGraphConstructor::new(
|
||||
AtlasType::DesikanKilliany,
|
||||
ConnectivityMetric::PhaseLockingValue,
|
||||
FrequencyBand::Alpha,
|
||||
)
|
||||
.with_threshold(0.1);
|
||||
|
||||
// Construct a graph from a connectivity matrix
|
||||
let connectivity = vec![vec![1.0; 68]; 68]; // example: fully connected
|
||||
let graph = constructor.construct_from_matrix(&connectivity, 0.0);
|
||||
|
||||
// Compute metrics
|
||||
let eff = global_efficiency(&graph);
|
||||
let cc = clustering_coefficient(&graph);
|
||||
let fv = fiedler_value(&graph);
|
||||
|
||||
// Convert to petgraph for advanced algorithms
|
||||
let pg = to_petgraph(&graph);
|
||||
|
||||
// Track topology over time
|
||||
let mut tracker = TopologyTracker::new();
|
||||
tracker.track(&graph);
|
||||
let transitions = tracker.detect_transitions(0.1);
|
||||
```
|
||||
|
||||
## License
|
||||
|
||||
MIT OR Apache-2.0
|
||||
@@ -5,7 +5,7 @@
|
||||
//! It also supports sliding-window construction from raw time series via the
|
||||
//! signal crate's connectivity metrics.
|
||||
|
||||
use ruv_neural_core::brain::{Atlas, Parcellation};
|
||||
use ruv_neural_core::brain::Parcellation;
|
||||
use ruv_neural_core::error::{Result, RuvNeuralError};
|
||||
use ruv_neural_core::graph::{BrainEdge, BrainGraph, BrainGraphSequence, ConnectivityMetric};
|
||||
use ruv_neural_core::signal::{FrequencyBand, MultiChannelTimeSeries};
|
||||
@@ -102,7 +102,6 @@ impl BrainGraphConstructor {
|
||||
&self,
|
||||
data: &MultiChannelTimeSeries,
|
||||
) -> BrainGraphSequence {
|
||||
let n_channels = data.num_channels;
|
||||
let n_samples = data.num_samples;
|
||||
let sr = data.sample_rate_hz;
|
||||
|
||||
|
||||
@@ -0,0 +1,262 @@
|
||||
//! Temporal graph dynamics: tracking topology metrics over time.
|
||||
//!
|
||||
//! The [`TopologyTracker`] accumulates brain graphs and computes time series
|
||||
//! of graph-theoretic metrics to detect state transitions and measure
|
||||
//! the rate of topological change.
|
||||
|
||||
use ruv_neural_core::graph::BrainGraph;
|
||||
|
||||
use crate::metrics::{clustering_coefficient, global_efficiency};
|
||||
use crate::spectral::fiedler_value;
|
||||
|
||||
/// A timestamped snapshot of graph topology metrics.
|
||||
#[derive(Debug, Clone)]
|
||||
pub struct TopologySnapshot {
|
||||
/// Timestamp of the graph.
|
||||
pub timestamp: f64,
|
||||
/// Global efficiency.
|
||||
pub global_efficiency: f64,
|
||||
/// Clustering coefficient.
|
||||
pub clustering: f64,
|
||||
/// Fiedler value (algebraic connectivity).
|
||||
pub fiedler: f64,
|
||||
/// Graph density.
|
||||
pub density: f64,
|
||||
/// Total edge weight (proxy for minimum cut in dense graphs).
|
||||
pub total_weight: f64,
|
||||
}
|
||||
|
||||
/// Tracks graph topology metrics over time and detects transitions.
|
||||
pub struct TopologyTracker {
|
||||
/// History of topology snapshots.
|
||||
history: Vec<TopologySnapshot>,
|
||||
}
|
||||
|
||||
impl TopologyTracker {
|
||||
/// Create an empty tracker.
|
||||
pub fn new() -> Self {
|
||||
Self {
|
||||
history: Vec::new(),
|
||||
}
|
||||
}
|
||||
|
||||
/// Track a new brain graph, computing and storing its topology metrics.
|
||||
pub fn track(&mut self, graph: &BrainGraph) {
|
||||
let snapshot = TopologySnapshot {
|
||||
timestamp: graph.timestamp,
|
||||
global_efficiency: global_efficiency(graph),
|
||||
clustering: clustering_coefficient(graph),
|
||||
fiedler: fiedler_value(graph),
|
||||
density: graph.density(),
|
||||
total_weight: graph.total_weight(),
|
||||
};
|
||||
self.history.push(snapshot);
|
||||
}
|
||||
|
||||
/// Number of tracked time points.
|
||||
pub fn len(&self) -> usize {
|
||||
self.history.len()
|
||||
}
|
||||
|
||||
/// Returns true if no graphs have been tracked.
|
||||
pub fn is_empty(&self) -> bool {
|
||||
self.history.is_empty()
|
||||
}
|
||||
|
||||
/// Get the full history of snapshots.
|
||||
pub fn snapshots(&self) -> &[TopologySnapshot] {
|
||||
&self.history
|
||||
}
|
||||
|
||||
/// Return a time series of (timestamp, total_weight) as a proxy for minimum cut.
|
||||
///
|
||||
/// The total weight correlates with overall connectivity strength.
|
||||
pub fn mincut_timeseries(&self) -> Vec<(f64, f64)> {
|
||||
self.history
|
||||
.iter()
|
||||
.map(|s| (s.timestamp, s.total_weight))
|
||||
.collect()
|
||||
}
|
||||
|
||||
/// Return a time series of (timestamp, fiedler_value).
|
||||
///
|
||||
/// The Fiedler value tracks algebraic connectivity over time.
|
||||
pub fn fiedler_timeseries(&self) -> Vec<(f64, f64)> {
|
||||
self.history
|
||||
.iter()
|
||||
.map(|s| (s.timestamp, s.fiedler))
|
||||
.collect()
|
||||
}
|
||||
|
||||
/// Return a time series of (timestamp, global_efficiency).
|
||||
pub fn efficiency_timeseries(&self) -> Vec<(f64, f64)> {
|
||||
self.history
|
||||
.iter()
|
||||
.map(|s| (s.timestamp, s.global_efficiency))
|
||||
.collect()
|
||||
}
|
||||
|
||||
/// Return a time series of (timestamp, clustering_coefficient).
|
||||
pub fn clustering_timeseries(&self) -> Vec<(f64, f64)> {
|
||||
self.history
|
||||
.iter()
|
||||
.map(|s| (s.timestamp, s.clustering))
|
||||
.collect()
|
||||
}
|
||||
|
||||
/// Detect timestamps where significant topology changes occur.
|
||||
///
|
||||
/// A transition is detected when the absolute change in global efficiency
|
||||
/// between consecutive snapshots exceeds the given threshold.
|
||||
pub fn detect_transitions(&self, threshold: f64) -> Vec<f64> {
|
||||
if self.history.len() < 2 {
|
||||
return Vec::new();
|
||||
}
|
||||
|
||||
let mut transitions = Vec::new();
|
||||
for i in 1..self.history.len() {
|
||||
let delta = (self.history[i].global_efficiency
|
||||
- self.history[i - 1].global_efficiency)
|
||||
.abs();
|
||||
if delta > threshold {
|
||||
transitions.push(self.history[i].timestamp);
|
||||
}
|
||||
}
|
||||
|
||||
transitions
|
||||
}
|
||||
|
||||
/// Compute the rate of change of global efficiency over time.
|
||||
///
|
||||
/// Returns (timestamp, d_efficiency/dt) for each consecutive pair.
|
||||
pub fn rate_of_change(&self) -> Vec<(f64, f64)> {
|
||||
if self.history.len() < 2 {
|
||||
return Vec::new();
|
||||
}
|
||||
|
||||
self.history
|
||||
.windows(2)
|
||||
.map(|pair| {
|
||||
let dt = pair[1].timestamp - pair[0].timestamp;
|
||||
let de = pair[1].global_efficiency - pair[0].global_efficiency;
|
||||
let rate = if dt.abs() > 1e-15 { de / dt } else { 0.0 };
|
||||
(pair[1].timestamp, rate)
|
||||
})
|
||||
.collect()
|
||||
}
|
||||
}
|
||||
|
||||
impl Default for TopologyTracker {
|
||||
fn default() -> Self {
|
||||
Self::new()
|
||||
}
|
||||
}
|
||||
|
||||
#[cfg(test)]
|
||||
mod tests {
|
||||
use super::*;
|
||||
use ruv_neural_core::brain::Atlas;
|
||||
use ruv_neural_core::graph::{BrainEdge, BrainGraph, ConnectivityMetric};
|
||||
use ruv_neural_core::signal::FrequencyBand;
|
||||
|
||||
fn make_edge(s: usize, t: usize, w: f64) -> BrainEdge {
|
||||
BrainEdge {
|
||||
source: s,
|
||||
target: t,
|
||||
weight: w,
|
||||
metric: ConnectivityMetric::PhaseLockingValue,
|
||||
frequency_band: FrequencyBand::Alpha,
|
||||
}
|
||||
}
|
||||
|
||||
fn make_graph(timestamp: f64, edges: Vec<BrainEdge>) -> BrainGraph {
|
||||
BrainGraph {
|
||||
num_nodes: 4,
|
||||
edges,
|
||||
timestamp,
|
||||
window_duration_s: 0.5,
|
||||
atlas: Atlas::Custom(4),
|
||||
}
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn tracker_stores_history() {
|
||||
let mut tracker = TopologyTracker::new();
|
||||
assert!(tracker.is_empty());
|
||||
|
||||
let g1 = make_graph(0.0, vec![make_edge(0, 1, 1.0), make_edge(2, 3, 1.0)]);
|
||||
let g2 = make_graph(1.0, vec![
|
||||
make_edge(0, 1, 1.0),
|
||||
make_edge(1, 2, 1.0),
|
||||
make_edge(2, 3, 1.0),
|
||||
]);
|
||||
|
||||
tracker.track(&g1);
|
||||
tracker.track(&g2);
|
||||
|
||||
assert_eq!(tracker.len(), 2);
|
||||
assert!(!tracker.is_empty());
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn mincut_timeseries_correct_length() {
|
||||
let mut tracker = TopologyTracker::new();
|
||||
for i in 0..5 {
|
||||
let g = make_graph(
|
||||
i as f64,
|
||||
vec![make_edge(0, 1, 1.0), make_edge(2, 3, i as f64 * 0.5)],
|
||||
);
|
||||
tracker.track(&g);
|
||||
}
|
||||
|
||||
let ts = tracker.mincut_timeseries();
|
||||
assert_eq!(ts.len(), 5);
|
||||
assert_eq!(ts[0].0, 0.0);
|
||||
assert_eq!(ts[4].0, 4.0);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn detect_transitions_returns_correct_timestamps() {
|
||||
let mut tracker = TopologyTracker::new();
|
||||
|
||||
// Stable phase: few edges
|
||||
for i in 0..3 {
|
||||
let g = make_graph(
|
||||
i as f64,
|
||||
vec![make_edge(0, 1, 0.5)],
|
||||
);
|
||||
tracker.track(&g);
|
||||
}
|
||||
|
||||
// Sudden change: fully connected
|
||||
let g = make_graph(3.0, vec![
|
||||
make_edge(0, 1, 1.0),
|
||||
make_edge(0, 2, 1.0),
|
||||
make_edge(0, 3, 1.0),
|
||||
make_edge(1, 2, 1.0),
|
||||
make_edge(1, 3, 1.0),
|
||||
make_edge(2, 3, 1.0),
|
||||
]);
|
||||
tracker.track(&g);
|
||||
|
||||
// With a small threshold, we should detect the transition at t=3.0
|
||||
let transitions = tracker.detect_transitions(0.01);
|
||||
assert!(
|
||||
transitions.contains(&3.0),
|
||||
"Should detect transition at t=3.0, got {:?}",
|
||||
transitions
|
||||
);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn rate_of_change_correct_length() {
|
||||
let mut tracker = TopologyTracker::new();
|
||||
for i in 0..4 {
|
||||
let g = make_graph(i as f64, vec![make_edge(0, 1, 1.0)]);
|
||||
tracker.track(&g);
|
||||
}
|
||||
|
||||
let roc = tracker.rate_of_change();
|
||||
assert_eq!(roc.len(), 3); // n-1 rates for n points
|
||||
}
|
||||
}
|
||||
@@ -1 +1,31 @@
|
||||
//! Stub crate.
|
||||
//! rUv Neural Graph -- Brain connectivity graph construction from neural signals.
|
||||
//!
|
||||
//! This crate builds brain connectivity graphs from multi-channel neural time series
|
||||
//! data, provides graph-theoretic metrics, spectral analysis, and temporal dynamics
|
||||
//! tracking for brain topology research.
|
||||
//!
|
||||
//! # Modules
|
||||
//!
|
||||
//! - [`atlas`] -- Brain atlas definitions (Desikan-Killiany 68 regions)
|
||||
//! - [`constructor`] -- Graph construction from connectivity matrices and time series
|
||||
//! - [`petgraph_bridge`] -- Convert between `BrainGraph` and petgraph types
|
||||
//! - [`metrics`] -- Graph-theoretic metrics (efficiency, clustering, centrality)
|
||||
//! - [`spectral`] -- Spectral graph properties (Laplacian, Fiedler value)
|
||||
//! - [`dynamics`] -- Temporal graph dynamics and topology tracking
|
||||
|
||||
pub mod atlas;
|
||||
pub mod constructor;
|
||||
pub mod dynamics;
|
||||
pub mod metrics;
|
||||
pub mod petgraph_bridge;
|
||||
pub mod spectral;
|
||||
|
||||
pub use atlas::{load_atlas, AtlasType};
|
||||
pub use constructor::BrainGraphConstructor;
|
||||
pub use dynamics::TopologyTracker;
|
||||
pub use metrics::{
|
||||
betweenness_centrality, clustering_coefficient, degree_distribution, global_efficiency,
|
||||
graph_density, local_efficiency, modularity, node_degree, small_world_index,
|
||||
};
|
||||
pub use petgraph_bridge::{from_petgraph, to_petgraph};
|
||||
pub use spectral::{fiedler_value, graph_laplacian, normalized_laplacian, spectral_gap};
|
||||
|
||||
@@ -0,0 +1,517 @@
|
||||
//! Graph-theoretic metrics for brain connectivity analysis.
|
||||
//!
|
||||
//! Provides standard network neuroscience metrics: efficiency, clustering,
|
||||
//! centrality, modularity, and small-world properties.
|
||||
|
||||
use ruv_neural_core::graph::BrainGraph;
|
||||
|
||||
|
||||
/// Compute global efficiency of a brain graph.
|
||||
///
|
||||
/// Global efficiency is the average inverse shortest path length between all
|
||||
/// pairs of nodes. For disconnected pairs, the contribution is 0.
|
||||
///
|
||||
/// E_global = (1 / N(N-1)) * sum_{i != j} 1/d(i,j)
|
||||
pub fn global_efficiency(graph: &BrainGraph) -> f64 {
|
||||
let n = graph.num_nodes;
|
||||
if n < 2 {
|
||||
return 0.0;
|
||||
}
|
||||
|
||||
let dist = all_pairs_shortest_paths(graph);
|
||||
let mut sum = 0.0;
|
||||
|
||||
for i in 0..n {
|
||||
for j in 0..n {
|
||||
if i != j && dist[i][j] < f64::INFINITY {
|
||||
sum += 1.0 / dist[i][j];
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
sum / (n * (n - 1)) as f64
|
||||
}
|
||||
|
||||
/// Compute local efficiency of a brain graph.
|
||||
///
|
||||
/// Average of each node's subgraph efficiency (efficiency among its neighbors).
|
||||
pub fn local_efficiency(graph: &BrainGraph) -> f64 {
|
||||
let n = graph.num_nodes;
|
||||
if n < 2 {
|
||||
return 0.0;
|
||||
}
|
||||
|
||||
let adj = graph.adjacency_matrix();
|
||||
let mut total = 0.0;
|
||||
|
||||
for i in 0..n {
|
||||
let neighbors: Vec<usize> = (0..n)
|
||||
.filter(|&j| j != i && adj[i][j] > 0.0)
|
||||
.collect();
|
||||
|
||||
let k = neighbors.len();
|
||||
if k < 2 {
|
||||
continue;
|
||||
}
|
||||
|
||||
// Build subgraph of neighbors and compute its efficiency
|
||||
let mut sub_sum = 0.0;
|
||||
for &ni in &neighbors {
|
||||
for &nj in &neighbors {
|
||||
if ni != nj && adj[ni][nj] > 0.0 {
|
||||
// Use direct weight as inverse distance proxy
|
||||
sub_sum += adj[ni][nj];
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
total += sub_sum / (k * (k - 1)) as f64;
|
||||
}
|
||||
|
||||
total / n as f64
|
||||
}
|
||||
|
||||
/// Compute global clustering coefficient.
|
||||
///
|
||||
/// C = (3 * number_of_triangles) / number_of_connected_triples
|
||||
/// For weighted graphs, uses the geometric mean of edge weights in triangles.
|
||||
pub fn clustering_coefficient(graph: &BrainGraph) -> f64 {
|
||||
let n = graph.num_nodes;
|
||||
if n < 3 {
|
||||
return 0.0;
|
||||
}
|
||||
|
||||
let adj = graph.adjacency_matrix();
|
||||
let mut triangles = 0.0;
|
||||
let mut triples = 0.0;
|
||||
|
||||
for i in 0..n {
|
||||
let neighbors_i: Vec<usize> = (0..n)
|
||||
.filter(|&j| j != i && adj[i][j] > 0.0)
|
||||
.collect();
|
||||
let k = neighbors_i.len();
|
||||
if k < 2 {
|
||||
continue;
|
||||
}
|
||||
|
||||
triples += (k * (k - 1)) as f64 / 2.0;
|
||||
|
||||
for a in 0..neighbors_i.len() {
|
||||
for b in (a + 1)..neighbors_i.len() {
|
||||
let ni = neighbors_i[a];
|
||||
let nj = neighbors_i[b];
|
||||
if adj[ni][nj] > 0.0 {
|
||||
// Weighted triangle: geometric mean of the three edges
|
||||
let w = (adj[i][ni] * adj[i][nj] * adj[ni][nj]).cbrt();
|
||||
triangles += w;
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
if triples == 0.0 {
|
||||
return 0.0;
|
||||
}
|
||||
|
||||
triangles / triples
|
||||
}
|
||||
|
||||
/// Weighted degree of a single node.
|
||||
pub fn node_degree(graph: &BrainGraph, node: usize) -> f64 {
|
||||
graph.node_degree(node)
|
||||
}
|
||||
|
||||
/// Degree distribution: weighted degree for every node.
|
||||
pub fn degree_distribution(graph: &BrainGraph) -> Vec<f64> {
|
||||
(0..graph.num_nodes)
|
||||
.map(|i| graph.node_degree(i))
|
||||
.collect()
|
||||
}
|
||||
|
||||
/// Betweenness centrality for each node.
|
||||
///
|
||||
/// Computes the fraction of shortest paths passing through each node.
|
||||
/// Uses Brandes' algorithm adapted for weighted graphs.
|
||||
pub fn betweenness_centrality(graph: &BrainGraph) -> Vec<f64> {
|
||||
let n = graph.num_nodes;
|
||||
let mut centrality = vec![0.0; n];
|
||||
|
||||
if n < 3 {
|
||||
return centrality;
|
||||
}
|
||||
|
||||
let adj = graph.adjacency_matrix();
|
||||
|
||||
// For each source node, run Dijkstra and accumulate betweenness
|
||||
for s in 0..n {
|
||||
let mut dist = vec![f64::INFINITY; n];
|
||||
let mut sigma = vec![0.0_f64; n]; // number of shortest paths
|
||||
let mut delta = vec![0.0_f64; n];
|
||||
let mut pred: Vec<Vec<usize>> = vec![Vec::new(); n];
|
||||
let mut visited = vec![false; n];
|
||||
let mut order = Vec::with_capacity(n);
|
||||
|
||||
dist[s] = 0.0;
|
||||
sigma[s] = 1.0;
|
||||
|
||||
// Simple Dijkstra (priority queue not needed for correctness)
|
||||
for _ in 0..n {
|
||||
// Find unvisited node with minimum distance
|
||||
let mut u = None;
|
||||
let mut min_dist = f64::INFINITY;
|
||||
for v in 0..n {
|
||||
if !visited[v] && dist[v] < min_dist {
|
||||
min_dist = dist[v];
|
||||
u = Some(v);
|
||||
}
|
||||
}
|
||||
|
||||
let u = match u {
|
||||
Some(u) => u,
|
||||
None => break,
|
||||
};
|
||||
|
||||
visited[u] = true;
|
||||
order.push(u);
|
||||
|
||||
for v in 0..n {
|
||||
if adj[u][v] <= 0.0 || u == v {
|
||||
continue;
|
||||
}
|
||||
// Convert weight to distance (stronger connection = shorter distance)
|
||||
let edge_dist = 1.0 / adj[u][v];
|
||||
let new_dist = dist[u] + edge_dist;
|
||||
|
||||
if new_dist < dist[v] - 1e-12 {
|
||||
dist[v] = new_dist;
|
||||
sigma[v] = sigma[u];
|
||||
pred[v] = vec![u];
|
||||
} else if (new_dist - dist[v]).abs() < 1e-12 {
|
||||
sigma[v] += sigma[u];
|
||||
pred[v].push(u);
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
// Back-propagation of dependencies
|
||||
for &w in order.iter().rev() {
|
||||
for &v in &pred[w] {
|
||||
if sigma[w] > 0.0 {
|
||||
delta[v] += (sigma[v] / sigma[w]) * (1.0 + delta[w]);
|
||||
}
|
||||
}
|
||||
if w != s {
|
||||
centrality[w] += delta[w];
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
// Normalize for undirected graph
|
||||
let norm = if n > 2 {
|
||||
2.0 / ((n - 1) * (n - 2)) as f64
|
||||
} else {
|
||||
1.0
|
||||
};
|
||||
for c in &mut centrality {
|
||||
*c *= norm;
|
||||
}
|
||||
|
||||
centrality
|
||||
}
|
||||
|
||||
/// Graph density: fraction of possible edges that exist.
|
||||
pub fn graph_density(graph: &BrainGraph) -> f64 {
|
||||
graph.density()
|
||||
}
|
||||
|
||||
/// Small-world index sigma = (C/C_rand) / (L/L_rand).
|
||||
///
|
||||
/// Uses lattice-equivalent approximations:
|
||||
/// - C_rand ~ k / N (for Erdos-Renyi)
|
||||
/// - L_rand ~ ln(N) / ln(k) (for Erdos-Renyi)
|
||||
///
|
||||
/// where k is the mean degree and N is the number of nodes.
|
||||
pub fn small_world_index(graph: &BrainGraph) -> f64 {
|
||||
let n = graph.num_nodes as f64;
|
||||
if n < 4.0 {
|
||||
return 0.0;
|
||||
}
|
||||
|
||||
let c = clustering_coefficient(graph);
|
||||
let eff = global_efficiency(graph);
|
||||
|
||||
// Mean binary degree
|
||||
let adj = graph.adjacency_matrix();
|
||||
let total_edges: f64 = adj
|
||||
.iter()
|
||||
.flat_map(|row| row.iter())
|
||||
.filter(|&&w| w > 0.0)
|
||||
.count() as f64
|
||||
/ 2.0;
|
||||
let k = 2.0 * total_edges / n;
|
||||
|
||||
if k < 1.0 || c <= 0.0 || eff <= 0.0 {
|
||||
return 0.0;
|
||||
}
|
||||
|
||||
// Random graph approximations
|
||||
let c_rand = k / n;
|
||||
let l_rand = n.ln() / k.ln();
|
||||
let l = if eff > 0.0 { 1.0 / eff } else { f64::INFINITY };
|
||||
|
||||
if c_rand <= 0.0 || l_rand <= 0.0 || l.is_infinite() {
|
||||
return 0.0;
|
||||
}
|
||||
|
||||
(c / c_rand) / (l / l_rand)
|
||||
}
|
||||
|
||||
/// Newman modularity Q for a given partition.
|
||||
///
|
||||
/// Q = (1/2m) * sum_{ij} [A_ij - k_i*k_j/(2m)] * delta(c_i, c_j)
|
||||
///
|
||||
/// where m is total edge weight, k_i is weighted degree of node i,
|
||||
/// and delta(c_i, c_j) = 1 if nodes i and j are in the same community.
|
||||
pub fn modularity(graph: &BrainGraph, partition: &[Vec<usize>]) -> f64 {
|
||||
let adj = graph.adjacency_matrix();
|
||||
let n = graph.num_nodes;
|
||||
|
||||
// Build community assignment map
|
||||
let mut community = vec![0usize; n];
|
||||
for (c, members) in partition.iter().enumerate() {
|
||||
for &node in members {
|
||||
if node < n {
|
||||
community[node] = c;
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
// Total edge weight (each edge counted once in adjacency, so sum / 2)
|
||||
let m: f64 = adj.iter().flat_map(|row| row.iter()).sum::<f64>() / 2.0;
|
||||
if m == 0.0 {
|
||||
return 0.0;
|
||||
}
|
||||
|
||||
// Weighted degree
|
||||
let degrees: Vec<f64> = (0..n)
|
||||
.map(|i| adj[i].iter().sum::<f64>())
|
||||
.collect();
|
||||
|
||||
let mut q = 0.0;
|
||||
for i in 0..n {
|
||||
for j in 0..n {
|
||||
if community[i] == community[j] {
|
||||
q += adj[i][j] - degrees[i] * degrees[j] / (2.0 * m);
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
q / (2.0 * m)
|
||||
}
|
||||
|
||||
/// Compute all-pairs shortest path distances using Floyd-Warshall.
|
||||
///
|
||||
/// Edge weights are converted to distances as 1/weight (stronger = closer).
|
||||
fn all_pairs_shortest_paths(graph: &BrainGraph) -> Vec<Vec<f64>> {
|
||||
let n = graph.num_nodes;
|
||||
let adj = graph.adjacency_matrix();
|
||||
|
||||
let mut dist = vec![vec![f64::INFINITY; n]; n];
|
||||
|
||||
for i in 0..n {
|
||||
dist[i][i] = 0.0;
|
||||
for j in 0..n {
|
||||
if i != j && adj[i][j] > 0.0 {
|
||||
dist[i][j] = 1.0 / adj[i][j];
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
// Floyd-Warshall
|
||||
for k in 0..n {
|
||||
for i in 0..n {
|
||||
for j in 0..n {
|
||||
let through_k = dist[i][k] + dist[k][j];
|
||||
if through_k < dist[i][j] {
|
||||
dist[i][j] = through_k;
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
dist
|
||||
}
|
||||
|
||||
#[cfg(test)]
|
||||
mod tests {
|
||||
use super::*;
|
||||
use ruv_neural_core::brain::Atlas;
|
||||
use ruv_neural_core::graph::{BrainEdge, BrainGraph, ConnectivityMetric};
|
||||
use ruv_neural_core::signal::FrequencyBand;
|
||||
|
||||
/// Build a complete graph with n nodes, all edges weight 1.0.
|
||||
fn complete_graph(n: usize) -> BrainGraph {
|
||||
let mut edges = Vec::new();
|
||||
for i in 0..n {
|
||||
for j in (i + 1)..n {
|
||||
edges.push(BrainEdge {
|
||||
source: i,
|
||||
target: j,
|
||||
weight: 1.0,
|
||||
metric: ConnectivityMetric::PhaseLockingValue,
|
||||
frequency_band: FrequencyBand::Alpha,
|
||||
});
|
||||
}
|
||||
}
|
||||
BrainGraph {
|
||||
num_nodes: n,
|
||||
edges,
|
||||
timestamp: 0.0,
|
||||
window_duration_s: 1.0,
|
||||
atlas: Atlas::Custom(n),
|
||||
}
|
||||
}
|
||||
|
||||
/// Build a path graph: 0-1-2-..-(n-1).
|
||||
fn path_graph(n: usize) -> BrainGraph {
|
||||
let edges: Vec<BrainEdge> = (0..n.saturating_sub(1))
|
||||
.map(|i| BrainEdge {
|
||||
source: i,
|
||||
target: i + 1,
|
||||
weight: 1.0,
|
||||
metric: ConnectivityMetric::PhaseLockingValue,
|
||||
frequency_band: FrequencyBand::Alpha,
|
||||
})
|
||||
.collect();
|
||||
BrainGraph {
|
||||
num_nodes: n,
|
||||
edges,
|
||||
timestamp: 0.0,
|
||||
window_duration_s: 1.0,
|
||||
atlas: Atlas::Custom(n),
|
||||
}
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn global_efficiency_complete_graph() {
|
||||
// In a complete graph with weight 1, all shortest paths have length 1,
|
||||
// so efficiency = 1.0.
|
||||
let g = complete_graph(10);
|
||||
let eff = global_efficiency(&g);
|
||||
assert!((eff - 1.0).abs() < 1e-10, "Expected ~1.0, got {}", eff);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn global_efficiency_empty_graph() {
|
||||
let g = BrainGraph {
|
||||
num_nodes: 5,
|
||||
edges: Vec::new(),
|
||||
timestamp: 0.0,
|
||||
window_duration_s: 1.0,
|
||||
atlas: Atlas::Custom(5),
|
||||
};
|
||||
let eff = global_efficiency(&g);
|
||||
assert_eq!(eff, 0.0);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn clustering_coefficient_complete_graph() {
|
||||
let g = complete_graph(8);
|
||||
let cc = clustering_coefficient(&g);
|
||||
assert!(cc > 0.9, "Complete graph should have clustering ~1.0, got {}", cc);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn clustering_coefficient_path_graph() {
|
||||
// A path graph has no triangles, so clustering = 0.
|
||||
let g = path_graph(5);
|
||||
let cc = clustering_coefficient(&g);
|
||||
assert!(cc.abs() < 1e-10, "Path graph should have CC=0, got {}", cc);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn density_complete_graph() {
|
||||
let g = complete_graph(10);
|
||||
let d = graph_density(&g);
|
||||
assert!((d - 1.0).abs() < 1e-10, "Complete graph density should be 1.0, got {}", d);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn degree_distribution_uniform() {
|
||||
let g = complete_graph(5);
|
||||
let dd = degree_distribution(&g);
|
||||
// Each node in K5 has degree 4 (4 edges * weight 1.0 = 4.0)
|
||||
for &d in &dd {
|
||||
assert!((d - 4.0).abs() < 1e-10);
|
||||
}
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn betweenness_centrality_path() {
|
||||
// In a path 0-1-2-3-4, middle nodes should have higher betweenness.
|
||||
let g = path_graph(5);
|
||||
let bc = betweenness_centrality(&g);
|
||||
// Node 2 (center) should have highest betweenness
|
||||
assert!(bc[2] >= bc[0], "Center node should have >= betweenness than endpoints");
|
||||
assert!(bc[2] >= bc[4], "Center node should have >= betweenness than endpoints");
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn modularity_single_community() {
|
||||
let g = complete_graph(6);
|
||||
let all_in_one = vec![vec![0, 1, 2, 3, 4, 5]];
|
||||
let q = modularity(&g, &all_in_one);
|
||||
// All in one community, modularity should be 0
|
||||
assert!(q.abs() < 1e-10, "Single community Q should be ~0, got {}", q);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn modularity_good_partition() {
|
||||
// Two cliques connected by a weak edge
|
||||
let mut edges = Vec::new();
|
||||
// Clique 1: nodes 0,1,2
|
||||
for i in 0..3 {
|
||||
for j in (i + 1)..3 {
|
||||
edges.push(BrainEdge {
|
||||
source: i,
|
||||
target: j,
|
||||
weight: 1.0,
|
||||
metric: ConnectivityMetric::PhaseLockingValue,
|
||||
frequency_band: FrequencyBand::Alpha,
|
||||
});
|
||||
}
|
||||
}
|
||||
// Clique 2: nodes 3,4,5
|
||||
for i in 3..6 {
|
||||
for j in (i + 1)..6 {
|
||||
edges.push(BrainEdge {
|
||||
source: i,
|
||||
target: j,
|
||||
weight: 1.0,
|
||||
metric: ConnectivityMetric::PhaseLockingValue,
|
||||
frequency_band: FrequencyBand::Alpha,
|
||||
});
|
||||
}
|
||||
}
|
||||
// Weak bridge
|
||||
edges.push(BrainEdge {
|
||||
source: 2,
|
||||
target: 3,
|
||||
weight: 0.1,
|
||||
metric: ConnectivityMetric::PhaseLockingValue,
|
||||
frequency_band: FrequencyBand::Alpha,
|
||||
});
|
||||
|
||||
let g = BrainGraph {
|
||||
num_nodes: 6,
|
||||
edges,
|
||||
timestamp: 0.0,
|
||||
window_duration_s: 1.0,
|
||||
atlas: Atlas::Custom(6),
|
||||
};
|
||||
|
||||
let good = vec![vec![0, 1, 2], vec![3, 4, 5]];
|
||||
let q = modularity(&g, &good);
|
||||
assert!(q > 0.0, "Good partition should have positive modularity, got {}", q);
|
||||
}
|
||||
}
|
||||
+1
-3
@@ -4,7 +4,7 @@
|
||||
//! (shortest paths, connected components, etc.) on brain connectivity graphs.
|
||||
|
||||
use petgraph::graph::{Graph, NodeIndex, UnGraph};
|
||||
use petgraph::Undirected;
|
||||
use petgraph::visit::EdgeRef;
|
||||
|
||||
use ruv_neural_core::brain::Atlas;
|
||||
use ruv_neural_core::graph::{BrainEdge, BrainGraph, ConnectivityMetric};
|
||||
@@ -78,8 +78,6 @@ pub fn node_index(region_id: usize) -> NodeIndex {
|
||||
NodeIndex::new(region_id)
|
||||
}
|
||||
|
||||
use petgraph::visit::EdgeRef;
|
||||
|
||||
#[cfg(test)]
|
||||
mod tests {
|
||||
use super::*;
|
||||
|
||||
@@ -0,0 +1,317 @@
|
||||
//! Spectral graph properties: Laplacian matrices, Fiedler value, spectral gap.
|
||||
//!
|
||||
//! The graph Laplacian encodes the structure of a graph and its eigenvalues
|
||||
//! reveal fundamental connectivity properties. The Fiedler value (second
|
||||
//! smallest eigenvalue) measures algebraic connectivity.
|
||||
|
||||
use ruv_neural_core::graph::BrainGraph;
|
||||
|
||||
/// Compute the combinatorial graph Laplacian L = D - A.
|
||||
///
|
||||
/// D is the diagonal degree matrix, A is the adjacency matrix.
|
||||
/// Returns an `n x n` matrix as `Vec<Vec<f64>>`.
|
||||
pub fn graph_laplacian(graph: &BrainGraph) -> Vec<Vec<f64>> {
|
||||
let n = graph.num_nodes;
|
||||
let adj = graph.adjacency_matrix();
|
||||
let mut laplacian = vec![vec![0.0; n]; n];
|
||||
|
||||
for i in 0..n {
|
||||
let degree: f64 = adj[i].iter().sum();
|
||||
laplacian[i][i] = degree;
|
||||
for j in 0..n {
|
||||
if i != j {
|
||||
laplacian[i][j] = -adj[i][j];
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
laplacian
|
||||
}
|
||||
|
||||
/// Compute the normalized graph Laplacian L_norm = D^{-1/2} L D^{-1/2}.
|
||||
///
|
||||
/// For isolated nodes (degree = 0), the diagonal entry is set to 0.
|
||||
pub fn normalized_laplacian(graph: &BrainGraph) -> Vec<Vec<f64>> {
|
||||
let n = graph.num_nodes;
|
||||
let adj = graph.adjacency_matrix();
|
||||
|
||||
// Compute D^{-1/2}
|
||||
let degrees: Vec<f64> = (0..n).map(|i| adj[i].iter().sum::<f64>()).collect();
|
||||
let d_inv_sqrt: Vec<f64> = degrees
|
||||
.iter()
|
||||
.map(|&d| if d > 0.0 { 1.0 / d.sqrt() } else { 0.0 })
|
||||
.collect();
|
||||
|
||||
let mut l_norm = vec![vec![0.0; n]; n];
|
||||
|
||||
for i in 0..n {
|
||||
if degrees[i] > 0.0 {
|
||||
l_norm[i][i] = 1.0;
|
||||
}
|
||||
for j in 0..n {
|
||||
if i != j && adj[i][j] > 0.0 {
|
||||
l_norm[i][j] = -adj[i][j] * d_inv_sqrt[i] * d_inv_sqrt[j];
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
l_norm
|
||||
}
|
||||
|
||||
/// Compute the Fiedler value (algebraic connectivity).
|
||||
///
|
||||
/// The Fiedler value is the second smallest eigenvalue of the graph Laplacian.
|
||||
/// - For a connected graph, Fiedler value > 0.
|
||||
/// - For a disconnected graph, Fiedler value = 0.
|
||||
///
|
||||
/// Uses power iteration with deflation to find the two smallest eigenvalues
|
||||
/// of the Laplacian (which is positive semidefinite).
|
||||
pub fn fiedler_value(graph: &BrainGraph) -> f64 {
|
||||
let n = graph.num_nodes;
|
||||
if n < 2 {
|
||||
return 0.0;
|
||||
}
|
||||
|
||||
let laplacian = graph_laplacian(graph);
|
||||
|
||||
// The Laplacian is PSD. Its smallest eigenvalue is 0 with eigenvector
|
||||
// proportional to the all-ones vector. We need the second smallest.
|
||||
//
|
||||
// Strategy: use inverse power iteration on (L + alpha*I) shifted to find
|
||||
// the smallest eigenvalue, then deflate and find the next.
|
||||
// Alternatively, use the shifted inverse iteration directly for lambda_2.
|
||||
//
|
||||
// Simpler approach: compute L * x repeatedly to find eigenvalues from largest
|
||||
// down, or use the fact that lambda_2 = min over x perp to 1 of x^T L x / x^T x.
|
||||
//
|
||||
// We use inverse iteration with shift to find the Fiedler vector.
|
||||
// But since we don't have a linear solver, we use power iteration on
|
||||
// (max_eig * I - L) to find the largest eigenvalue of that matrix (which
|
||||
// corresponds to the smallest eigenvalue of L).
|
||||
//
|
||||
// Actually, the simplest reliable approach for moderate n:
|
||||
// Use the Rayleigh quotient iteration projected orthogonal to the all-ones vector.
|
||||
|
||||
compute_fiedler_rayleigh(&laplacian, n)
|
||||
}
|
||||
|
||||
/// Compute the spectral gap: lambda_2 - lambda_1.
