Files
ruvnet--RuView/vendor/sublinear-time-solver/dist/mcp/tools/temporal-attractor-handlers.d.ts
T
ruv 4b1005524e feat: complete vendor repos, add edge intelligence and WASM modules
- Add 154 missing vendor files (gitignore was filtering them)
  - vendor/midstream: 564 files (was 561)
  - vendor/sublinear-time-solver: 1190 files (was 1039)
- Add ESP32 edge processing (ADR-039): presence, vitals, fall detection
- Add WASM programmable sensing (ADR-040/041) with wasm3 runtime
- Add firmware CI workflow (.github/workflows/firmware-ci.yml)
- Add wifi-densepose-wasm-edge crate for edge WASM modules
- Update sensing server, provision.py, UI components

Co-Authored-By: claude-flow <ruv@ruv.net>
2026-03-02 23:53:25 -05:00

98 lines
2.7 KiB
TypeScript

/**
* Temporal Attractor Studio Handlers
* WASM-based implementation for chaos analysis tools
*/
export declare const temporalAttractorHandlers: {
chaos_analyze: (args: any) => Promise<{
lambda: any;
is_chaotic: any;
chaos_level: any;
lyapunov_time: any;
doubling_time: any;
safe_prediction_steps: any;
pairs_found: any;
interpretation: string;
}>;
temporal_delay_embed: (args: any) => Promise<{
original_length: any;
embedded_vectors: number;
embedding_dim: any;
tau: any;
data: any;
}>;
temporal_predict: (args: any) => Promise<{
initialized: boolean;
reservoir_size: any;
training_complete?: undefined;
mse?: undefined;
n_samples?: undefined;
input?: undefined;
prediction?: undefined;
trajectory?: undefined;
n_steps?: undefined;
} | {
training_complete: boolean;
mse: any;
n_samples: any;
initialized?: undefined;
reservoir_size?: undefined;
input?: undefined;
prediction?: undefined;
trajectory?: undefined;
n_steps?: undefined;
} | {
input: any;
prediction: any;
initialized?: undefined;
reservoir_size?: undefined;
training_complete?: undefined;
mse?: undefined;
n_samples?: undefined;
trajectory?: undefined;
n_steps?: undefined;
} | {
input: any;
trajectory: any;
n_steps: any;
initialized?: undefined;
reservoir_size?: undefined;
training_complete?: undefined;
mse?: undefined;
n_samples?: undefined;
prediction?: undefined;
}>;
temporal_fractal_dimension: (args: any) => Promise<{
fractal_dimension: any;
interpretation: string;
}>;
temporal_regime_changes: (args: any) => Promise<{
n_windows: any;
lyapunov_values: any;
changes_detected: boolean;
max_lambda: number;
min_lambda: number;
variance: number;
}>;
temporal_generate_attractor: (args: any) => Promise<{
system: any;
n_points: any;
dimensions: any;
dt: any;
data: any;
}>;
temporal_interpret_chaos: (args: any) => Promise<any>;
temporal_recommend_parameters: (args: any) => Promise<any>;
temporal_attractor_pullback: (args: any) => Promise<{
ensemble_size: any;
evolution_time: any;
snapshots: any[];
drift: any[];
convergence_rate: number;
}>;
temporal_kaplan_yorke_dimension: (args: any) => Promise<{
kaplan_yorke_dimension: number;
lyapunov_spectrum: any;
interpretation: string;
}>;
};