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https://github.com/ruvnet/RuView
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feat: vendor midstream and sublinear-time-solver libraries
Add ruvnet/midstream (AIMDS real-time inference) and ruvnet/sublinear-time-solver (sublinear optimization algorithms) as vendored dependencies under vendor/. Co-Authored-By: claude-flow <ruv@ruv.net>
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# Pattern Detection APIs - Quick Reference
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## TL;DR
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✅ **Both required APIs are fully implemented and working**
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- `find_similar()` - Find similar patterns in time series using DTW
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- `detect_pattern()` - Detect if a pattern exists (boolean)
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**Location**: `/workspaces/midstream/crates/temporal-compare/src/lib.rs`
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**Status**: Production-ready, tested, documented
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---
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## Quick Start
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```rust
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use temporal_compare::TemporalComparator;
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// Create comparator
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let comparator: TemporalComparator<f64> = TemporalComparator::new(100, 1000);
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// Example data
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let series = vec![1.0, 2.0, 3.0, 4.0, 5.0, 3.0, 4.0, 5.0];
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let pattern = vec![3.0, 4.0, 5.0];
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// Find all similar patterns
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let matches = comparator.find_similar(&series, &pattern, 1.0);
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println!("Found {} matches: {:?}", matches.len(), matches);
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// Output: Found 2 matches: [(2, 0.0), (5, 0.0)]
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// Check if pattern exists
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let exists = comparator.detect_pattern(&series, &pattern, 1.0);
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println!("Pattern exists: {}", exists);
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// Output: Pattern exists: true
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```
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---
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## API Reference
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### `find_similar()`
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Find all occurrences of a pattern within a time series.
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```rust
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pub fn find_similar(
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&self,
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series: &[f64], // Time series to search in
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pattern: &[f64], // Pattern to find
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threshold: f64 // Maximum DTW distance (lower = stricter)
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) -> Vec<(usize, f64)> // Returns: (start_index, distance)
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```
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**Returns**: Vector of (index, distance) tuples, sorted by distance (best matches first)
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**Algorithm**: Dynamic Time Warping (DTW) with sliding window
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**Examples**:
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```rust
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// Exact matches
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let series = vec![1.0, 2.0, 3.0, 4.0, 5.0, 3.0, 4.0, 5.0];
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let pattern = vec![3.0, 4.0, 5.0];
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let matches = comparator.find_similar(&series, &pattern, 0.5);
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// Returns: [(2, 0.0), (5, 0.0)]
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// Approximate matches
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let series = vec![1.0, 2.0, 3.1, 4.2, 4.9];
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let pattern = vec![3.0, 4.0, 5.0];
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let matches = comparator.find_similar(&series, &pattern, 1.5);
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// Returns: [(2, ~0.4)] - approximate match found
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```
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---
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### `detect_pattern()`
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Check if a pattern exists anywhere in the time series.