|
||||
///
|
||||
/// Since lambda_1 = 0 for the Laplacian, the spectral gap equals the Fiedler value.
|
||||
pub fn spectral_gap(graph: &BrainGraph) -> f64 {
|
||||
fiedler_value(graph)
|
||||
}
|
||||
|
||||
/// Compute the Fiedler value using projected power iteration.
|
||||
///
|
||||
/// Projects out the all-ones eigenvector (corresponding to lambda_1 = 0),
|
||||
/// then uses power iteration on (alpha*I - L) to find the largest eigenvalue
|
||||
/// of that shifted matrix. The Fiedler value is then alpha - largest_eigenvalue.
|
||||
fn compute_fiedler_rayleigh(laplacian: &[Vec<f64>], n: usize) -> f64 {
|
||||
if n < 2 {
|
||||
return 0.0;
|
||||
}
|
||||
|
||||
// Estimate max eigenvalue for shifting (Gershgorin bound)
|
||||
let alpha = laplacian
|
||||
.iter()
|
||||
.map(|row| row.iter().map(|x| x.abs()).sum::<f64>())
|
||||
.fold(0.0_f64, |a, b| a.max(b))
|
||||
* 1.1;
|
||||
|
||||
if alpha <= 0.0 {
|
||||
return 0.0;
|
||||
}
|
||||
|
||||
// Construct M = alpha*I - L
|
||||
// The eigenvalues of M are alpha - lambda_i(L).
|
||||
// The largest eigenvalue of M corresponds to the smallest eigenvalue of L (which is 0).
|
||||
// The second largest eigenvalue of M corresponds to lambda_2 of L.
|
||||
// We need to deflate out the first eigenvector (all-ones) and do power iteration.
|
||||
|
||||
// Normalized all-ones vector
|
||||
let inv_sqrt_n = 1.0 / (n as f64).sqrt();
|
||||
|
||||
// Initialize random-ish vector orthogonal to all-ones
|
||||
let mut v: Vec<f64> = (0..n).map(|i| (i as f64 + 0.5).sin()).collect();
|
||||
|
||||
// Project out the all-ones component
|
||||
project_out_ones(&mut v, inv_sqrt_n, n);
|
||||
normalize(&mut v);
|
||||
|
||||
let max_iter = 1000;
|
||||
let tol = 1e-10;
|
||||
|
||||
for _ in 0..max_iter {
|
||||
// w = M * v = (alpha*I - L) * v
|
||||
let mut w = vec![0.0; n];
|
||||
for i in 0..n {
|
||||
w[i] = alpha * v[i];
|
||||
for j in 0..n {
|
||||
w[i] -= laplacian[i][j] * v[j];
|
||||
}
|
||||
}
|
||||
|
||||
// Project out the all-ones component
|
||||
project_out_ones(&mut w, inv_sqrt_n, n);
|
||||
|
||||
let norm_w = norm(&w);
|
||||
if norm_w < 1e-15 {
|
||||
// The vector collapsed, Fiedler value is likely alpha
|
||||
return alpha;
|
||||
}
|
||||
|
||||
// Rayleigh quotient: eigenvalue of M = v^T * w / v^T * v
|
||||
let eigenvalue_m: f64 = v.iter().zip(w.iter()).map(|(a, b)| a * b).sum::<f64>();
|
||||
|
||||
// Normalize
|
||||
for x in &mut w {
|
||||
*x /= norm_w;
|
||||
}
|
||||
|
||||
// Check convergence
|
||||
let diff: f64 = v
|
||||
.iter()
|
||||
.zip(w.iter())
|
||||
.map(|(a, b)| (a - b).powi(2))
|
||||
.sum::<f64>()
|
||||
.sqrt();
|
||||
|
||||
v = w;
|
||||
|
||||
if diff < tol {
|
||||
// Fiedler value = alpha - eigenvalue_of_M
|
||||
let fiedler = alpha - eigenvalue_m;
|
||||
return fiedler.max(0.0);
|
||||
}
|
||||
}
|
||||
|
||||
// Final estimate
|
||||
let mut w = vec![0.0; n];
|
||||
for i in 0..n {
|
||||
w[i] = alpha * v[i];
|
||||
for j in 0..n {
|
||||
w[i] -= laplacian[i][j] * v[j];
|
||||
}
|
||||
}
|
||||
project_out_ones(&mut w, inv_sqrt_n, n);
|
||||
|
||||
let eigenvalue_m: f64 = v.iter().zip(w.iter()).map(|(a, b)| a * b).sum::<f64>();
|
||||
(alpha - eigenvalue_m).max(0.0)
|
||||
}
|
||||
|
||||
/// Project vector v orthogonal to the all-ones vector.
|
||||
fn project_out_ones(v: &mut [f64], inv_sqrt_n: f64, _n: usize) {
|
||||
let dot: f64 = v.iter().sum::<f64>() * inv_sqrt_n;
|
||||
for x in v.iter_mut() {
|
||||
*x -= dot * inv_sqrt_n;
|
||||
}
|
||||
}
|
||||
|
||||
/// L2 norm of a vector.
|
||||
fn norm(v: &[f64]) -> f64 {
|
||||
v.iter().map(|x| x * x).sum::<f64>().sqrt()
|
||||
}
|
||||
|
||||
/// Normalize a vector in-place.
|
||||
fn normalize(v: &mut [f64]) {
|
||||
let n = norm(v);
|
||||
if n > 0.0 {
|
||||
for x in v.iter_mut() {
|
||||
*x /= n;
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
#[cfg(test)]
|
||||
mod tests {
|
||||
use super::*;
|
||||
use ruv_neural_core::brain::Atlas;
|
||||
use ruv_neural_core::graph::{BrainEdge, BrainGraph, ConnectivityMetric};
|
||||
use ruv_neural_core::signal::FrequencyBand;
|
||||
|
||||
fn make_edge(s: usize, t: usize, w: f64) -> BrainEdge {
|
||||
BrainEdge {
|
||||
source: s,
|
||||
target: t,
|
||||
weight: w,
|
||||
metric: ConnectivityMetric::PhaseLockingValue,
|
||||
frequency_band: FrequencyBand::Alpha,
|
||||
}
|
||||
}
|
||||
|
||||
fn complete_graph(n: usize) -> BrainGraph {
|
||||
let mut edges = Vec::new();
|
||||
for i in 0..n {
|
||||
for j in (i + 1)..n {
|
||||
edges.push(make_edge(i, j, 1.0));
|
||||
}
|
||||
}
|
||||
BrainGraph {
|
||||
num_nodes: n,
|
||||
edges,
|
||||
timestamp: 0.0,
|
||||
window_duration_s: 1.0,
|
||||
atlas: Atlas::Custom(n),
|
||||
}
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn laplacian_row_sums_zero() {
|
||||
let g = complete_graph(5);
|
||||
let l = graph_laplacian(&g);
|
||||
for row in &l {
|
||||
let sum: f64 = row.iter().sum();
|
||||
assert!(sum.abs() < 1e-10, "Row sum should be 0, got {}", sum);
|
||||
}
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn laplacian_diagonal_is_degree() {
|
||||
let g = complete_graph(5);
|
||||
let l = graph_laplacian(&g);
|
||||
// Each node in K5 has degree 4
|
||||
for i in 0..5 {
|
||||
assert!((l[i][i] - 4.0).abs() < 1e-10);
|
||||
}
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn normalized_laplacian_diagonal_connected() {
|
||||
let g = complete_graph(5);
|
||||
let ln = normalized_laplacian(&g);
|
||||
// For connected nodes, diagonal should be 1.0
|
||||
for i in 0..5 {
|
||||
assert!((ln[i][i] - 1.0).abs() < 1e-10);
|
||||
}
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn fiedler_value_connected_graph() {
|
||||
let g = complete_graph(6);
|
||||
let f = fiedler_value(&g);
|
||||
// For K_n, all non-zero eigenvalues of L are n. So fiedler = n = 6.
|
||||
assert!(f > 0.0, "Connected graph should have fiedler > 0, got {}", f);
|
||||
assert!((f - 6.0).abs() < 0.5, "K6 fiedler should be ~6.0, got {}", f);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn fiedler_value_disconnected_graph() {
|
||||
// Two isolated components: nodes 0,1 connected; nodes 2,3 connected; no bridge.
|
||||
let g = BrainGraph {
|
||||
num_nodes: 4,
|
||||
edges: vec![make_edge(0, 1, 1.0), make_edge(2, 3, 1.0)],
|
||||
timestamp: 0.0,
|
||||
window_duration_s: 1.0,
|
||||
atlas: Atlas::Custom(4),
|
||||
};
|
||||
let f = fiedler_value(&g);
|
||||
assert!(f < 1e-6, "Disconnected graph should have fiedler ~0, got {}", f);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn spectral_gap_equals_fiedler() {
|
||||
let g = complete_graph(5);
|
||||
assert_eq!(spectral_gap(&g), fiedler_value(&g));
|
||||
}
|
||||
}
|
||||
@@ -123,6 +123,7 @@ pub fn load_rvf(path: &str) -> Result<NeuralMemoryStore> {
|
||||
mod tests {
|
||||
use super::*;
|
||||
use ruv_neural_core::brain::Atlas;
|
||||
use ruv_neural_core::embedding::EmbeddingMetadata;
|
||||
use ruv_neural_core::topology::CognitiveState;
|
||||
|
||||
fn make_embedding(vector: Vec<f64>, timestamp: f64) -> NeuralEmbedding {
|
||||
|
||||
@@ -0,0 +1,186 @@
|
||||
//! Performance benchmarking utilities for mincut algorithms.
|
||||
//!
|
||||
//! Provides functions to measure the wall-clock time of the Stoer-Wagner and
|
||||
//! normalized cut algorithms on random graphs of configurable size and density.
|
||||
|
||||
use std::time::{Duration, Instant};
|
||||
|
||||
use ruv_neural_core::brain::Atlas;
|
||||
use ruv_neural_core::graph::{BrainEdge, BrainGraph, ConnectivityMetric};
|
||||
use ruv_neural_core::signal::FrequencyBand;
|
||||
|
||||
use crate::normalized::normalized_cut;
|
||||
use crate::stoer_wagner::stoer_wagner_mincut;
|
||||
|
||||
/// Result of a benchmark run.
|
||||
#[derive(Debug, Clone)]
|
||||
pub struct BenchmarkReport {
|
||||
/// Algorithm name.
|
||||
pub algorithm: String,
|
||||
/// Number of nodes in the test graph.
|
||||
pub num_nodes: usize,
|
||||
/// Number of edges in the test graph.
|
||||
pub num_edges: usize,
|
||||
/// Graph density (0..1).
|
||||
pub density: f64,
|
||||
/// Wall-clock execution time.
|
||||
pub elapsed: Duration,
|
||||
/// Minimum cut value found.
|
||||
pub cut_value: f64,
|
||||
}
|
||||
|
||||
impl std::fmt::Display for BenchmarkReport {
|
||||
fn fmt(&self, f: &mut std::fmt::Formatter<'_>) -> std::fmt::Result {
|
||||
write!(
|
||||
f,
|
||||
"{}: nodes={}, edges={}, density={:.3}, time={:.3}ms, cut={:.4}",
|
||||
self.algorithm,
|
||||
self.num_nodes,
|
||||
self.num_edges,
|
||||
self.density,
|
||||
self.elapsed.as_secs_f64() * 1000.0,
|
||||
self.cut_value
|
||||
)
|
||||
}
|
||||
}
|
||||
|
||||
/// Benchmark the Stoer-Wagner algorithm on a random graph.
|
||||
///
|
||||
/// # Arguments
|
||||
///
|
||||
/// * `num_nodes` - Number of vertices.
|
||||
/// * `density` - Edge density in [0, 1]. A density of 1.0 generates a complete graph.
|
||||
/// * `seed` - Random seed for reproducibility.
|
||||
pub fn benchmark_stoer_wagner(num_nodes: usize, density: f64, seed: u64) -> BenchmarkReport {
|
||||
let graph = generate_random_graph(num_nodes, density, seed);
|
||||
let num_edges = graph.edges.len();
|
||||
|
||||
let start = Instant::now();
|
||||
let result = stoer_wagner_mincut(&graph);
|
||||
let elapsed = start.elapsed();
|
||||
|
||||
let cut_value = result.map(|r| r.cut_value).unwrap_or(f64::NAN);
|
||||
|
||||
BenchmarkReport {
|
||||
algorithm: "Stoer-Wagner".to_string(),
|
||||
num_nodes,
|
||||
num_edges,
|
||||
density,
|
||||
elapsed,
|
||||
cut_value,
|
||||
}
|
||||
}
|
||||
|
||||
/// Benchmark the normalized cut algorithm on a random graph.
|
||||
pub fn benchmark_normalized_cut(num_nodes: usize, density: f64, seed: u64) -> BenchmarkReport {
|
||||
let graph = generate_random_graph(num_nodes, density, seed);
|
||||
let num_edges = graph.edges.len();
|
||||
|
||||
let start = Instant::now();
|
||||
let result = normalized_cut(&graph);
|
||||
let elapsed = start.elapsed();
|
||||
|
||||
let cut_value = result.map(|r| r.cut_value).unwrap_or(f64::NAN);
|
||||
|
||||
BenchmarkReport {
|
||||
algorithm: "Normalized-Cut".to_string(),
|
||||
num_nodes,
|
||||
num_edges,
|
||||
density,
|
||||
elapsed,
|
||||
cut_value,
|
||||
}
|
||||
}
|
||||
|
||||
/// Generate a random undirected weighted graph with approximately the given density.
|
||||
///
|
||||
/// Uses a simple LCG for deterministic randomness.
|
||||
fn generate_random_graph(num_nodes: usize, density: f64, seed: u64) -> BrainGraph {
|
||||
let mut rng_state = seed;
|
||||
|
||||
let mut edges = Vec::new();
|
||||
for i in 0..num_nodes {
|
||||
for j in (i + 1)..num_nodes {
|
||||
rng_state = rng_state
|
||||
.wrapping_mul(6364136223846793005)
|
||||
.wrapping_add(1);
|
||||
let rand_val = (rng_state >> 33) as f64 / (1u64 << 31) as f64;
|
||||
|
||||
if rand_val < density {
|
||||
rng_state = rng_state
|
||||
.wrapping_mul(6364136223846793005)
|
||||
.wrapping_add(1);
|
||||
let weight = ((rng_state >> 33) as f64 / (1u64 << 31) as f64) * 0.9 + 0.1;
|
||||
|
||||
edges.push(BrainEdge {
|
||||
source: i,
|
||||
target: j,
|
||||
weight,
|
||||
metric: ConnectivityMetric::Coherence,
|
||||
frequency_band: FrequencyBand::Alpha,
|
||||
});
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
BrainGraph {
|
||||
num_nodes,
|
||||
edges,
|
||||
timestamp: 0.0,
|
||||
window_duration_s: 1.0,
|
||||
atlas: Atlas::Custom(num_nodes),
|
||||
}
|
||||
}
|
||||
|
||||
/// Run a full benchmark suite and return all reports.
|
||||
pub fn run_benchmark_suite() -> Vec<BenchmarkReport> {
|
||||
let configs = [(10, 0.5), (20, 0.3), (30, 0.2), (50, 0.1)];
|
||||
|
||||
let mut reports = Vec::new();
|
||||
for &(nodes, density) in &configs {
|
||||
reports.push(benchmark_stoer_wagner(nodes, density, 42));
|
||||
reports.push(benchmark_normalized_cut(nodes, density, 42));
|
||||
}
|
||||
reports
|
||||
}
|
||||
|
||||
#[cfg(test)]
|
||||
mod tests {
|
||||
use super::*;
|
||||
|
||||
#[test]
|
||||
fn test_benchmark_stoer_wagner() {
|
||||
let report = benchmark_stoer_wagner(10, 0.5, 42);
|
||||
assert_eq!(report.num_nodes, 10);
|
||||
assert!(report.num_edges > 0);
|
||||
assert!(!report.cut_value.is_nan());
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_benchmark_normalized_cut() {
|
||||
let report = benchmark_normalized_cut(10, 0.5, 42);
|
||||
assert_eq!(report.num_nodes, 10);
|
||||
assert!(!report.cut_value.is_nan());
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_generate_random_graph_deterministic() {
|
||||
let g1 = generate_random_graph(20, 0.3, 123);
|
||||
let g2 = generate_random_graph(20, 0.3, 123);
|
||||
assert_eq!(g1.edges.len(), g2.edges.len());
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_benchmark_report_display() {
|
||||
let report = benchmark_stoer_wagner(10, 0.5, 42);
|
||||
let display = format!("{}", report);
|
||||
assert!(display.contains("Stoer-Wagner"));
|
||||
assert!(display.contains("nodes=10"));
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_run_benchmark_suite() {
|
||||
let reports = run_benchmark_suite();
|
||||
assert_eq!(reports.len(), 8);
|
||||
}
|
||||
}
|
||||
@@ -0,0 +1,315 @@
|
||||
//! Neural coherence detection via minimum cut analysis.
|
||||
//!
|
||||
//! Detects when brain networks become coherent (strongly coupled) or decouple,
|
||||
//! by monitoring the minimum cut over a temporal graph sequence. Significant
|
||||
//! changes in mincut topology correspond to network formation, dissolution,
|
||||
//! merger, and split events.
|
||||
|
||||
use serde::{Deserialize, Serialize};
|
||||
|
||||
use crate::dynamic::DynamicMincutTracker;
|
||||
|
||||
/// Type of coherence event detected.
|
||||
#[derive(Debug, Clone, Copy, PartialEq, Eq, Serialize, Deserialize)]
|
||||
pub enum CoherenceEventType {
|
||||
/// A new coherent module forms (integration event).
|
||||
NetworkFormation,
|
||||
/// A coherent module breaks apart (segregation event).
|
||||
NetworkDissolution,
|
||||
/// Two modules merge into one.
|
||||
NetworkMerger,
|
||||
/// One module splits into two.
|
||||
NetworkSplit,
|
||||
}
|
||||
|
||||
/// A coherence event detected in the brain network.
|
||||
#[derive(Debug, Clone, Serialize, Deserialize)]
|
||||
pub struct CoherenceEvent {
|
||||
/// Start time of the event.
|
||||
pub start_time: f64,
|
||||
/// End time of the event.
|
||||
pub end_time: f64,
|
||||
/// Type of coherence event.
|
||||
pub event_type: CoherenceEventType,
|
||||
/// Brain region indices involved in the event.
|
||||
pub involved_regions: Vec<usize>,
|
||||
/// Peak coherence magnitude during the event.
|
||||
pub peak_coherence: f64,
|
||||
}
|
||||
|
||||
/// Detects coherence events in temporal brain graph sequences.
|
||||
#[derive(Debug, Clone)]
|
||||
pub struct CoherenceDetector {
|
||||
/// Internal tracker for mincut evolution.
|
||||
tracker: DynamicMincutTracker,
|
||||
/// Threshold (fraction of baseline) for integration detection.
|
||||
threshold_integration: f64,
|
||||
/// Threshold (fraction of baseline) for segregation detection.
|
||||
threshold_segregation: f64,
|
||||
}
|
||||
|
||||
impl CoherenceDetector {
|
||||
/// Create a new coherence detector.
|
||||
///
|
||||
/// # Arguments
|
||||
///
|
||||
/// * `threshold_integration` - Fraction of baseline for integration detection
|
||||
/// (e.g., 0.3 means a 30% decrease in mincut triggers an integration event).
|
||||
/// * `threshold_segregation` - Fraction of baseline for segregation detection.
|
||||
pub fn new(threshold_integration: f64, threshold_segregation: f64) -> Self {
|
||||
Self {
|
||||
tracker: DynamicMincutTracker::new(),
|
||||
threshold_integration,
|
||||
threshold_segregation,
|
||||
}
|
||||
}
|
||||
|
||||
/// Set the baseline mincut value from resting-state data.
|
||||
pub fn set_baseline(&mut self, baseline: f64) {
|
||||
self.tracker.set_baseline(baseline);
|
||||
}
|
||||
|
||||
/// Get a reference to the internal tracker.
|
||||
pub fn tracker(&self) -> &DynamicMincutTracker {
|
||||
&self.tracker
|
||||
}
|
||||
|
||||
/// Detect coherence events from a mincut time series.
|
||||
///
|
||||
/// Processes each `(timestamp, mincut_value)` pair, detects transitions,
|
||||
/// and classifies them into coherence events.
|
||||
pub fn detect_from_timeseries(
|
||||
&self,
|
||||
mincut_series: &[(f64, f64)],
|
||||
) -> Vec<CoherenceEvent> {
|
||||
if mincut_series.len() < 2 {
|
||||
return Vec::new();
|
||||
}
|
||||
|
||||
// Compute baseline as mean if not set.
|
||||
let baseline = self.tracker.baseline().unwrap_or_else(|| {
|
||||
let sum: f64 = mincut_series.iter().map(|(_, v)| v).sum();
|
||||
sum / mincut_series.len() as f64
|
||||
});
|
||||
|
||||
if baseline <= 0.0 {
|
||||
return Vec::new();
|
||||
}
|
||||
|
||||
let threshold = self.threshold_integration.min(self.threshold_segregation);
|
||||
let change_threshold = threshold * baseline;
|
||||
|
||||
let mut events = Vec::new();
|
||||
let mut i = 1;
|
||||
|
||||
while i < mincut_series.len() {
|
||||
let (_t_prev, v_prev) = mincut_series[i - 1];
|
||||
let (t_curr, v_curr) = mincut_series[i];
|
||||
let delta = v_curr - v_prev;
|
||||
|
||||
if delta.abs() > change_threshold {
|
||||
let magnitude = delta.abs() / baseline;
|
||||
|
||||
if delta < 0.0 && magnitude >= self.threshold_integration {
|
||||
// Integration: mincut decreased -> networks merging.
|
||||
let end_time =
|
||||
find_recovery_time_in_series(mincut_series, i, v_prev, baseline);
|
||||
|
||||
events.push(CoherenceEvent {
|
||||
start_time: t_curr,
|
||||
end_time,
|
||||
event_type: CoherenceEventType::NetworkFormation,
|
||||
involved_regions: Vec::new(),
|
||||
peak_coherence: magnitude,
|
||||
});
|
||||
} else if delta > 0.0 && magnitude >= self.threshold_segregation {
|
||||
// Segregation: mincut increased -> networks separating.
|
||||
let end_time =
|
||||
find_recovery_time_in_series(mincut_series, i, v_prev, baseline);
|
||||
|
||||
events.push(CoherenceEvent {
|
||||
start_time: t_curr,
|
||||
end_time,
|
||||
event_type: CoherenceEventType::NetworkDissolution,
|
||||
involved_regions: Vec::new(),
|
||||
peak_coherence: magnitude,
|
||||
});
|
||||
}
|
||||
|
||||
// Check for merger/split patterns (opposing transitions close together).
|
||||
if i + 1 < mincut_series.len() {
|
||||
let (t_next, v_next) = mincut_series[i + 1];
|
||||
let dt = t_next - t_curr;
|
||||
let delta_next = v_next - v_curr;
|
||||
|
||||
if dt < 2.0 && delta_next.abs() > change_threshold {
|
||||
if delta < 0.0 && delta_next > 0.0 {
|
||||
events.push(CoherenceEvent {
|
||||
start_time: t_curr,
|
||||
end_time: t_next,
|
||||
event_type: CoherenceEventType::NetworkSplit,
|
||||
involved_regions: Vec::new(),
|
||||
peak_coherence: magnitude.max(delta_next.abs() / baseline),
|
||||
});
|
||||
i += 1;
|
||||
} else if delta > 0.0 && delta_next < 0.0 {
|
||||
events.push(CoherenceEvent {
|
||||
start_time: t_curr,
|
||||
end_time: t_next,
|
||||
event_type: CoherenceEventType::NetworkMerger,
|
||||
involved_regions: Vec::new(),
|
||||
peak_coherence: magnitude.max(delta_next.abs() / baseline),
|
||||
});
|
||||
i += 1;
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
i += 1;
|
||||
}
|
||||
|
||||
events
|
||||
}
|
||||
|
||||
/// Detect coherence events by processing a brain graph sequence.
|
||||
///
|
||||
/// Updates the internal tracker with each graph and then analyzes the
|
||||
/// resulting mincut time series.
|
||||
pub fn detect_coherence_events(
|
||||
&mut self,
|
||||
sequence: &ruv_neural_core::graph::BrainGraphSequence,
|
||||
) -> ruv_neural_core::Result<Vec<CoherenceEvent>> {
|
||||
for graph in &sequence.graphs {
|
||||
self.tracker.update(graph)?;
|
||||
}
|
||||
|
||||
let timeseries = self.tracker.mincut_timeseries();
|
||||
Ok(self.detect_from_timeseries(×eries))
|
||||
}
|
||||
}
|
||||
|
||||
/// Find the time when the mincut recovers to near the original value.
|
||||
fn find_recovery_time_in_series(
|
||||
series: &[(f64, f64)],
|
||||
start_idx: usize,
|
||||
original_value: f64,
|
||||
baseline: f64,
|
||||
) -> f64 {
|
||||
let recovery_threshold = 0.1 * baseline;
|
||||
|
||||
for &(t, v) in series.iter().skip(start_idx + 1) {
|
||||
if (v - original_value).abs() < recovery_threshold {
|
||||
return t;
|
||||
}
|
||||
}
|
||||
|
||||
// No recovery found; return last timestamp.
|
||||
series.last().map_or(series[start_idx].0, |&(t, _)| t)
|
||||
}
|
||||
|
||||
#[cfg(test)]
|
||||
mod tests {
|
||||
use super::*;
|
||||
|
||||
#[test]
|
||||
fn test_coherence_event_types_serialization() {
|
||||
for event_type in [
|
||||
CoherenceEventType::NetworkFormation,
|
||||
CoherenceEventType::NetworkDissolution,
|
||||
CoherenceEventType::NetworkMerger,
|
||||
CoherenceEventType::NetworkSplit,
|
||||
] {
|
||||
let json = serde_json::to_string(&event_type).unwrap();
|
||||
let back: CoherenceEventType = serde_json::from_str(&json).unwrap();
|
||||
assert_eq!(back, event_type);
|
||||
}
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_coherence_event_serialization() {
|
||||
let event = CoherenceEvent {
|
||||
start_time: 0.0,
|
||||
end_time: 1.0,
|
||||
event_type: CoherenceEventType::NetworkFormation,
|
||||
involved_regions: vec![0, 1, 2],
|
||||
peak_coherence: 0.8,
|
||||
};
|
||||
let json = serde_json::to_string(&event).unwrap();
|
||||
let back: CoherenceEvent = serde_json::from_str(&json).unwrap();
|
||||
assert_eq!(back.event_type, CoherenceEventType::NetworkFormation);
|
||||
assert!((back.peak_coherence - 0.8).abs() < 1e-9);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_detect_no_events_for_constant_series() {
|
||||
let detector = CoherenceDetector::new(0.3, 0.3);
|
||||
let series: Vec<(f64, f64)> = (0..10)
|
||||
.map(|i| (i as f64, 5.0))
|
||||
.collect();
|
||||
let events = detector.detect_from_timeseries(&series);
|
||||
assert!(events.is_empty());
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_detect_formation_event() {
|
||||
let mut detector = CoherenceDetector::new(0.2, 0.2);
|
||||
detector.set_baseline(5.0);
|
||||
|
||||
// Constant, then a sudden drop in mincut (integration).
|
||||
let series = vec![
|
||||
(0.0, 5.0),
|
||||
(1.0, 5.0),
|
||||
(2.0, 5.0),
|
||||
(3.0, 1.0), // big drop
|
||||
(4.0, 1.0),
|
||||
(5.0, 5.0), // recovery
|
||||
];
|
||||
|
||||
let events = detector.detect_from_timeseries(&series);
|
||||
assert!(
|
||||
!events.is_empty(),
|
||||
"Should detect a formation event from a large mincut decrease"
|
||||
);
|
||||
// First event should be a formation (integration).
|
||||
assert_eq!(events[0].event_type, CoherenceEventType::NetworkFormation);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_detect_dissolution_event() {
|
||||
let mut detector = CoherenceDetector::new(0.2, 0.2);
|
||||
detector.set_baseline(5.0);
|
||||
|
||||
// Sudden increase in mincut (segregation).
|
||||
let series = vec![
|
||||
(0.0, 5.0),
|
||||
(1.0, 5.0),
|
||||
(2.0, 15.0), // big jump
|
||||
(3.0, 15.0),
|
||||
];
|
||||
|
||||
let events = detector.detect_from_timeseries(&series);
|
||||
let dissolution_events: Vec<_> = events
|
||||
.iter()
|
||||
.filter(|e| e.event_type == CoherenceEventType::NetworkDissolution)
|
||||
.collect();
|
||||
assert!(
|
||||
!dissolution_events.is_empty(),
|
||||
"Should detect a dissolution event from a large mincut increase"
|
||||
);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_detector_empty_series() {
|
||||
let detector = CoherenceDetector::new(0.3, 0.3);
|
||||
let events = detector.detect_from_timeseries(&[]);
|
||||
assert!(events.is_empty());
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_detector_single_point() {
|
||||
let detector = CoherenceDetector::new(0.3, 0.3);
|
||||
let events = detector.detect_from_timeseries(&[(0.0, 5.0)]);
|
||||
assert!(events.is_empty());
|
||||
}
|
||||
}
|
||||
@@ -9,7 +9,7 @@ use serde::{Deserialize, Serialize};
|
||||
|
||||
use ruv_neural_core::graph::BrainGraph;
|
||||
use ruv_neural_core::topology::MincutResult;
|
||||
use ruv_neural_core::{Result, RuvNeuralError};
|
||||
use ruv_neural_core::Result;
|
||||
|
||||
use crate::stoer_wagner::stoer_wagner_mincut;
|
||||
|
||||
@@ -161,7 +161,9 @@ impl DynamicMincutTracker {
|
||||
///
|
||||
/// The integration index is defined as:
|
||||
///
|
||||
/// I(t) = 1.0 - mincut(t) / max_mincut
|
||||
/// ```text
|
||||
/// I(t) = 1.0 - mincut(t) / max_mincut
|
||||
/// ```
|
||||
///
|
||||
/// High values (close to 1) indicate integrated states; low values indicate
|
||||
/// segregated states.
|
||||
@@ -352,7 +354,7 @@ mod tests {
|
||||
fn test_integration_index() {
|
||||
let mut tracker = DynamicMincutTracker::new();
|
||||
for i in 0..3 {
|
||||
let g = make_graph(i as f64, (i as f64 + 1.0));
|
||||
let g = make_graph(i as f64, i as f64 + 1.0);
|
||||
tracker.update(&g).unwrap();
|
||||
}
|
||||
|
||||
|
||||
@@ -5,7 +5,7 @@
|
||||
//! optimization.
|
||||
|
||||
use ruv_neural_core::graph::{BrainEdge, BrainGraph};
|
||||
use ruv_neural_core::topology::{MincutResult, MultiPartition};
|
||||
use ruv_neural_core::topology::MultiPartition;
|
||||
use ruv_neural_core::{Result, RuvNeuralError};
|
||||
|
||||
use crate::normalized::normalized_cut;
|
||||
|
||||
@@ -2,7 +2,9 @@
|
||||
//!
|
||||
//! The normalized cut objective is:
|
||||
//!
|
||||
//! Ncut(A, B) = cut(A,B) / vol(A) + cut(A,B) / vol(B)
|
||||
//! ```text
|
||||
//! Ncut(A, B) = cut(A,B) / vol(A) + cut(A,B) / vol(B)
|
||||
//! ```
|
||||
//!
|
||||
//! where vol(S) = sum of degrees of nodes in S.
|
||||
//!
|
||||
|
||||
+37
-9
@@ -234,10 +234,24 @@ pub fn cheeger_constant(graph: &BrainGraph) -> Result<f64> {
|
||||
}
|
||||
}
|
||||
|
||||
/// Cheeger inequality bounds relating the Fiedler value lambda_2 to the
|
||||
/// Cheeger constant h(G):
|
||||
/// Cheeger inequality bounds relating the Fiedler value lambda_2 of the
|
||||
/// **unnormalized** Laplacian to the conductance h(G).
|
||||
///
|
||||
/// lambda_2 / 2 <= h(G) <= sqrt(2 * lambda_2)
|
||||
/// For the unnormalized Laplacian with maximum degree d_max:
|
||||
///
|
||||
/// ```text
|
||||
/// lambda_2 / (2 * d_max) <= h(G) <= sqrt(2 * lambda_2 / d_min)
|
||||
/// ```
|
||||
///
|
||||
/// For convenience when d_max is unknown, this function uses the normalized
|
||||
/// Laplacian relationship:
|
||||
///
|
||||
/// ```text
|
||||
/// lambda_2_norm / 2 <= h(G) <= sqrt(2 * lambda_2_norm)
|
||||
/// ```
|
||||
///
|
||||
/// The `fiedler_value` parameter should be from the **normalized** Laplacian
|
||||
/// (i.e., `unnormalized_lambda_2 / d_max` is a conservative approximation).
|
||||
///
|
||||
/// Returns `(lower_bound, upper_bound)`.
|
||||
pub fn cheeger_bound(fiedler_value: f64) -> (f64, f64) {
|
||||
@@ -331,7 +345,11 @@ mod tests {
|
||||
);
|
||||
}
|
||||
|
||||
/// Cheeger bounds: lambda_2/2 <= h(G) <= sqrt(2*lambda_2).
|
||||
/// Cheeger bounds using normalized Laplacian eigenvalue.
|
||||
///
|
||||
/// For the unnormalized Laplacian eigenvalue lambda_2 and max degree d_max,
|
||||
/// the normalized eigenvalue is lambda_2_norm = lambda_2 / d_max, and the
|
||||
/// Cheeger inequality states: lambda_2_norm / 2 <= h(G) <= sqrt(2 * lambda_2_norm).
|
||||
#[test]
|
||||
fn test_cheeger_bounds_hold() {
|
||||
let graph = BrainGraph {
|
||||
@@ -349,21 +367,31 @@ mod tests {
|
||||
|
||||
let (fiedler_value, _) = fiedler_decomposition(&graph).unwrap();
|
||||
let h = cheeger_constant(&graph).unwrap();
|
||||
let (lower, upper) = cheeger_bound(fiedler_value);
|
||||
|
||||
// For conductance (cut/vol), the Cheeger inequality uses the normalized
|
||||
// Laplacian eigenvalue. For C4 with unit weights, d_max = 2, so:
|
||||
// lambda_2_norm = lambda_2 / d_max
|
||||
let adj = graph.adjacency_matrix();
|
||||
let d_max: f64 = (0..graph.num_nodes)
|
||||
.map(|i| adj[i].iter().sum::<f64>())
|
||||
.fold(f64::NEG_INFINITY, f64::max);
|
||||
let lambda_2_norm = fiedler_value / d_max;
|
||||
|
||||
let (lower, upper) = cheeger_bound(lambda_2_norm);
|
||||
|
||||
assert!(
|
||||
h >= lower - 1e-6,
|
||||
"Cheeger h={} should be >= lower bound {} (lambda2={})",
|
||||
"Cheeger h={} should be >= lower bound {} (lambda2_norm={})",
|
||||
h,
|
||||
lower,
|
||||
fiedler_value
|
||||
lambda_2_norm
|
||||
);
|
||||
assert!(
|
||||
h <= upper + 1e-6,
|
||||
"Cheeger h={} should be <= upper bound {} (lambda2={})",
|
||||
"Cheeger h={} should be <= upper bound {} (lambda2_norm={})",
|
||||
h,
|
||||
upper,
|
||||
fiedler_value
|
||||
lambda_2_norm
|
||||
);
|
||||
}
|
||||
|
||||
|
||||
@@ -0,0 +1,130 @@
|
||||
# ruv-neural-sensor
|
||||
|
||||
**rUv Neural** -- Sensor data acquisition for NV diamond, OPM, EEG, and simulated sources.
|
||||
|
||||
Part of the [rUv Neural](https://github.com/ruvnet/RuView) brain topology analysis pipeline.
|
||||
|
||||
## Overview
|
||||
|
||||
`ruv-neural-sensor` provides a uniform `SensorSource` trait interface for acquiring multi-channel neural signal data from multiple sensor modalities. Each sensor backend is feature-gated so you only compile what you need.
|
||||
|
||||
## Supported Sensor Types
|
||||
|
||||
| Sensor | Feature Flag | Sensitivity | Description |
|
||||
|--------|-------------|-------------|-------------|
|
||||
| Simulated | `simulator` (default) | Configurable | Synthetic data for testing and development |
|
||||
| NV Diamond | `nv_diamond` | ~10 fT/sqrt(Hz) | Nitrogen-vacancy diamond magnetometer |
|
||||
| OPM | `opm` | ~7 fT/sqrt(Hz) | Optically pumped magnetometer (SERF mode) |
|
||||
| EEG | `eeg` | ~1000 fT/sqrt(Hz) | Electroencephalography (10-20 system) |
|
||||
|
||||
## Feature Flags
|
||||
|
||||
| Feature | Default | Description |
|
||||
|---------|---------|-------------|
|
||||
| `simulator` | Yes | Simulated sensor array with configurable noise, oscillations, and events |
|
||||
| `nv_diamond` | No | NV diamond magnetometer with ODMR signal processing stub |
|
||||
| `opm` | No | OPM array with SERF mode, cross-talk matrix, active shielding |
|
||||
| `eeg` | No | EEG with 10-20 electrode system, impedance tracking |
|
||||
|
||||
## Usage
|
||||
|
||||
### Basic Simulator
|
||||
|
||||
```rust
|
||||
use ruv_neural_sensor::simulator::SimulatedSensorArray;
|
||||
use ruv_neural_sensor::SensorSource;
|
||||
|
||||
// Create a 16-channel simulator at 1000 Hz with 10 fT/sqrt(Hz) noise.
|
||||
let mut sim = SimulatedSensorArray::new(16, 1000.0);
|
||||
|
||||
// Inject alpha rhythm (10 Hz, 100 fT amplitude).
|
||||
sim.inject_alpha(100.0);
|
||||
|
||||
// Acquire 1 second of data.
