Create SCA.py

SCA.py

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+import numpy as np
+from cnn_model import build_cnn_model
+
+# -------------------------
+# Fonction objectif pour SCA
+# -------------------------
+def objective_function(x, X_train, y_train):
+    filter1 = int(x[0])
+    filter2 = int(x[1])
+    filter3 = int(x[2])
+    learning_rate = float(x[3])
+    dropout = float(x[4])
+
+    model = build_cnn_model(
+        input_shape=(X_train.shape[1], X_train.shape[2]),
+        num_classes=y_train.shape[1],
+        filter1=filter1,
+        filter2=filter2,
+        filter3=filter3,
+        learning_rate=learning_rate,
+        dropout=dropout
+    )
+
+    history = model.fit(
+        X_train, y_train,
+        validation_split=0.2,
+        epochs=3,  # rapide pour optimisation
+        batch_size=32,
+        verbose=0
+    )
+
+    val_acc = history.history['val_accuracy'][-1]
+    return 1 - val_acc  # minimiser 1 - précision
+
+# -------------------------
+# Implémentation simple de SCA
+# -------------------------
+def devsca(objf, lb, ub, dim, N, T, X_train, y_train):
+    X = np.random.uniform(0, 1, (N, dim)) * (np.array(ub) - np.array(lb)) + np.array(lb)
+    fitness = np.array([objf(ind, X_train, y_train) for ind in X])
+    best_idx = np.argmin(fitness)
+    best_X = X[best_idx].copy()
+    best_fit = fitness[best_idx]
+    convergence_curve = []
+
+    for t in range(T):
+        r1 = np.linspace(2, 0, T)[t]  # diminue avec le temps
+        for i in range(N):
+            for j in range(dim):
+                r2 = 2 * np.pi * np.random.rand()
+                r3 = 2 * np.random.rand()
+                r4 = np.random.rand()
+                if r4 < 0.5:
+                    X[i, j] = X[i, j] + r1 * np.sin(r2) * abs(r3 * best_X[j] - X[i, j])
+                else:
+                    X[i, j] = X[i, j] + r1 * np.cos(r2) * abs(r3 * best_X[j] - X[i, j])
+
+            X[i] = np.clip(X[i], lb, ub)
+
+            fit = objf(X[i], X_train, y_train)
+            if fit < best_fit:
+                best_fit = fit
+                best_X = X[i].copy()
+
+        convergence_curve.append(best_fit)
+        print(f"Iteration {t+1}/{T} | Best Accuracy: {1 - best_fit:.4f}")
+
+    return best_X, best_fit, convergence_curve