File size: 3,612 Bytes
b39fe3e | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 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.") |