|
||||
let data = sim.read_chunk(1000).unwrap();
|
||||
assert_eq!(data.num_channels, 16);
|
||||
assert_eq!(data.num_samples, 1000);
|
||||
```
|
||||
|
||||
### Custom Noise Floor
|
||||
|
||||
```rust
|
||||
use ruv_neural_sensor::simulator::SimulatedSensorArray;
|
||||
|
||||
let mut sim = SimulatedSensorArray::new(8, 500.0)
|
||||
.with_noise(5.0); // 5 fT/sqrt(Hz) noise density
|
||||
```
|
||||
|
||||
### Injecting Events
|
||||
|
||||
```rust
|
||||
use ruv_neural_sensor::simulator::{SimulatedSensorArray, SensorEvent};
|
||||
use ruv_neural_sensor::SensorSource;
|
||||
|
||||
let mut sim = SimulatedSensorArray::new(4, 1000.0);
|
||||
sim.inject_event(SensorEvent::Spike {
|
||||
channel: 0,
|
||||
amplitude_ft: 500.0,
|
||||
sample_offset: 100,
|
||||
});
|
||||
let data = sim.read_chunk(200).unwrap();
|
||||
```
|
||||
|
||||
## Calibration
|
||||
|
||||
The `calibration` module provides tools for sensor gain/offset correction and cross-sensor alignment.
|
||||
|
||||
```rust
|
||||
use ruv_neural_sensor::calibration::{CalibrationData, calibrate_channel, estimate_noise_floor, cross_calibrate};
|
||||
|
||||
// Define calibration data.
|
||||
let cal = CalibrationData {
|
||||
gains: vec![2.0, 1.5],
|
||||
offsets: vec![10.0, 5.0],
|
||||
noise_floors: vec![1.0, 2.0],
|
||||
};
|
||||
|
||||
// Apply correction: corrected = (raw - offset) * gain
|
||||
let corrected = calibrate_channel(100.0, 0, &cal);
|
||||
|
||||
// Estimate noise floor from a quiet recording.
|
||||
let quiet_data = vec![0.1, -0.2, 0.15, -0.1];
|
||||
let noise = estimate_noise_floor(&quiet_data);
|
||||
|
||||
// Cross-calibrate two sensors.
|
||||
let reference = vec![10.0, 20.0, 30.0];
|
||||
let target = vec![5.0, 10.0, 15.0];
|
||||
let (gain, offset) = cross_calibrate(&reference, &target);
|
||||
```
|
||||
|
||||
## Quality Monitoring
|
||||
|
||||
The `quality` module tracks real-time signal quality across channels.
|
||||
|
||||
```rust
|
||||
use ruv_neural_sensor::quality::{QualityMonitor, SignalQuality};
|
||||
|
||||
let mut monitor = QualityMonitor::new(4);
|
||||
|
||||
// Check quality of 4 channels.
|
||||
let ch0 = vec![/* ... */];
|
||||
let ch1 = vec![/* ... */];
|
||||
let ch2 = vec![/* ... */];
|
||||
let ch3 = vec![/* ... */];
|
||||
let qualities = monitor.check_quality(&[&ch0, &ch1, &ch2, &ch3]);
|
||||
|
||||
for (i, q) in qualities.iter().enumerate() {
|
||||
if q.below_threshold() {
|
||||
println!("Channel {i}: quality below threshold (SNR={:.1} dB)", q.snr_db);
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
**Alert thresholds:**
|
||||
- SNR < 3 dB
|
||||
- Artifact probability > 0.5
|
||||
- Saturation detected
|
||||
|
||||
## License
|
||||
|
||||
MIT OR Apache-2.0
|
||||
@@ -0,0 +1,60 @@
|
||||
//! Sensor calibration utilities for gain/offset correction and cross-calibration.
|
||||
|
||||
/// Calibration data for a sensor array.
|
||||
pub struct CalibrationData {
|
||||
/// Per-channel gain factors.
|
||||
pub gains: Vec<f64>,
|
||||
/// Per-channel DC offsets to subtract.
|
||||
pub offsets: Vec<f64>,
|
||||
/// Per-channel noise floor estimates (fT RMS).
|
||||
pub noise_floors: Vec<f64>,
|
||||
}
|
||||
|
||||
/// Apply gain and offset correction to a single sample on a given channel.
|
||||
///
|
||||
/// `corrected = (raw - offset) * gain`
|
||||
pub fn calibrate_channel(raw: f64, channel: usize, cal: &CalibrationData) -> f64 {
|
||||
let offset = cal.offsets.get(channel).copied().unwrap_or(0.0);
|
||||
let gain = cal.gains.get(channel).copied().unwrap_or(1.0);
|
||||
(raw - offset) * gain
|
||||
}
|
||||
|
||||
/// Estimate the noise floor (RMS) of a quiet signal segment.
|
||||
pub fn estimate_noise_floor(signal: &[f64]) -> f64 {
|
||||
if signal.is_empty() {
|
||||
return 0.0;
|
||||
}
|
||||
let mean_sq = signal.iter().map(|x| x * x).sum::<f64>() / signal.len() as f64;
|
||||
mean_sq.sqrt()
|
||||
}
|
||||
|
||||
/// Cross-calibrate a target channel against a reference channel.
|
||||
///
|
||||
/// Returns `(gain, offset)` such that `target * gain + offset ~ reference`.
|
||||
/// Uses simple linear regression.
|
||||
pub fn cross_calibrate(reference: &[f64], target: &[f64]) -> (f64, f64) {
|
||||
let n = reference.len().min(target.len());
|
||||
if n == 0 {
|
||||
return (1.0, 0.0);
|
||||
}
|
||||
|
||||
let mean_r = reference[..n].iter().sum::<f64>() / n as f64;
|
||||
let mean_t = target[..n].iter().sum::<f64>() / n as f64;
|
||||
|
||||
let mut num = 0.0;
|
||||
let mut den = 0.0;
|
||||
for i in 0..n {
|
||||
let dr = reference[i] - mean_r;
|
||||
let dt = target[i] - mean_t;
|
||||
num += dr * dt;
|
||||
den += dt * dt;
|
||||
}
|
||||
|
||||
if den.abs() < 1e-15 {
|
||||
return (1.0, mean_r - mean_t);
|
||||
}
|
||||
|
||||
let gain = num / den;
|
||||
let offset = mean_r - gain * mean_t;
|
||||
(gain, offset)
|
||||
}
|
||||
@@ -0,0 +1,214 @@
|
||||
//! EEG (Electroencephalography) interface.
|
||||
//!
|
||||
//! Provides a sensor interface for standard EEG systems using the 10-20
|
||||
//! international electrode placement system. Included as a comparison/fallback
|
||||
//! modality alongside higher-sensitivity magnetometer arrays.
|
||||
|
||||
use ruv_neural_core::error::{Result, RuvNeuralError};
|
||||
use ruv_neural_core::sensor::{SensorArray, SensorChannel, SensorType};
|
||||
use ruv_neural_core::signal::MultiChannelTimeSeries;
|
||||
use ruv_neural_core::traits::SensorSource;
|
||||
use serde::{Deserialize, Serialize};
|
||||
use std::f64::consts::PI;
|
||||
|
||||
/// Standard 10-20 system electrode labels (21 channels).
|
||||
pub const STANDARD_10_20_LABELS: &[&str] = &[
|
||||
"Fp1", "Fp2", "F7", "F3", "Fz", "F4", "F8", "T3", "C3", "Cz", "C4", "T4", "T5", "P3",
|
||||
"Pz", "P4", "T6", "O1", "Oz", "O2", "A1",
|
||||
];
|
||||
|
||||
/// Standard 10-20 system approximate positions on a unit sphere (nasion-inion axis = Y).
|
||||
fn standard_10_20_positions() -> Vec<[f64; 3]> {
|
||||
// Simplified spherical positions for the 21-channel 10-20 montage.
|
||||
let r = 0.09; // ~9 cm radius
|
||||
STANDARD_10_20_LABELS
|
||||
.iter()
|
||||
.enumerate()
|
||||
.map(|(i, _)| {
|
||||
let phi = 2.0 * PI * i as f64 / STANDARD_10_20_LABELS.len() as f64;
|
||||
let theta = PI / 3.0 + (i as f64 / STANDARD_10_20_LABELS.len() as f64) * PI / 3.0;
|
||||
[
|
||||
r * theta.sin() * phi.cos(),
|
||||
r * theta.sin() * phi.sin(),
|
||||
r * theta.cos(),
|
||||
]
|
||||
})
|
||||
.collect()
|
||||
}
|
||||
|
||||
/// Configuration for an EEG sensor array.
|
||||
#[derive(Debug, Clone, Serialize, Deserialize)]
|
||||
pub struct EegConfig {
|
||||
/// Number of EEG channels.
|
||||
pub num_channels: usize,
|
||||
/// Sample rate in Hz.
|
||||
pub sample_rate_hz: f64,
|
||||
/// Channel labels (e.g., "Fp1", "Fz", etc.).
|
||||
pub labels: Vec<String>,
|
||||
/// Channel positions in head-frame coordinates.
|
||||
pub positions: Vec<[f64; 3]>,
|
||||
/// Reference electrode label (e.g., "A1" for linked ears).
|
||||
pub reference: String,
|
||||
/// Per-channel impedance in kOhm (None = not measured yet).
|
||||
pub impedances_kohm: Vec<Option<f64>>,
|
||||
}
|
||||
|
||||
impl Default for EegConfig {
|
||||
fn default() -> Self {
|
||||
let labels: Vec<String> = STANDARD_10_20_LABELS.iter().map(|s| s.to_string()).collect();
|
||||
let num_channels = labels.len();
|
||||
let positions = standard_10_20_positions();
|
||||
Self {
|
||||
num_channels,
|
||||
sample_rate_hz: 256.0,
|
||||
labels,
|
||||
positions,
|
||||
reference: "A1".to_string(),
|
||||
impedances_kohm: vec![None; num_channels],
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
/// EEG sensor array.
|
||||
///
|
||||
/// Provides the [`SensorSource`] interface for EEG acquisition.
|
||||
/// Currently operates as a simulated backend.
|
||||
#[derive(Debug)]
|
||||
pub struct EegArray {
|
||||
config: EegConfig,
|
||||
array: SensorArray,
|
||||
sample_counter: u64,
|
||||
}
|
||||
|
||||
impl EegArray {
|
||||
/// Create a new EEG array from configuration.
|
||||
pub fn new(config: EegConfig) -> Self {
|
||||
let channels = (0..config.num_channels)
|
||||
.map(|i| {
|
||||
let pos = config.positions.get(i).copied().unwrap_or([0.0, 0.0, 0.0]);
|
||||
let label = config
|
||||
.labels
|
||||
.get(i)
|
||||
.cloned()
|
||||
.unwrap_or_else(|| format!("EEG-{}", i));
|
||||
SensorChannel {
|
||||
id: i,
|
||||
sensor_type: SensorType::Eeg,
|
||||
position: pos,
|
||||
orientation: [0.0, 0.0, 1.0],
|
||||
// EEG sensitivity is much lower than magnetometers.
|
||||
sensitivity_ft_sqrt_hz: 1000.0,
|
||||
sample_rate_hz: config.sample_rate_hz,
|
||||
label,
|
||||
}
|
||||
})
|
||||
.collect();
|
||||
|
||||
let array = SensorArray {
|
||||
channels,
|
||||
sensor_type: SensorType::Eeg,
|
||||
name: "EegArray".to_string(),
|
||||
};
|
||||
|
||||
Self {
|
||||
config,
|
||||
array,
|
||||
sample_counter: 0,
|
||||
}
|
||||
}
|
||||
|
||||
/// Returns the sensor array metadata.
|
||||
pub fn sensor_array(&self) -> &SensorArray {
|
||||
&self.array
|
||||
}
|
||||
|
||||
/// Update impedance measurement for a channel.
|
||||
pub fn set_impedance(&mut self, channel: usize, impedance_kohm: f64) -> Result<()> {
|
||||
if channel >= self.config.num_channels {
|
||||
return Err(RuvNeuralError::ChannelOutOfRange {
|
||||
channel,
|
||||
max: self.config.num_channels - 1,
|
||||
});
|
||||
}
|
||||
self.config.impedances_kohm[channel] = Some(impedance_kohm);
|
||||
Ok(())
|
||||
}
|
||||
|
||||
/// Check if all channels have acceptable impedance (< 5 kOhm).
|
||||
pub fn impedance_ok(&self) -> bool {
|
||||
self.config.impedances_kohm.iter().all(|imp| {
|
||||
imp.map_or(false, |v| v < 5.0)
|
||||
})
|
||||
}
|
||||
|
||||
/// Get channels with high impedance (> threshold kOhm).
|
||||
pub fn high_impedance_channels(&self, threshold_kohm: f64) -> Vec<usize> {
|
||||
self.config
|
||||
.impedances_kohm
|
||||
.iter()
|
||||
.enumerate()
|
||||
.filter_map(|(i, imp)| {
|
||||
imp.and_then(|v| if v > threshold_kohm { Some(i) } else { None })
|
||||
})
|
||||
.collect()
|
||||
}
|
||||
|
||||
/// Get the reference electrode label.
|
||||
pub fn reference(&self) -> &str {
|
||||
&self.config.reference
|
||||
}
|
||||
|
||||
/// Re-reference data to average reference.
|
||||
///
|
||||
/// Subtracts the mean across channels at each time point.
|
||||
pub fn average_reference(data: &mut [Vec<f64>]) {
|
||||
if data.is_empty() {
|
||||
return;
|
||||
}
|
||||
let num_samples = data[0].len();
|
||||
let num_channels = data.len();
|
||||
for s in 0..num_samples {
|
||||
let mean: f64 = data.iter().map(|ch| ch[s]).sum::<f64>() / num_channels as f64;
|
||||
for ch in data.iter_mut() {
|
||||
ch[s] -= mean;
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
impl SensorSource for EegArray {
|
||||
fn sensor_type(&self) -> SensorType {
|
||||
SensorType::Eeg
|
||||
}
|
||||
|
||||
fn num_channels(&self) -> usize {
|
||||
self.config.num_channels
|
||||
}
|
||||
|
||||
fn sample_rate_hz(&self) -> f64 {
|
||||
self.config.sample_rate_hz
|
||||
}
|
||||
|
||||
fn read_chunk(&mut self, num_samples: usize) -> Result<MultiChannelTimeSeries> {
|
||||
let timestamp = self.sample_counter as f64 / self.config.sample_rate_hz;
|
||||
|
||||
// Generate simulated EEG: microvolts scale (converted to fT-equivalent units).
|
||||
let mut rng = rand::thread_rng();
|
||||
let data: Vec<Vec<f64>> = (0..self.config.num_channels)
|
||||
.map(|_ch| {
|
||||
// EEG noise ~50 uV RMS, simulated as white noise.
|
||||
let sigma = 50.0; // uV
|
||||
(0..num_samples)
|
||||
.map(|_| {
|
||||
let u1: f64 = rand::Rng::gen::<f64>(&mut rng).max(1e-15);
|
||||
let u2: f64 = rand::Rng::gen(&mut rng);
|
||||
sigma * (-2.0 * u1.ln()).sqrt() * (2.0 * PI * u2).cos()
|
||||
})
|
||||
.collect()
|
||||
})
|
||||
.collect();
|
||||
|
||||
self.sample_counter += num_samples as u64;
|
||||
MultiChannelTimeSeries::new(data, self.config.sample_rate_hz, timestamp)
|
||||
}
|
||||
}
|
||||
@@ -171,12 +171,20 @@ mod tests {
|
||||
.map(|i| 100.0 * (2.0 * std::f64::consts::PI * 10.0 * i as f64 / 1000.0).sin())
|
||||
.collect();
|
||||
|
||||
// Channel 1: mostly noise.
|
||||
let bad_signal: Vec<f64> = (0..1000).map(|i| (i as f64 * 0.001).sin() * 0.01).collect();
|
||||
// Channel 1: high-frequency noise (alternating values = maximum first-difference noise).
|
||||
let bad_signal: Vec<f64> = (0..1000)
|
||||
.map(|i| if i % 2 == 0 { 1.0 } else { -1.0 })
|
||||
.collect();
|
||||
|
||||
let qualities = monitor.check_quality(&[&good_signal, &bad_signal]);
|
||||
assert_eq!(qualities.len(), 2);
|
||||
assert!(qualities[0].snr_db > qualities[1].snr_db);
|
||||
// Smooth sinusoid should have higher SNR than alternating noise.
|
||||
assert!(
|
||||
qualities[0].snr_db > qualities[1].snr_db,
|
||||
"Good SNR ({}) should be > bad SNR ({})",
|
||||
qualities[0].snr_db,
|
||||
qualities[1].snr_db,
|
||||
);
|
||||
}
|
||||
|
||||
#[test]
|
||||
|
||||
@@ -117,6 +117,11 @@ impl NvDiamondArray {
|
||||
}
|
||||
}
|
||||
|
||||
/// Returns the sensor array metadata.
|
||||
pub fn sensor_array(&self) -> &SensorArray {
|
||||
&self.array
|
||||
}
|
||||
|
||||
/// Set custom calibration data.
|
||||
pub fn with_calibration(mut self, calibration: NvCalibration) -> Result<Self> {
|
||||
if calibration.sensitivity_ft_per_count.len() != self.config.num_channels {
|
||||
|
||||
@@ -116,6 +116,11 @@ impl OpmArray {
|
||||
}
|
||||
}
|
||||
|
||||
/// Returns the sensor array metadata.
|
||||
pub fn sensor_array(&self) -> &SensorArray {
|
||||
&self.array
|
||||
}
|
||||
|
||||
/// Apply cross-talk compensation to raw channel data.
|
||||
///
|
||||
/// Multiplies the raw data vector by the inverse cross-talk matrix.
|
||||
|
||||
@@ -0,0 +1,95 @@
|
||||
//! Signal quality monitoring for neural sensor channels.
|
||||
|
||||
/// Signal quality metrics for a single channel.
|
||||
pub struct SignalQuality {
|
||||
/// Signal-to-noise ratio in dB.
|
||||
pub snr_db: f64,
|
||||
/// Probability of artifact contamination in [0, 1].
|
||||
pub artifact_probability: f64,
|
||||
/// Whether the channel is saturated (clipping).
|
||||
pub saturated: bool,
|
||||
}
|
||||
|
||||
impl SignalQuality {
|
||||
/// Returns true if signal quality is below acceptable thresholds.
|
||||
///
|
||||
/// Thresholds: SNR < 3 dB or artifact_probability > 0.5.
|
||||
pub fn below_threshold(&self) -> bool {
|
||||
self.snr_db < 3.0 || self.artifact_probability > 0.5
|
||||
}
|
||||
}
|
||||
|
||||
/// Real-time signal quality monitor for multi-channel data.
|
||||
pub struct QualityMonitor {
|
||||
num_channels: usize,
|
||||
}
|
||||
|
||||
impl QualityMonitor {
|
||||
/// Create a new quality monitor for the given number of channels.
|
||||
pub fn new(num_channels: usize) -> Self {
|
||||
Self { num_channels }
|
||||
}
|
||||
|
||||
/// Check signal quality for each channel.
|
||||
///
|
||||
/// Each element in `signals` is a slice of samples for one channel.
|
||||
pub fn check_quality(&mut self, signals: &[&[f64]]) -> Vec<SignalQuality> {
|
||||
let n = signals.len().min(self.num_channels);
|
||||
(0..n)
|
||||
.map(|i| {
|
||||
let signal = signals[i];
|
||||
let snr_db = estimate_snr_db(signal);
|
||||
let saturated = detect_saturation(signal);
|
||||
let artifact_probability = if saturated { 0.9 } else { 0.0 };
|
||||
SignalQuality {
|
||||
snr_db,
|
||||
artifact_probability,
|
||||
saturated,
|
||||
}
|
||||
})
|
||||
.collect()
|
||||
}
|
||||
}
|
||||
|
||||
/// Estimate SNR in dB from a signal segment.
|
||||
fn estimate_snr_db(signal: &[f64]) -> f64 {
|
||||
if signal.is_empty() {
|
||||
return 0.0;
|
||||
}
|
||||
let mean = signal.iter().sum::<f64>() / signal.len() as f64;
|
||||
let variance = signal.iter().map(|x| (x - mean).powi(2)).sum::<f64>() / signal.len() as f64;
|
||||
let rms = variance.sqrt();
|
||||
if rms < 1e-15 {
|
||||
return 0.0;
|
||||
}
|
||||
let n = signal.len();
|
||||
if n < 4 {
|
||||
return 20.0 * rms.log10();
|
||||
}
|
||||
// Estimate noise as std of first differences (captures high-freq content).
|
||||
let diff_var = signal
|
||||
.windows(2)
|
||||
.map(|w| (w[1] - w[0]).powi(2))
|
||||
.sum::<f64>()
|
||||
/ (n - 1) as f64;
|
||||
let noise_power = diff_var / 2.0;
|
||||
let signal_power = variance;
|
||||
if noise_power < 1e-15 {
|
||||
return 60.0;
|
||||
}
|
||||
10.0 * (signal_power / noise_power).log10()
|
||||
}
|
||||
|
||||
/// Detect if a signal is saturated (extreme repeated values).
|
||||
fn detect_saturation(signal: &[f64]) -> bool {
|
||||
if signal.len() < 10 {
|
||||
return false;
|
||||
}
|
||||
let max_abs = signal.iter().map(|x| x.abs()).fold(0.0_f64, f64::max);
|
||||
if max_abs < 1e-10 {
|
||||
return false;
|
||||
}
|
||||
let threshold = max_abs * 0.999;
|
||||
let clipped_count = signal.iter().filter(|x| x.abs() >= threshold).count();
|
||||
clipped_count as f64 / signal.len() as f64 > 0.1
|
||||
}
|
||||
@@ -148,6 +148,11 @@ impl SimulatedSensorArray {
|
||||
self.pending_events.push(event);
|
||||
}
|
||||
|
||||
/// Returns the sensor array metadata.
|
||||
pub fn sensor_array(&self) -> &SensorArray {
|
||||
&self.array
|
||||
}
|
||||
|
||||
/// Add a custom oscillation component to all channels.
|
||||
pub fn add_oscillation(&mut self, frequency_hz: f64, amplitude_ft: f64) {
|
||||
self.oscillations.push(OscillationComponent {
|
||||
|
||||
@@ -0,0 +1,140 @@
|
||||
# rUv Neural Signal
|
||||
|
||||
Digital signal processing for neural magnetic field data.
|
||||
|
||||
Part of the **rUv Neural** workspace for brain topology analysis via non-invasive neural sensing.
|
||||
|
||||
## Capabilities
|
||||
|
||||
| Module | Description |
|
||||
|--------|-------------|
|
||||
| `filter` | Butterworth IIR bandpass, notch, highpass, lowpass filters (SOS form, zero-phase) |
|
||||
| `spectral` | Power spectral density (Welch), STFT, band power, spectral entropy, peak frequency |
|
||||
| `hilbert` | FFT-based Hilbert transform for instantaneous phase and amplitude |
|
||||
| `artifact` | Eye blink, muscle artifact, and cardiac (QRS) detection and rejection |
|
||||
| `connectivity` | Phase Locking Value, coherence, imaginary coherence, amplitude envelope correlation |
|
||||
| `preprocessing` | Configurable multi-stage pipeline (notch + bandpass + artifact rejection) |
|
||||
|
||||
## Feature Flags
|
||||
|
||||
| Flag | Description |
|
||||
|------|-------------|
|
||||
| `std` (default) | Standard library support |
|
||||
| `simd` | SIMD-accelerated processing (future) |
|
||||
|
||||
## Usage
|
||||
|
||||
### Preprocessing Pipeline
|
||||
|
||||
```rust
|
||||
use ruv_neural_core::signal::MultiChannelTimeSeries;
|
||||
use ruv_neural_signal::PreprocessingPipeline;
|
||||
|
||||
// Load your multi-channel neural recording
|
||||
let raw_data = MultiChannelTimeSeries::new(channels, 1000.0, 0.0).unwrap();
|
||||
|
||||
// Default pipeline: 50 Hz notch -> 1-200 Hz bandpass -> artifact rejection
|
||||
let pipeline = PreprocessingPipeline::default_pipeline(1000.0);
|
||||
let clean_data = pipeline.process(&raw_data).unwrap();
|
||||
|
||||
// Or build a custom pipeline
|
||||
let mut custom = PreprocessingPipeline::new(1000.0);
|
||||
custom.add_notch(60.0, 2.0); // 60 Hz for US power grid
|
||||
custom.add_bandpass(0.5, 100.0, 4); // Wider passband
|
||||
custom.add_artifact_rejection();
|
||||
let result = custom.process(&raw_data).unwrap();
|
||||
```
|
||||
|
||||
### Spectral Analysis
|
||||
|
||||
```rust
|
||||
use ruv_neural_signal::{compute_psd, band_power, spectral_entropy, peak_frequency};
|
||||
use ruv_neural_core::signal::FrequencyBand;
|
||||
|
||||
let (freqs, psd) = compute_psd(&signal, 1000.0, 512);
|
||||
let alpha_power = band_power(&psd, &freqs, FrequencyBand::Alpha);
|
||||
let entropy = spectral_entropy(&psd);
|
||||
let peak = peak_frequency(&psd, &freqs);
|
||||
```
|
||||
|
||||
### Connectivity
|
||||
|
||||
```rust
|
||||
use ruv_neural_signal::{phase_locking_value, coherence, compute_all_pairs, ConnectivityMetric};
|
||||
use ruv_neural_core::signal::FrequencyBand;
|
||||
|
||||
// Pairwise PLV in the alpha band
|
||||
let plv = phase_locking_value(&ch_a, &ch_b, 1000.0, FrequencyBand::Alpha);
|
||||
|
||||
// Full connectivity matrix
|
||||
let matrix = compute_all_pairs(&data, ConnectivityMetric::Plv, FrequencyBand::Alpha);
|
||||
```
|
||||
|
||||
### Hilbert Transform
|
||||
|
||||
```rust
|
||||
use ruv_neural_signal::{hilbert_transform, instantaneous_phase, instantaneous_amplitude};
|
||||
|
||||
let analytic = hilbert_transform(&signal);
|
||||
let phase = instantaneous_phase(&signal);
|
||||
let envelope = instantaneous_amplitude(&signal);
|
||||
```
|
||||
|
||||
## Mathematical Formulations
|
||||
|
||||
### Butterworth Filter
|
||||
|
||||
The Butterworth filter maximizes flatness in the passband. The magnitude response of an Nth-order Butterworth lowpass filter is:
|
||||
|
||||
```
|
||||
|H(jw)|^2 = 1 / (1 + (w/wc)^(2N))
|
||||
```
|
||||
|
||||
Implemented as cascaded second-order sections (biquads) via bilinear transform for numerical stability. Zero-phase filtering is achieved by forward-backward (filtfilt) application.
|
||||
|
||||
### Welch's Method (PSD)
|
||||
|
||||
The signal is divided into overlapping segments (50% overlap), each windowed with a Hann window, and the averaged periodogram is computed:
|
||||
|
||||
```
|
||||
PSD(f) = (1 / (M * fs * W)) * sum_m |X_m(f)|^2
|
||||
```
|
||||
|
||||
where M is the number of segments, fs is the sample rate, and W is the window power.
|
||||
|
||||
### Phase Locking Value
|
||||
|
||||
```
|
||||
PLV = |<exp(j * (phi_a(t) - phi_b(t)))>|
|
||||
```
|
||||
|
||||
Instantaneous phases are extracted via the Hilbert transform after bandpass filtering.
|
||||
|
||||
### Hilbert Transform
|
||||
|
||||
The analytic signal is computed via the FFT:
|
||||
1. Compute X(f) = FFT(x(t))
|
||||
2. Zero negative frequencies, double positive frequencies
|
||||
3. z(t) = IFFT(X_analytic(f))
|
||||
|
||||
Instantaneous amplitude = |z(t)|, instantaneous phase = arg(z(t)).
|
||||
|
||||
### Spectral Entropy
|
||||
|
||||
```
|
||||
H = -sum(p_k * log2(p_k))
|
||||
```
|
||||
|
||||
where p_k = PSD(f_k) / sum(PSD) is the normalized power distribution.
|
||||
|
||||
## Performance Notes
|
||||
|
||||
- All filters use SOS (second-order sections) cascade for numerical stability with high filter orders
|
||||
- Zero-phase filtering (forward-backward) eliminates phase distortion at the cost of 2x computation
|
||||
- FFT operations use the `rustfft` crate (pure Rust, no external dependencies)
|
||||
- Connectivity matrix computation is O(N^2) in the number of channels; each pair requires bandpass filtering + Hilbert transform
|
||||
- The `simd` feature flag is reserved for future SIMD-accelerated inner loops
|
||||
|
||||
## License
|
||||
|
||||
MIT OR Apache-2.0
|
||||
@@ -1,67 +1,391 @@
|
||||
//! Artifact detection and rejection for neural signals.
|
||||
//! Artifact detection and rejection for neural recordings.
|
||||
//!
|
||||
//! Detects common physiological and environmental artifacts:
|
||||
//! - Eye blinks: large slow deflections (primarily frontal channels)
|
||||
//! - Muscle artifacts: high-frequency broadband power bursts
|
||||
//! - Cardiac artifacts: QRS complex detection
|
||||
//!
|
||||
//! Provides functions to mark and remove/interpolate artifact periods.
|
||||
|
||||
/// Detect eye blink artifacts by amplitude threshold.
|
||||
pub fn detect_eye_blinks(signal: &[f64], threshold: f64) -> Vec<(usize, usize)> {
|
||||
let mut artifacts = Vec::new();
|
||||
use ruv_neural_core::signal::MultiChannelTimeSeries;
|
||||
|
||||
use crate::filter::{BandpassFilter, HighpassFilter, LowpassFilter};
|
||||
|
||||
/// Detect eye blink artifacts in a single channel.
|
||||
///
|
||||
/// Eye blinks produce large, slow voltage deflections (1-5 Hz)
|
||||
/// with amplitudes 5-10x the background signal. Detection uses:
|
||||
/// 1. Lowpass filter to isolate slow components
|
||||
/// 2. Amplitude thresholding at `mean + 3*std`
|
||||
/// 3. Merging of nearby detections
|
||||
///
|
||||
/// # Arguments
|
||||
/// * `signal` - Single-channel time series
|
||||
/// * `sample_rate` - Sampling rate in Hz
|
||||
///
|
||||
/// # Returns
|
||||
/// Vector of (start_sample, end_sample) ranges for detected blinks.
|
||||
pub fn detect_eye_blinks(signal: &[f64], sample_rate: f64) -> Vec<(usize, usize)> {
|
||||
if signal.len() < (sample_rate * 0.2) as usize {
|
||||
return Vec::new();
|
||||
}
|
||||
|
||||
// Lowpass filter at 5 Hz to isolate blink waveform
|
||||
let lp = LowpassFilter::new(2, 5.0, sample_rate);
|
||||
let filtered = lp.apply(signal);
|
||||
|
||||
// Compute absolute values
|
||||
let abs_signal: Vec<f64> = filtered.iter().map(|x| x.abs()).collect();
|
||||
|
||||
// Compute mean and std of the absolute filtered signal
|
||||
let mean = abs_signal.iter().sum::<f64>() / abs_signal.len() as f64;
|
||||
let variance = abs_signal
|
||||
.iter()
|
||||
.map(|x| (x - mean).powi(2))
|
||||
.sum::<f64>()
|
||||
/ abs_signal.len() as f64;
|
||||
let std_dev = variance.sqrt();
|
||||
|
||||
// Threshold at mean + 3*std
|
||||
let threshold = mean + 3.0 * std_dev;
|
||||
|
||||
// Find contiguous regions above threshold
|
||||
let mut ranges = Vec::new();
|
||||
let mut in_artifact = false;
|
||||
let mut start = 0;
|
||||
for (i, &val) in signal.iter().enumerate() {
|
||||
if val.abs() > threshold && !in_artifact {
|
||||
|
||||
for (i, &val) in abs_signal.iter().enumerate() {
|
||||
if val > threshold && !in_artifact {
|
||||
in_artifact = true;
|
||||
start = i;
|
||||
} else if val.abs() <= threshold && in_artifact {
|
||||
} else if val <= threshold && in_artifact {
|
||||
in_artifact = false;
|
||||
artifacts.push((start, i));
|
||||
ranges.push((start, i));
|
||||
}
|
||||
}
|
||||
if in_artifact {
|
||||
artifacts.push((start, signal.len()));
|
||||
ranges.push((start, abs_signal.len()));
|
||||
}
|
||||
artifacts
|
||||
|
||||
// Extend ranges by 50ms on each side (blink onset/offset)
|
||||
let pad = (sample_rate * 0.05) as usize;
|
||||
let merged = merge_ranges_with_padding(&ranges, pad, signal.len());
|
||||
|
||||
merged
|
||||
}
|
||||
|
||||
/// Detect muscle artifacts via high-frequency power.
|
||||
pub fn detect_muscle_artifact(signal: &[f64], threshold: f64) -> Vec<(usize, usize)> {
|
||||
// Simplified: detect rapid changes
|
||||
let mut artifacts = Vec::new();
|
||||
if signal.len() < 2 { return artifacts; }
|
||||
/// Detect muscle artifact in a single channel.
|
||||
///
|
||||
/// Muscle artifacts produce broadband high-frequency power (>30 Hz).
|
||||
/// Detection uses:
|
||||
/// 1. Highpass filter at 30 Hz
|
||||
/// 2. Compute sliding window RMS
|
||||
/// 3. Threshold at mean + 3*std of RMS
|
||||
///
|
||||
/// # Returns
|
||||
/// Vector of (start_sample, end_sample) ranges for detected artifacts.
|
||||
pub fn detect_muscle_artifact(signal: &[f64], sample_rate: f64) -> Vec<(usize, usize)> {
|
||||
if signal.len() < (sample_rate * 0.1) as usize {
|
||||
return Vec::new();
|
||||
}
|
||||
|
||||
// Highpass filter at 30 Hz to isolate muscle activity
|
||||
let hp = HighpassFilter::new(2, 30.0, sample_rate);
|
||||
let filtered = hp.apply(signal);
|
||||
|
||||
// Sliding window RMS (50ms window)
|
||||
let window_len = (sample_rate * 0.05) as usize;
|
||||
let window_len = window_len.max(1);
|
||||
let n = filtered.len();
|
||||
let mut rms_signal = vec![0.0; n];
|
||||
|
||||
// Compute running sum of squares
|
||||
let mut sum_sq = 0.0;
|
||||
for i in 0..n {
|
||||
sum_sq += filtered[i] * filtered[i];
|
||||
if i >= window_len {
|
||||
sum_sq -= filtered[i - window_len] * filtered[i - window_len];
|
||||
}
|
||||
let count = (i + 1).min(window_len);
|
||||
rms_signal[i] = (sum_sq / count as f64).sqrt();
|
||||
}
|
||||
|
||||
// Threshold at mean + 3*std of RMS
|
||||
let mean = rms_signal.iter().sum::<f64>() / n as f64;
|
||||
let variance = rms_signal
|
||||
.iter()
|
||||
.map(|x| (x - mean).powi(2))
|
||||
.sum::<f64>()
|
||||
/ n as f64;
|
||||
let std_dev = variance.sqrt();
|
||||
let threshold = mean + 3.0 * std_dev;
|
||||
|
||||
let mut ranges = Vec::new();
|
||||
let mut in_artifact = false;
|
||||
let mut start = 0;
|
||||
for i in 1..signal.len() {
|
||||
let diff = (signal[i] - signal[i - 1]).abs();
|
||||
if diff > threshold && !in_artifact {
|
||||
|
||||
for (i, &val) in rms_signal.iter().enumerate() {
|
||||
if val > threshold && !in_artifact {
|
||||
in_artifact = true;
|
||||
start = i;
|
||||
} else if diff <= threshold && in_artifact {
|
||||
} else if val <= threshold && in_artifact {
|
||||
in_artifact = false;
|
||||
artifacts.push((start, i));
|
||||
ranges.push((start, i));
|
||||
}
|
||||
}
|
||||
if in_artifact {
|
||||
artifacts.push((start, signal.len()));
|
||||
ranges.push((start, n));
|
||||
}
|
||||
artifacts
|
||||
|
||||
let pad = (sample_rate * 0.025) as usize;
|
||||
merge_ranges_with_padding(&ranges, pad, signal.len())
|
||||
}
|
||||
|
||||
/// Detect cardiac artifacts.
|
||||
pub fn detect_cardiac(signal: &[f64], sample_rate_hz: f64) -> Vec<(usize, usize)> {
|
||||
let _ = sample_rate_hz;
|
||||
detect_eye_blinks(signal, 3.0 * std_dev(signal))
|
||||
}
|
||||
/// Detect cardiac (QRS complex) artifact peaks in a single channel.
|
||||
///
|
||||
/// Uses a simplified Pan-Tompkins-style approach:
|
||||
/// 1. Bandpass filter 5-15 Hz
|
||||
/// 2. Differentiate and square
|
||||
/// 3. Moving window integration
|
||||
/// 4. Threshold-based peak detection with refractory period
|
||||
///
|
||||
/// # Returns
|
||||
/// Vector of sample indices where QRS peaks are detected.
|
||||
pub fn detect_cardiac(signal: &[f64], sample_rate: f64) -> Vec<usize> {
|
||||
if signal.len() < (sample_rate * 0.5) as usize {
|
||||
return Vec::new();
|
||||
}
|
||||
|
||||
/// Reject artifacts by zeroing detected intervals.
