更新 function.py

function.py

@@ -28,59 +28,7 @@ name = "董秋"
 
 
 # 这里写自己的代码实现
-def train_svm(X, y, learning_rate=0.01, iterations=1000):
-    """
-    支持向量机训练函数。
-
-    Args:
-    - X (numpy array): 特征数据。
-    - y (numpy array): 标签数据。
-    - other_params (str): 其它参数，可选设置。若需设置其它参数，必须设置其默认值，否则将**无法通过**自动评测，**成绩为零**。
-
-    Returns:
-    - numpy array: 训练得到的权重。
-    """
-    n_samples, n_features = X.shape
-    n_classes = len(np.unique(y))
-    weights = np.zeros((n_classes, n_features))
-    
-    for _ in range(iterations):
-        for idx, x_i in enumerate(X):
-            for c in range(n_classes):
-                y_i = 1 if y[idx] == c else -1
-                if y_i * (np.dot(weights[c], x_i)) < 1:
-                    weights[c] += learning_rate * (y_i * x_i - (2 * 1/iterations) * weights[c])
-                else:
-                    weights[c] -= learning_rate * (2 * 1/iterations) * weights[c]
-    
-    return weights
-
-def predict_svm(inX, weights):
-    """
-    支持向量机预测函数。
-
-    Args:
-    - inX (numpy array): 输入待分类的样本。
-    - weights (numpy array): 训练得到的权重。
-
-    Returns:
-    - int: 预测的分类标签。
-    """
-   scores = [np.dot(w, inX) for w in weights]
-    return np.argmax(scores)
-
-# 这里也写自己的代码实现
 def load_data(filepath):
-    """
-    从文件中加载数据，若不自行正确实现，则无法通过评测，**不记录成绩**。
-
-    Args:
-    - directory (str): 数据文件路径。
-
-    Returns:
-    - np.array: 特征数据。
-    - np.array: 标签数据。
-    """
     data = []
     labels = []
     with open(filepath, 'r') as file:
@@ -101,6 +49,27 @@ def load_data(filepath):
     
     return np.array(data), np.array(labels)
 
+def train_svm(X, y, learning_rate=0.01, iterations=1000):
+    n_samples, n_features = X.shape
+    n_classes = len(np.unique(y))
+    weights = np.zeros((n_classes, n_features))
+    
+    for _ in range(iterations):
+        for idx, x_i in enumerate(X):
+            for c in range(n_classes):
+                y_i = 1 if y[idx] == c else -1
+                if y_i * (np.dot(weights[c], x_i)) < 1:
+                    weights[c] += learning_rate * (y_i * x_i - (2 * 1/iterations) * weights[c])
+                else:
+                    weights[c] -= learning_rate * (2 * 1/iterations) * weights[c]
+    
+    return weights
+
+def predict_svm(inX, weights):
+    scores = [np.dot(w, inX) for w in weights]
+    return np.argmax(scores)
+
+
 
 if __name__ == "__main__":
     # 以下代码可用于测试，可自行随意修改