# Venomoussaversai — Particle Manipulation integration scaffold
# Paste your particle-manipulation function into `particle_step` below.
# This code simulates signals, applies the algorithm, trains a small mapper,
# and saves a model representing "your" pattern space.

import numpy as np
import pickle
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score

# ---------- PLACEHOLDER: insert your particle algorithm here ----------
# Example interface: def particle_step(state: np.ndarray, input_vec: np.ndarray) -> np.ndarray
# The function should take a current particle state and an input vector, and return updated state.
def particle_step(state: np.ndarray, input_vec: np.ndarray) -> np.ndarray:
    # --- REPLACE THIS WITH YOUR ALGORITHM ---
    # tiny example: weighted update with tanh nonlinearity
    W = np.sin(np.arange(state.size) + 1.0)  # placeholder weights
    new = np.tanh(state * 0.9 + input_vec.dot(W) * 0.1)
    return new
# --------------------------------------------------------------------

class ParticleManipulator:
    def __init__(self, dim=64):
        self.dim = dim
        # initial particle states (can be randomized or seeded from your profile)
        self.state = np.random.randn(dim) * 0.01

    def step(self, input_vec):
        # ensure input vector length compatibility
        inp = np.asarray(input_vec).ravel()
        if inp.size == 0:
            inp = np.zeros(self.dim)
        # broadcast or pad/truncate to dim
        if inp.size < self.dim:
            x = np.pad(inp, (0, self.dim - inp.size))
        else:
            x = inp[:self.dim]
        self.state = particle_step(self.state, x)
        return self.state

# ---------- Simple signal simulator ----------
def simulate_signals(n_samples=500, dim=16, n_classes=4, noise=0.05, seed=0):
    rng = np.random.RandomState(seed)
    X = []
    y = []
    for cls in range(n_classes):
        base = rng.randn(dim) * (0.5 + cls*0.2) + cls*0.7
        for i in range(n_samples // n_classes):
            sample = base + rng.randn(dim) * noise
            X.append(sample)
            y.append(cls)
    return np.array(X), np.array(y)

# ---------- Build dataset by running particle manipulator ----------
def build_dataset(manip, raw_X):
    features = []
    for raw in raw_X:
        st = manip.step(raw)            # run particle update
        feat = st.copy()[:manip.dim]    # derive features (you can add spectral transforms)
        features.append(feat)
    return np.array(features)

# ---------- Training pipeline ----------
if __name__ == "__main__":
    # simulate raw sensor inputs (replace simulate_signals with real EEG/ECG files if available)
    raw_X, y = simulate_signals(n_samples=800, dim=32, n_classes=4)
    manip = ParticleManipulator(dim=32)

    X = build_dataset(manip, raw_X)
    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

    clf = RandomForestClassifier(n_estimators=100, random_state=42)
    clf.fit(X_train, y_train)
    preds = clf.predict(X_test)
    print("Accuracy:", accuracy_score(y_test, preds))

    # Save the trained model + manipulator state as your "mind snapshot"
    artifact = {
        "model": clf,
        "particle_state": manip.state,
        "meta": {"owner": "Ananthu Sajeev", "artifact_type": "venomous_mind_snapshot_v1"}
    }
    with open("venomous_mind_snapshot.pkl", "wb") as f:
        pickle.dump(artifact, f)

    print("Saved venomous_mind_snapshot.pkl — this file is your digital pattern snapshot.")