|
||||
pub fn reject_artifacts(signal: &mut [f64], artifacts: &[(usize, usize)]) {
|
||||
for &(start, end) in artifacts {
|
||||
for sample in signal[start..end.min(signal.len())].iter_mut() {
|
||||
*sample = 0.0;
|
||||
// Bandpass 5-15 Hz to isolate QRS complex
|
||||
let bp = BandpassFilter::new(2, 5.0, 15.0, sample_rate);
|
||||
let filtered = bp.apply(signal);
|
||||
|
||||
// Differentiate
|
||||
let n = filtered.len();
|
||||
let mut diff = vec![0.0; n];
|
||||
for i in 1..n {
|
||||
diff[i] = filtered[i] - filtered[i - 1];
|
||||
}
|
||||
|
||||
// Square
|
||||
let squared: Vec<f64> = diff.iter().map(|x| x * x).collect();
|
||||
|
||||
// Moving window integration (150ms window)
|
||||
let win_len = (sample_rate * 0.15) as usize;
|
||||
let win_len = win_len.max(1);
|
||||
let mut integrated = vec![0.0; n];
|
||||
let mut sum = 0.0;
|
||||
|
||||
for i in 0..n {
|
||||
sum += squared[i];
|
||||
if i >= win_len {
|
||||
sum -= squared[i - win_len];
|
||||
}
|
||||
integrated[i] = sum / win_len.min(i + 1) as f64;
|
||||
}
|
||||
|
||||
// Threshold: mean + 0.5*std (tuned for cardiac artifacts which are periodic)
|
||||
let mean = integrated.iter().sum::<f64>() / n as f64;
|
||||
let variance = integrated
|
||||
.iter()
|
||||
.map(|x| (x - mean).powi(2))
|
||||
.sum::<f64>()
|
||||
/ n as f64;
|
||||
let std_dev = variance.sqrt();
|
||||
let threshold = mean + 0.5 * std_dev;
|
||||
|
||||
// Find peaks above threshold with refractory period (200ms)
|
||||
let refractory = (sample_rate * 0.2) as usize;
|
||||
let mut peaks = Vec::new();
|
||||
let mut last_peak: Option<usize> = None;
|
||||
|
||||
for i in 1..(n - 1) {
|
||||
if integrated[i] > threshold
|
||||
&& integrated[i] > integrated[i - 1]
|
||||
&& integrated[i] >= integrated[i + 1]
|
||||
{
|
||||
if let Some(lp) = last_peak {
|
||||
if i - lp < refractory {
|
||||
continue;
|
||||
}
|
||||
}
|
||||
peaks.push(i);
|
||||
last_peak = Some(i);
|
||||
}
|
||||
}
|
||||
|
||||
peaks
|
||||
}
|
||||
|
||||
fn std_dev(signal: &[f64]) -> f64 {
|
||||
let n = signal.len() as f64;
|
||||
if n <= 1.0 { return 0.0; }
|
||||
let mean = signal.iter().sum::<f64>() / n;
|
||||
let var = signal.iter().map(|x| (x - mean).powi(2)).sum::<f64>() / (n - 1.0);
|
||||
var.sqrt()
|
||||
/// Remove artifacts from multi-channel data by linear interpolation.
|
||||
///
|
||||
/// For each artifact range, replaces the data with a linear interpolation
|
||||
/// between the sample before the range and the sample after the range.
|
||||
///
|
||||
/// # Arguments
|
||||
/// * `data` - Multi-channel time series
|
||||
/// * `artifact_ranges` - Sorted, non-overlapping (start, end) sample ranges
|
||||
///
|
||||
/// # Returns
|
||||
/// A new `MultiChannelTimeSeries` with artifacts interpolated out.
|
||||
pub fn reject_artifacts(
|
||||
data: &MultiChannelTimeSeries,
|
||||
artifact_ranges: &[(usize, usize)],
|
||||
) -> MultiChannelTimeSeries {
|
||||
let mut clean_data = data.data.clone();
|
||||
|
||||
for channel in &mut clean_data {
|
||||
let n = channel.len();
|
||||
for &(start, end) in artifact_ranges {
|
||||
let start = start.min(n);
|
||||
let end = end.min(n);
|
||||
if start >= end {
|
||||
continue;
|
||||
}
|
||||
|
||||
// Get boundary values for interpolation
|
||||
let val_before = if start > 0 { channel[start - 1] } else { 0.0 };
|
||||
let val_after = if end < n { channel[end] } else { 0.0 };
|
||||
let span = (end - start) as f64;
|
||||
|
||||
// Linear interpolation across the artifact
|
||||
// frac goes from 1/(span+1) to span/(span+1), excluding boundaries
|
||||
let intervals = span + 1.0;
|
||||
for i in start..end {
|
||||
let frac = (i - start + 1) as f64 / intervals;
|
||||
channel[i] = val_before * (1.0 - frac) + val_after * frac;
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
MultiChannelTimeSeries {
|
||||
data: clean_data,
|
||||
sample_rate_hz: data.sample_rate_hz,
|
||||
num_channels: data.num_channels,
|
||||
num_samples: data.num_samples,
|
||||
timestamp_start: data.timestamp_start,
|
||||
}
|
||||
}
|
||||
|
||||
/// Merge artifact ranges and add padding on each side.
|
||||
fn merge_ranges_with_padding(
|
||||
ranges: &[(usize, usize)],
|
||||
pad: usize,
|
||||
max_len: usize,
|
||||
) -> Vec<(usize, usize)> {
|
||||
if ranges.is_empty() {
|
||||
return Vec::new();
|
||||
}
|
||||
|
||||
// Pad each range
|
||||
let padded: Vec<(usize, usize)> = ranges
|
||||
.iter()
|
||||
.map(|&(s, e)| (s.saturating_sub(pad), (e + pad).min(max_len)))
|
||||
.collect();
|
||||
|
||||
// Merge overlapping ranges
|
||||
let mut merged = Vec::new();
|
||||
let (mut cur_start, mut cur_end) = padded[0];
|
||||
|
||||
for &(s, e) in &padded[1..] {
|
||||
if s <= cur_end {
|
||||
cur_end = cur_end.max(e);
|
||||
} else {
|
||||
merged.push((cur_start, cur_end));
|
||||
cur_start = s;
|
||||
cur_end = e;
|
||||
}
|
||||
}
|
||||
merged.push((cur_start, cur_end));
|
||||
|
||||
merged
|
||||
}
|
||||
|
||||
#[cfg(test)]
|
||||
mod tests {
|
||||
use super::*;
|
||||
use ruv_neural_core::signal::MultiChannelTimeSeries;
|
||||
|
||||
#[test]
|
||||
fn detect_eye_blinks_finds_large_deflections() {
|
||||
let sr = 1000.0;
|
||||
let n = 5000;
|
||||
// Create signal with a large slow deflection (simulated blink)
|
||||
let mut signal = vec![0.0; n];
|
||||
// Normal background: small random-like variation
|
||||
for i in 0..n {
|
||||
signal[i] = 0.01 * ((i as f64 * 0.1).sin());
|
||||
}
|
||||
// Insert a blink: large Gaussian-like bump at sample 2500
|
||||
for i in 2400..2600 {
|
||||
let t = (i as f64 - 2500.0) / 30.0;
|
||||
signal[i] += 5.0 * (-t * t / 2.0).exp();
|
||||
}
|
||||
|
||||
let blinks = detect_eye_blinks(&signal, sr);
|
||||
// Should detect at least one blink near sample 2500
|
||||
assert!(
|
||||
!blinks.is_empty(),
|
||||
"Should detect the simulated eye blink"
|
||||
);
|
||||
|
||||
// At least one range should overlap with 2400..2600
|
||||
let found = blinks.iter().any(|&(s, e)| s < 2600 && e > 2400);
|
||||
assert!(found, "Blink range should overlap with injected artifact");
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn reject_artifacts_interpolates_correctly() {
|
||||
let data = MultiChannelTimeSeries {
|
||||
data: vec![vec![1.0, 2.0, 100.0, 100.0, 5.0, 6.0]],
|
||||
sample_rate_hz: 1000.0,
|
||||
num_channels: 1,
|
||||
num_samples: 6,
|
||||
timestamp_start: 0.0,
|
||||
};
|
||||
|
||||
let cleaned = reject_artifacts(&data, &[(2, 4)]);
|
||||
|
||||
// Samples 2 and 3 should be linearly interpolated between 2.0 and 5.0
|
||||
assert!((cleaned.data[0][2] - 3.0).abs() < 0.01);
|
||||
assert!((cleaned.data[0][3] - 4.0).abs() < 0.01);
|
||||
|
||||
// Non-artifact samples should be unchanged
|
||||
assert!((cleaned.data[0][0] - 1.0).abs() < 1e-10);
|
||||
assert!((cleaned.data[0][4] - 5.0).abs() < 1e-10);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn detect_cardiac_finds_periodic_peaks() {
|
||||
let sr = 1000.0;
|
||||
let duration = 3.0;
|
||||
let n = (sr * duration) as usize;
|
||||
let mut signal = vec![0.0; n];
|
||||
|
||||
// Simulate cardiac artifact: periodic QRS-like spikes at ~1 Hz
|
||||
let heart_rate_hz = 1.0;
|
||||
let interval = (sr / heart_rate_hz) as usize;
|
||||
|
||||
for beat in 0..3 {
|
||||
let center = beat * interval + interval / 2;
|
||||
if center >= n {
|
||||
break;
|
||||
}
|
||||
// QRS complex: sharp spike ~10ms wide
|
||||
let half_width = (sr * 0.005) as usize;
|
||||
for i in center.saturating_sub(half_width)..(center + half_width).min(n) {
|
||||
let t = (i as f64 - center as f64) / (half_width as f64);
|
||||
signal[i] = 10.0 * (-t * t * 5.0).exp();
|
||||
}
|
||||
}
|
||||
|
||||
let peaks = detect_cardiac(&signal, sr);
|
||||
|
||||
// Should find roughly 3 peaks
|
||||
assert!(
|
||||
peaks.len() >= 1,
|
||||
"Should detect at least one cardiac peak, found {}",
|
||||
peaks.len()
|
||||
);
|
||||
}
|
||||
}
|
||||
|
||||
+374
-75
@@ -1,111 +1,282 @@
|
||||
//! Connectivity metrics between neural signal channels.
|
||||
//! Cross-channel coupling and connectivity metrics.
|
||||
//!
|
||||
//! Provides PLV, coherence, imaginary coherence, and amplitude envelope correlation.
|
||||
//! Provides measures of functional connectivity between neural signals:
|
||||
//! - Phase Locking Value (PLV)
|
||||
//! - Magnitude-squared coherence
|
||||
//! - Imaginary coherence (robust to volume conduction)
|
||||
//! - Amplitude envelope correlation
|
||||
//! - Full connectivity matrix computation
|
||||
|
||||
use crate::hilbert::{hilbert_transform, instantaneous_amplitude};
|
||||
use num_complex::Complex;
|
||||
use ruv_neural_core::signal::{FrequencyBand, MultiChannelTimeSeries};
|
||||
use rustfft::FftPlanner;
|
||||
use serde::{Deserialize, Serialize};
|
||||
use std::f64::consts::PI;
|
||||
|
||||
/// Compute the Phase Locking Value between two signals.
|
||||
use crate::filter::BandpassFilter;
|
||||
use crate::hilbert::hilbert_transform;
|
||||
|
||||
/// Type of connectivity metric to compute.
|
||||
#[derive(Debug, Clone, Copy, PartialEq, Eq, Serialize, Deserialize)]
|
||||
pub enum ConnectivityMetric {
|
||||
/// Phase Locking Value.
|
||||
Plv,
|
||||
/// Amplitude envelope correlation.
|
||||
Aec,
|
||||
}
|
||||
|
||||
/// Compute the Phase Locking Value (PLV) between two signals.
|
||||
///
|
||||
/// PLV measures the consistency of phase difference between two signals.
|
||||
/// Returns a value in [0, 1] where 1 = perfectly phase-locked.
|
||||
pub fn phase_locking_value(signal_a: &[f64], signal_b: &[f64]) -> f64 {
|
||||
/// PLV = |mean(exp(j * (phase_a - phase_b)))|
|
||||
///
|
||||
/// The signals are first bandpass-filtered to the specified frequency band,
|
||||
/// then the Hilbert transform extracts instantaneous phase.
|
||||
///
|
||||
/// PLV = 1.0 indicates perfect phase synchrony;
|
||||
/// PLV ~ 0.0 indicates no consistent phase relationship.
|
||||
///
|
||||
/// # Arguments
|
||||
/// * `signal_a` - First channel time series
|
||||
/// * `signal_b` - Second channel time series
|
||||
/// * `sample_rate` - Sampling rate in Hz
|
||||
/// * `band` - Frequency band for phase extraction
|
||||
pub fn phase_locking_value(
|
||||
signal_a: &[f64],
|
||||
signal_b: &[f64],
|
||||
sample_rate: f64,
|
||||
band: FrequencyBand,
|
||||
) -> f64 {
|
||||
let n = signal_a.len().min(signal_b.len());
|
||||
if n == 0 {
|
||||
if n < 4 {
|
||||
return 0.0;
|
||||
}
|
||||
let analytic_a = hilbert_transform(signal_a);
|
||||
let analytic_b = hilbert_transform(signal_b);
|
||||
|
||||
let sum: Complex<f64> = analytic_a[..n]
|
||||
.iter()
|
||||
.zip(analytic_b[..n].iter())
|
||||
.map(|(a, b)| {
|
||||
let phase_diff = (a / a.norm()) * (b / b.norm()).conj();
|
||||
if phase_diff.norm() > 0.0 {
|
||||
phase_diff / phase_diff.norm()
|
||||
} else {
|
||||
Complex::new(0.0, 0.0)
|
||||
}
|
||||
})
|
||||
.fold(Complex::new(0.0, 0.0), |acc, x| acc + x);
|
||||
let (low, high) = band.range_hz();
|
||||
let bp = BandpassFilter::new(2, low, high, sample_rate);
|
||||
|
||||
let filtered_a = bp.apply(&signal_a[..n]);
|
||||
let filtered_b = bp.apply(&signal_b[..n]);
|
||||
|
||||
let analytic_a = hilbert_transform(&filtered_a);
|
||||
let analytic_b = hilbert_transform(&filtered_b);
|
||||
|
||||
// Compute mean of exp(j*(phase_a - phase_b))
|
||||
let mut sum = Complex::new(0.0, 0.0);
|
||||
for i in 0..n {
|
||||
let phase_a = analytic_a[i].im.atan2(analytic_a[i].re);
|
||||
let phase_b = analytic_b[i].im.atan2(analytic_b[i].re);
|
||||
let diff = phase_a - phase_b;
|
||||
sum += Complex::new(diff.cos(), diff.sin());
|
||||
}
|
||||
|
||||
(sum / n as f64).norm()
|
||||
}
|
||||
|
||||
/// Compute magnitude-squared coherence between two signals.
|
||||
pub fn coherence(signal_a: &[f64], signal_b: &[f64]) -> f64 {
|
||||
///
|
||||
/// Coh(f) = |S_ab(f)|^2 / (S_aa(f) * S_bb(f))
|
||||
///
|
||||
/// Uses Welch's method with overlapping segments and Hann window.
|
||||
///
|
||||
/// # Returns
|
||||
/// Vector of (frequency, coherence) pairs.
|
||||
pub fn coherence(
|
||||
signal_a: &[f64],
|
||||
signal_b: &[f64],
|
||||
sample_rate: f64,
|
||||
) -> Vec<(f64, f64)> {
|
||||
let n = signal_a.len().min(signal_b.len());
|
||||
if n == 0 {
|
||||
return 0.0;
|
||||
return Vec::new();
|
||||
}
|
||||
// Simplified: correlation of instantaneous amplitudes
|
||||
let amp_a = instantaneous_amplitude(signal_a);
|
||||
let amp_b = instantaneous_amplitude(signal_b);
|
||||
pearson_correlation(&_a[..n], &_b[..n]).abs()
|
||||
|
||||
let window_size = 256.min(n);
|
||||
let overlap = window_size / 2;
|
||||
let hop = window_size - overlap;
|
||||
|
||||
let window = hann_window(window_size);
|
||||
let num_freqs = window_size / 2 + 1;
|
||||
|
||||
let mut planner = FftPlanner::new();
|
||||
let fft = planner.plan_fft_forward(window_size);
|
||||
|
||||
let mut saa = vec![0.0; num_freqs];
|
||||
let mut sbb = vec![0.0; num_freqs];
|
||||
let mut sab = vec![Complex::new(0.0, 0.0); num_freqs];
|
||||
let mut num_segments = 0;
|
||||
|
||||
let mut start = 0;
|
||||
while start + window_size <= n {
|
||||
let mut fa: Vec<Complex<f64>> = (0..window_size)
|
||||
.map(|i| Complex::new(signal_a[start + i] * window[i], 0.0))
|
||||
.collect();
|
||||
let mut fb: Vec<Complex<f64>> = (0..window_size)
|
||||
.map(|i| Complex::new(signal_b[start + i] * window[i], 0.0))
|
||||
.collect();
|
||||
|
||||
fft.process(&mut fa);
|
||||
fft.process(&mut fb);
|
||||
|
||||
for k in 0..num_freqs {
|
||||
saa[k] += fa[k].norm_sqr();
|
||||
sbb[k] += fb[k].norm_sqr();
|
||||
sab[k] += fa[k] * fb[k].conj();
|
||||
}
|
||||
num_segments += 1;
|
||||
start += hop;
|
||||
}
|
||||
|
||||
if num_segments == 0 {
|
||||
return Vec::new();
|
||||
}
|
||||
|
||||
let freq_res = sample_rate / window_size as f64;
|
||||
(0..num_freqs)
|
||||
.map(|k| {
|
||||
let freq = k as f64 * freq_res;
|
||||
let denom = saa[k] * sbb[k];
|
||||
let coh = if denom > 1e-30 {
|
||||
sab[k].norm_sqr() / denom
|
||||
} else {
|
||||
0.0
|
||||
};
|
||||
(freq, coh.min(1.0))
|
||||
})
|
||||
.collect()
|
||||
}
|
||||
|
||||
/// Compute imaginary coherence (volume-conduction robust).
|
||||
pub fn imaginary_coherence(signal_a: &[f64], signal_b: &[f64]) -> f64 {
|
||||
/// Compute imaginary coherence between two signals.
|
||||
///
|
||||
/// ImCoh(f) = Im(S_ab(f)) / sqrt(S_aa(f) * S_bb(f))
|
||||
///
|
||||
/// The imaginary part of coherence is robust to volume conduction
|
||||
/// artifacts, which produce zero-lag (purely real) correlations.
|
||||
///
|
||||
/// # Returns
|
||||
/// Vector of (frequency, imaginary_coherence) pairs.
|
||||
pub fn imaginary_coherence(
|
||||
signal_a: &[f64],
|
||||
signal_b: &[f64],
|
||||
sample_rate: f64,
|
||||
) -> Vec<(f64, f64)> {
|
||||
let n = signal_a.len().min(signal_b.len());
|
||||
if n == 0 {
|
||||
return 0.0;
|
||||
return Vec::new();
|
||||
}
|
||||
let analytic_a = hilbert_transform(signal_a);
|
||||
let analytic_b = hilbert_transform(signal_b);
|
||||
|
||||
let cross_spec: Complex<f64> = analytic_a[..n]
|
||||
.iter()
|
||||
.zip(analytic_b[..n].iter())
|
||||
.map(|(a, b)| a * b.conj())
|
||||
.fold(Complex::new(0.0, 0.0), |acc, x| acc + x);
|
||||
let window_size = 256.min(n);
|
||||
let overlap = window_size / 2;
|
||||
let hop = window_size - overlap;
|
||||
|
||||
let psd_a: f64 = analytic_a[..n].iter().map(|a| a.norm_sqr()).sum();
|
||||
let psd_b: f64 = analytic_b[..n].iter().map(|b| b.norm_sqr()).sum();
|
||||
let denom = (psd_a * psd_b).sqrt();
|
||||
if denom == 0.0 {
|
||||
return 0.0;
|
||||
let window = hann_window(window_size);
|
||||
let num_freqs = window_size / 2 + 1;
|
||||
|
||||
let mut planner = FftPlanner::new();
|
||||
let fft = planner.plan_fft_forward(window_size);
|
||||
|
||||
let mut saa = vec![0.0; num_freqs];
|
||||
let mut sbb = vec![0.0; num_freqs];
|
||||
let mut sab = vec![Complex::new(0.0, 0.0); num_freqs];
|
||||
let mut num_segments = 0;
|
||||
|
||||
let mut start = 0;
|
||||
while start + window_size <= n {
|
||||
let mut fa: Vec<Complex<f64>> = (0..window_size)
|
||||
.map(|i| Complex::new(signal_a[start + i] * window[i], 0.0))
|
||||
.collect();
|
||||
let mut fb: Vec<Complex<f64>> = (0..window_size)
|
||||
.map(|i| Complex::new(signal_b[start + i] * window[i], 0.0))
|
||||
.collect();
|
||||
|
||||
fft.process(&mut fa);
|
||||
fft.process(&mut fb);
|
||||
|
||||
for k in 0..num_freqs {
|
||||
saa[k] += fa[k].norm_sqr();
|
||||
sbb[k] += fb[k].norm_sqr();
|
||||
sab[k] += fa[k] * fb[k].conj();
|
||||
}
|
||||
num_segments += 1;
|
||||
start += hop;
|
||||
}
|
||||
(cross_spec.im / denom).abs()
|
||||
|
||||
if num_segments == 0 {
|
||||
return Vec::new();
|
||||
}
|
||||
|
||||
let freq_res = sample_rate / window_size as f64;
|
||||
(0..num_freqs)
|
||||
.map(|k| {
|
||||
let freq = k as f64 * freq_res;
|
||||
let denom = (saa[k] * sbb[k]).sqrt();
|
||||
let im_coh = if denom > 1e-30 {
|
||||
sab[k].im / denom
|
||||
} else {
|
||||
0.0
|
||||
};
|
||||
(freq, im_coh)
|
||||
})
|
||||
.collect()
|
||||
}
|
||||
|
||||
/// Compute amplitude envelope correlation between two signals.
|
||||
pub fn amplitude_envelope_correlation(signal_a: &[f64], signal_b: &[f64]) -> f64 {
|
||||
///
|
||||
/// 1. Bandpass filter both signals to the specified frequency band
|
||||
/// 2. Extract amplitude envelopes via Hilbert transform
|
||||
/// 3. Compute Pearson correlation of the envelopes
|
||||
///
|
||||
/// # Returns
|
||||
/// Correlation coefficient in [-1, 1].
|
||||
pub fn amplitude_envelope_correlation(
|
||||
signal_a: &[f64],
|
||||
signal_b: &[f64],
|
||||
sample_rate: f64,
|
||||
band: FrequencyBand,
|
||||
) -> f64 {
|
||||
let n = signal_a.len().min(signal_b.len());
|
||||
if n == 0 {
|
||||
if n < 4 {
|
||||
return 0.0;
|
||||
}
|
||||
let env_a = instantaneous_amplitude(signal_a);
|
||||
let env_b = instantaneous_amplitude(signal_b);
|
||||
pearson_correlation(&env_a[..n], &env_b[..n])
|
||||
|
||||
let (low, high) = band.range_hz();
|
||||
let bp = BandpassFilter::new(2, low, high, sample_rate);
|
||||
|
||||
let filtered_a = bp.apply(&signal_a[..n]);
|
||||
let filtered_b = bp.apply(&signal_b[..n]);
|
||||
|
||||
let env_a = crate::hilbert::instantaneous_amplitude(&filtered_a);
|
||||
let env_b = crate::hilbert::instantaneous_amplitude(&filtered_b);
|
||||
|
||||
pearson_correlation(&env_a, &env_b)
|
||||
}
|
||||
|
||||
/// Compute all-pairs connectivity for a set of channels.
|
||||
/// Compute a full connectivity matrix for all channel pairs.
|
||||
///
|
||||
/// Returns a symmetric matrix `result[i][j]` with connectivity between channel i and j.
|
||||
/// # Arguments
|
||||
/// * `data` - Multi-channel time series
|
||||
/// * `metric` - Which connectivity metric to use
|
||||
/// * `band` - Frequency band (for PLV and AEC)
|
||||
///
|
||||
/// # Returns
|
||||
/// NxN matrix where entry [i][j] is the connectivity between channels i and j.
|
||||
pub fn compute_all_pairs(
|
||||
channels: &[Vec<f64>],
|
||||
metric: &crate::ConnectivityMetric,
|
||||
data: &MultiChannelTimeSeries,
|
||||
metric: ConnectivityMetric,
|
||||
band: FrequencyBand,
|
||||
) -> Vec<Vec<f64>> {
|
||||
let n = channels.len();
|
||||
let mut matrix = vec![vec![0.0; n]; n];
|
||||
let nc = data.num_channels;
|
||||
let sr = data.sample_rate_hz;
|
||||
let mut matrix = vec![vec![0.0; nc]; nc];
|
||||
|
||||
for i in 0..n {
|
||||
matrix[i][i] = 1.0; // Self-connectivity = 1
|
||||
for j in (i + 1)..n {
|
||||
for i in 0..nc {
|
||||
matrix[i][i] = 1.0; // Self-connectivity is 1.0
|
||||
for j in (i + 1)..nc {
|
||||
let val = match metric {
|
||||
crate::ConnectivityMetric::Plv => {
|
||||
phase_locking_value(&channels[i], &channels[j])
|
||||
ConnectivityMetric::Plv => {
|
||||
phase_locking_value(&data.data[i], &data.data[j], sr, band)
|
||||
}
|
||||
crate::ConnectivityMetric::Coherence => {
|
||||
coherence(&channels[i], &channels[j])
|
||||
}
|
||||
crate::ConnectivityMetric::ImaginaryCoherence => {
|
||||
imaginary_coherence(&channels[i], &channels[j])
|
||||
}
|
||||
crate::ConnectivityMetric::AmplitudeEnvelopeCorrelation => {
|
||||
amplitude_envelope_correlation(&channels[i], &channels[j])
|
||||
ConnectivityMetric::Aec => {
|
||||
amplitude_envelope_correlation(&data.data[i], &data.data[j], sr, band)
|
||||
}
|
||||
};
|
||||
matrix[i][j] = val;
|
||||
@@ -116,28 +287,156 @@ pub fn compute_all_pairs(
|
||||
matrix
|
||||
}
|
||||
|
||||
/// Pearson correlation coefficient between two slices.
|
||||
/// Pearson correlation coefficient between two vectors.
|
||||
fn pearson_correlation(a: &[f64], b: &[f64]) -> f64 {
|
||||
let n = a.len().min(b.len()) as f64;
|
||||
if n <= 1.0 {
|
||||
let n = a.len().min(b.len());
|
||||
if n == 0 {
|
||||
return 0.0;
|
||||
}
|
||||
let mean_a = a.iter().sum::<f64>() / n;
|
||||
let mean_b = b.iter().sum::<f64>() / n;
|
||||
|
||||
let mean_a = a[..n].iter().sum::<f64>() / n as f64;
|
||||
let mean_b = b[..n].iter().sum::<f64>() / n as f64;
|
||||
|
||||
let mut cov = 0.0;
|
||||
let mut var_a = 0.0;
|
||||
let mut var_b = 0.0;
|
||||
for i in 0..n as usize {
|
||||
|
||||
for i in 0..n {
|
||||
let da = a[i] - mean_a;
|
||||
let db = b[i] - mean_b;
|
||||
cov += da * db;
|
||||
var_a += da * da;
|
||||
var_b += db * db;
|
||||
}
|
||||
|
||||
let denom = (var_a * var_b).sqrt();
|
||||
if denom == 0.0 {
|
||||
if denom < 1e-30 {
|
||||
0.0
|
||||
} else {
|
||||
cov / denom
|
||||
}
|
||||
}
|
||||
|
||||
/// Generate a Hann window (local copy for this module).
|
||||
fn hann_window(length: usize) -> Vec<f64> {
|
||||
(0..length)
|
||||
.map(|i| 0.5 * (1.0 - (2.0 * PI * i as f64 / (length - 1).max(1) as f64).cos()))
|
||||
.collect()
|
||||
}
|
||||
|
||||
#[cfg(test)]
|
||||
mod tests {
|
||||
use super::*;
|
||||
use approx::assert_abs_diff_eq;
|
||||
use std::f64::consts::PI;
|
||||
|
||||
#[test]
|
||||
fn plv_of_identical_signals_is_one() {
|
||||
let sr = 1000.0;
|
||||
let n = 2000;
|
||||
let signal: Vec<f64> = (0..n)
|
||||
.map(|i| {
|
||||
let t = i as f64 / sr;
|
||||
(2.0 * PI * 10.0 * t).sin()
|
||||
})
|
||||
.collect();
|
||||
|
||||
let plv = phase_locking_value(&signal, &signal, sr, FrequencyBand::Alpha);
|
||||
|
||||
assert!(
|
||||
plv > 0.9,
|
||||
"PLV of identical signals should be ~1.0, got {plv}"
|
||||
);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn plv_of_unrelated_signals_is_low() {
|
||||
let sr = 1000.0;
|
||||
let n = 4000;
|
||||
// Two signals at different frequencies
|
||||
let signal_a: Vec<f64> = (0..n)
|
||||
.map(|i| {
|
||||
let t = i as f64 / sr;
|
||||
(2.0 * PI * 10.0 * t).sin()
|
||||
})
|
||||
.collect();
|
||||
let signal_b: Vec<f64> = (0..n)
|
||||
.map(|i| {
|
||||
let t = i as f64 / sr;
|
||||
(2.0 * PI * 11.3 * t).sin() + 0.5 * (2.0 * PI * 9.7 * t).cos()
|
||||
})
|
||||
.collect();
|
||||
|
||||
let plv = phase_locking_value(&signal_a, &signal_b, sr, FrequencyBand::Alpha);
|
||||
|
||||
assert!(
|
||||
plv < 0.7,
|
||||
"PLV of unrelated signals should be low, got {plv}"
|
||||
);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn coherence_of_identical_signals_is_one() {
|
||||
let sr = 1000.0;
|
||||
let n = 2000;
|
||||
let signal: Vec<f64> = (0..n)
|
||||
.map(|i| {
|
||||
let t = i as f64 / sr;
|
||||
(2.0 * PI * 20.0 * t).sin()
|
||||
})
|
||||
.collect();
|
||||
|
||||
let coh = coherence(&signal, &signal, sr);
|
||||
|
||||
// At the signal frequency (~20 Hz), coherence should be ~1.0
|
||||
let peak_coh = coh
|
||||
.iter()
|
||||
.filter(|(f, _)| *f > 15.0 && *f < 25.0)
|
||||
.map(|(_, c)| *c)
|
||||
.max_by(|a, b| a.partial_cmp(b).unwrap())
|
||||
.unwrap_or(0.0);
|
||||
|
||||
assert!(
|
||||
peak_coh > 0.95,
|
||||
"Coherence of identical signals should be ~1.0 at signal freq, got {peak_coh}"
|
||||
);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn compute_all_pairs_returns_symmetric_matrix() {
|
||||
let data = MultiChannelTimeSeries {
|
||||
data: vec![
|
||||
(0..1000)
|
||||
.map(|i| (2.0 * PI * 10.0 * i as f64 / 1000.0).sin())
|
||||
.collect(),
|
||||
(0..1000)
|
||||
.map(|i| (2.0 * PI * 10.0 * i as f64 / 1000.0).cos())
|
||||
.collect(),
|
||||
(0..1000)
|
||||
.map(|i| (2.0 * PI * 10.0 * i as f64 / 1000.0 + 0.3).sin())
|
||||
.collect(),
|
||||
],
|
||||
sample_rate_hz: 1000.0,
|
||||
num_channels: 3,
|
||||
num_samples: 1000,
|
||||
timestamp_start: 0.0,
|
||||
};
|
||||
|
||||
let matrix = compute_all_pairs(&data, ConnectivityMetric::Plv, FrequencyBand::Alpha);
|
||||
|
||||
assert_eq!(matrix.len(), 3);
|
||||
assert_eq!(matrix[0].len(), 3);
|
||||
|
||||
// Diagonal should be 1.0
|
||||
for i in 0..3 {
|
||||
assert_abs_diff_eq!(matrix[i][i], 1.0, epsilon = 1e-10);
|
||||
}
|
||||
|
||||
// Should be symmetric
|
||||
for i in 0..3 {
|
||||
for j in 0..3 {
|
||||
assert_abs_diff_eq!(matrix[i][j], matrix[j][i], epsilon = 1e-10);
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
@@ -1,134 +1,511 @@
|
||||
//! Digital filters for neural signal processing.
|
||||
//!
|
||||
//! Provides Butterworth IIR filters: bandpass, highpass, lowpass, and notch.
|
||||
//! Implements Butterworth IIR filters in second-order sections (SOS) form
|
||||
//! for numerical stability. Supports bandpass, notch (band-reject),
|
||||
//! highpass, and lowpass configurations.
|
||||
//!
|
||||
//! All filters implement the [`SignalProcessor`] trait for uniform usage.
|
||||
|
||||
use crate::SignalProcessor;
|
||||
use serde::{Deserialize, Serialize};
|
||||
use std::f64::consts::PI;
|
||||
|
||||
/// Butterworth bandpass filter.
|
||||
#[derive(Debug, Clone)]
|
||||
/// Trait for signal processing operations.
|
||||
pub trait SignalProcessor {
|
||||
/// Apply the processor to a signal, returning the filtered output.
|
||||
fn process(&self, signal: &[f64]) -> Vec<f64>;
|
||||
}
|
||||
|
||||
/// A single second-order section (biquad) with coefficients.
|
||||
///
|
||||
/// Transfer function: H(z) = (b0 + b1*z^-1 + b2*z^-2) / (1 + a1*z^-1 + a2*z^-2)
|
||||
#[derive(Debug, Clone, Serialize, Deserialize)]
|
||||
pub struct SecondOrderSection {
|
||||
pub b0: f64,
|
||||
pub b1: f64,
|
||||
pub b2: f64,
|
||||
pub a1: f64,
|
||||
pub a2: f64,
|
||||
}
|
||||
|
||||
impl SecondOrderSection {
|
||||
/// Apply this biquad section to a signal using Direct Form II Transposed.
|
||||
fn apply(&self, signal: &[f64]) -> Vec<f64> {
|
||||
let n = signal.len();
|
||||
let mut output = vec![0.0; n];
|
||||
let mut w1 = 0.0;
|
||||
let mut w2 = 0.0;
|
||||
|
||||
for i in 0..n {
|
||||
let x = signal[i];
|
||||
let y = self.b0 * x + w1;
|
||||
w1 = self.b1 * x - self.a1 * y + w2;
|
||||
w2 = self.b2 * x - self.a2 * y;
|
||||
output[i] = y;
|
||||
}
|
||||
|
||||
output
|
||||
}
|
||||
}
|
||||
|
||||
/// Apply a cascade of second-order sections to a signal (forward-backward
|
||||
/// for zero-phase filtering).
|
||||
fn apply_sos_filtfilt(sections: &[SecondOrderSection], signal: &[f64]) -> Vec<f64> {
|
||||
if signal.is_empty() {
|
||||
return Vec::new();
|
||||
}
|
||||
|
||||
// Forward pass through all sections
|
||||
let mut result = signal.to_vec();
|
||||
for sos in sections {
|
||||
result = sos.apply(&result);
|
||||
}
|
||||
|
||||
// Reverse
|
||||
result.reverse();
|
||||
|
||||
// Backward pass through all sections
|
||||
for sos in sections {
|
||||
result = sos.apply(&result);
|
||||
}
|
||||
|
||||
// Reverse back to original order
|
||||
result.reverse();
|
||||
|
||||
result
|
||||
}
|
||||
|
||||
/// Design Butterworth analog prototype poles for a given order.
|
||||
/// Returns poles on the unit circle in the left half of the s-plane.
|
||||
fn butterworth_poles(order: usize) -> Vec<(f64, f64)> {
|
||||
let mut poles = Vec::new();
|
||||
for k in 0..order {
|
||||
let theta = PI * (2 * k + order + 1) as f64 / (2 * order) as f64;
|
||||
poles.push((theta.cos(), theta.sin()));
|
||||
}
|
||||
poles
|
||||
}
|
||||
|
||||
/// Prewarp a frequency from digital to analog domain.
|
||||
fn prewarp(freq_hz: f64, sample_rate: f64) -> f64 {
|
||||
2.0 * sample_rate * (PI * freq_hz / sample_rate).tan()
|
||||
}
|
||||
|
||||
/// Design a lowpass second-order section from analog prototype poles
|
||||
/// using the bilinear transform.
|
||||
fn design_lowpass_sos(pole_re: f64, pole_im: f64, wc: f64, fs: f64) -> SecondOrderSection {
|
||||
let t = 1.0 / (2.0 * fs);
|
||||
|
||||
if pole_im.abs() < 1e-14 {
|
||||
// Real pole -> embed in SOS with b2=0, a2=0
|
||||
let s_re = wc * pole_re;
|
||||
let d = 1.0 - s_re * t;
|
||||
let n = -(s_re * t);
|
||||
SecondOrderSection {
|
||||
b0: n / d,
|
||||
b1: n / d,
|
||||
b2: 0.0,
|
||||
a1: -(1.0 + s_re * t) / d,
|
||||
a2: 0.0,
|
||||
}
|
||||
} else {
|
||||
// Complex conjugate pair
|
||||
let s_re = wc * pole_re;
|
||||
let s_im = wc * pole_im;
|
||||
let denom = (1.0 - s_re * t).powi(2) + (s_im * t).powi(2);
|
||||
let a1 = 2.0 * ((s_re * t).powi(2) + (s_im * t).powi(2) - 1.0) / denom;
|
||||
let a2 = ((1.0 + s_re * t).powi(2) + (s_im * t).powi(2)) / denom;
|
||||
let num_gain = (wc * t).powi(2) / denom;
|
||||
SecondOrderSection {
|
||||
b0: num_gain,
|
||||
b1: 2.0 * num_gain,
|
||||
b2: num_gain,
|
||||
a1,
|
||||
a2,
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
/// Design a highpass second-order section from analog prototype poles.
|
||||
fn design_highpass_sos(pole_re: f64, pole_im: f64, wc: f64, fs: f64) -> SecondOrderSection {
|
||||
let t = 1.0 / (2.0 * fs);
|
||||
|
||||
if pole_im.abs() < 1e-14 {
|
||||
// Real pole
|
||||
let alpha = wc / (-pole_re);
|
||||
let d = 1.0 + alpha * t;
|
||||
SecondOrderSection {
|
||||
b0: 1.0 / d,
|
||||
b1: -1.0 / d,
|
||||
b2: 0.0,
|
||||
a1: -(1.0 - alpha * t) / d,
|
||||
a2: 0.0,
|
||||
}
|
||||
} else {
|
||||
// Complex conjugate pair: HP transform s -> wc/s
|
||||
let mag_sq = pole_re.powi(2) + pole_im.powi(2);
|
||||
let hp_re = wc * pole_re / mag_sq;
|
||||
let hp_im = -wc * pole_im / mag_sq;
|
||||
|
||||
let denom = (1.0 - hp_re * t).powi(2) + (hp_im * t).powi(2);
|
||||
let a1 = 2.0 * ((hp_re * t).powi(2) + (hp_im * t).powi(2) - 1.0) / denom;
|
||||
let a2 = ((1.0 + hp_re * t).powi(2) + (hp_im * t).powi(2)) / denom;
|
||||
let num_gain = 1.0 / denom;
|
||||
SecondOrderSection {
|
||||
b0: num_gain,
|
||||
b1: -2.0 * num_gain,
|
||||
b2: num_gain,
|
||||
a1,
|
||||
a2,
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
/// Design Butterworth lowpass filter as cascade of second-order sections.
