抽象整活儿

function.py

@@ -41,17 +41,7 @@ def train_svm(X, y, C=0.20, max_iter=1000, learning_rate=0.01):
     Returns:
     - numpy array: 训练得到的权重。
     """
-    m, n = X.shape
+    return None
-    w = np.zeros(n)
-    b = 0
-    for i in range(max_iter):
-        for j in range(m):
-            if y[j] * (np.dot(X[j], w) + b) < 1:
-                w = w - learning_rate * (2 * w - C * y[j] * X[j])
-                b = b + learning_rate * C * y[j]
-            else:
-                w = w - learning_rate * 2 * w
-    return (w, b)
 
 
 def predict_svm(inX, weight):
@@ -65,8 +55,7 @@ def predict_svm(inX, weight):
     Returns:
     - int: 预测的分类标签。
     """
-    result = np.dot(inX, weight[0]) + weight[1]
+    return 1
-    return 1 if result > 0 else -1
 
 
 # 这里也写自己的代码实现