Upload Particle_manipulation.py

Particle_manipulation.py

@@ -0,0 +1,87 @@
+# 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.")