|
||||
fn design_butterworth_lowpass(order: usize, cutoff_hz: f64, sample_rate: f64) -> Vec<SecondOrderSection> {
|
||||
let wc = prewarp(cutoff_hz, sample_rate);
|
||||
let poles = butterworth_poles(order);
|
||||
let mut sections = Vec::new();
|
||||
|
||||
let mut i = 0;
|
||||
while i < poles.len() {
|
||||
if poles[i].1.abs() < 1e-14 {
|
||||
sections.push(design_lowpass_sos(poles[i].0, 0.0, wc, sample_rate));
|
||||
i += 1;
|
||||
} else {
|
||||
sections.push(design_lowpass_sos(poles[i].0, poles[i].1, wc, sample_rate));
|
||||
i += 2;
|
||||
}
|
||||
}
|
||||
|
||||
sections
|
||||
}
|
||||
|
||||
/// Design Butterworth highpass filter as cascade of second-order sections.
|
||||
fn design_butterworth_highpass(order: usize, cutoff_hz: f64, sample_rate: f64) -> Vec<SecondOrderSection> {
|
||||
let wc = prewarp(cutoff_hz, sample_rate);
|
||||
let poles = butterworth_poles(order);
|
||||
let mut sections = Vec::new();
|
||||
|
||||
let mut i = 0;
|
||||
while i < poles.len() {
|
||||
if poles[i].1.abs() < 1e-14 {
|
||||
sections.push(design_highpass_sos(poles[i].0, 0.0, wc, sample_rate));
|
||||
i += 1;
|
||||
} else {
|
||||
sections.push(design_highpass_sos(poles[i].0, poles[i].1, wc, sample_rate));
|
||||
i += 2;
|
||||
}
|
||||
}
|
||||
|
||||
sections
|
||||
}
|
||||
|
||||
/// Butterworth IIR bandpass filter using cascaded second-order sections.
|
||||
///
|
||||
/// Applies a zero-phase (forward-backward) filter for no phase distortion.
|
||||
#[derive(Debug, Clone, Serialize, Deserialize)]
|
||||
pub struct BandpassFilter {
|
||||
low_hz: f64,
|
||||
high_hz: f64,
|
||||
order: usize,
|
||||
sample_rate_hz: f64,
|
||||
/// Filter order (per lowpass/highpass stage).
|
||||
pub order: usize,
|
||||
/// Lower cutoff frequency in Hz.
|
||||
pub low_hz: f64,
|
||||
/// Upper cutoff frequency in Hz.
|
||||
pub high_hz: f64,
|
||||
/// Sampling rate in Hz.
|
||||
pub sample_rate: f64,
|
||||
/// Highpass SOS sections (for low_hz cutoff).
|
||||
hp_sections: Vec<SecondOrderSection>,
|
||||
/// Lowpass SOS sections (for high_hz cutoff).
|
||||
lp_sections: Vec<SecondOrderSection>,
|
||||
}
|
||||
|
||||
impl BandpassFilter {
|
||||
/// Create a new bandpass filter.
|
||||
pub fn new(low_hz: f64, high_hz: f64, order: usize, sample_rate_hz: f64) -> Self {
|
||||
Self { low_hz, high_hz, order, sample_rate_hz }
|
||||
/// Create a new Butterworth bandpass filter.
|
||||
///
|
||||
/// # Arguments
|
||||
/// * `order` - Filter order (typically 2-6)
|
||||
/// * `low_hz` - Lower cutoff frequency in Hz
|
||||
/// * `high_hz` - Upper cutoff frequency in Hz
|
||||
/// * `sample_rate` - Sampling rate in Hz
|
||||
pub fn new(order: usize, low_hz: f64, high_hz: f64, sample_rate: f64) -> Self {
|
||||
let hp_sections = design_butterworth_highpass(order, low_hz, sample_rate);
|
||||
let lp_sections = design_butterworth_lowpass(order, high_hz, sample_rate);
|
||||
Self {
|
||||
order,
|
||||
low_hz,
|
||||
high_hz,
|
||||
sample_rate,
|
||||
hp_sections,
|
||||
lp_sections,
|
||||
}
|
||||
}
|
||||
|
||||
/// Apply the bandpass filter to a signal.
|
||||
pub fn apply(&self, signal: &[f64]) -> Vec<f64> {
|
||||
let hp_out = apply_sos_filtfilt(&self.hp_sections, signal);
|
||||
apply_sos_filtfilt(&self.lp_sections, &hp_out)
|
||||
}
|
||||
}
|
||||
|
||||
impl SignalProcessor for BandpassFilter {
|
||||
fn apply(&self, signal: &[f64]) -> Vec<f64> {
|
||||
// Simple moving-average approximation for compilation.
|
||||
// A production implementation would use proper Butterworth coefficients.
|
||||
let n = signal.len();
|
||||
if n < 3 {
|
||||
return signal.to_vec();
|
||||
}
|
||||
let mut out = vec![0.0; n];
|
||||
// Remove DC (highpass effect) then smooth (lowpass effect)
|
||||
let mean: f64 = signal.iter().sum::<f64>() / n as f64;
|
||||
let dc_removed: Vec<f64> = signal.iter().map(|x| x - mean).collect();
|
||||
// Simple smoothing kernel
|
||||
let kernel_size = (self.sample_rate_hz / self.high_hz).max(1.0).min(n as f64 / 2.0) as usize;
|
||||
let kernel_size = kernel_size.max(1);
|
||||
for i in 0..n {
|
||||
let start = i.saturating_sub(kernel_size / 2);
|
||||
let end = (i + kernel_size / 2 + 1).min(n);
|
||||
let sum: f64 = dc_removed[start..end].iter().sum();
|
||||
out[i] = sum / (end - start) as f64;
|
||||
}
|
||||
out
|
||||
}
|
||||
|
||||
fn name(&self) -> &str {
|
||||
"BandpassFilter"
|
||||
fn process(&self, signal: &[f64]) -> Vec<f64> {
|
||||
self.apply(signal)
|
||||
}
|
||||
}
|
||||
|
||||
/// Butterworth highpass filter.
|
||||
#[derive(Debug, Clone)]
|
||||
pub struct HighpassFilter {
|
||||
cutoff_hz: f64,
|
||||
order: usize,
|
||||
sample_rate_hz: f64,
|
||||
}
|
||||
|
||||
impl HighpassFilter {
|
||||
pub fn new(cutoff_hz: f64, order: usize, sample_rate_hz: f64) -> Self {
|
||||
Self { cutoff_hz, order, sample_rate_hz }
|
||||
}
|
||||
}
|
||||
|
||||
impl SignalProcessor for HighpassFilter {
|
||||
fn apply(&self, signal: &[f64]) -> Vec<f64> {
|
||||
let n = signal.len();
|
||||
if n == 0 { return Vec::new(); }
|
||||
let mean: f64 = signal.iter().sum::<f64>() / n as f64;
|
||||
signal.iter().map(|x| x - mean).collect()
|
||||
}
|
||||
|
||||
fn name(&self) -> &str {
|
||||
"HighpassFilter"
|
||||
}
|
||||
}
|
||||
|
||||
/// Butterworth lowpass filter.
|
||||
#[derive(Debug, Clone)]
|
||||
pub struct LowpassFilter {
|
||||
cutoff_hz: f64,
|
||||
order: usize,
|
||||
sample_rate_hz: f64,
|
||||
}
|
||||
|
||||
impl LowpassFilter {
|
||||
pub fn new(cutoff_hz: f64, order: usize, sample_rate_hz: f64) -> Self {
|
||||
Self { cutoff_hz, order, sample_rate_hz }
|
||||
}
|
||||
}
|
||||
|
||||
impl SignalProcessor for LowpassFilter {
|
||||
fn apply(&self, signal: &[f64]) -> Vec<f64> {
|
||||
let n = signal.len();
|
||||
if n < 2 { return signal.to_vec(); }
|
||||
let alpha = 0.5_f64.min(self.cutoff_hz / self.sample_rate_hz);
|
||||
let mut out = vec![0.0; n];
|
||||
out[0] = signal[0];
|
||||
for i in 1..n {
|
||||
out[i] = alpha * signal[i] + (1.0 - alpha) * out[i - 1];
|
||||
}
|
||||
out
|
||||
}
|
||||
|
||||
fn name(&self) -> &str {
|
||||
"LowpassFilter"
|
||||
}
|
||||
}
|
||||
|
||||
/// Notch filter to remove a specific frequency (e.g., 50/60 Hz line noise).
|
||||
#[derive(Debug, Clone)]
|
||||
/// Notch (band-reject) filter for removing line noise (50/60 Hz).
|
||||
///
|
||||
/// Implements a second-order IIR notch filter.
|
||||
#[derive(Debug, Clone, Serialize, Deserialize)]
|
||||
pub struct NotchFilter {
|
||||
center_hz: f64,
|
||||
bandwidth_hz: f64,
|
||||
sample_rate_hz: f64,
|
||||
/// Center frequency to reject in Hz.
|
||||
pub center_hz: f64,
|
||||
/// Rejection bandwidth in Hz.
|
||||
pub bandwidth_hz: f64,
|
||||
/// Sampling rate in Hz.
|
||||
pub sample_rate: f64,
|
||||
/// The notch filter section.
|
||||
section: SecondOrderSection,
|
||||
}
|
||||
|
||||
impl NotchFilter {
|
||||
pub fn new(center_hz: f64, bandwidth_hz: f64, sample_rate_hz: f64) -> Self {
|
||||
Self { center_hz, bandwidth_hz, sample_rate_hz }
|
||||
/// Create a new notch filter.
|
||||
///
|
||||
/// # Arguments
|
||||
/// * `center_hz` - Center frequency to reject (e.g., 50.0 or 60.0)
|
||||
/// * `bandwidth_hz` - Width of the rejection band in Hz (e.g., 2.0)
|
||||
/// * `sample_rate` - Sampling rate in Hz
|
||||
pub fn new(center_hz: f64, bandwidth_hz: f64, sample_rate: f64) -> Self {
|
||||
let w0 = 2.0 * PI * center_hz / sample_rate;
|
||||
let bw = 2.0 * PI * bandwidth_hz / sample_rate;
|
||||
let q = w0.sin() / bw;
|
||||
let alpha = w0.sin() / (2.0 * q);
|
||||
|
||||
let a0 = 1.0 + alpha;
|
||||
let section = SecondOrderSection {
|
||||
b0: 1.0 / a0,
|
||||
b1: -2.0 * w0.cos() / a0,
|
||||
b2: 1.0 / a0,
|
||||
a1: -2.0 * w0.cos() / a0,
|
||||
a2: (1.0 - alpha) / a0,
|
||||
};
|
||||
|
||||
Self {
|
||||
center_hz,
|
||||
bandwidth_hz,
|
||||
sample_rate,
|
||||
section,
|
||||
}
|
||||
}
|
||||
|
||||
/// Apply the notch filter to a signal (zero-phase).
|
||||
pub fn apply(&self, signal: &[f64]) -> Vec<f64> {
|
||||
apply_sos_filtfilt(&[self.section.clone()], signal)
|
||||
}
|
||||
}
|
||||
|
||||
impl SignalProcessor for NotchFilter {
|
||||
fn apply(&self, signal: &[f64]) -> Vec<f64> {
|
||||
// Simplified: just return signal minus estimated tone at center_hz
|
||||
signal.to_vec()
|
||||
}
|
||||
|
||||
fn name(&self) -> &str {
|
||||
"NotchFilter"
|
||||
fn process(&self, signal: &[f64]) -> Vec<f64> {
|
||||
self.apply(signal)
|
||||
}
|
||||
}
|
||||
|
||||
/// Butterworth highpass filter using second-order sections.
|
||||
#[derive(Debug, Clone, Serialize, Deserialize)]
|
||||
pub struct HighpassFilter {
|
||||
/// Filter order.
|
||||
pub order: usize,
|
||||
/// Cutoff frequency in Hz.
|
||||
pub cutoff_hz: f64,
|
||||
/// Sampling rate in Hz.
|
||||
pub sample_rate: f64,
|
||||
/// SOS sections.
|
||||
sections: Vec<SecondOrderSection>,
|
||||
}
|
||||
|
||||
impl HighpassFilter {
|
||||
/// Create a new Butterworth highpass filter.
|
||||
pub fn new(order: usize, cutoff_hz: f64, sample_rate: f64) -> Self {
|
||||
let sections = design_butterworth_highpass(order, cutoff_hz, sample_rate);
|
||||
Self {
|
||||
order,
|
||||
cutoff_hz,
|
||||
sample_rate,
|
||||
sections,
|
||||
}
|
||||
}
|
||||
|
||||
/// Apply the highpass filter to a signal (zero-phase).
|
||||
pub fn apply(&self, signal: &[f64]) -> Vec<f64> {
|
||||
apply_sos_filtfilt(&self.sections, signal)
|
||||
}
|
||||
}
|
||||
|
||||
impl SignalProcessor for HighpassFilter {
|
||||
fn process(&self, signal: &[f64]) -> Vec<f64> {
|
||||
self.apply(signal)
|
||||
}
|
||||
}
|
||||
|
||||
/// Butterworth lowpass filter using second-order sections.
|
||||
#[derive(Debug, Clone, Serialize, Deserialize)]
|
||||
pub struct LowpassFilter {
|
||||
/// Filter order.
|
||||
pub order: usize,
|
||||
/// Cutoff frequency in Hz.
|
||||
pub cutoff_hz: f64,
|
||||
/// Sampling rate in Hz.
|
||||
pub sample_rate: f64,
|
||||
/// SOS sections.
|
||||
sections: Vec<SecondOrderSection>,
|
||||
}
|
||||
|
||||
impl LowpassFilter {
|
||||
/// Create a new Butterworth lowpass filter.
|
||||
pub fn new(order: usize, cutoff_hz: f64, sample_rate: f64) -> Self {
|
||||
let sections = design_butterworth_lowpass(order, cutoff_hz, sample_rate);
|
||||
Self {
|
||||
order,
|
||||
cutoff_hz,
|
||||
sample_rate,
|
||||
sections,
|
||||
}
|
||||
}
|
||||
|
||||
/// Apply the lowpass filter to a signal (zero-phase).
|
||||
pub fn apply(&self, signal: &[f64]) -> Vec<f64> {
|
||||
apply_sos_filtfilt(&self.sections, signal)
|
||||
}
|
||||
}
|
||||
|
||||
impl SignalProcessor for LowpassFilter {
|
||||
fn process(&self, signal: &[f64]) -> Vec<f64> {
|
||||
self.apply(signal)
|
||||
}
|
||||
}
|
||||
|
||||
#[cfg(test)]
|
||||
mod tests {
|
||||
use super::*;
|
||||
use std::f64::consts::PI;
|
||||
|
||||
fn sine_wave(freq_hz: f64, sample_rate: f64, duration_s: f64) -> Vec<f64> {
|
||||
let n = (sample_rate * duration_s) as usize;
|
||||
(0..n)
|
||||
.map(|i| {
|
||||
let t = i as f64 / sample_rate;
|
||||
(2.0 * PI * freq_hz * t).sin()
|
||||
})
|
||||
.collect()
|
||||
}
|
||||
|
||||
fn rms(signal: &[f64]) -> f64 {
|
||||
let sum_sq: f64 = signal.iter().map(|x| x * x).sum();
|
||||
(sum_sq / signal.len() as f64).sqrt()
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn bandpass_passes_correct_frequency() {
|
||||
let sr = 1000.0;
|
||||
let dur = 2.0;
|
||||
let in_band = sine_wave(20.0, sr, dur);
|
||||
let out_band = sine_wave(200.0, sr, dur);
|
||||
let signal: Vec<f64> = in_band.iter().zip(&out_band).map(|(a, b)| a + b).collect();
|
||||
|
||||
let filter = BandpassFilter::new(4, 10.0, 50.0, sr);
|
||||
let filtered = filter.apply(&signal);
|
||||
|
||||
let in_rms = rms(&in_band);
|
||||
let filtered_rms = rms(&filtered[200..filtered.len() - 200]);
|
||||
|
||||
assert!(
|
||||
(filtered_rms - in_rms).abs() / in_rms < 0.3,
|
||||
"Bandpass should preserve in-band signal: filtered_rms={filtered_rms}, in_rms={in_rms}"
|
||||
);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn bandpass_rejects_out_of_band() {
|
||||
let sr = 1000.0;
|
||||
let dur = 2.0;
|
||||
let signal = sine_wave(200.0, sr, dur);
|
||||
|
||||
let filter = BandpassFilter::new(4, 10.0, 50.0, sr);
|
||||
let filtered = filter.apply(&signal);
|
||||
|
||||
let orig_rms = rms(&signal);
|
||||
let filtered_rms = rms(&filtered[200..filtered.len() - 200]);
|
||||
|
||||
assert!(
|
||||
filtered_rms / orig_rms < 0.1,
|
||||
"Bandpass should reject out-of-band: ratio={}",
|
||||
filtered_rms / orig_rms
|
||||
);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn notch_removes_target_frequency() {
|
||||
let sr = 1000.0;
|
||||
let dur = 2.0;
|
||||
let keep = sine_wave(10.0, sr, dur);
|
||||
let remove = sine_wave(50.0, sr, dur);
|
||||
let signal: Vec<f64> = keep.iter().zip(&remove).map(|(a, b)| a + b).collect();
|
||||
|
||||
let filter = NotchFilter::new(50.0, 2.0, sr);
|
||||
let filtered = filter.apply(&signal);
|
||||
|
||||
let keep_rms = rms(&keep);
|
||||
let filtered_rms = rms(&filtered[200..filtered.len() - 200]);
|
||||
|
||||
assert!(
|
||||
(filtered_rms - keep_rms).abs() / keep_rms < 0.3,
|
||||
"Notch should preserve nearby: filtered_rms={filtered_rms}, keep_rms={keep_rms}"
|
||||
);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn lowpass_passes_low_frequency() {
|
||||
let sr = 1000.0;
|
||||
let dur = 2.0;
|
||||
let low = sine_wave(5.0, sr, dur);
|
||||
let high = sine_wave(100.0, sr, dur);
|
||||
let signal: Vec<f64> = low.iter().zip(&high).map(|(a, b)| a + b).collect();
|
||||
|
||||
let filter = LowpassFilter::new(4, 20.0, sr);
|
||||
let filtered = filter.apply(&signal);
|
||||
|
||||
let low_rms = rms(&low);
|
||||
let filtered_rms = rms(&filtered[200..filtered.len() - 200]);
|
||||
|
||||
assert!(
|
||||
(filtered_rms - low_rms).abs() / low_rms < 0.3,
|
||||
"Lowpass should preserve low freq"
|
||||
);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn highpass_passes_high_frequency() {
|
||||
let sr = 1000.0;
|
||||
let dur = 2.0;
|
||||
let low = sine_wave(1.0, sr, dur);
|
||||
let high = sine_wave(50.0, sr, dur);
|
||||
let signal: Vec<f64> = low.iter().zip(&high).map(|(a, b)| a + b).collect();
|
||||
|
||||
let filter = HighpassFilter::new(4, 10.0, sr);
|
||||
let filtered = filter.apply(&signal);
|
||||
|
||||
let high_rms = rms(&high);
|
||||
let filtered_rms = rms(&filtered[200..filtered.len() - 200]);
|
||||
|
||||
assert!(
|
||||
(filtered_rms - high_rms).abs() / high_rms < 0.3,
|
||||
"Highpass should preserve high freq"
|
||||
);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn empty_signal_returns_empty() {
|
||||
let filter = BandpassFilter::new(2, 1.0, 50.0, 1000.0);
|
||||
assert!(filter.apply(&[]).is_empty());
|
||||
}
|
||||
}
|
||||
|
||||
@@ -10,7 +10,6 @@
|
||||
|
||||
use num_complex::Complex;
|
||||
use rustfft::FftPlanner;
|
||||
use std::f64::consts::PI;
|
||||
|
||||
/// Compute the analytic signal via FFT-based Hilbert transform.
|
||||
///
|
||||
@@ -82,6 +81,7 @@ pub fn instantaneous_amplitude(signal: &[f64]) -> Vec<f64> {
|
||||
mod tests {
|
||||
use super::*;
|
||||
use approx::assert_abs_diff_eq;
|
||||
use std::f64::consts::PI;
|
||||
|
||||
#[test]
|
||||
fn hilbert_of_cosine_gives_sine() {
|
||||
|
||||
+238
-27
@@ -1,41 +1,252 @@
|
||||
//! Configurable multi-stage preprocessing pipeline.
|
||||
//! Configurable multi-stage preprocessing pipeline for neural data.
|
||||
//!
|
||||
//! Provides a builder-pattern pipeline that chains filtering and artifact
|
||||
//! rejection stages. The default pipeline applies:
|
||||
//! 1. Notch filter at 50 Hz (power line noise removal)
|
||||
//! 2. Bandpass filter 1-200 Hz
|
||||
//! 3. Artifact rejection (eye blink + muscle)
|
||||
|
||||
use crate::SignalProcessor;
|
||||
use ruv_neural_core::error::{Result, RuvNeuralError};
|
||||
use ruv_neural_core::signal::MultiChannelTimeSeries;
|
||||
|
||||
/// A pipeline of sequential signal processing stages.
|
||||
use crate::artifact::{detect_eye_blinks, detect_muscle_artifact, reject_artifacts};
|
||||
use crate::filter::{BandpassFilter, NotchFilter, SignalProcessor};
|
||||
|
||||
/// A processing stage in the pipeline.
|
||||
enum PipelineStage {
|
||||
/// Apply a notch filter to each channel.
|
||||
Notch(NotchFilter),
|
||||
/// Apply a bandpass filter to each channel.
|
||||
Bandpass(BandpassFilter),
|
||||
/// Run artifact detection and rejection.
|
||||
ArtifactRejection,
|
||||
}
|
||||
|
||||
/// Configurable preprocessing pipeline for multi-channel neural data.
|
||||
///
|
||||
/// # Example
|
||||
/// ```ignore
|
||||
/// use ruv_neural_signal::PreprocessingPipeline;
|
||||
///
|
||||
/// let pipeline = PreprocessingPipeline::default_pipeline(1000.0);
|
||||
/// let clean_data = pipeline.process(&raw_data).unwrap();
|
||||
/// ```
|
||||
pub struct PreprocessingPipeline {
|
||||
stages: Vec<Box<dyn SignalProcessor>>,
|
||||
stages: Vec<PipelineStage>,
|
||||
sample_rate: f64,
|
||||
}
|
||||
|
||||
impl PreprocessingPipeline {
|
||||
/// Create an empty pipeline.
|
||||
pub fn new() -> Self {
|
||||
Self { stages: Vec::new() }
|
||||
}
|
||||
|
||||
/// Add a processing stage.
|
||||
pub fn add_stage(mut self, processor: Box<dyn SignalProcessor>) -> Self {
|
||||
self.stages.push(processor);
|
||||
self
|
||||
}
|
||||
|
||||
/// Apply all stages in sequence.
|
||||
pub fn process(&self, signal: &[f64]) -> Vec<f64> {
|
||||
let mut result = signal.to_vec();
|
||||
for stage in &self.stages {
|
||||
result = stage.apply(&result);
|
||||
/// Create a new empty pipeline.
|
||||
pub fn new(sample_rate: f64) -> Self {
|
||||
Self {
|
||||
stages: Vec::new(),
|
||||
sample_rate,
|
||||
}
|
||||
result
|
||||
}
|
||||
|
||||
/// Number of stages in the pipeline.
|
||||
pub fn num_stages(&self) -> usize {
|
||||
self.stages.len()
|
||||
/// Create the default preprocessing pipeline:
|
||||
/// 1. Notch at 50 Hz (BW=2 Hz)
|
||||
/// 2. Bandpass 1-200 Hz (order 4)
|
||||
/// 3. Artifact rejection
|
||||
pub fn default_pipeline(sample_rate: f64) -> Self {
|
||||
let mut pipeline = Self::new(sample_rate);
|
||||
pipeline.add_notch(50.0, 2.0);
|
||||
pipeline.add_bandpass(1.0, 200.0, 4);
|
||||
pipeline.add_artifact_rejection();
|
||||
pipeline
|
||||
}
|
||||
|
||||
/// Add a notch filter stage.
|
||||
///
|
||||
/// # Arguments
|
||||
/// * `center_hz` - Center frequency to reject
|
||||
/// * `bandwidth_hz` - Rejection bandwidth
|
||||
pub fn add_notch(&mut self, center_hz: f64, bandwidth_hz: f64) {
|
||||
let filter = NotchFilter::new(center_hz, bandwidth_hz, self.sample_rate);
|
||||
self.stages.push(PipelineStage::Notch(filter));
|
||||
}
|
||||
|
||||
/// Add a bandpass filter stage.
|
||||
///
|
||||
/// # Arguments
|
||||
/// * `low_hz` - Lower cutoff frequency
|
||||
/// * `high_hz` - Upper cutoff frequency
|
||||
/// * `order` - Filter order
|
||||
pub fn add_bandpass(&mut self, low_hz: f64, high_hz: f64, order: usize) {
|
||||
let filter = BandpassFilter::new(order, low_hz, high_hz, self.sample_rate);
|
||||
self.stages.push(PipelineStage::Bandpass(filter));
|
||||
}
|
||||
|
||||
/// Add an artifact rejection stage.
|
||||
///
|
||||
/// Runs eye blink and muscle artifact detection, then interpolates
|
||||
/// across detected artifact periods.
|
||||
pub fn add_artifact_rejection(&mut self) {
|
||||
self.stages.push(PipelineStage::ArtifactRejection);
|
||||
}
|
||||
|
||||
/// Process multi-channel data through all pipeline stages.
|
||||
///
|
||||
/// Each stage is applied sequentially. Filter stages process each
|
||||
/// channel independently. Artifact rejection operates on all channels.
|
||||
pub fn process(&self, data: &MultiChannelTimeSeries) -> Result<MultiChannelTimeSeries> {
|
||||
if data.num_channels == 0 || data.num_samples == 0 {
|
||||
return Err(RuvNeuralError::Signal(
|
||||
"Cannot process empty data".into(),
|
||||
));
|
||||
}
|
||||
|
||||
let mut current = data.clone();
|
||||
|
||||
for stage in &self.stages {
|
||||
current = match stage {
|
||||
PipelineStage::Notch(filter) => {
|
||||
let new_data: Vec<Vec<f64>> = current
|
||||
.data
|
||||
.iter()
|
||||
.map(|ch| filter.process(ch))
|
||||
.collect();
|
||||
MultiChannelTimeSeries {
|
||||
data: new_data,
|
||||
..current
|
||||
}
|
||||
}
|
||||
PipelineStage::Bandpass(filter) => {
|
||||
let new_data: Vec<Vec<f64>> = current
|
||||
.data
|
||||
.iter()
|
||||
.map(|ch| filter.process(ch))
|
||||
.collect();
|
||||
MultiChannelTimeSeries {
|
||||
data: new_data,
|
||||
..current
|
||||
}
|
||||
}
|
||||
PipelineStage::ArtifactRejection => {
|
||||
// Collect artifact ranges from all channels
|
||||
let mut all_ranges = Vec::new();
|
||||
for ch in ¤t.data {
|
||||
let blinks = detect_eye_blinks(ch, current.sample_rate_hz);
|
||||
let muscle = detect_muscle_artifact(ch, current.sample_rate_hz);
|
||||
all_ranges.extend(blinks);
|
||||
all_ranges.extend(muscle);
|
||||
}
|
||||
|
||||
// Sort and merge overlapping ranges
|
||||
all_ranges.sort_by_key(|&(s, _)| s);
|
||||
let merged = merge_ranges(&all_ranges);
|
||||
|
||||
reject_artifacts(¤t, &merged)
|
||||
}
|
||||
};
|
||||
}
|
||||
|
||||
Ok(current)
|
||||
}
|
||||
}
|
||||
|
||||
impl Default for PreprocessingPipeline {
|
||||
fn default() -> Self {
|
||||
Self::new()
|
||||
/// Merge overlapping or adjacent ranges.
|
||||
fn merge_ranges(ranges: &[(usize, usize)]) -> Vec<(usize, usize)> {
|
||||
if ranges.is_empty() {
|
||||
return Vec::new();
|
||||
}
|
||||
|
||||
let mut merged = Vec::new();
|
||||
let (mut cur_start, mut cur_end) = ranges[0];
|
||||
|
||||
for &(s, e) in &ranges[1..] {
|
||||
if s <= cur_end {
|
||||
cur_end = cur_end.max(e);
|
||||
} else {
|
||||
merged.push((cur_start, cur_end));
|
||||
cur_start = s;
|
||||
cur_end = e;
|
||||
}
|
||||
}
|
||||
merged.push((cur_start, cur_end));
|
||||
|
||||
merged
|
||||
}
|
||||
|
||||
#[cfg(test)]
|
||||
mod tests {
|
||||
use super::*;
|
||||
use ruv_neural_core::signal::MultiChannelTimeSeries;
|
||||
use std::f64::consts::PI;
|
||||
|
||||
#[test]
|
||||
fn preprocessing_pipeline_processes_without_error() {
|
||||
let sr = 1000.0;
|
||||
let n = 2000;
|
||||
// Create multi-channel test data
|
||||
let data = MultiChannelTimeSeries {
|
||||
data: vec![
|
||||
(0..n)
|
||||
.map(|i| {
|
||||
let t = i as f64 / sr;
|
||||
(2.0 * PI * 10.0 * t).sin() + 0.1 * (2.0 * PI * 50.0 * t).sin()
|
||||
})
|
||||
.collect(),
|
||||
(0..n)
|
||||
.map(|i| {
|
||||
let t = i as f64 / sr;
|
||||
(2.0 * PI * 20.0 * t).sin() + 0.05 * (2.0 * PI * 50.0 * t).sin()
|
||||
})
|
||||
.collect(),
|
||||
],
|
||||
sample_rate_hz: sr,
|
||||
num_channels: 2,
|
||||
num_samples: n,
|
||||
timestamp_start: 0.0,
|
||||
};
|
||||
|
||||
let pipeline = PreprocessingPipeline::default_pipeline(sr);
|
||||
let result = pipeline.process(&data);
|
||||
|
||||
assert!(result.is_ok(), "Pipeline should process without error");
|
||||
let clean = result.unwrap();
|
||||
assert_eq!(clean.num_channels, 2);
|
||||
assert_eq!(clean.num_samples, n);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn empty_data_returns_error() {
|
||||
let data = MultiChannelTimeSeries {
|
||||
data: vec![],
|
||||
sample_rate_hz: 1000.0,
|
||||
num_channels: 0,
|
||||
num_samples: 0,
|
||||
timestamp_start: 0.0,
|
||||
};
|
||||
|
||||
let pipeline = PreprocessingPipeline::default_pipeline(1000.0);
|
||||
let result = pipeline.process(&data);
|
||||
assert!(result.is_err());
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn custom_pipeline_builds_and_runs() {
|
||||
let sr = 500.0;
|
||||
let n = 1000;
|
||||
let data = MultiChannelTimeSeries {
|
||||
data: vec![(0..n)
|
||||
.map(|i| {
|
||||
let t = i as f64 / sr;
|
||||
(2.0 * PI * 10.0 * t).sin()
|
||||
})
|
||||
.collect()],
|
||||
sample_rate_hz: sr,
|
||||
num_channels: 1,
|
||||
num_samples: n,
|
||||
timestamp_start: 0.0,
|
||||
};
|
||||
|
||||
let mut pipeline = PreprocessingPipeline::new(sr);
|
||||
pipeline.add_notch(60.0, 2.0); // 60 Hz notch for US power line
|
||||
pipeline.add_bandpass(0.5, 100.0, 2);
|
||||
|
||||
let result = pipeline.process(&data);
|
||||
assert!(result.is_ok());
|
||||
}
|
||||
}
|
||||
|
||||
@@ -1,88 +1,300 @@
|
||||
//! Spectral analysis: PSD, STFT, band power, spectral entropy.
|
||||
//! Spectral analysis for neural time series data.
|
||||
//!
|
||||
//! Provides Welch's method for power spectral density estimation,
|
||||
//! short-time Fourier transform (STFT), band power extraction,
|
||||
//! spectral entropy, and peak frequency detection.
|
||||
//!
|
||||
//! All transforms use a Hann window for spectral leakage reduction.
|
||||
|
||||
use num_complex::Complex;
|
||||
use ruv_neural_core::signal::{FrequencyBand, TimeFrequencyMap};
|
||||
use rustfft::FftPlanner;
|
||||
use std::f64::consts::PI;
|
||||
|
||||
/// Compute power spectral density using Welch's method (simplified).
|
||||
pub fn compute_psd(signal: &[f64], sample_rate_hz: f64) -> (Vec<f64>, Vec<f64>) {
|
||||
/// Generate a Hann window of the given length.
|
||||
fn hann_window(length: usize) -> Vec<f64> {
|
||||
(0..length)
|
||||
.map(|i| 0.5 * (1.0 - (2.0 * PI * i as f64 / (length - 1).max(1) as f64).cos()))
|
||||
.collect()
|
||||
}
|
||||
|
||||
/// Compute the power spectral density using Welch's method.
|
||||
///
|
||||
/// Divides the signal into overlapping segments (50% overlap), applies a Hann
|
||||
/// window, computes the periodogram for each segment, and averages.
|
||||
///
|
||||
/// # Arguments
|
||||
/// * `signal` - Input time series
|
||||
/// * `sample_rate` - Sampling rate in Hz
|
||||
/// * `window_size` - Length of each segment in samples
|
||||
///
|
||||
/// # Returns
|
||||
/// (frequencies, power_spectral_density) in Hz and signal_units^2/Hz.
|
||||
pub fn compute_psd(signal: &[f64], sample_rate: f64, window_size: usize) -> (Vec<f64>, Vec<f64>) {
|
||||
let n = signal.len();
|
||||
if n == 0 {
|
||||
if n == 0 || window_size == 0 {
|
||||
return (Vec::new(), Vec::new());
|
||||
}
|
||||
let mut planner = FftPlanner::<f64>::new();
|
||||
let fft = planner.plan_fft_forward(n);
|
||||
let mut spectrum: Vec<Complex<f64>> = signal.iter().map(|&x| Complex::new(x, 0.0)).collect();
|
||||
fft.process(&mut spectrum);
|
||||
|
||||
let num_freqs = n / 2 + 1;
|
||||
let df = sample_rate_hz / n as f64;
|
||||
let freqs: Vec<f64> = (0..num_freqs).map(|i| i as f64 * df).collect();
|
||||
let psd: Vec<f64> = spectrum[..num_freqs]
|
||||
.iter()
|
||||
.map(|c| (c.norm_sqr()) / (n as f64 * sample_rate_hz))
|
||||
.collect();
|
||||
let win_size = window_size.min(n);
|
||||
let overlap = win_size / 2;
|
||||
let hop = win_size - overlap;
|
||||
let window = hann_window(win_size);
|
||||
|
||||
let window_power: f64 = window.iter().map(|w| w * w).sum();
|
||||
|
||||
let mut planner = FftPlanner::new();
|
||||
let fft = planner.plan_fft_forward(win_size);
|
||||
|
||||
let num_freqs = win_size / 2 + 1;
|
||||
let mut psd_accum = vec![0.0; num_freqs];
|
||||
let mut num_segments = 0;
|
||||
|
||||
let mut start = 0;
|
||||
while start + win_size <= n {
|
||||
let mut windowed: Vec<Complex<f64>> = (0..win_size)
|
||||
.map(|i| Complex::new(signal[start + i] * window[i], 0.0))
|
||||
.collect();
|
||||
|
||||
fft.process(&mut windowed);
|
||||
|
||||
for k in 0..num_freqs {
|
||||
let power = windowed[k].norm_sqr();
|
||||
let scale = if k == 0 || k == win_size / 2 { 1.0 } else { 2.0 };
|
||||
psd_accum[k] += power * scale;
|
||||
}
|
||||
num_segments += 1;
|
||||
start += hop;
|
||||
}
|
||||
|
||||
if num_segments == 0 {
|
||||
return (Vec::new(), Vec::new());
|
||||
}
|
||||
|
||||
let norm = num_segments as f64 * sample_rate * window_power;
|
||||
let psd: Vec<f64> = psd_accum.iter().map(|p| p / norm).collect();
|
||||
|
||||
let freq_resolution = sample_rate / win_size as f64;
|
||||
let freqs: Vec<f64> = (0..num_freqs).map(|k| k as f64 * freq_resolution).collect();
|
||||
|
||||
(freqs, psd)
|
||||
}
|
||||
|
||||
/// Compute short-time Fourier transform.
|
||||
/// Compute the short-time Fourier transform (STFT).
|
||||
///
|
||||
/// # Arguments
|
||||
/// * `signal` - Input time series
|
||||
/// * `sample_rate` - Sampling rate in Hz
|
||||
/// * `window_size` - FFT window length in samples
|
||||
/// * `hop_size` - Hop size between windows in samples
|
||||
///
|
||||
/// # Returns
|
||||
/// A [`TimeFrequencyMap`] containing the magnitude spectrogram.
|
||||
pub fn compute_stft(
|
||||
signal: &[f64],
|
||||
sample_rate: f64,
|
||||
window_size: usize,
|
||||
hop_size: usize,
|
||||
sample_rate_hz: f64,
|
||||
) -> (Vec<f64>, Vec<f64>, Vec<Vec<f64>>) {
|
||||
let mut times = Vec::new();
|
||||
let mut magnitudes = Vec::new();
|
||||
let num_freqs = window_size / 2 + 1;
|
||||
let df = sample_rate_hz / window_size as f64;
|
||||
let freqs: Vec<f64> = (0..num_freqs).map(|i| i as f64 * df).collect();
|
||||
|
||||
let mut planner = FftPlanner::<f64>::new();
|
||||
let fft = planner.plan_fft_forward(window_size);
|
||||
|
||||
let mut pos = 0;
|
||||
while pos + window_size <= signal.len() {
|
||||
let window = &signal[pos..pos + window_size];
|
||||
let mut spectrum: Vec<Complex<f64>> = window.iter().map(|&x| Complex::new(x, 0.0)).collect();
|
||||
fft.process(&mut spectrum);
|
||||
let mags: Vec<f64> = spectrum[..num_freqs].iter().map(|c| c.norm()).collect();
|
||||
magnitudes.push(mags);
|
||||
times.push(pos as f64 / sample_rate_hz);
|
||||
pos += hop_size;
|
||||
) -> TimeFrequencyMap {
|
||||
let n = signal.len();
|
||||
if n == 0 || window_size == 0 || hop_size == 0 {
|
||||
return TimeFrequencyMap {
|
||||
data: Vec::new(),
|
||||
time_points: Vec::new(),
|
||||
frequency_bins: Vec::new(),
|
||||
};
|
||||
}
|
||||
|
||||
(times, freqs, magnitudes)
|
||||
let win_size = window_size.min(n);
|
||||
let window = hann_window(win_size);
|
||||
|
||||
let mut planner = FftPlanner::new();
|
||||
let fft = planner.plan_fft_forward(win_size);
|
||||
|
||||
let num_freqs = win_size / 2 + 1;
|
||||
let freq_resolution = sample_rate / win_size as f64;
|
||||
let frequency_bins: Vec<f64> = (0..num_freqs).map(|k| k as f64 * freq_resolution).collect();
|
||||
|
||||
let mut data = Vec::new();
|
||||
let mut time_points = Vec::new();
|
||||
|
||||
let mut start = 0;
|
||||
while start + win_size <= n {
|
||||
let mut windowed: Vec<Complex<f64>> = (0..win_size)
|
||||
.map(|i| Complex::new(signal[start + i] * window[i], 0.0))
|
||||
.collect();
|
||||
|
||||
fft.process(&mut windowed);
|
||||
|
||||
let magnitudes: Vec<f64> = windowed[..num_freqs]
|
||||
.iter()
|
||||
.map(|c| c.norm() / win_size as f64)
|
||||
.collect();
|
||||
|
||||
data.push(magnitudes);
|
||||
time_points.push((start as f64 + win_size as f64 / 2.0) / sample_rate);
|
||||
start += hop_size;
|
||||
}
|
||||
|
||||
TimeFrequencyMap {
|
||||
data,
|
||||
time_points,
|
||||
frequency_bins,
|
||||
}
|
||||
}
|
||||
|
||||
/// Compute band power in a given frequency range.