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```rust
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pub fn detect_pattern(
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&self,
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series: &[f64], // Time series to search in
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pattern: &[f64], // Pattern to detect
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threshold: f64 // Maximum DTW distance
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) -> bool // Returns: true if found, false otherwise
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```
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**Returns**: Boolean indicating pattern presence
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**Algorithm**: DTW-based, early-exit on first match
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**Examples**:
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```rust
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// Pattern exists
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let series = vec![1.0, 2.0, 3.0, 4.0, 5.0];
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let pattern = vec![3.0, 4.0, 5.0];
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let found = comparator.detect_pattern(&series, &pattern, 1.0);
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// Returns: true
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// Pattern doesn't exist
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let series = vec![1.0, 2.0, 3.0];
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let pattern = vec![10.0, 20.0, 30.0];
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let found = comparator.detect_pattern(&series, &pattern, 0.5);
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// Returns: false
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```
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---
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## Advanced APIs
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### Generic Type Support
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Work with any comparable type, not just f64:
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```rust
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let comparator: TemporalComparator<i32> = TemporalComparator::new(100, 1000);
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let haystack = vec![1, 2, 3, 4, 5, 3, 4, 5];
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let needle = vec![3, 4, 5];
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let matches = comparator.find_similar_generic(&haystack, &needle, 0.1)?;
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// Returns: Vec<SimilarityMatch>
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```
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### Automatic Pattern Discovery
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Find recurring patterns without knowing what to look for:
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```rust
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let comparator: TemporalComparator<char> = TemporalComparator::new(100, 1000);
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let sequence = vec!['a', 'b', 'c', 'a', 'b', 'c', 'a', 'b', 'c'];
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let patterns = comparator.detect_recurring_patterns(&sequence, 2, 4)?;
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// Returns: Vec<Pattern<char>> with detected patterns
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```
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### Fuzzy Pattern Matching
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Group similar pattern variations:
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```rust
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let comparator: TemporalComparator<i32> = TemporalComparator::new(100, 1000);
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let sequence = vec![1, 2, 3, 1, 2, 4, 1, 2, 3];
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let patterns = comparator.detect_fuzzy_patterns(&sequence, 3, 3, 0.7)?;
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// Groups [1,2,3] and [1,2,4] as similar patterns
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```
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---
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## Understanding Threshold
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The `threshold` parameter controls how strict the matching is:
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- **Lower threshold** = More strict (exact matches only)
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- **Higher threshold** = More lenient (allows variation)
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```rust
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// Very strict - only exact or near-exact matches
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let strict = comparator.find_similar(&series, &pattern, 0.1);
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// Moderate - some variation allowed
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let moderate = comparator.find_similar(&series, &pattern, 1.0);
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// Lenient - significant variation allowed
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let lenient = comparator.find_similar(&series, &pattern, 5.0);
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```
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**Recommendation**: Start with `1.0` and adjust based on results.
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---
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## Performance Tips
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### Caching
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All methods use intelligent caching automatically:
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```rust
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let comparator: TemporalComparator<f64> = TemporalComparator::new(100, 1000);
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// First call - computes DTW
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let matches1 = comparator.find_similar(&series, &pattern, 1.0);
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// Second call - uses cache (much faster)
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let matches2 = comparator.find_similar(&series, &pattern, 1.0);
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// Check cache performance
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let stats = comparator.cache_stats();
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println!("Cache hit rate: {:.2}%", stats.hit_rate() * 100.0);
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```
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### Clear Cache
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If memory is a concern:
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```rust
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comparator.clear_cache();
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```
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---
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## Edge Cases Handled
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✅ Empty patterns
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```rust
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let matches = comparator.find_similar(&series, &[], 1.0);
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// Returns: []
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```
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✅ Pattern longer than series
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```rust
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let matches = comparator.find_similar(&[1.0, 2.0], &[1.0, 2.0, 3.0, 4.0], 1.0);
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// Returns: []
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```
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✅ Single element patterns
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```rust
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let matches = comparator.find_similar(&[1.0, 2.0, 3.0, 2.0], &[2.0], 0.5);
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// Returns: [(1, 0.0), (3, 0.0)]
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```
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---
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## Testing
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### Run Unit Tests
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```bash
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cd /workspaces/midstream/crates/temporal-compare
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cargo test
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```
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### Run Integration Tests
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```bash
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cargo test --test temporal_compare_api_test
