更新 function.py

function.py

@@ -43,14 +43,10 @@ def train_logreg(X, y, learning_rate=0.01, iterations=1000, batch_size=256):
     m, n = X.shape
     weights = np.zeros(n)
     for i in range(iterations):
-        for j in range(0, m, batch_size):
-            end = min(j + batch_size, m)
-            X_batch = X[j:end]
-            y_batch = y[j:end]
-            z = np.dot(X_batch, weights)
-            h = sigmoid(z)
-            gradient = np.dot(X_batch.T, (h - y_batch)) / len(X_batch)
-            weights -= learning_rate * gradient
+        z = np.dot(X, weights)
+        h = sigmoid(z)
+        gradient = np.dot(X.T, (h - y)) / m
+        weights -= learning_rate * gradient
     return weights
 
 def predict_logreg(inX, weights):