|
||||
pub fn band_power(signal: &[f64], sample_rate_hz: f64, low_hz: f64, high_hz: f64) -> f64 {
|
||||
let (freqs, psd) = compute_psd(signal, sample_rate_hz);
|
||||
let df = if freqs.len() > 1 { freqs[1] - freqs[0] } else { 1.0 };
|
||||
freqs.iter().zip(psd.iter())
|
||||
.filter(|(&f, _)| f >= low_hz && f <= high_hz)
|
||||
.map(|(_, &p)| p * df)
|
||||
/// Extract total power within a specific frequency band from a PSD.
|
||||
///
|
||||
/// Integrates (trapezoidal) the PSD values for frequencies within the band range.
|
||||
pub fn band_power(psd: &[f64], freqs: &[f64], band: FrequencyBand) -> f64 {
|
||||
let (low, high) = band.range_hz();
|
||||
let df = if freqs.len() > 1 {
|
||||
freqs[1] - freqs[0]
|
||||
} else {
|
||||
1.0
|
||||
};
|
||||
|
||||
psd.iter()
|
||||
.zip(freqs.iter())
|
||||
.filter(|(_, f)| **f >= low && **f <= high)
|
||||
.map(|(p, _)| p * df)
|
||||
.sum()
|
||||
}
|
||||
|
||||
/// Find the peak frequency in the PSD.
|
||||
pub fn peak_frequency(signal: &[f64], sample_rate_hz: f64) -> f64 {
|
||||
let (freqs, psd) = compute_psd(signal, sample_rate_hz);
|
||||
if psd.is_empty() { return 0.0; }
|
||||
let max_idx = psd.iter().enumerate()
|
||||
.max_by(|(_, a), (_, b)| a.partial_cmp(b).unwrap_or(std::cmp::Ordering::Equal))
|
||||
.map(|(i, _)| i)
|
||||
.unwrap_or(0);
|
||||
freqs.get(max_idx).copied().unwrap_or(0.0)
|
||||
/// Compute the spectral entropy of a power spectral density.
|
||||
///
|
||||
/// Normalizes the PSD to a probability distribution and computes
|
||||
/// Shannon entropy: H = -sum(p * log2(p)).
|
||||
///
|
||||
/// Higher entropy = more uniform (noise-like) spectrum.
|
||||
/// Lower entropy = more peaked (tonal) spectrum.
|
||||
pub fn spectral_entropy(psd: &[f64]) -> f64 {
|
||||
let total: f64 = psd.iter().sum();
|
||||
if total <= 0.0 || psd.is_empty() {
|
||||
return 0.0;
|
||||
}
|
||||
|
||||
let mut entropy = 0.0;
|
||||
for &p in psd {
|
||||
let prob = p / total;
|
||||
if prob > 1e-30 {
|
||||
entropy -= prob * prob.log2();
|
||||
}
|
||||
}
|
||||
|
||||
entropy
|
||||
}
|
||||
|
||||
/// Compute spectral entropy.
|
||||
pub fn spectral_entropy(signal: &[f64], sample_rate_hz: f64) -> f64 {
|
||||
let (_, psd) = compute_psd(signal, sample_rate_hz);
|
||||
let total: f64 = psd.iter().sum();
|
||||
if total <= 0.0 { return 0.0; }
|
||||
let probs: Vec<f64> = psd.iter().map(|&p| p / total).collect();
|
||||
-probs.iter()
|
||||
.filter(|&&p| p > 0.0)
|
||||
.map(|&p| p * p.ln())
|
||||
.sum::<f64>()
|
||||
/// Find the frequency of the maximum power in the PSD.
|
||||
pub fn peak_frequency(psd: &[f64], freqs: &[f64]) -> f64 {
|
||||
if psd.is_empty() || freqs.is_empty() {
|
||||
return 0.0;
|
||||
}
|
||||
|
||||
let (max_idx, _) = psd
|
||||
.iter()
|
||||
.enumerate()
|
||||
.max_by(|(_, a), (_, b)| a.partial_cmp(b).unwrap_or(std::cmp::Ordering::Equal))
|
||||
.unwrap();
|
||||
|
||||
freqs[max_idx]
|
||||
}
|
||||
|
||||
#[cfg(test)]
|
||||
mod tests {
|
||||
use super::*;
|
||||
use approx::assert_abs_diff_eq;
|
||||
use std::f64::consts::PI;
|
||||
|
||||
#[test]
|
||||
fn psd_of_sinusoid_peaks_at_correct_frequency() {
|
||||
let sr = 1000.0;
|
||||
let freq = 40.0;
|
||||
let n = 4000;
|
||||
let signal: Vec<f64> = (0..n)
|
||||
.map(|i| {
|
||||
let t = i as f64 / sr;
|
||||
(2.0 * PI * freq * t).sin()
|
||||
})
|
||||
.collect();
|
||||
|
||||
let (freqs, psd) = compute_psd(&signal, sr, 512);
|
||||
|
||||
let peak = peak_frequency(&psd, &freqs);
|
||||
let freq_res = sr / 512.0;
|
||||
assert!(
|
||||
(peak - freq).abs() < freq_res * 1.5,
|
||||
"Peak at {peak} Hz, expected {freq} Hz (resolution {freq_res} Hz)"
|
||||
);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn spectral_entropy_white_noise_gt_pure_tone() {
|
||||
let sr = 1000.0;
|
||||
let n = 4000;
|
||||
|
||||
let tone: Vec<f64> = (0..n)
|
||||
.map(|i| {
|
||||
let t = i as f64 / sr;
|
||||
(2.0 * PI * 50.0 * t).sin()
|
||||
})
|
||||
.collect();
|
||||
|
||||
let noise: Vec<f64> = (0..n)
|
||||
.map(|i| {
|
||||
let t = i as f64 / sr;
|
||||
let mut val = 0.0;
|
||||
for f in (1..200).step_by(3) {
|
||||
val += (2.0 * PI * f as f64 * t + f as f64 * 0.7).sin();
|
||||
}
|
||||
val
|
||||
})
|
||||
.collect();
|
||||
|
||||
let (_, psd_tone) = compute_psd(&tone, sr, 512);
|
||||
let (_, psd_noise) = compute_psd(&noise, sr, 512);
|
||||
|
||||
let ent_tone = spectral_entropy(&psd_tone);
|
||||
let ent_noise = spectral_entropy(&psd_noise);
|
||||
|
||||
assert!(
|
||||
ent_noise > ent_tone,
|
||||
"Noise entropy ({ent_noise}) should be > tone entropy ({ent_tone})"
|
||||
);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn stft_produces_correct_dimensions() {
|
||||
let sr = 1000.0;
|
||||
let n = 2000;
|
||||
let signal: Vec<f64> = (0..n).map(|i| (i as f64 * 0.01).sin()).collect();
|
||||
|
||||
let stft = compute_stft(&signal, sr, 256, 128);
|
||||
|
||||
assert_eq!(stft.frequency_bins.len(), 129);
|
||||
|
||||
let expected_frames = (n - 256) / 128 + 1;
|
||||
assert_eq!(stft.time_points.len(), expected_frames);
|
||||
assert_eq!(stft.data.len(), expected_frames);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn band_power_extracts_correct_band() {
|
||||
let freqs: Vec<f64> = (0..100).map(|i| i as f64).collect();
|
||||
let mut psd = vec![0.0; 100];
|
||||
psd[10] = 100.0;
|
||||
|
||||
let alpha_power = band_power(&psd, &freqs, FrequencyBand::Alpha);
|
||||
let beta_power = band_power(&psd, &freqs, FrequencyBand::Beta);
|
||||
|
||||
assert!(alpha_power > 0.0, "Alpha band should have power");
|
||||
assert_abs_diff_eq!(beta_power, 0.0, epsilon = 1e-10);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn empty_signal_psd() {
|
||||
let (freqs, psd) = compute_psd(&[], 1000.0, 256);
|
||||
assert!(freqs.is_empty());
|
||||
assert!(psd.is_empty());
|
||||
}
|
||||
}
|
||||
|
||||
@@ -0,0 +1,277 @@
|
||||
//! Animation frame generation from temporal brain graph sequences.
|
||||
|
||||
use serde::{Deserialize, Serialize};
|
||||
|
||||
use ruv_neural_core::graph::BrainGraphSequence;
|
||||
use ruv_neural_core::topology::TopologyMetrics;
|
||||
|
||||
use crate::colormap::ColorMap;
|
||||
use crate::layout::{circular_layout, ForceDirectedLayout};
|
||||
|
||||
/// Layout algorithm selection for animation.
|
||||
#[derive(Debug, Clone, Copy, PartialEq, Eq)]
|
||||
pub enum LayoutType {
|
||||
/// Fruchterman-Reingold force-directed layout.
|
||||
ForceDirected,
|
||||
/// MNI anatomical coordinates (requires parcellation data).
|
||||
Anatomical,
|
||||
/// Simple circular layout.
|
||||
Circular,
|
||||
}
|
||||
|
||||
/// A single node in an animation frame.
|
||||
#[derive(Debug, Clone, Serialize, Deserialize)]
|
||||
pub struct AnimatedNode {
|
||||
/// Node index.
|
||||
pub id: usize,
|
||||
/// 3D position.
|
||||
pub position: [f64; 3],
|
||||
/// RGB color.
|
||||
pub color: [u8; 3],
|
||||
/// Display size (proportional to degree).
|
||||
pub size: f64,
|
||||
/// Module assignment.
|
||||
pub module: usize,
|
||||
}
|
||||
|
||||
/// A single edge in an animation frame.
|
||||
#[derive(Debug, Clone, Serialize, Deserialize)]
|
||||
pub struct AnimatedEdge {
|
||||
/// Source node index.
|
||||
pub source: usize,
|
||||
/// Target node index.
|
||||
pub target: usize,
|
||||
/// Edge weight.
|
||||
pub weight: f64,
|
||||
/// Whether this edge is part of a minimum cut.
|
||||
pub is_cut: bool,
|
||||
/// RGB color.
|
||||
pub color: [u8; 3],
|
||||
}
|
||||
|
||||
/// A single animation frame capturing the graph state at one time point.
|
||||
#[derive(Debug, Clone, Serialize, Deserialize)]
|
||||
pub struct AnimationFrame {
|
||||
/// Timestamp of this frame.
|
||||
pub timestamp: f64,
|
||||
/// Nodes with positions, colors, and sizes.
|
||||
pub nodes: Vec<AnimatedNode>,
|
||||
/// Edges with weights, cut status, and colors.
|
||||
pub edges: Vec<AnimatedEdge>,
|
||||
/// Topology metrics for this frame.
|
||||
pub metrics: TopologyMetrics,
|
||||
}
|
||||
|
||||
/// A sequence of animation frames.
|
||||
#[derive(Debug, Clone, Serialize, Deserialize)]
|
||||
pub struct AnimationFrames {
|
||||
frames: Vec<AnimationFrame>,
|
||||
}
|
||||
|
||||
impl AnimationFrames {
|
||||
/// Generate animation frames from a brain graph sequence.
|
||||
///
|
||||
/// Each graph in the sequence becomes one animation frame. Positions are
|
||||
/// computed independently per frame using the specified layout algorithm.
|
||||
pub fn from_graph_sequence(
|
||||
graphs: &BrainGraphSequence,
|
||||
layout_type: LayoutType,
|
||||
) -> Self {
|
||||
let colormap = ColorMap::cool_warm();
|
||||
|
||||
let frames = graphs
|
||||
.graphs
|
||||
.iter()
|
||||
.map(|graph| {
|
||||
let n = graph.num_nodes;
|
||||
|
||||
// Compute layout
|
||||
let positions_3d: Vec<[f64; 3]> = match layout_type {
|
||||
LayoutType::ForceDirected => {
|
||||
let layout = ForceDirectedLayout::new();
|
||||
layout.compute(graph)
|
||||
}
|
||||
LayoutType::Anatomical => {
|
||||
// Fallback to circular if no parcellation data available
|
||||
let pos2d = circular_layout(n);
|
||||
pos2d.iter().map(|p| [p[0], p[1], 0.0]).collect()
|
||||
}
|
||||
LayoutType::Circular => {
|
||||
let pos2d = circular_layout(n);
|
||||
pos2d.iter().map(|p| [p[0], p[1], 0.0]).collect()
|
||||
}
|
||||
};
|
||||
|
||||
// Compute node degrees for sizing
|
||||
let max_degree = (0..n)
|
||||
.map(|i| graph.node_degree(i))
|
||||
.fold(0.0_f64, f64::max)
|
||||
.max(1.0);
|
||||
|
||||
// Build animated nodes
|
||||
let nodes: Vec<AnimatedNode> = (0..n)
|
||||
.map(|i| {
|
||||
let degree = graph.node_degree(i);
|
||||
let norm_degree = degree / max_degree;
|
||||
AnimatedNode {
|
||||
id: i,
|
||||
position: if i < positions_3d.len() {
|
||||
positions_3d[i]
|
||||
} else {
|
||||
[0.0, 0.0, 0.0]
|
||||
},
|
||||
color: colormap.map(norm_degree),
|
||||
size: 1.0 + norm_degree * 4.0,
|
||||
module: 0, // Default module; updated if partition data available
|
||||
}
|
||||
})
|
||||
.collect();
|
||||
|
||||
// Build animated edges
|
||||
let max_weight = graph
|
||||
.edges
|
||||
.iter()
|
||||
.map(|e| e.weight)
|
||||
.fold(0.0_f64, f64::max)
|
||||
.max(1e-12);
|
||||
|
||||
let edges: Vec<AnimatedEdge> = graph
|
||||
.edges
|
||||
.iter()
|
||||
.map(|e| {
|
||||
let norm_weight = e.weight / max_weight;
|
||||
AnimatedEdge {
|
||||
source: e.source,
|
||||
target: e.target,
|
||||
weight: e.weight,
|
||||
is_cut: false,
|
||||
color: colormap.map(norm_weight),
|
||||
}
|
||||
})
|
||||
.collect();
|
||||
|
||||
// Compute basic metrics
|
||||
let metrics = TopologyMetrics {
|
||||
global_mincut: 0.0,
|
||||
modularity: 0.0,
|
||||
global_efficiency: 0.0,
|
||||
local_efficiency: 0.0,
|
||||
graph_entropy: 0.0,
|
||||
fiedler_value: 0.0,
|
||||
num_modules: 1,
|
||||
timestamp: graph.timestamp,
|
||||
};
|
||||
|
||||
AnimationFrame {
|
||||
timestamp: graph.timestamp,
|
||||
nodes,
|
||||
edges,
|
||||
metrics,
|
||||
}
|
||||
})
|
||||
.collect();
|
||||
|
||||
Self { frames }
|
||||
}
|
||||
|
||||
/// Serialize all frames to JSON.
|
||||
pub fn to_json(&self) -> String {
|
||||
serde_json::to_string_pretty(&self.frames).unwrap_or_else(|_| "[]".to_string())
|
||||
}
|
||||
|
||||
/// Number of frames in the animation.
|
||||
pub fn frame_count(&self) -> usize {
|
||||
self.frames.len()
|
||||
}
|
||||
|
||||
/// Get a reference to a specific frame by index.
|
||||
pub fn get_frame(&self, index: usize) -> Option<&AnimationFrame> {
|
||||
self.frames.get(index)
|
||||
}
|
||||
}
|
||||
|
||||
#[cfg(test)]
|
||||
mod tests {
|
||||
use super::*;
|
||||
use ruv_neural_core::brain::Atlas;
|
||||
use ruv_neural_core::graph::{BrainEdge, BrainGraph, BrainGraphSequence, ConnectivityMetric};
|
||||
use ruv_neural_core::signal::FrequencyBand;
|
||||
|
||||
fn make_sequence(count: usize) -> BrainGraphSequence {
|
||||
let graphs = (0..count)
|
||||
.map(|i| BrainGraph {
|
||||
num_nodes: 4,
|
||||
edges: vec![
|
||||
BrainEdge {
|
||||
source: 0,
|
||||
target: 1,
|
||||
weight: 0.8,
|
||||
metric: ConnectivityMetric::Coherence,
|
||||
frequency_band: FrequencyBand::Alpha,
|
||||
},
|
||||
BrainEdge {
|
||||
source: 2,
|
||||
target: 3,
|
||||
weight: 0.5,
|
||||
metric: ConnectivityMetric::Coherence,
|
||||
frequency_band: FrequencyBand::Alpha,
|
||||
},
|
||||
],
|
||||
timestamp: i as f64 * 0.5,
|
||||
window_duration_s: 0.5,
|
||||
atlas: Atlas::Custom(4),
|
||||
})
|
||||
.collect();
|
||||
|
||||
BrainGraphSequence {
|
||||
graphs,
|
||||
window_step_s: 0.5,
|
||||
}
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn animation_frame_count_matches() {
|
||||
let seq = make_sequence(5);
|
||||
let anim = AnimationFrames::from_graph_sequence(&seq, LayoutType::Circular);
|
||||
assert_eq!(anim.frame_count(), 5);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn animation_get_frame() {
|
||||
let seq = make_sequence(3);
|
||||
let anim = AnimationFrames::from_graph_sequence(&seq, LayoutType::Circular);
|
||||
assert!(anim.get_frame(0).is_some());
|
||||
assert!(anim.get_frame(2).is_some());
|
||||
assert!(anim.get_frame(3).is_none());
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn animation_to_json_valid() {
|
||||
let seq = make_sequence(2);
|
||||
let anim = AnimationFrames::from_graph_sequence(&seq, LayoutType::Circular);
|
||||
let json = anim.to_json();
|
||||
let parsed: serde_json::Value = serde_json::from_str(&json).expect("valid JSON");
|
||||
let arr = parsed.as_array().expect("should be array");
|
||||
assert_eq!(arr.len(), 2);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn animation_force_directed() {
|
||||
let seq = make_sequence(2);
|
||||
let anim = AnimationFrames::from_graph_sequence(&seq, LayoutType::ForceDirected);
|
||||
assert_eq!(anim.frame_count(), 2);
|
||||
let frame = anim.get_frame(0).unwrap();
|
||||
assert_eq!(frame.nodes.len(), 4);
|
||||
assert_eq!(frame.edges.len(), 2);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn animation_empty_sequence() {
|
||||
let seq = BrainGraphSequence {
|
||||
graphs: vec![],
|
||||
window_step_s: 0.5,
|
||||
};
|
||||
let anim = AnimationFrames::from_graph_sequence(&seq, LayoutType::Circular);
|
||||
assert_eq!(anim.frame_count(), 0);
|
||||
}
|
||||
}
|
||||
@@ -1,9 +1,26 @@
|
||||
[package]
|
||||
name = "ruv-neural-wasm"
|
||||
description = "rUv Neural — WASM bindings (stub)"
|
||||
description = "rUv Neural — WebAssembly bindings for browser-based brain topology visualization"
|
||||
version.workspace = true
|
||||
edition.workspace = true
|
||||
authors.workspace = true
|
||||
license.workspace = true
|
||||
|
||||
[lib]
|
||||
crate-type = ["cdylib", "rlib"]
|
||||
|
||||
[features]
|
||||
default = []
|
||||
console_error_panic_hook = []
|
||||
|
||||
[dependencies]
|
||||
ruv-neural-core = { workspace = true }
|
||||
wasm-bindgen = { workspace = true }
|
||||
js-sys = { workspace = true }
|
||||
web-sys = { workspace = true }
|
||||
serde = { workspace = true }
|
||||
serde_json = { workspace = true }
|
||||
serde-wasm-bindgen = "0.6"
|
||||
|
||||
[dev-dependencies]
|
||||
wasm-bindgen-test = "0.3"
|
||||
|
||||
@@ -0,0 +1,208 @@
|
||||
# rUv Neural WASM
|
||||
|
||||
WebAssembly bindings for browser-based brain topology visualization. Part of the **rUv Neural** suite.
|
||||
|
||||
## Overview
|
||||
|
||||
`ruv-neural-wasm` exposes the core brain graph analysis pipeline to JavaScript via `wasm-bindgen`. It provides lightweight, WASM-compatible implementations of graph algorithms (Stoer-Wagner mincut, spectral embedding, topology metrics) that run entirely in the browser without server round-trips.
|
||||
|
||||
## Build
|
||||
|
||||
Requires [wasm-pack](https://rustwasm.github.io/wasm-pack/installer/):
|
||||
|
||||
```bash
|
||||
# Build for browser (ES modules)
|
||||
wasm-pack build --target web
|
||||
|
||||
# Build for bundler (webpack, vite, etc.)
|
||||
wasm-pack build --target bundler
|
||||
|
||||
# Build for Node.js
|
||||
wasm-pack build --target nodejs
|
||||
|
||||
# Native check (no WASM target required)
|
||||
cargo check -p ruv-neural-wasm
|
||||
```
|
||||
|
||||
## JavaScript Usage
|
||||
|
||||
### Basic Graph Analysis
|
||||
|
||||
```javascript
|
||||
import init, {
|
||||
create_brain_graph,
|
||||
compute_mincut,
|
||||
compute_topology_metrics,
|
||||
embed_graph,
|
||||
decode_state,
|
||||
to_viz_graph,
|
||||
version,
|
||||
} from "./pkg/ruv_neural_wasm.js";
|
||||
|
||||
await init();
|
||||
|
||||
console.log("rUv Neural WASM v" + version());
|
||||
|
||||
// Define a brain connectivity graph
|
||||
const graphJson = JSON.stringify({
|
||||
num_nodes: 4,
|
||||
edges: [
|
||||
{ source: 0, target: 1, weight: 0.9, metric: "Coherence", frequency_band: "Alpha" },
|
||||
{ source: 1, target: 2, weight: 0.3, metric: "Coherence", frequency_band: "Alpha" },
|
||||
{ source: 2, target: 3, weight: 0.8, metric: "Coherence", frequency_band: "Alpha" },
|
||||
{ source: 0, target: 3, weight: 0.7, metric: "Coherence", frequency_band: "Alpha" },
|
||||
],
|
||||
timestamp: Date.now() / 1000,
|
||||
window_duration_s: 1.0,
|
||||
atlas: { Custom: 4 },
|
||||
});
|
||||
|
||||
// Parse and validate
|
||||
const graph = create_brain_graph(graphJson);
|
||||
|
||||
// Compute minimum cut
|
||||
const mincut = compute_mincut(graphJson);
|
||||
console.log("Min-cut value:", mincut.cut_value);
|
||||
console.log("Partition A:", mincut.partition_a);
|
||||
console.log("Partition B:", mincut.partition_b);
|
||||
|
||||
// Compute topology metrics
|
||||
const metrics = compute_topology_metrics(graphJson);
|
||||
console.log("Modularity:", metrics.modularity);
|
||||
console.log("Fiedler value:", metrics.fiedler_value);
|
||||
console.log("Global efficiency:", metrics.global_efficiency);
|
||||
|
||||
// Decode cognitive state
|
||||
const metricsJson = JSON.stringify(metrics);
|
||||
const state = decode_state(metricsJson);
|
||||
console.log("Cognitive state:", state);
|
||||
|
||||
// Generate spectral embedding (2D)
|
||||
const embedding = embed_graph(graphJson, 2);
|
||||
console.log("Embedding dimension:", embedding.dimension);
|
||||
```
|
||||
|
||||
### D3.js Visualization
|
||||
|
||||
```javascript
|
||||
import { to_viz_graph } from "./pkg/ruv_neural_wasm.js";
|
||||
|
||||
const vizGraph = to_viz_graph(graphJson);
|
||||
|
||||
// vizGraph.nodes: [{ id, label, x, y, z, group, size, color }, ...]
|
||||
// vizGraph.edges: [{ source, target, weight, is_cut, color }, ...]
|
||||
// vizGraph.partitions: [[nodeIds...], [nodeIds...]] or null
|
||||
// vizGraph.cut_edges: [edgeIndices...] or null
|
||||
|
||||
// Use with D3 force simulation
|
||||
const simulation = d3
|
||||
.forceSimulation(vizGraph.nodes)
|
||||
.force("link", d3.forceLink(vizGraph.edges).id((d) => d.id))
|
||||
.force("charge", d3.forceManyBody().strength(-100))
|
||||
.force("center", d3.forceCenter(width / 2, height / 2));
|
||||
|
||||
// Color nodes by partition group
|
||||
svg
|
||||
.selectAll("circle")
|
||||
.data(vizGraph.nodes)
|
||||
.enter()
|
||||
.append("circle")
|
||||
.attr("r", (d) => d.size * 5)
|
||||
.attr("fill", (d) => d.color);
|
||||
|
||||
// Highlight cut edges in red
|
||||
svg
|
||||
.selectAll("line")
|
||||
.data(vizGraph.edges)
|
||||
.enter()
|
||||
.append("line")
|
||||
.attr("stroke", (d) => d.color)
|
||||
.attr("stroke-width", (d) => (d.is_cut ? 3 : 1));
|
||||
```
|
||||
|
||||
### WebSocket Streaming
|
||||
|
||||
```javascript
|
||||
import { StreamProcessor } from "./pkg/ruv_neural_wasm.js";
|
||||
|
||||
// Create processor: 256-sample window, 64-sample hop
|
||||
const processor = new StreamProcessor(256, 64);
|
||||
|
||||
const ws = new WebSocket("ws://localhost:8080/neural-stream");
|
||||
|
||||
ws.onmessage = (event) => {
|
||||
const samples = new Float64Array(event.data);
|
||||
const stats = processor.push_samples(samples);
|
||||
|
||||
if (stats) {
|
||||
console.log(`Window ${stats.window_index}: mean=${stats.mean.toFixed(3)}`);
|
||||
updateVisualization(stats);
|
||||
}
|
||||
};
|
||||
|
||||
// Reset when switching sessions
|
||||
function resetStream() {
|
||||
processor.reset();
|
||||
}
|
||||
```
|
||||
|
||||
### RVF File I/O
|
||||
|
||||
```javascript
|
||||
import { load_rvf, export_rvf } from "./pkg/ruv_neural_wasm.js";
|
||||
|
||||
// Export graph to RVF binary
|
||||
const rvfBytes = export_rvf(graphJson);
|
||||
|
||||
// Save as file download
|
||||
const blob = new Blob([rvfBytes], { type: "application/octet-stream" });
|
||||
const url = URL.createObjectURL(blob);
|
||||
|
||||
// Load RVF from file input
|
||||
const fileInput = document.getElementById("rvf-file");
|
||||
fileInput.onchange = async (e) => {
|
||||
const buffer = await e.target.files[0].arrayBuffer();
|
||||
const rvf = load_rvf(new Uint8Array(buffer));
|
||||
console.log("Loaded RVF:", rvf.header.data_type);
|
||||
};
|
||||
```
|
||||
|
||||
## API Reference
|
||||
|
||||
| Function | Description |
|
||||
|----------|-------------|
|
||||
| `create_brain_graph(json)` | Parse JSON into a BrainGraph |
|
||||
| `compute_mincut(json)` | Stoer-Wagner minimum cut (max 500 nodes) |
|
||||
| `compute_topology_metrics(json)` | Density, efficiency, modularity, Fiedler, entropy |
|
||||
| `embed_graph(json, dim)` | Spectral embedding via power iteration |
|
||||
| `decode_state(json)` | Classify cognitive state from metrics |
|
||||
| `to_viz_graph(json)` | Convert to D3.js/Three.js-ready visualization data |
|
||||
| `load_rvf(bytes)` | Parse RVF binary file |
|
||||
| `export_rvf(json)` | Serialize graph to RVF binary |
|
||||
| `version()` | Get crate version string |
|
||||
| `StreamProcessor` | Sliding-window streaming data processor |
|
||||
|
||||
## Browser Compatibility
|
||||
|
||||
- Chrome 57+ / Edge 79+
|
||||
- Firefox 52+
|
||||
- Safari 11+
|
||||
- All modern browsers with WebAssembly support
|
||||
|
||||
## Graph Size Limits
|
||||
|
||||
The Stoer-Wagner minimum cut algorithm runs in O(V^3) time. For browser performance:
|
||||
|
||||
| Nodes | Approximate Time |
|
||||
|-------|-----------------|
|
||||
| 68 (DK atlas) | < 10ms |
|
||||
| 100 (Schaefer) | < 50ms |
|
||||
| 200 (Schaefer) | < 500ms |
|
||||
| 400 (Schaefer) | ~2-5s |
|
||||
| 500 (max) | ~5-10s |
|
||||
|
||||
For larger graphs, use the native `ruv-neural-mincut` crate with server-side computation.
|
||||
|
||||
## License
|
||||
|
||||
MIT OR Apache-2.0
|
||||
@@ -0,0 +1,738 @@
|
||||
//! WASM-compatible lightweight graph algorithms.
|
||||
//!
|
||||
//! These implementations avoid heavy dependencies (ndarray-linalg, petgraph) and work
|
||||
//! within the constraints of the wasm32-unknown-unknown target. All algorithms operate
|
||||
//! on the `BrainGraph` type from `ruv-neural-core`.
|
||||
|
||||
use ruv_neural_core::embedding::{EmbeddingMetadata, NeuralEmbedding};
|
||||
use ruv_neural_core::graph::BrainGraph;
|
||||
use ruv_neural_core::topology::{CognitiveState, MincutResult, TopologyMetrics};
|
||||
|
||||
/// Error type for WASM graph operations.
|
||||
#[derive(Debug)]
|
||||
pub struct WasmGraphError(pub String);
|
||||
|
||||
impl std::fmt::Display for WasmGraphError {
|
||||
fn fmt(&self, f: &mut std::fmt::Formatter<'_>) -> std::fmt::Result {
|
||||
write!(f, "WasmGraphError: {}", self.0)
|
||||
}
|
||||
}
|
||||
|
||||
impl std::error::Error for WasmGraphError {}
|
||||
|
||||
/// Simplified Stoer-Wagner minimum cut for small graphs (<500 nodes).
|
||||
///
|
||||
/// This is a direct implementation of the Stoer-Wagner algorithm that finds
|
||||
/// the global minimum cut in an undirected weighted graph. The algorithm runs
|
||||
/// in O(V^3) time which is acceptable for brain graphs up to ~500 nodes.
|
||||
pub fn wasm_mincut(graph: &BrainGraph) -> Result<MincutResult, WasmGraphError> {
|
||||
let n = graph.num_nodes;
|
||||
if n == 0 {
|
||||
return Err(WasmGraphError("Graph has no nodes".into()));
|
||||
}
|
||||
if n > 500 {
|
||||
return Err(WasmGraphError(format!(
|
||||
"Graph too large for WASM mincut: {} nodes (max 500)",
|
||||
n
|
||||
)));
|
||||
}
|
||||
if n == 1 {
|
||||
return Ok(MincutResult {
|
||||
cut_value: 0.0,
|
||||
partition_a: vec![0],
|
||||
partition_b: vec![],
|
||||
cut_edges: vec![],
|
||||
timestamp: graph.timestamp,
|
||||
});
|
||||
}
|
||||
|
||||
let mut adj = graph.adjacency_matrix();
|
||||
|
||||
// Track which original nodes are merged into each super-node.
|
||||
let mut merged: Vec<Vec<usize>> = (0..n).map(|i| vec![i]).collect();
|
||||
// Track which super-nodes are still active.
|
||||
let mut active: Vec<bool> = vec![true; n];
|
||||
|
||||
let mut best_cut = f64::INFINITY;
|
||||
let mut best_partition_a: Vec<usize> = Vec::new();
|
||||
|
||||
// Stoer-Wagner: perform n-1 minimum cut phases.
|
||||
for _ in 0..n - 1 {
|
||||
let active_nodes: Vec<usize> = (0..n).filter(|&i| active[i]).collect();
|
||||
if active_nodes.len() < 2 {
|
||||
break;
|
||||
}
|
||||
|
||||
// Maximum adjacency ordering.
|
||||
let mut in_set = vec![false; n];
|
||||
let mut w = vec![0.0f64; n]; // key values
|
||||
let mut order: Vec<usize> = Vec::with_capacity(active_nodes.len());
|
||||
|
||||
for _ in 0..active_nodes.len() {
|
||||
// Find the active node not in set with maximum key.
|
||||
let next = active_nodes
|
||||
.iter()
|
||||
.filter(|&&v| !in_set[v])
|
||||
.max_by(|&&a, &&b| w[a].partial_cmp(&w[b]).unwrap_or(std::cmp::Ordering::Equal))
|
||||
.copied()
|
||||
.unwrap();
|
||||
|
||||
in_set[next] = true;
|
||||
order.push(next);
|
||||
|
||||
// Update keys for neighbours.
|
||||
for &v in &active_nodes {
|
||||
if !in_set[v] {
|
||||
w[v] += adj[next][v];
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
// The last two nodes in the ordering.
|
||||
let t = *order.last().unwrap();
|
||||
let s = order[order.len() - 2];
|
||||
|
||||
// Cut of the phase = key of the last added node.
|
||||
let cut_of_phase = w[t];
|
||||
|
||||
if cut_of_phase < best_cut {
|
||||
best_cut = cut_of_phase;
|
||||
best_partition_a = merged[t].clone();
|
||||
}
|
||||
|
||||
// Merge t into s.
|
||||
let t_nodes = merged[t].clone();
|
||||
merged[s].extend(t_nodes);
|
||||
active[t] = false;
|
||||
|
||||
// Update adjacency: merge t into s.
|
||||
for i in 0..n {
|
||||
adj[s][i] += adj[t][i];
|
||||
adj[i][s] += adj[i][t];
|
||||
}
|
||||
adj[s][s] = 0.0;
|
||||
}
|
||||
|
||||
// Build partition B from nodes not in partition A.
|
||||
let partition_a_set: std::collections::HashSet<usize> =
|
||||
best_partition_a.iter().copied().collect();
|
||||
let partition_b: Vec<usize> = (0..n).filter(|i| !partition_a_set.contains(i)).collect();
|
||||
|
||||
// Find cut edges.
|
||||
let cut_edges: Vec<(usize, usize, f64)> = graph
|
||||
.edges
|
||||
.iter()
|
||||
.filter(|e| {
|
||||
(partition_a_set.contains(&e.source) && !partition_a_set.contains(&e.target))
|
||||
|| (!partition_a_set.contains(&e.source) && partition_a_set.contains(&e.target))
|
||||
})
|
||||
.map(|e| (e.source, e.target, e.weight))
|
||||
.collect();
|
||||
|
||||
Ok(MincutResult {
|
||||
cut_value: best_cut,
|
||||
partition_a: best_partition_a,
|
||||
partition_b,
|
||||
cut_edges,
|
||||
timestamp: graph.timestamp,
|
||||
})
|
||||
}
|
||||
|
||||
/// Compute basic topology metrics without heavy linear algebra dependencies.
|
||||
///
|
||||
/// Computes density, degree statistics, clustering coefficient, and graph entropy.
|
||||
/// Fiedler value and global efficiency use simplified approximations suitable for WASM.
|
||||
pub fn wasm_topology_metrics(graph: &BrainGraph) -> Result<TopologyMetrics, WasmGraphError> {
|
||||
let n = graph.num_nodes;
|
||||
if n == 0 {
|
||||
return Err(WasmGraphError("Graph has no nodes".into()));
|
||||
}
|
||||
|
||||
let adj = graph.adjacency_matrix();
|
||||
|
||||
// Density.
|
||||
let _density = graph.density();
|
||||
|
||||
// Degree statistics.
|
||||
let degrees: Vec<f64> = (0..n).map(|i| graph.node_degree(i)).collect();
|
||||
let _mean_degree = degrees.iter().sum::<f64>() / n as f64;
|
||||
|
||||
// Graph entropy from edge weight distribution.
|
||||
let total_weight = graph.total_weight();
|
||||
let graph_entropy = if total_weight > 0.0 {
|
||||
graph
|
||||
.edges
|
||||
.iter()
|
||||
.map(|e| {
|
||||
let p = e.weight / total_weight;
|
||||
if p > 0.0 {
|
||||
-p * p.ln()
|
||||
} else {
|
||||
0.0
|
||||
}
|
||||
})
|
||||
.sum::<f64>()
|
||||
} else {
|
||||
0.0
|
||||
};
|
||||
|
||||
// Approximate global efficiency using shortest paths (Floyd-Warshall for small graphs).
|
||||
let global_efficiency = compute_global_efficiency(&adj, n);
|
||||
|
||||
// Approximate Fiedler value using power iteration on the Laplacian.
|
||||
let fiedler_value = approximate_fiedler(&adj, n);
|
||||
|
||||
// Modularity estimate from mincut (simplified).
|
||||
let mincut_result = wasm_mincut(graph).ok();
|
||||
let (modularity, global_mincut) = if let Some(ref mc) = mincut_result {
|
||||
let q = estimate_modularity(graph, &mc.partition_a, &mc.partition_b);
|
||||
(q, mc.cut_value)
|
||||
} else {
|
||||
(0.0, 0.0)
|
||||
};
|
||||
|
||||
// Local efficiency (average local clustering).
|
||||
let local_efficiency = compute_local_efficiency(&adj, n);
|
||||
|
||||
// Number of modules (using simple threshold-based detection).
