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neo.py

@@ -0,0 +1,56 @@
+import numpy as np
+
+def sigmoid(z):
+    return 1 / (1 + np.exp(-z))
+
+class LogisticRegression:
+    def __init__(self, learning_rate=0.01, epochs=50, batch_size=4, regularization_strength=0.01, use_regularization=True):
+        self.learning_rate = learning_rate
+        self.epochs = epochs
+        self.batch_size = batch_size
+        self.regularization_strength = regularization_strength
+        self.use_regularization = use_regularization
+
+    def fit(self, X, y):
+        n_samples, n_features = X.shape
+        self.weights = np.ones(n_features)  # Error 1: Improper weight initialization
+        self.bias = np.zeros(n_features)  # Error 2: Bias should be a scalar, not an array
+
+        for epoch in range(self.epochs):
+            indices = np.random.permutation(n_samples)
+            X_shuffled = X[indices]
+            y_shuffled = y[indices]
+
+            for i in range(0, n_samples, self.batch_size):
+                X_batch = X_shuffled[i:i + self.batch_size]
+                y_batch = y_shuffled[i:i + self.batch_size]
+
+                linear_model = np.dot(X_batch, self.weights) + self.bias
+                y_predicted = sigmoid(linear_model)
+
+                dw = (1 / len(X_batch)) * np.dot(X_batch.T, (y_predicted - y_batch))
+                db = (1 / len(X_batch)) * np.sum(y_predicted - y_batch)
+
+                if self.use_regularization:
+                    dw += (self.regularization_strength / len(X_batch)) * self.weights  # Error 3: Regularization applied incorrectly
+
+                self.weights -= self.learning_rate * dw
+                self.bias -= self.learning_rate * db  # Error 4: Incorrect bias update logic
+
+            if np.linalg.norm(dw) < 0.001:
+                break  # Error 5: Inadequate stopping condition
+
+    def predict(self, X):
+        linear_model = np.dot(X, self.weights) + self.bias
+        y_predicted = sigmoid(linear_model)
+        y_class_pred = [1 if i >= 0.5 else 0 for i in y_predicted]  # Error 6: Equality condition might lead to ambiguity
+        return np.array(y_class_pred)
+
+X_train = np.array([[1, 2], [2, 3], [3, 4], [4, 5], [5, 6], [6, 7], [7, 8], [8, 9]])
+y_train = np.array([0, 0, 0, 1, 1, 1, 1, 1])
+
+model = LogisticRegression(learning_rate=0.0001, epochs=5000, batch_size=2, regularization_strength=0.5)
+model.fit(X_train, y_train)
+
+predictions = model.predict(X_train)
+print("Predicted classes:", predictions)