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```
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### Run Example
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```bash
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cargo run --example pattern_detection_demo
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```
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---
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## Documentation
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### Generated Docs
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```bash
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cargo doc --no-deps --open
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```
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### Documentation Files
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- `/workspaces/midstream/docs/temporal_compare_api_verification.md` - Detailed API verification
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- `/workspaces/midstream/docs/PATTERN_DETECTION_IMPLEMENTATION.md` - Implementation guide
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- `/workspaces/midstream/docs/IMPLEMENTATION_COMPLETE_SUMMARY.md` - Task completion summary
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---
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## Common Use Cases
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### 1. Time Series Anomaly Detection
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```rust
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// Define normal pattern
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let normal_pattern = vec![1.0, 2.0, 3.0, 2.0, 1.0];
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// Check if it appears in recent data
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let recent_data = vec![5.0, 10.0, 15.0, 20.0, 25.0]; // Anomalous
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let is_normal = comparator.detect_pattern(&recent_data, &normal_pattern, 1.0);
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// Returns: false (anomaly detected)
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```
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### 2. Sensor Data Analysis
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```rust
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// Find all occurrences of a spike pattern
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let sensor_data = vec![10.0, 10.0, 50.0, 10.0, 10.0, 50.0, 10.0];
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let spike_pattern = vec![10.0, 50.0, 10.0];
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let spikes = comparator.find_similar(&sensor_data, &spike_pattern, 2.0);
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// Returns: [(1, 0.0), (4, 0.0)] - two spike events
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```
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### 3. Signal Processing
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```rust
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// Detect repeating waveforms
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let signal = vec![0.0, 1.0, 0.0, -1.0, 0.0, 1.0, 0.0, -1.0];
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let waveform = vec![0.0, 1.0, 0.0, -1.0];
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let cycles = comparator.find_similar(&signal, &waveform, 0.5);
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// Returns: [(0, 0.0), (4, 0.0)] - two complete cycles
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```
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### 4. Market Data Pattern Recognition
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```rust
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// Find price patterns (e.g., double top)
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let prices = vec![100.0, 110.0, 105.0, 110.0, 100.0];
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let double_top = vec![105.0, 110.0, 105.0];
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let patterns = comparator.find_similar(&prices, &double_top, 2.0);
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// Detects double top pattern
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```
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---
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## Error Handling
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All advanced APIs return `Result<T, TemporalError>`:
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```rust
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use temporal_compare::TemporalError;
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match comparator.detect_recurring_patterns(&sequence, 2, 4) {
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Ok(patterns) => {
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println!("Found {} patterns", patterns.len());
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}
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Err(TemporalError::InvalidPatternLength(min, max)) => {
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eprintln!("Invalid lengths: min={}, max={}", min, max);
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}
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Err(e) => {
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eprintln!("Error: {}", e);
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}
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}
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```
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---
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## Type Support
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Works with any type that implements required traits:
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```rust
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// f64 (built-in methods)
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let comp_f64: TemporalComparator<f64> = TemporalComparator::new(100, 1000);
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// Integers
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let comp_i32: TemporalComparator<i32> = TemporalComparator::new(100, 1000);
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// Characters
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let comp_char: TemporalComparator<char> = TemporalComparator::new(100, 1000);
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// Custom types (must impl Clone + PartialEq + Debug + Serialize + Hash + Eq)
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#[derive(Clone, PartialEq, Debug, Serialize, Hash, Eq)]
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struct CustomValue(i32);
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let comp_custom: TemporalComparator<CustomValue> = TemporalComparator::new(100, 1000);
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```
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---
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## Summary
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| Feature | Status | Location |
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|---------|--------|----------|
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| `find_similar()` | ✅ Complete | Lines 468-505 |
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| `detect_pattern()` | ✅ Complete | Lines 531-536 |
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| Generic API | ✅ Complete | Lines 563-633 |
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| Recurring patterns | ✅ Complete | Lines 659-740 |
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| Fuzzy matching | ✅ Complete | Lines 766-858 |
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| DTW algorithm | ✅ Complete | Lines 249-304 |
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| Caching | ✅ Complete | Throughout |
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| Documentation | ✅ Complete | Doc comments |
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| Tests | ✅ Complete | 30+ tests |
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| Examples | ✅ Complete | Demo file |
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**All pattern detection APIs are production-ready and fully functional.**
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---
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## Support
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- **Source**: `/workspaces/midstream/crates/temporal-compare/src/lib.rs`
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- **Tests**: `/workspaces/midstream/crates/temporal-compare/src/lib.rs` (unit tests)
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- **Integration Tests**: `/workspaces/midstream/tests/temporal_compare_api_test.rs`
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- **Example**: `/workspaces/midstream/examples/pattern_detection_demo.rs`
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- **Docs**: `/workspaces/midstream/docs/PATTERN_DETECTION_*.md`
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