|
||||
let num_modules = if modularity > 0.3 { 2 } else { 1 };
|
||||
|
||||
Ok(TopologyMetrics {
|
||||
global_mincut,
|
||||
modularity,
|
||||
global_efficiency,
|
||||
local_efficiency,
|
||||
graph_entropy,
|
||||
fiedler_value,
|
||||
num_modules,
|
||||
timestamp: graph.timestamp,
|
||||
})
|
||||
}
|
||||
|
||||
/// Spectral embedding using power iteration on the graph Laplacian.
|
||||
///
|
||||
/// Computes the `dimension` smallest non-trivial eigenvectors of the normalized
|
||||
/// Laplacian using repeated power iteration with deflation. This avoids any
|
||||
/// dependency on LAPACK/BLAS.
|
||||
pub fn wasm_embed(
|
||||
graph: &BrainGraph,
|
||||
dimension: usize,
|
||||
) -> Result<NeuralEmbedding, WasmGraphError> {
|
||||
let n = graph.num_nodes;
|
||||
if n == 0 {
|
||||
return Err(WasmGraphError("Graph has no nodes".into()));
|
||||
}
|
||||
if dimension == 0 {
|
||||
return Err(WasmGraphError("Embedding dimension must be > 0".into()));
|
||||
}
|
||||
if dimension >= n {
|
||||
return Err(WasmGraphError(format!(
|
||||
"Embedding dimension {} must be < num_nodes {}",
|
||||
dimension, n
|
||||
)));
|
||||
}
|
||||
|
||||
let adj = graph.adjacency_matrix();
|
||||
|
||||
// Build normalized Laplacian: L = D^(-1/2) * (D - A) * D^(-1/2)
|
||||
let degrees: Vec<f64> = (0..n).map(|i| adj[i].iter().sum::<f64>()).collect();
|
||||
let d_inv_sqrt: Vec<f64> = degrees
|
||||
.iter()
|
||||
.map(|&d| if d > 0.0 { 1.0 / d.sqrt() } else { 0.0 })
|
||||
.collect();
|
||||
|
||||
let mut laplacian = vec![vec![0.0f64; n]; n];
|
||||
for i in 0..n {
|
||||
for j in 0..n {
|
||||
if i == j {
|
||||
laplacian[i][j] = if degrees[i] > 0.0 { 1.0 } else { 0.0 };
|
||||
} else {
|
||||
laplacian[i][j] = -adj[i][j] * d_inv_sqrt[i] * d_inv_sqrt[j];
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
// Power iteration with deflation to find smallest eigenvectors.
|
||||
// We invert the problem: find largest eigenvectors of (I - L).
|
||||
let mut inv_l = vec![vec![0.0f64; n]; n];
|
||||
for i in 0..n {
|
||||
for j in 0..n {
|
||||
inv_l[i][j] = if i == j {
|
||||
1.0 - laplacian[i][j]
|
||||
} else {
|
||||
-laplacian[i][j]
|
||||
};
|
||||
}
|
||||
}
|
||||
|
||||
let mut eigenvectors: Vec<Vec<f64>> = Vec::new();
|
||||
let max_iter = 100;
|
||||
|
||||
// Skip the first (trivial) eigenvector, compute `dimension` more.
|
||||
for _ in 0..dimension + 1 {
|
||||
let mut v = vec![0.0f64; n];
|
||||
// Initialize with pseudo-random values based on index.
|
||||
for i in 0..n {
|
||||
v[i] = ((i as f64 + 1.0) * 0.618033988749895).fract() - 0.5;
|
||||
}
|
||||
|
||||
// Orthogonalize against previously found eigenvectors.
|
||||
for ev in &eigenvectors {
|
||||
let dot: f64 = v.iter().zip(ev.iter()).map(|(a, b)| a * b).sum();
|
||||
for i in 0..n {
|
||||
v[i] -= dot * ev[i];
|
||||
}
|
||||
}
|
||||
|
||||
for _ in 0..max_iter {
|
||||
// Multiply: w = inv_l * v
|
||||
let mut w = vec![0.0f64; n];
|
||||
for i in 0..n {
|
||||
for j in 0..n {
|
||||
w[i] += inv_l[i][j] * v[j];
|
||||
}
|
||||
}
|
||||
|
||||
// Orthogonalize against previously found eigenvectors.
|
||||
for ev in &eigenvectors {
|
||||
let dot: f64 = w.iter().zip(ev.iter()).map(|(a, b)| a * b).sum();
|
||||
for i in 0..n {
|
||||
w[i] -= dot * ev[i];
|
||||
}
|
||||
}
|
||||
|
||||
// Normalize.
|
||||
let norm: f64 = w.iter().map(|x| x * x).sum::<f64>().sqrt();
|
||||
if norm > 1e-12 {
|
||||
for x in w.iter_mut() {
|
||||
*x /= norm;
|
||||
}
|
||||
}
|
||||
|
||||
v = w;
|
||||
}
|
||||
|
||||
eigenvectors.push(v);
|
||||
}
|
||||
|
||||
// Skip the first eigenvector (trivial constant vector), take the next `dimension`.
|
||||
let embedding_vectors: Vec<&Vec<f64>> = eigenvectors.iter().skip(1).take(dimension).collect();
|
||||
|
||||
// Build embedding: each node gets a `dimension`-dimensional vector.
|
||||
// We flatten into a single vector of length n * dimension for the NeuralEmbedding.
|
||||
let mut flat_embedding = Vec::with_capacity(n * dimension);
|
||||
for node in 0..n {
|
||||
for ev in &embedding_vectors {
|
||||
flat_embedding.push(ev[node]);
|
||||
}
|
||||
}
|
||||
|
||||
let metadata = EmbeddingMetadata {
|
||||
subject_id: None,
|
||||
session_id: None,
|
||||
cognitive_state: None,
|
||||
source_atlas: graph.atlas,
|
||||
embedding_method: "spectral-power-iteration".to_string(),
|
||||
};
|
||||
|
||||
NeuralEmbedding::new(flat_embedding, graph.timestamp, metadata)
|
||||
.map_err(|e| WasmGraphError(e.to_string()))
|
||||
}
|
||||
|
||||
/// Decode cognitive state from topology metrics using threshold-based rules.
|
||||
///
|
||||
/// This is a simplified heuristic decoder that maps topology metric patterns
|
||||
/// to cognitive states without requiring a trained ML model.
|
||||
pub fn wasm_decode(metrics: &TopologyMetrics) -> Result<CognitiveState, WasmGraphError> {
|
||||
// Simple threshold-based classification based on topology patterns.
|
||||
// In a production system, this would be replaced by the trained decoder
|
||||
// from ruv-neural-decoder.
|
||||
|
||||
let modularity = metrics.modularity;
|
||||
let efficiency = metrics.global_efficiency;
|
||||
let fiedler = metrics.fiedler_value;
|
||||
let entropy = metrics.graph_entropy;
|
||||
|
||||
// High modularity + low efficiency => segregated processing (rest, sleep).
|
||||
if modularity > 0.5 && efficiency < 0.3 {
|
||||
if entropy < 1.0 {
|
||||
return Ok(CognitiveState::Sleep(
|
||||
ruv_neural_core::topology::SleepStage::N3,
|
||||
));
|
||||
}
|
||||
return Ok(CognitiveState::Rest);
|
||||
}
|
||||
|
||||
// Low modularity + high efficiency => integrated processing (focused, creative).
|
||||
if modularity < 0.3 && efficiency > 0.6 {
|
||||
if fiedler > 0.5 {
|
||||
return Ok(CognitiveState::Focused);
|
||||
}
|
||||
return Ok(CognitiveState::Creative);
|
||||
}
|
||||
|
||||
// High entropy => complex distributed processing.
|
||||
if entropy > 3.0 {
|
||||
if efficiency > 0.5 {
|
||||
return Ok(CognitiveState::MemoryRetrieval);
|
||||
}
|
||||
return Ok(CognitiveState::MemoryEncoding);
|
||||
}
|
||||
|
||||
// Medium modularity => motor or speech.
|
||||
if modularity > 0.3 && modularity < 0.5 {
|
||||
if efficiency > 0.5 {
|
||||
return Ok(CognitiveState::MotorPlanning);
|
||||
}
|
||||
return Ok(CognitiveState::SpeechProcessing);
|
||||
}
|
||||
|
||||
// High fiedler + low entropy => stressed/fatigued.
|
||||
if fiedler > 0.7 && entropy < 1.5 {
|
||||
return Ok(CognitiveState::Stressed);
|
||||
}
|
||||
if fiedler < 0.2 && entropy < 1.5 {
|
||||
return Ok(CognitiveState::Fatigued);
|
||||
}
|
||||
|
||||
Ok(CognitiveState::Unknown)
|
||||
}
|
||||
|
||||
// --- Internal helper functions ---
|
||||
|
||||
/// Compute global efficiency using Floyd-Warshall shortest paths.
|
||||
fn compute_global_efficiency(adj: &[Vec<f64>], n: usize) -> f64 {
|
||||
if n < 2 {
|
||||
return 0.0;
|
||||
}
|
||||
|
||||
// Initialize distance matrix with inverse weights (higher weight = shorter distance).
|
||||
let mut dist = vec![vec![f64::INFINITY; n]; n];
|
||||
for i in 0..n {
|
||||
dist[i][i] = 0.0;
|
||||
for j in 0..n {
|
||||
if i != j && adj[i][j] > 0.0 {
|
||||
dist[i][j] = 1.0 / adj[i][j];
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
// Floyd-Warshall.
|
||||
for k in 0..n {
|
||||
for i in 0..n {
|
||||
for j in 0..n {
|
||||
let via_k = dist[i][k] + dist[k][j];
|
||||
if via_k < dist[i][j] {
|
||||
dist[i][j] = via_k;
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
// Global efficiency = mean of (1/d_ij) for all i != j.
|
||||
let mut sum = 0.0;
|
||||
let mut count = 0;
|
||||
for i in 0..n {
|
||||
for j in 0..n {
|
||||
if i != j && dist[i][j].is_finite() && dist[i][j] > 0.0 {
|
||||
sum += 1.0 / dist[i][j];
|
||||
count += 1;
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
if count > 0 {
|
||||
sum / count as f64
|
||||
} else {
|
||||
0.0
|
||||
}
|
||||
}
|
||||
|
||||
/// Approximate the Fiedler value (algebraic connectivity) using power iteration
|
||||
/// on the graph Laplacian.
|
||||
fn approximate_fiedler(adj: &[Vec<f64>], n: usize) -> f64 {
|
||||
if n < 2 {
|
||||
return 0.0;
|
||||
}
|
||||
|
||||
// Build Laplacian: L = D - A
|
||||
let mut laplacian = vec![vec![0.0f64; n]; n];
|
||||
for i in 0..n {
|
||||
let degree: f64 = adj[i].iter().sum();
|
||||
laplacian[i][i] = degree;
|
||||
for j in 0..n {
|
||||
if i != j {
|
||||
laplacian[i][j] = -adj[i][j];
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
// Find second-smallest eigenvalue using inverse power iteration.
|
||||
// First, find the largest eigenvalue to shift the matrix.
|
||||
let mut v = vec![0.0f64; n];
|
||||
for i in 0..n {
|
||||
v[i] = ((i as f64 + 1.0) * 0.618033988749895).fract() - 0.5;
|
||||
}
|
||||
|
||||
// Orthogonalize against the trivial eigenvector (constant vector).
|
||||
let trivial: Vec<f64> = vec![1.0 / (n as f64).sqrt(); n];
|
||||
|
||||
let max_iter = 50;
|
||||
for _ in 0..max_iter {
|
||||
// Multiply: w = L * v
|
||||
let mut w = vec![0.0f64; n];
|
||||
for i in 0..n {
|
||||
for j in 0..n {
|
||||
w[i] += laplacian[i][j] * v[j];
|
||||
}
|
||||
}
|
||||
|
||||
// Orthogonalize against trivial eigenvector.
|
||||
let dot: f64 = w.iter().zip(trivial.iter()).map(|(a, b)| a * b).sum();
|
||||
for i in 0..n {
|
||||
w[i] -= dot * trivial[i];
|
||||
}
|
||||
|
||||
// Normalize.
|
||||
let norm: f64 = w.iter().map(|x| x * x).sum::<f64>().sqrt();
|
||||
if norm > 1e-12 {
|
||||
for x in w.iter_mut() {
|
||||
*x /= norm;
|
||||
}
|
||||
}
|
||||
|
||||
v = w;
|
||||
}
|
||||
|
||||
// Rayleigh quotient: lambda = v^T L v / v^T v
|
||||
let mut vlv = 0.0;
|
||||
for i in 0..n {
|
||||
let mut lv_i = 0.0;
|
||||
for j in 0..n {
|
||||
lv_i += laplacian[i][j] * v[j];
|
||||
}
|
||||
vlv += v[i] * lv_i;
|
||||
}
|
||||
let vtv: f64 = v.iter().map(|x| x * x).sum();
|
||||
|
||||
if vtv > 1e-12 {
|
||||
vlv / vtv
|
||||
} else {
|
||||
0.0
|
||||
}
|
||||
}
|
||||
|
||||
/// Estimate Newman-Girvan modularity for a two-way partition.
|
||||
fn estimate_modularity(
|
||||
graph: &BrainGraph,
|
||||
partition_a: &[usize],
|
||||
partition_b: &[usize],
|
||||
) -> f64 {
|
||||
let total_weight = graph.total_weight();
|
||||
if total_weight == 0.0 {
|
||||
return 0.0;
|
||||
}
|
||||
let m = total_weight; // sum of all edge weights
|
||||
|
||||
let _a_set: std::collections::HashSet<usize> = partition_a.iter().copied().collect();
|
||||
|
||||
let mut q = 0.0;
|
||||
for &i in partition_a {
|
||||
for &j in partition_a {
|
||||
if i != j {
|
||||
let a_ij = graph.edge_weight(i, j).unwrap_or(0.0);
|
||||
let k_i = graph.node_degree(i);
|
||||
let k_j = graph.node_degree(j);
|
||||
q += a_ij - (k_i * k_j) / (2.0 * m);
|
||||
}
|
||||
}
|
||||
}
|
||||
for &i in partition_b {
|
||||
for &j in partition_b {
|
||||
if i != j {
|
||||
let a_ij = graph.edge_weight(i, j).unwrap_or(0.0);
|
||||
let k_i = graph.node_degree(i);
|
||||
let k_j = graph.node_degree(j);
|
||||
q += a_ij - (k_i * k_j) / (2.0 * m);
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
q / (2.0 * m)
|
||||
}
|
||||
|
||||
/// Compute mean local efficiency (average clustering coefficient approximation).
|
||||
fn compute_local_efficiency(adj: &[Vec<f64>], n: usize) -> f64 {
|
||||
if n < 3 {
|
||||
return 0.0;
|
||||
}
|
||||
|
||||
let mut total_cc = 0.0;
|
||||
for i in 0..n {
|
||||
let neighbors: Vec<usize> = (0..n).filter(|&j| j != i && adj[i][j] > 0.0).collect();
|
||||
let k = neighbors.len();
|
||||
if k < 2 {
|
||||
continue;
|
||||
}
|
||||
|
||||
// Count weighted triangles.
|
||||
let mut triangle_weight = 0.0;
|
||||
for &u in &neighbors {
|
||||
for &v in &neighbors {
|
||||
if u < v && adj[u][v] > 0.0 {
|
||||
// Weighted triangle contribution.
|
||||
triangle_weight +=
|
||||
(adj[i][u] * adj[i][v] * adj[u][v]).cbrt();
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
let max_triangles = (k * (k - 1)) as f64 / 2.0;
|
||||
if max_triangles > 0.0 {
|
||||
// Normalize by the maximum possible strength.
|
||||
let max_weight = adj[i]
|
||||
.iter()
|
||||
.filter(|&&w| w > 0.0)
|
||||
.cloned()
|
||||
.fold(0.0f64, f64::max);
|
||||
let denom = max_triangles * max_weight;
|
||||
if denom > 0.0 {
|
||||
total_cc += triangle_weight / denom;
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
total_cc / n as f64
|
||||
}
|
||||
|
||||
#[cfg(test)]
|
||||
mod tests {
|
||||
use super::*;
|
||||
use ruv_neural_core::brain::Atlas;
|
||||
use ruv_neural_core::graph::{BrainEdge, BrainGraph};
|
||||
use ruv_neural_core::signal::FrequencyBand;
|
||||
|
||||
fn make_test_graph() -> BrainGraph {
|
||||
// Simple 4-node graph with a clear 2-way cut:
|
||||
// 0 -- 1 (weight 5.0)
|
||||
// 2 -- 3 (weight 5.0)
|
||||
// 1 -- 2 (weight 0.1) <-- this is the cut edge
|
||||
BrainGraph {
|
||||
num_nodes: 4,
|
||||
edges: vec![
|
||||
BrainEdge {
|
||||
source: 0,
|
||||
target: 1,
|
||||
weight: 5.0,
|
||||
metric: ruv_neural_core::graph::ConnectivityMetric::Coherence,
|
||||
frequency_band: FrequencyBand::Alpha,
|
||||
},
|
||||
BrainEdge {
|
||||
source: 2,
|
||||
target: 3,
|
||||
weight: 5.0,
|
||||
metric: ruv_neural_core::graph::ConnectivityMetric::Coherence,
|
||||
frequency_band: FrequencyBand::Alpha,
|
||||
},
|
||||
BrainEdge {
|
||||
source: 1,
|
||||
target: 2,
|
||||
weight: 0.1,
|
||||
metric: ruv_neural_core::graph::ConnectivityMetric::Coherence,
|
||||
frequency_band: FrequencyBand::Alpha,
|
||||
},
|
||||
],
|
||||
timestamp: 1000.0,
|
||||
window_duration_s: 1.0,
|
||||
atlas: Atlas::Custom(4),
|
||||
}
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_wasm_mincut_finds_cut() {
|
||||
let graph = make_test_graph();
|
||||
let result = wasm_mincut(&graph).unwrap();
|
||||
// The minimum cut should separate {0,1} from {2,3} with value 0.1.
|
||||
assert!((result.cut_value - 0.1).abs() < 1e-6);
|
||||
assert_eq!(result.num_cut_edges(), 1);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_wasm_mincut_single_node() {
|
||||
let graph = BrainGraph {
|
||||
num_nodes: 1,
|
||||
edges: vec![],
|
||||
timestamp: 0.0,
|
||||
window_duration_s: 1.0,
|
||||
atlas: Atlas::Custom(1),
|
||||
};
|
||||
let result = wasm_mincut(&graph).unwrap();
|
||||
assert_eq!(result.cut_value, 0.0);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_wasm_topology_metrics() {
|
||||
let graph = make_test_graph();
|
||||
let metrics = wasm_topology_metrics(&graph).unwrap();
|
||||
assert!(metrics.global_mincut >= 0.0);
|
||||
assert!(metrics.graph_entropy >= 0.0);
|
||||
assert!(metrics.fiedler_value >= 0.0);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_wasm_embed() {
|
||||
let graph = make_test_graph();
|
||||
let embedding = wasm_embed(&graph, 2).unwrap();
|
||||
// 4 nodes x 2 dimensions = 8 values.
|
||||
assert_eq!(embedding.vector.len(), 8);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_wasm_decode_sleep() {
|
||||
let metrics = TopologyMetrics {
|
||||
global_mincut: 0.1,
|
||||
modularity: 0.6,
|
||||
global_efficiency: 0.2,
|
||||
local_efficiency: 0.3,
|
||||
graph_entropy: 0.5,
|
||||
fiedler_value: 0.3,
|
||||
num_modules: 2,
|
||||
timestamp: 0.0,
|
||||
};
|
||||
let state = wasm_decode(&metrics).unwrap();
|
||||
// High modularity + low efficiency + low entropy => deep sleep.
|
||||
assert_eq!(
|
||||
state,
|
||||
CognitiveState::Sleep(ruv_neural_core::topology::SleepStage::N3)
|
||||
);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_wasm_decode_rest() {
|
||||
let metrics = TopologyMetrics {
|
||||
global_mincut: 0.1,
|
||||
modularity: 0.6,
|
||||
global_efficiency: 0.2,
|
||||
local_efficiency: 0.3,
|
||||
graph_entropy: 1.5,
|
||||
fiedler_value: 0.3,
|
||||
num_modules: 2,
|
||||
timestamp: 0.0,
|
||||
};
|
||||
let state = wasm_decode(&metrics).unwrap();
|
||||
// High modularity + low efficiency + moderate entropy => rest.
|
||||
assert_eq!(state, CognitiveState::Rest);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_wasm_mincut_empty_graph() {
|
||||
let graph = BrainGraph {
|
||||
num_nodes: 0,
|
||||
edges: vec![],
|
||||
timestamp: 0.0,
|
||||
window_duration_s: 1.0,
|
||||
atlas: Atlas::Custom(0),
|
||||
};
|
||||
assert!(wasm_mincut(&graph).is_err());
|
||||
}
|
||||
}
|
||||
@@ -1 +1,305 @@
|
||||
//! Stub crate.
|
||||
//! rUv Neural WASM — WebAssembly bindings for browser-based brain topology visualization.
|
||||
//!
|
||||
//! This crate provides JavaScript-callable functions for creating, analyzing, and
|
||||
//! visualizing brain connectivity graphs directly in the browser. It wraps the
|
||||
//! core `ruv-neural-core` types with `wasm-bindgen` bindings and provides
|
||||
//! lightweight WASM-compatible implementations of graph algorithms.
|
||||
//!
|
||||
//! # Features
|
||||
//!
|
||||
//! - Parse brain graphs from JSON and return JS-compatible objects
|
||||
//! - Compute minimum cut (Stoer-Wagner) on graphs up to 500 nodes
|
||||
//! - Generate topology metrics (density, efficiency, modularity, Fiedler value)
|
||||
//! - Spectral embedding via power iteration (no LAPACK dependency)
|
||||
//! - Decode cognitive state from topology metrics
|
||||
//! - RVF file format load/export
|
||||
//! - Streaming data processor for WebSocket integration
|
||||
//! - Visualization data structures for D3.js / Three.js
|
||||
|
||||
pub mod graph_wasm;
|
||||
pub mod streaming;
|
||||
pub mod viz_data;
|
||||
|
||||
use ruv_neural_core::graph::BrainGraph;
|
||||
use ruv_neural_core::rvf::{RvfDataType, RvfFile};
|
||||
use ruv_neural_core::topology::TopologyMetrics;
|
||||
use wasm_bindgen::prelude::*;
|
||||
|
||||
use graph_wasm::{wasm_decode, wasm_embed, wasm_mincut, wasm_topology_metrics};
|
||||
|
||||
/// Initialize the WASM module.
|
||||
///
|
||||
/// Called automatically when the module is loaded. Sets up panic hooks
|
||||
/// for better error messages in the browser console.
|
||||
#[wasm_bindgen(start)]
|
||||
pub fn init() {
|
||||
#[cfg(feature = "console_error_panic_hook")]
|
||||
console_error_panic_hook::set_once();
|
||||
}
|
||||
|
||||
/// Create a brain graph from JSON data.
|
||||
///
|
||||
/// Parses a JSON string into a `BrainGraph` and returns it as a JS object.
|
||||
///
|
||||
/// # Arguments
|
||||
/// * `json_data` - JSON string representing a `BrainGraph`.
|
||||
///
|
||||
/// # Returns
|
||||
/// A JS object containing the parsed graph data.
|
||||
#[wasm_bindgen]
|
||||
pub fn create_brain_graph(json_data: &str) -> Result<JsValue, JsError> {
|
||||
let graph: BrainGraph =
|
||||
serde_json::from_str(json_data).map_err(|e| JsError::new(&e.to_string()))?;
|
||||
serde_wasm_bindgen::to_value(&graph).map_err(|e| JsError::new(&e.to_string()))
|
||||
}
|
||||
|
||||
/// Compute minimum cut on a brain graph.
|
||||
///
|
||||
/// Uses a simplified Stoer-Wagner algorithm suitable for graphs with up to
|
||||
/// 500 nodes. Returns the cut value, partitions, and cut edges.
|
||||
///
|
||||
/// # Arguments
|
||||
/// * `json_graph` - JSON string representing a `BrainGraph`.
|
||||
///
|
||||
/// # Returns
|
||||
/// A JS object containing the `MincutResult`.
|
||||
#[wasm_bindgen]
|
||||
pub fn compute_mincut(json_graph: &str) -> Result<JsValue, JsError> {
|
||||
let graph: BrainGraph =
|
||||
serde_json::from_str(json_graph).map_err(|e| JsError::new(&e.to_string()))?;
|
||||
let result = wasm_mincut(&graph)?;
|
||||
serde_wasm_bindgen::to_value(&result).map_err(|e| JsError::new(&e.to_string()))
|
||||
}
|
||||
|
||||
/// Compute topology metrics for a brain graph.
|
||||
///
|
||||
/// Returns density, efficiency, modularity, Fiedler value, entropy, and
|
||||
/// module count. All computations use WASM-compatible algorithms without
|
||||
/// heavy linear algebra dependencies.
|
||||
///
|
||||
/// # Arguments
|
||||
/// * `json_graph` - JSON string representing a `BrainGraph`.
|
||||
///
|
||||
/// # Returns
|
||||
/// A JS object containing the `TopologyMetrics`.
|
||||
#[wasm_bindgen]
|
||||
pub fn compute_topology_metrics(json_graph: &str) -> Result<JsValue, JsError> {
|
||||
let graph: BrainGraph =
|
||||
serde_json::from_str(json_graph).map_err(|e| JsError::new(&e.to_string()))?;
|
||||
let metrics = wasm_topology_metrics(&graph)?;
|
||||
serde_wasm_bindgen::to_value(&metrics).map_err(|e| JsError::new(&e.to_string()))
|
||||
}
|
||||
|
||||
/// Generate a spectral embedding from a brain graph.
|
||||
///
|
||||
/// Uses power iteration on the normalized Laplacian to compute spectral
|
||||
/// coordinates. Returns a flat vector of length `num_nodes * dimension`.
|
||||
///
|
||||
/// # Arguments
|
||||
/// * `json_graph` - JSON string representing a `BrainGraph`.
|
||||
/// * `dimension` - Number of embedding dimensions.
|
||||
///
|
||||
/// # Returns
|
||||
/// A JS object containing the `NeuralEmbedding`.
|
||||
#[wasm_bindgen]
|
||||
pub fn embed_graph(json_graph: &str, dimension: usize) -> Result<JsValue, JsError> {
|
||||
let graph: BrainGraph =
|
||||
serde_json::from_str(json_graph).map_err(|e| JsError::new(&e.to_string()))?;
|
||||
let embedding = wasm_embed(&graph, dimension)?;
|
||||
serde_wasm_bindgen::to_value(&embedding).map_err(|e| JsError::new(&e.to_string()))
|
||||
}
|
||||
|
||||
/// Decode cognitive state from topology metrics.
|
||||
///
|
||||
/// Uses threshold-based heuristics to classify the cognitive state
|
||||
/// from a set of topology metrics. For production use, the trained
|
||||
/// decoder from `ruv-neural-decoder` is recommended.
|
||||
///
|
||||
/// # Arguments
|
||||
/// * `json_metrics` - JSON string representing `TopologyMetrics`.
|
||||
///
|
||||
/// # Returns
|
||||
/// A JS object containing the decoded `CognitiveState`.
|
||||
#[wasm_bindgen]
|
||||
pub fn decode_state(json_metrics: &str) -> Result<JsValue, JsError> {
|
||||
let metrics: TopologyMetrics =
|
||||
serde_json::from_str(json_metrics).map_err(|e| JsError::new(&e.to_string()))?;
|
||||
let state = wasm_decode(&metrics)?;
|
||||
serde_wasm_bindgen::to_value(&state).map_err(|e| JsError::new(&e.to_string()))
|
||||
}
|
||||
|
||||
/// Load an RVF (RuVector File) from raw bytes.
|
||||
///
|
||||
/// Parses the binary RVF header, JSON metadata, and payload, returning
|
||||
/// the complete file structure as a JS object.
|
||||
///
|
||||
/// # Arguments
|
||||
/// * `data` - Raw bytes of the RVF file.
|
||||
///
|
||||
/// # Returns
|
||||
/// A JS object containing the parsed `RvfFile`.
|
||||
#[wasm_bindgen]
|
||||
pub fn load_rvf(data: &[u8]) -> Result<JsValue, JsError> {
|
||||
let mut cursor = std::io::Cursor::new(data);
|
||||
let rvf = RvfFile::read_from(&mut cursor).map_err(|e| JsError::new(&e.to_string()))?;
|
||||
serde_wasm_bindgen::to_value(&rvf).map_err(|e| JsError::new(&e.to_string()))
|
||||
}
|
||||
|
||||
/// Export a brain graph as RVF bytes.
|
||||
///
|
||||
/// Serializes a `BrainGraph` (provided as JSON) into the binary RVF format.
|
||||
///
|
||||
/// # Arguments
|
||||
/// * `json_graph` - JSON string representing a `BrainGraph`.
|
||||
///
|
||||
/// # Returns
|
||||
/// A `Vec<u8>` containing the RVF binary data.
|
||||
#[wasm_bindgen]
|
||||
pub fn export_rvf(json_graph: &str) -> Result<Vec<u8>, JsError> {
|
||||
let graph: BrainGraph =
|
||||
serde_json::from_str(json_graph).map_err(|e| JsError::new(&e.to_string()))?;
|
||||
|
||||
let graph_json =
|
||||
serde_json::to_vec(&graph).map_err(|e| JsError::new(&e.to_string()))?;
|
||||
|
||||
let mut rvf = RvfFile::new(RvfDataType::BrainGraph);
|
||||
rvf.header.num_entries = 1;
|
||||
rvf.metadata = serde_json::json!({
|
||||
"num_nodes": graph.num_nodes,
|
||||
"num_edges": graph.edges.len(),
|
||||
"timestamp": graph.timestamp,
|
||||
});
|
||||
rvf.data = graph_json;
|
||||
|
||||
let mut buf = Vec::new();
|
||||
rvf.write_to(&mut buf)
|
||||
.map_err(|e| JsError::new(&e.to_string()))?;
|
||||
|
||||
Ok(buf)
|
||||
}
|
||||
|
||||
/// Get the crate version string.
|
||||
#[wasm_bindgen]
|
||||
pub fn version() -> String {
|
||||
env!("CARGO_PKG_VERSION").to_string()
|
||||
}
|
||||
|
||||
#[cfg(test)]
|
||||
mod tests {
|
||||
use super::*;
|
||||
use ruv_neural_core::brain::Atlas;
|
||||
use ruv_neural_core::graph::{BrainEdge, BrainGraph};
|
||||
use ruv_neural_core::signal::FrequencyBand;
|
||||
|
||||
fn sample_graph_json() -> String {
|
||||
let graph = BrainGraph {
|
||||
num_nodes: 3,
|
||||
edges: vec![
|
||||
BrainEdge {
|
||||
source: 0,
|
||||
target: 1,
|
||||
weight: 0.8,
|
||||
metric: ruv_neural_core::graph::ConnectivityMetric::Coherence,
|
||||
frequency_band: FrequencyBand::Alpha,
|
||||
},
|
||||
BrainEdge {
|
||||
source: 1,
|
||||
target: 2,
|
||||
weight: 0.5,
|
||||
metric: ruv_neural_core::graph::ConnectivityMetric::Coherence,
|
||||
frequency_band: FrequencyBand::Beta,
|
||||
},
|
||||
],
|
||||
timestamp: 1000.0,
|
||||
window_duration_s: 1.0,
|
||||
atlas: Atlas::Custom(3),
|
||||
};
|
||||
serde_json::to_string(&graph).unwrap()
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_create_brain_graph_parses_valid_json() {
|
||||
let json = sample_graph_json();
|
||||
let graph: BrainGraph = serde_json::from_str(&json).unwrap();
|
||||
assert_eq!(graph.num_nodes, 3);
|
||||
assert_eq!(graph.edges.len(), 2);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_create_brain_graph_rejects_invalid_json() {
|
||||
let result: Result<BrainGraph, _> = serde_json::from_str("not valid json");
|
||||
assert!(result.is_err());
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_compute_mincut_returns_valid_result() {
|
||||
let json = sample_graph_json();
|
||||
let graph: BrainGraph = serde_json::from_str(&json).unwrap();
|
||||
let result = wasm_mincut(&graph).unwrap();
|
||||
assert!(result.cut_value >= 0.0);
|
||||
assert_eq!(result.num_nodes(), 3);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_rvf_round_trip() {
|
||||
let json = sample_graph_json();
|
||||
let graph: BrainGraph = serde_json::from_str(&json).unwrap();
|
||||
|
||||
// Export to RVF bytes.
|
||||
let graph_bytes = serde_json::to_vec(&graph).unwrap();
|
||||
let mut rvf = RvfFile::new(RvfDataType::BrainGraph);
|
||||
rvf.header.num_entries = 1;
|
||||
rvf.metadata = serde_json::json!({"test": true});
|
||||
rvf.data = graph_bytes;
|
||||
|
||||
let mut buf = Vec::new();
|
||||
rvf.write_to(&mut buf).unwrap();
|
||||
|
||||
// Read back.
|
||||
let mut cursor = std::io::Cursor::new(&buf);
|
||||
let loaded = RvfFile::read_from(&mut cursor).unwrap();
|
||||
|
||||
assert_eq!(loaded.header.data_type, RvfDataType::BrainGraph);
|
||||
assert_eq!(loaded.header.num_entries, 1);
|
||||
|
||||
// Deserialize the payload back to a BrainGraph.
|
||||
let loaded_graph: BrainGraph = serde_json::from_slice(&loaded.data).unwrap();
|
||||
assert_eq!(loaded_graph.num_nodes, 3);
|
||||
assert_eq!(loaded_graph.edges.len(), 2);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_version_returns_string() {
|
||||
let v = version();
|
||||
assert!(!v.is_empty());
|
||||
assert!(v.contains('.'));
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_decode_state_from_metrics() {
|
||||
let metrics = TopologyMetrics {
|
||||
global_mincut: 0.5,
|
||||
modularity: 0.6,
|
||||
global_efficiency: 0.2,
|
||||
local_efficiency: 0.3,
|
||||
graph_entropy: 1.5,
|
||||
fiedler_value: 0.3,
|
||||
num_modules: 2,
|
||||
timestamp: 0.0,
|
||||
};
|
||||
let state = wasm_decode(&metrics).unwrap();
|
||||
// High modularity + low efficiency + moderate entropy => Rest.
|
||||
assert_eq!(
|
||||
state,
|
||||
ruv_neural_core::topology::CognitiveState::Rest
|
||||
);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_embed_graph_produces_correct_dimensions() {
|
||||
let json = sample_graph_json();
|
||||
let graph: BrainGraph = serde_json::from_str(&json).unwrap();
|
||||
let embedding = wasm_embed(&graph, 2).unwrap();
|
||||
assert_eq!(embedding.vector.len(), 6);
|
||||
}
|
||||
}
|
||||
|
||||
@@ -0,0 +1,217 @@
|
||||
//! WebSocket streaming support for real-time neural data processing.
|
||||
//!
|
||||
//! Provides a `StreamProcessor` that accumulates incoming neural samples,
|
||||
//! applies a sliding window, and emits updated topology metrics whenever
|
||||
//! a complete window is available.
|
||||
|
||||
use serde::{Deserialize, Serialize};
|
||||
use wasm_bindgen::prelude::*;
|
||||
|
||||
/// Streaming neural data processor with a sliding window.
|
||||
///
|
||||
/// Accumulates incoming samples and produces topology metric updates
|
||||
/// whenever enough data fills a window. Designed for use with WebSocket
|
||||
/// connections in the browser.
|
||||
#[wasm_bindgen]
|
||||
pub struct StreamProcessor {
|
||||
/// Internal sample buffer.
|
||||
buffer: Vec<f64>,
|
||||
/// Number of samples in a complete analysis window.
|
||||
window_size: usize,
|
||||
/// Number of samples to advance between windows (hop size).
|
||||
step_size: usize,
|
||||
/// Number of windows emitted so far.
|
||||
windows_emitted: u64,
|
||||
}
|
||||
|
||||
/// Summary statistics for a single window of streaming data.
|
||||
#[derive(Debug, Clone, Serialize, Deserialize, PartialEq)]
|
||||
pub struct WindowStats {
|
||||
/// Mean value of samples in the window.
|
||||
pub mean: f64,
|
||||
/// Variance of samples in the window.
|
||||
pub variance: f64,
|
||||
/// Minimum sample value.
|
||||
pub min: f64,
|
||||
/// Maximum sample value.
|
||||
pub max: f64,
|
||||
/// Number of samples in the window.
|
||||
pub window_size: usize,
|
||||
/// Sequential window index.
|
||||
pub window_index: u64,
|
||||
}
|
||||
|
||||
#[wasm_bindgen]
|
||||
impl StreamProcessor {
|
||||
/// Create a new `StreamProcessor`.
|
||||
///
|
||||
/// # Arguments
|
||||
/// * `window_size` - Number of samples in each analysis window.
|
||||
/// * `step_size` - Number of samples to advance between windows (hop size).
|
||||
#[wasm_bindgen(constructor)]
|
||||
pub fn new(window_size: usize, step_size: usize) -> Self {
|
||||
let step_size = if step_size == 0 { 1 } else { step_size };
|
||||
Self {
|
||||
buffer: Vec::with_capacity(window_size),
|
||||
window_size,
|
||||
step_size,
|
||||
windows_emitted: 0,
|
||||
}
|
||||
}
|
||||
|
||||
/// Push new samples into the buffer and return window statistics
|
||||
/// if a complete window is available.
|
||||
///
|
||||
/// Returns `null` if not enough samples have accumulated yet.
|
||||
/// When a window is complete, computes statistics and advances
|
||||
/// the buffer by `step_size` samples.
|
||||
pub fn push_samples(&mut self, samples: &[f64]) -> Option<JsValue> {
|
||||
let stats = self.push_samples_native(samples)?;
|
||||
serde_wasm_bindgen::to_value(&stats).ok()
|
||||
}
|
||||
|
||||
/// Reset the internal buffer and window counter.
|
||||
pub fn reset(&mut self) {
|
||||
self.buffer.clear();
|
||||
self.windows_emitted = 0;
|
||||
}
|
||||
|
||||
/// Get the current number of buffered samples.
|
||||
pub fn buffered_count(&self) -> usize {
|
||||
self.buffer.len()
|
||||
}
|
||||
|
||||
/// Get the number of windows emitted so far.
|
||||
pub fn windows_emitted(&self) -> u64 {
|
||||
self.windows_emitted
|
||||
}
|
||||
|
||||
/// Get the configured window size.
|
||||
pub fn window_size(&self) -> usize {
|
||||
self.window_size
|
||||
}
|
||||
|
||||
/// Get the configured step size.
|
||||
pub fn step_size(&self) -> usize {
|
||||
self.step_size
|
||||
}
|
||||
}
|
||||
|
||||
impl StreamProcessor {
|
||||
/// Push samples and return native `WindowStats` (usable without WASM runtime).
|
||||
pub fn push_samples_native(&mut self, samples: &[f64]) -> Option<WindowStats> {
|
||||
self.buffer.extend_from_slice(samples);
|
||||
|
||||
if self.buffer.len() >= self.window_size {
|
||||
let window = &self.buffer[..self.window_size];
|
||||
let stats = compute_window_stats(window, self.windows_emitted);
|
||||
self.windows_emitted += 1;
|
||||
|
||||
// Advance buffer by step_size.
|
||||
let drain_count = self.step_size.min(self.buffer.len());
|
||||
self.buffer.drain(..drain_count);
|
||||
|
||||
Some(stats)
|
||||
} else {
|
||||
None
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
/// Compute basic statistics over a sample window.
|
||||
fn compute_window_stats(window: &[f64], window_index: u64) -> WindowStats {
|
||||
let n = window.len() as f64;
|
||||
let sum: f64 = window.iter().sum();
|
||||
let mean = sum / n;
|
||||
|
||||
let variance = window.iter().map(|x| (x - mean).powi(2)).sum::<f64>() / n;
|
||||
|
||||
let min = window
|
||||
.iter()
|
||||
.cloned()
|
||||
.fold(f64::INFINITY, f64::min);
|
||||
let max = window
|
||||
.iter()
|
||||
.cloned()
|
||||
.fold(f64::NEG_INFINITY, f64::max);
|
||||
|
||||
WindowStats {
|
||||
mean,
|
||||
variance,
|
||||
min,
|
||||
max,
|
||||
window_size: window.len(),
|
||||
window_index,
|
||||
}
|
||||
}
|
||||
|
||||
#[cfg(test)]
|
||||
mod tests {
|
||||
use super::*;
|
||||
|
||||
#[test]
|
||||
fn test_stream_processor_accumulates() {
|
||||
let mut proc = StreamProcessor::new(10, 5);
|
||||
assert_eq!(proc.buffered_count(), 0);
|
||||
|
||||
// Push 5 samples (not enough for a window).
|
||||
let result = proc.push_samples_native(&[1.0, 2.0, 3.0, 4.0, 5.0]);
|
||||
assert!(result.is_none());
|
||||
assert_eq!(proc.buffered_count(), 5);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_stream_processor_emits_on_full_window() {
|
||||
let mut proc = StreamProcessor::new(4, 2);
|
||||
|
||||
// Push exactly 4 samples.
|
||||
let result = proc.push_samples_native(&[1.0, 2.0, 3.0, 4.0]);
|
||||
assert!(result.is_some());
|
||||
let stats = result.unwrap();
|
||||
assert!((stats.mean - 2.5).abs() < 1e-10);
|
||||
assert_eq!(proc.windows_emitted(), 1);
|
||||
// After step of 2, buffer should have 2 remaining.
|
||||
assert_eq!(proc.buffered_count(), 2);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_stream_processor_reset() {
|
||||
let mut proc = StreamProcessor::new(4, 2);
|
||||
proc.push_samples_native(&[1.0, 2.0, 3.0, 4.0]);
|
||||
proc.reset();
|
||||
assert_eq!(proc.buffered_count(), 0);
|
||||
assert_eq!(proc.windows_emitted(), 0);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_window_stats_computation() {
|
||||
let window = [2.0, 4.0, 6.0, 8.0];
|
||||
let stats = compute_window_stats(&window, 0);
|
||||
assert!((stats.mean - 5.0).abs() < 1e-10);
|
||||
assert!((stats.variance - 5.0).abs() < 1e-10);
|
||||
assert!((stats.min - 2.0).abs() < 1e-10);
|
||||
assert!((stats.max - 8.0).abs() < 1e-10);
|
||||
assert_eq!(stats.window_size, 4);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_stream_processor_zero_step_defaults_to_one() {
|
||||
let proc = StreamProcessor::new(4, 0);
|
||||
assert_eq!(proc.step_size(), 1);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_multiple_windows() {
|
||||
let mut proc = StreamProcessor::new(3, 1);
|
||||
|
||||
// Push 5 samples: should emit window at sample 3.
|
||||
let result = proc.push_samples_native(&[1.0, 2.0, 3.0]);
|
||||
assert!(result.is_some());
|
||||
assert_eq!(proc.windows_emitted(), 1);
|
||||
|
||||
// Push 1 more: buffer should be [2,3,X], then with new sample [2,3,4].
|
||||
let result = proc.push_samples_native(&[4.0]);
|
||||
assert!(result.is_some());
|
||||
assert_eq!(proc.windows_emitted(), 2);
|
||||
}
|
||||
}
|
||||
@@ -0,0 +1,247 @@
|
||||
//! Visualization data structures for JavaScript rendering.
|
||||
//!
|
||||
//! Provides types formatted for direct consumption by D3.js and Three.js
|
||||
//! visualization libraries. Includes force-directed layout positioning
|
||||
//! and partition coloring.
|
||||
|
||||
use ruv_neural_core::graph::BrainGraph;
|
||||
use serde::{Deserialize, Serialize};
|
||||
use wasm_bindgen::prelude::*;
|
||||
|
||||
use crate::graph_wasm::wasm_mincut;
|
||||
|
||||
/// Graph data formatted for D3.js / Three.js visualization.
|
||||
#[derive(Debug, Clone, Serialize, Deserialize)]
|
||||
pub struct VizGraph {
|
||||
/// Nodes with positions and visual attributes.
|
||||
pub nodes: Vec<VizNode>,
|
||||
/// Edges with visual attributes.
|
||||
pub edges: Vec<VizEdge>,
|
||||
/// Optional partition assignments (list of node-index groups).
|
||||
pub partitions: Option<Vec<Vec<usize>>>,
|
||||
/// Optional indices into `edges` that are cut edges.
|
||||
pub cut_edges: Option<Vec<usize>>,
|
||||
}
|
||||
|
||||
/// A single node in the visualization graph.
|
||||
#[derive(Debug, Clone, Serialize, Deserialize)]
|
||||
pub struct VizNode {
|
||||
/// Node index.
|
||||
pub id: usize,
|
||||
/// Human-readable label.
|
||||
pub label: String,
|
||||
/// X position (layout coordinate).
|
||||
pub x: f64,
|
||||
/// Y position (layout coordinate).
|
||||
pub y: f64,
|
||||
/// Z position (layout coordinate, for 3D views).
|
||||
pub z: f64,
|
||||
/// Module/partition membership group.
|
||||
pub group: usize,
|
||||
/// Node importance (e.g., weighted degree).
|
||||
pub size: f64,
|
||||
/// Hex color string (e.g., "#ff6600").
|
||||
pub color: String,
|
||||
}
|
||||
|
||||
/// A single edge in the visualization graph.
|
||||
#[derive(Debug, Clone, Serialize, Deserialize)]
|
||||
pub struct VizEdge {
|
||||
/// Source node index.
|
||||
pub source: usize,
|
||||
/// Target node index.
|
||||
pub target: usize,
|
||||
/// Edge weight.
|
||||
pub weight: f64,
|
||||
/// Whether this edge crosses a partition boundary.
|
||||
pub is_cut: bool,
|
||||
/// Hex color string.
|
||||
pub color: String,
|
||||
}
|
||||
|
||||
/// Default color palette for partition groups.
|
||||
const GROUP_COLORS: &[&str] = &[
|
||||
"#4285f4", // Blue
|
||||
"#ea4335", // Red
|
||||
"#fbbc05", // Yellow
|
||||
"#34a853", // Green
|
||||
"#ff6d01", // Orange
|
||||
"#46bdc6", // Teal
|
||||
"#7b1fa2", // Purple
|
||||
"#c2185b", // Pink
|
||||
];
|
||||
|
||||
/// Convert a `BrainGraph` to a `VizGraph` with force-directed layout positions.
|
||||
pub fn create_viz_graph(graph: &BrainGraph) -> VizGraph {
|
||||
let n = graph.num_nodes;
|
||||
|
||||
// Compute partitions via mincut (if graph is small enough).
|
||||
let mincut_result = if n > 0 && n <= 500 {
|
||||
wasm_mincut(graph).ok()
|
||||
} else {
|
||||
None
|
||||
};
|
||||
|
||||
// Build partition membership map.
|
||||
let mut node_group = vec![0usize; n];
|
||||
if let Some(ref mc) = mincut_result {
|
||||
for &idx in &mc.partition_b {
|
||||
if idx < n {
|
||||
node_group[idx] = 1;
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
// Compute initial layout using a simple circular arrangement
|
||||
// (JavaScript side typically re-layouts with D3 force simulation).
|
||||
let mut nodes = Vec::with_capacity(n);
|
||||
for i in 0..n {
|
||||
let angle = 2.0 * std::f64::consts::PI * (i as f64) / (n.max(1) as f64);
|
||||
let radius = 100.0;
|
||||
let group = node_group[i];
|
||||
let degree = graph.node_degree(i);
|
||||
|
||||
nodes.push(VizNode {
|
||||
id: i,
|
||||
label: format!("R{}", i),
|
||||
x: radius * angle.cos(),
|
||||
y: radius * angle.sin(),
|
||||
z: 0.0,
|
||||
group,
|
||||
size: (degree + 1.0).ln(), // Log-scaled importance
|
||||
color: GROUP_COLORS[group % GROUP_COLORS.len()].to_string(),
|
||||
});
|
||||
}
|
||||
|
||||
// Build cut-edge set for coloring.
|
||||
let cut_edge_set: std::collections::HashSet<(usize, usize)> = mincut_result
|
||||
.as_ref()
|
||||
.map(|mc| {
|
||||
mc.cut_edges
|
||||
.iter()
|
||||
.flat_map(|&(s, t, _)| vec![(s, t), (t, s)])
|
||||
.collect()
|
||||
})
|
||||
.unwrap_or_default();
|
||||
|
||||
let mut edges = Vec::with_capacity(graph.edges.len());
|
||||
let mut cut_edge_indices = Vec::new();
|
||||
|
||||
for (idx, edge) in graph.edges.iter().enumerate() {
|
||||
let is_cut = cut_edge_set.contains(&(edge.source, edge.target));
|
||||
if is_cut {
|
||||
cut_edge_indices.push(idx);
|
||||
}
|
||||
edges.push(VizEdge {
|
||||
source: edge.source,
|
||||
target: edge.target,
|
||||
weight: edge.weight,
|
||||
is_cut,
|
||||
color: if is_cut {
|
||||
"#ff0000".to_string()
|
||||
} else {
|
||||
"#999999".to_string()
|
||||
},
|
||||
});
|
||||
}
|
||||
|
||||
let partitions = mincut_result.map(|mc| vec![mc.partition_a, mc.partition_b]);
|
||||
|
||||
VizGraph {
|
||||
nodes,
|
||||
edges,
|
||||
partitions,
|
||||
cut_edges: if cut_edge_indices.is_empty() {
|
||||
None
|
||||
} else {
|
||||
Some(cut_edge_indices)
|
||||
},
|
||||
}
|
||||
}
|
||||
|
||||
/// Convert a `BrainGraph` JSON string to a `VizGraph` for rendering.
|
||||
#[wasm_bindgen]
|
||||
pub fn to_viz_graph(json_graph: &str) -> Result<JsValue, JsError> {
|
||||
let graph: BrainGraph =
|
||||
serde_json::from_str(json_graph).map_err(|e| JsError::new(&e.to_string()))?;
|
||||
let viz = create_viz_graph(&graph);
|
||||
serde_wasm_bindgen::to_value(&viz).map_err(|e| JsError::new(&e.to_string()))
|
||||
}
|
||||
|
||||
#[cfg(test)]
|
||||
mod tests {
|
||||
use super::*;
|
||||
use ruv_neural_core::brain::Atlas;
|
||||
use ruv_neural_core::graph::{BrainEdge, BrainGraph};
|
||||
use ruv_neural_core::signal::FrequencyBand;
|
||||
|
||||
fn make_test_graph() -> BrainGraph {
|
||||
BrainGraph {
|
||||
num_nodes: 4,
|
||||
edges: vec![
|
||||
BrainEdge {
|
||||
source: 0,
|
||||
target: 1,
|
||||
weight: 5.0,
|
||||
metric: ruv_neural_core::graph::ConnectivityMetric::Coherence,
|
||||
frequency_band: FrequencyBand::Alpha,
|
||||
},
|
||||
BrainEdge {
|
||||
source: 2,
|
||||
target: 3,
|
||||
weight: 5.0,
|
||||
metric: ruv_neural_core::graph::ConnectivityMetric::Coherence,
|
||||
frequency_band: FrequencyBand::Alpha,
|
||||
},
|
||||
BrainEdge {
|
||||
source: 1,
|
||||
target: 2,
|
||||
weight: 0.1,
|
||||
metric: ruv_neural_core::graph::ConnectivityMetric::Coherence,
|
||||
frequency_band: FrequencyBand::Alpha,
|
||||
},
|
||||
],
|
||||
timestamp: 1000.0,
|
||||
window_duration_s: 1.0,
|
||||
atlas: Atlas::Custom(4),
|
||||
}
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_viz_graph_creation() {
|
||||
let graph = make_test_graph();
|
||||
let viz = create_viz_graph(&graph);
|
||||
assert_eq!(viz.nodes.len(), 4);
|
||||
assert_eq!(viz.edges.len(), 3);
|
||||
// Should have partitions from mincut.
|
||||
assert!(viz.partitions.is_some());
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_viz_graph_serializes() {
|
||||
let graph = make_test_graph();
|
||||
let viz = create_viz_graph(&graph);
|
||||
let json = serde_json::to_string(&viz).unwrap();
|
||||
assert!(json.contains("\"nodes\""));
|
||||
assert!(json.contains("\"edges\""));
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_viz_node_has_position() {
|
||||
let graph = make_test_graph();
|
||||
let viz = create_viz_graph(&graph);
|
||||
for node in &viz.nodes {
|
||||
// Nodes should have non-zero positions (circular layout).
|
||||
assert!(node.x != 0.0 || node.y != 0.0 || node.id == 0);
|
||||
}
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_cut_edges_marked() {
|
||||
let graph = make_test_graph();
|
||||
let viz = create_viz_graph(&graph);
|
||||
let cut_count = viz.edges.iter().filter(|e| e.is_cut).count();
|
||||
// Should have at least one cut edge.
|
||||
assert!(cut_count >= 1);
|
||||
}
|
||||
}
|
||||
@@ -0,0 +1,558 @@
|
||||
//! Workspace-level integration tests for the rUv Neural crate ecosystem.
|
||||
//!
|
||||
//! These tests verify that all crates compose correctly and that the full
|
||||
//! pipeline (simulate -> preprocess -> graph -> mincut -> embed -> decode)
|
||||
//! produces consistent results across crate boundaries.
|
||||
//!
|
||||
//! Gate with `cfg(feature = "integration")` so these only run when all crates
|
||||
//! are built together (they require the full workspace).
|
||||
|
||||
#![cfg(feature = "integration")]
|
||||
|
||||
use ruv_neural_core::error::Result;
|
||||
use ruv_neural_core::graph::{BrainEdge, BrainGraph, ConnectivityMetric};
|
||||
use ruv_neural_core::signal::{FrequencyBand, MultiChannelTimeSeries};
|
||||
use ruv_neural_core::topology::MincutResult;
|
||||
use ruv_neural_core::traits::SensorSource;
|
||||
use ruv_neural_core::{Atlas, BrainRegion, Hemisphere, Lobe};
|
||||
|
||||
// ---------------------------------------------------------------------------
|
||||
// 1. Cross-crate type compatibility
|
||||
// ---------------------------------------------------------------------------
|
||||
|
||||
#[test]
|
||||
fn core_types_are_send_and_sync() {
|
||||
fn assert_send_sync<T: Send + Sync>() {}
|
||||
assert_send_sync::<BrainGraph>();
|
||||
assert_send_sync::<BrainEdge>();
|
||||
assert_send_sync::<MincutResult>();
|
||||
assert_send_sync::<MultiChannelTimeSeries>();
|
||||
assert_send_sync::<ruv_neural_core::embedding::NeuralEmbedding>();
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn core_enums_roundtrip_serde() {
|
||||
let atlas = Atlas::DesikanKilliany68;
|
||||
let json = serde_json::to_string(&atlas).unwrap();
|
||||
let back: Atlas = serde_json::from_str(&json).unwrap();
|
||||
assert_eq!(atlas, back);
|
||||
|
||||
let metric = ConnectivityMetric::PhaseLockingValue;
|
||||
let json = serde_json::to_string(&metric).unwrap();
|
||||
let back: ConnectivityMetric = serde_json::from_str(&json).unwrap();
|
||||
assert_eq!(metric, back);
|
||||
|
||||
let band = FrequencyBand::Alpha;
|
||||
let json = serde_json::to_string(&band).unwrap();
|
||||
let back: FrequencyBand = serde_json::from_str(&json).unwrap();
|
||||
assert_eq!(band, back);
|
||||
}
|
||||
|
||||
// ---------------------------------------------------------------------------
|
||||
// 2. Sensor -> Signal pipeline
|
||||
// ---------------------------------------------------------------------------
|
||||
|
||||
#[test]
|
||||
fn simulator_produces_valid_multichannel_data() {
|
||||
use ruv_neural_sensor::simulator::SimulatedSensorArray;
|
||||
|
||||
let mut sim = SimulatedSensorArray::new(16, 1000.0);
|
||||
let data = sim.read_chunk(500).expect("sensor read failed");
|
||||
|
||||
assert_eq!(data.num_channels, 16);
|
||||
assert_eq!(data.num_samples, 500);
|
||||
assert_eq!(data.sample_rate_hz, 1000.0);
|
||||
assert_eq!(data.data.len(), 16);
|
||||
for ch in &data.data {
|
||||
assert_eq!(ch.len(), 500);
|
||||
}
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn simulator_with_alpha_injection() {
|
||||
use ruv_neural_sensor::simulator::SimulatedSensorArray;
|
||||
|
||||
let mut sim = SimulatedSensorArray::new(8, 1000.0);
|
||||
sim.inject_alpha(200.0);
|
||||
let data = sim.read_chunk(2000).expect("sensor read failed");
|
||||
|
||||
// With alpha injection, signals should have non-trivial variance.
|
||||
let ch0 = &data.data[0];
|
||||
let mean: f64 = ch0.iter().sum::<f64>() / ch0.len() as f64;
|
||||
let variance: f64 = ch0.iter().map(|x| (x - mean).powi(2)).sum::<f64>() / ch0.len() as f64;
|
||||
assert!(
|
||||
variance > 0.0,
|
||||
"Expected non-zero variance with alpha injection"
|
||||
);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn preprocessing_pipeline_processes_channel_data() {
|
||||
use ruv_neural_signal::PreprocessingPipeline;
|
||||
|
||||
let pipeline = PreprocessingPipeline::new();
|
||||
assert_eq!(pipeline.num_stages(), 0, "Default pipeline has no stages");
|
||||
|
||||
// Process a simple signal through the empty pipeline (identity).
|
||||
let signal: Vec<f64> = (0..100).map(|i| (i as f64 * 0.1).sin()).collect();
|
||||
let result = pipeline.process(&signal);
|
||||
assert_eq!(result.len(), signal.len());
|
||||
}
|
||||
|
||||
// ---------------------------------------------------------------------------
|
||||
// 3. Signal -> Graph -> Mincut pipeline
|
||||
// ---------------------------------------------------------------------------
|
||||
|
||||
#[test]
|
||||
fn connectivity_matrix_from_signals() {
|
||||
use ruv_neural_signal::{compute_all_pairs, ConnectivityMetric};
|
||||
|
||||
// Create 4 channels of synthetic sinusoidal data.
|
||||
let n = 1000;
|
||||
let channels: Vec<Vec<f64>> = (0..4)
|
||||
.map(|ch| {
|
||||
(0..n)
|
||||
.map(|t| {
|
||||
let phase = ch as f64 * 0.5;
|
||||
(2.0 * std::f64::consts::PI * 10.0 * t as f64 / 1000.0 + phase).sin()
|
||||
})
|
||||
.collect()
|
||||
})
|
||||
.collect();
|
||||
|
||||
let matrix = compute_all_pairs(&channels, &ConnectivityMetric::PhaseLockingValue);
|
||||
assert_eq!(matrix.len(), 4);
|
||||
for row in &matrix {
|
||||
assert_eq!(row.len(), 4);
|
||||
}
|
||||
|
||||
// Diagonal should be 1.0 (self-PLV) or at least the highest value.
|
||||
for i in 0..4 {
|
||||
assert!(
|
||||
matrix[i][i] >= 0.99,
|
||||
"Self-PLV should be ~1.0, got {}",
|
||||
matrix[i][i]
|
||||
);
|
||||
}
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn brain_graph_construction_and_mincut() {
|
||||
// Build a small BrainGraph manually and run Stoer-Wagner.
|
||||
let edges = vec![
|
||||
BrainEdge {
|
||||
source: 0,
|
||||
target: 1,
|
||||
weight: 0.9,
|
||||
metric: ConnectivityMetric::PhaseLockingValue,
|
||||
frequency_band: FrequencyBand::Alpha,
|
||||
},
|
||||
BrainEdge {
|
||||
source: 1,
|
||||
target: 2,
|
||||
weight: 0.8,
|
||||
metric: ConnectivityMetric::PhaseLockingValue,
|
||||
frequency_band: FrequencyBand::Alpha,
|
||||
},
|
||||
BrainEdge {
|
||||
source: 2,
|
||||
target: 3,
|
||||
weight: 0.1,
|
||||
metric: ConnectivityMetric::PhaseLockingValue,
|
||||
frequency_band: FrequencyBand::Alpha,
|
||||
},
|
||||
BrainEdge {
|
||||
source: 3,
|
||||
target: 4,
|
||||
weight: 0.85,
|
||||
metric: ConnectivityMetric::PhaseLockingValue,
|
||||
frequency_band: FrequencyBand::Alpha,
|
||||
},
|
||||
BrainEdge {
|
||||
source: 0,
|
||||
target: 2,
|
||||
weight: 0.7,
|
||||
metric: ConnectivityMetric::PhaseLockingValue,
|
||||
frequency_band: FrequencyBand::Alpha,
|
||||
},
|
||||
];
|
||||
|
||||
let graph = BrainGraph {
|
||||
num_nodes: 5,
|
||||
edges,
|
||||
timestamp: 0.0,
|
||||
window_duration_s: 1.0,
|
||||
atlas: Atlas::DesikanKilliany68,
|
||||
};
|
||||
|
||||
// Verify graph utilities.
|
||||
assert!(graph.density() > 0.0);
|
||||
assert!(graph.total_weight() > 0.0);
|
||||
assert_eq!(graph.adjacency_matrix().len(), 5);
|
||||
|
||||
// Run Stoer-Wagner mincut.
|
||||
let result = ruv_neural_mincut::stoer_wagner_mincut(&graph).expect("mincut failed");
|
||||
assert!(result.cut_value > 0.0, "Cut value must be positive");
|
||||
assert!(
|
||||
!result.partition_a.is_empty() && !result.partition_b.is_empty(),
|
||||
"Both partitions must be non-empty"
|
||||
);
|
||||
assert_eq!(
|
||||
result.partition_a.len() + result.partition_b.len(),
|
||||
5,
|
||||
"Partitions must cover all nodes"
|
||||
);
|
||||
|
||||
// The weakest link (0.1 between nodes 2-3) should likely be cut.
|
||||
assert!(
|
||||
result.cut_value <= 0.2,
|
||||
"Expected cut near the weak edge (0.1), got {}",
|
||||
result.cut_value
|
||||
);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn normalized_cut_produces_valid_partition() {
|
||||
let edges = vec![
|
||||
BrainEdge {
|
||||
source: 0,
|
||||
target: 1,
|
||||
weight: 0.9,
|
||||
metric: ConnectivityMetric::Coherence,
|
||||
frequency_band: FrequencyBand::Beta,
|
||||
},
|
||||
BrainEdge {
|
||||
source: 1,
|
||||
target: 2,
|
||||
weight: 0.05,
|
||||
metric: ConnectivityMetric::Coherence,
|
||||
frequency_band: FrequencyBand::Beta,
|
||||
},
|
||||
BrainEdge {
|
||||
source: 2,
|
||||
target: 3,
|
||||
weight: 0.85,
|
||||
metric: ConnectivityMetric::Coherence,
|
||||
frequency_band: FrequencyBand::Beta,
|
||||
},
|
||||
];
|
||||
|
||||
let graph = BrainGraph {
|
||||
num_nodes: 4,
|
||||
edges,
|
||||
timestamp: 1.0,
|
||||
window_duration_s: 1.0,
|
||||
atlas: Atlas::DesikanKilliany68,
|
||||
};
|
||||
|
||||
let result = ruv_neural_mincut::normalized_cut(&graph).expect("normalized cut failed");
|
||||
assert!(result.cut_value >= 0.0);
|
||||
assert_eq!(result.partition_a.len() + result.partition_b.len(), 4);
|
||||
}
|
||||
|
||||
// ---------------------------------------------------------------------------
|
||||
// 4. Mincut -> Embed pipeline
|
||||
// ---------------------------------------------------------------------------
|
||||
|
||||
#[test]
|
||||
fn neural_embedding_creation_and_serialization() {
|
||||
use ruv_neural_embed::NeuralEmbedding;
|
||||
|
||||
let embedding = NeuralEmbedding::new(vec![1.0, 2.0, 3.0, 4.0], 0.0, "spectral")
|
||||
.expect("embedding creation failed");
|
||||
|
||||
assert_eq!(embedding.dimension, 4);
|
||||
assert_eq!(embedding.values.len(), 4);
|
||||
assert_eq!(embedding.method, "spectral");
|
||||
assert!((embedding.norm() - (1.0_f64 + 4.0 + 9.0 + 16.0).sqrt()).abs() < 1e-10);
|
||||
|
||||
// Serde roundtrip.
|
||||
let json = serde_json::to_string(&embedding).unwrap();
|
||||
let back: NeuralEmbedding = serde_json::from_str(&json).unwrap();
|
||||
assert_eq!(back.dimension, 4);
|
||||
assert_eq!(back.values, embedding.values);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn zero_embedding_has_zero_norm() {
|
||||
use ruv_neural_embed::NeuralEmbedding;
|
||||
|
||||
let zero = NeuralEmbedding::zeros(16, 0.0, "test");
|
||||
assert_eq!(zero.dimension, 16);
|
||||
assert!((zero.norm() - 0.0).abs() < 1e-15);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn empty_embedding_is_rejected() {
|
||||
use ruv_neural_embed::NeuralEmbedding;
|
||||
|
||||
let result = NeuralEmbedding::new(vec![], 0.0, "empty");
|
||||
assert!(result.is_err(), "Empty embedding should be rejected");
|
||||
}
|
||||
|
||||
// ---------------------------------------------------------------------------
|
||||
// 5. Decoder types (from non-stub decoder crate)
|
||||
// ---------------------------------------------------------------------------
|
||||
|
||||
#[test]
|
||||
fn decoder_types_exist_and_are_constructible() {
|
||||
// Verify that decoder public types can be referenced.
|
||||
// This is a compile-time check more than a runtime check.
|
||||
let _: fn() -> &str = || {
|
||||
let _ = std::any::type_name::<ruv_neural_decoder::KnnDecoder>();
|
||||
let _ = std::any::type_name::<ruv_neural_decoder::ThresholdDecoder>();
|
||||
let _ = std::any::type_name::<ruv_neural_decoder::TransitionDecoder>();
|
||||
let _ = std::any::type_name::<ruv_neural_decoder::ClinicalScorer>();
|
||||
let _ = std::any::type_name::<ruv_neural_decoder::DecoderPipeline>();
|
||||
"ok"
|
||||
};
|
||||
}
|
||||
|
||||
// ---------------------------------------------------------------------------
|
||||
// 6. Core traits are object-safe (can be used as trait objects)
|
||||
// ---------------------------------------------------------------------------
|
||||
|
||||
#[test]
|
||||
fn core_traits_are_object_safe() {
|
||||
use ruv_neural_core::traits::*;
|
||||
|
||||
// These lines verify the traits can be used as `dyn Trait`.
|
||||
// If a trait is not object-safe, this will fail to compile.
|
||||
fn _accept_sensor(_: &dyn SensorSource) {}
|
||||
fn _accept_signal(_: &dyn SignalProcessor) {}
|
||||
fn _accept_graph(_: &dyn GraphConstructor) {}
|
||||
fn _accept_topology(_: &dyn TopologyAnalyzer) {}
|
||||
fn _accept_embedding(_: &dyn EmbeddingGenerator) {}
|
||||
fn _accept_decoder(_: &dyn StateDecoder) {}
|
||||
fn _accept_memory(_: &mut dyn NeuralMemory) {}
|
||||
}
|
||||
|
||||
// ---------------------------------------------------------------------------
|
||||
// 7. Full pipeline: simulate -> preprocess -> connectivity -> graph -> mincut
|
||||
// ---------------------------------------------------------------------------
|
||||
|
||||
#[test]
|
||||
fn full_pipeline_simulate_to_mincut() {
|
||||
use ruv_neural_sensor::simulator::SimulatedSensorArray;
|
||||
use ruv_neural_signal::{compute_all_pairs, ConnectivityMetric};
|
||||
|
||||
// Step 1: Simulate sensor data (16 channels, 1s at 1000 Hz).
|
||||
let mut sim = SimulatedSensorArray::new(16, 1000.0);
|
||||
sim.inject_alpha(150.0);
|
||||
let data = sim.read_chunk(1000).expect("sensor read failed");
|
||||
assert_eq!(data.data.len(), 16);
|
||||
|
||||
// Step 2: Compute pairwise connectivity matrix (PLV).
|
||||
let matrix = compute_all_pairs(&data.data, &ConnectivityMetric::PhaseLockingValue);
|
||||
assert_eq!(matrix.len(), 16);
|
||||
|
||||
// Step 3: Build BrainGraph from connectivity matrix.
|
||||
let threshold = 0.3;
|
||||
let mut edges = Vec::new();
|
||||
for i in 0..16 {
|
||||
for j in (i + 1)..16 {
|
||||
if matrix[i][j] > threshold {
|
||||
edges.push(BrainEdge {
|
||||
source: i,
|
||||
target: j,
|
||||
weight: matrix[i][j],
|
||||
metric: ConnectivityMetric::PhaseLockingValue,
|
||||
frequency_band: FrequencyBand::Alpha,
|
||||
});
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
let graph = BrainGraph {
|
||||
num_nodes: 16,
|
||||
edges,
|
||||
timestamp: data.timestamp_start,
|
||||
window_duration_s: 1.0,
|
||||
atlas: Atlas::DesikanKilliany68,
|
||||
};
|
||||
|
||||
// Step 4: Run Stoer-Wagner mincut.
|
||||
if graph.edges.is_empty() {
|
||||
// If no edges pass threshold, the graph is disconnected — that is valid.
|
||||
return;
|
||||
}
|
||||
let result = ruv_neural_mincut::stoer_wagner_mincut(&graph).expect("mincut failed");
|
||||
assert!(result.cut_value >= 0.0);
|
||||
assert_eq!(
|
||||
result.partition_a.len() + result.partition_b.len(),
|
||||
16,
|
||||
"Partitions must cover all 16 nodes"
|
||||
);
|
||||
|
||||
// Step 5: Create embedding from topology result.
|
||||
let feature_vec = vec![
|
||||
result.cut_value,
|
||||
result.balance_ratio(),
|
||||
result.num_cut_edges() as f64,
|
||||
graph.density(),
|
||||
graph.total_weight(),
|
||||
];
|
||||
let embedding = ruv_neural_embed::NeuralEmbedding::new(feature_vec, data.timestamp_start, "topology")
|
||||
.expect("embedding failed");
|
||||
assert_eq!(embedding.dimension, 5);
|
||||
assert!(embedding.norm() > 0.0);
|
||||
}
|
||||
|
||||
// ---------------------------------------------------------------------------
|
||||
// 8. BrainGraph serde roundtrip
|
||||
// ---------------------------------------------------------------------------
|
||||
|
||||
#[test]
|
||||
fn brain_graph_serde_roundtrip() {
|
||||
let graph = BrainGraph {
|
||||
num_nodes: 3,
|
||||
edges: vec![
|
||||
BrainEdge {
|
||||
source: 0,
|
||||
target: 1,
|
||||
weight: 0.5,
|
||||
metric: ConnectivityMetric::PhaseLockingValue,
|
||||
frequency_band: FrequencyBand::Alpha,
|
||||
},
|
||||
BrainEdge {
|
||||
source: 1,
|
||||
target: 2,
|
||||
weight: 0.7,
|
||||
metric: ConnectivityMetric::Coherence,
|
||||
frequency_band: FrequencyBand::Gamma,
|
||||
},
|
||||
],
|
||||
timestamp: 42.0,
|
||||
window_duration_s: 2.0,
|
||||
atlas: Atlas::DesikanKilliany68,
|
||||
};
|
||||
|
||||
let json = serde_json::to_string_pretty(&graph).unwrap();
|
||||
let back: BrainGraph = serde_json::from_str(&json).unwrap();
|
||||
|
||||
assert_eq!(back.num_nodes, graph.num_nodes);
|
||||
assert_eq!(back.edges.len(), graph.edges.len());
|
||||
assert!((back.timestamp - graph.timestamp).abs() < 1e-10);
|
||||
assert_eq!(back.atlas, graph.atlas);
|
||||
}
|
||||
|
||||
// ---------------------------------------------------------------------------
|
||||
// 9. Multiway cut (multiple partitions)
|
||||
// ---------------------------------------------------------------------------
|
||||
|
||||
#[test]
|
||||
fn multiway_cut_produces_valid_partitions() {
|
||||
// Build a graph with 3 clear clusters connected by weak edges.
|
||||
let mut edges = Vec::new();
|
||||
|
||||
// Cluster A: nodes 0, 1, 2 (strong internal edges).
|
||||
for &(s, t) in &[(0, 1), (1, 2), (0, 2)] {
|
||||
edges.push(BrainEdge {
|
||||
source: s,
|
||||
target: t,
|
||||
weight: 0.9,
|
||||
metric: ConnectivityMetric::PhaseLockingValue,
|
||||
frequency_band: FrequencyBand::Alpha,
|
||||
});
|
||||
}
|
||||
|
||||
// Cluster B: nodes 3, 4, 5 (strong internal edges).
|
||||
for &(s, t) in &[(3, 4), (4, 5), (3, 5)] {
|
||||
edges.push(BrainEdge {
|
||||
source: s,
|
||||
target: t,
|
||||
weight: 0.85,
|
||||
metric: ConnectivityMetric::PhaseLockingValue,
|
||||
frequency_band: FrequencyBand::Alpha,
|
||||
});
|
||||
}
|
||||
|
||||
// Cluster C: nodes 6, 7, 8 (strong internal edges).
|
||||
for &(s, t) in &[(6, 7), (7, 8), (6, 8)] {
|
||||
edges.push(BrainEdge {
|
||||
source: s,
|
||||
target: t,
|
||||
weight: 0.88,
|
||||
metric: ConnectivityMetric::PhaseLockingValue,
|
||||
frequency_band: FrequencyBand::Alpha,
|
||||
});
|
||||
}
|
||||
|
||||
// Weak inter-cluster bridges.
|
||||
edges.push(BrainEdge {
|
||||
source: 2,
|
||||
target: 3,
|
||||
weight: 0.05,
|
||||
metric: ConnectivityMetric::PhaseLockingValue,
|
||||
frequency_band: FrequencyBand::Alpha,
|
||||
});
|
||||
edges.push(BrainEdge {
|
||||
source: 5,
|
||||
target: 6,
|
||||
weight: 0.04,
|
||||
metric: ConnectivityMetric::PhaseLockingValue,
|
||||
frequency_band: FrequencyBand::Alpha,
|
||||
});
|
||||
|
||||
let graph = BrainGraph {
|
||||
num_nodes: 9,
|
||||
edges,
|
||||
timestamp: 0.0,
|
||||
window_duration_s: 1.0,
|
||||
atlas: Atlas::DesikanKilliany68,
|
||||
};
|
||||
|
||||
let partitions = ruv_neural_mincut::multiway_cut(&graph, 3).expect("multiway cut failed");
|
||||
assert!(
|
||||
partitions.num_partitions() >= 2,
|
||||
"Expected at least 2 partitions"
|
||||
);
|
||||
assert_eq!(
|
||||
partitions.num_nodes(),
|
||||
9,
|
||||
"All nodes must be assigned to a partition"
|
||||
);
|
||||
}
|
||||
|
||||
// ---------------------------------------------------------------------------
|
||||
// 10. Spectral cut analysis
|
||||
// ---------------------------------------------------------------------------
|
||||
|
||||
#[test]
|
||||
fn spectral_bisection_produces_valid_split() {
|
||||
let edges = vec![
|
||||
BrainEdge {
|
||||
source: 0,
|
||||
target: 1,
|
||||
weight: 0.9,
|
||||
metric: ConnectivityMetric::PhaseLockingValue,
|
||||
frequency_band: FrequencyBand::Alpha,
|
||||
},
|
||||
BrainEdge {
|
||||
source: 1,
|
||||
target: 2,
|
||||
weight: 0.05,
|
||||
metric: ConnectivityMetric::PhaseLockingValue,
|
||||
frequency_band: FrequencyBand::Alpha,
|
||||
},
|
||||
BrainEdge {
|
||||
source: 2,
|
||||
target: 3,
|
||||
weight: 0.85,
|
||||
metric: ConnectivityMetric::PhaseLockingValue,
|
||||
frequency_band: FrequencyBand::Alpha,
|
||||
},
|
||||
];
|
||||
|
||||
let graph = BrainGraph {
|
||||
num_nodes: 4,
|
||||
edges,
|
||||
timestamp: 0.0,
|
||||
window_duration_s: 1.0,
|
||||
atlas: Atlas::DesikanKilliany68,
|
||||
};
|
||||
|
||||
let result = ruv_neural_mincut::spectral_bisection(&graph).expect("spectral bisection failed");
|
||||
assert!(result.cut_value >= 0.0);
|
||||
assert_eq!(result.partition_a.len() + result.partition_b.len(), 4);
|
||||
}
|
||||
Reference in New Issue
Block a user