added codes

2024-09-29 21:58:56 +02:00
parent efdc7bd11e
commit fae683c3f1
8 changed files with 405 additions and 187 deletions
@@ -0,0 +1,76 @@
+import numpy as np
+class DecisionTreeRegressor:
+    def __init__(self, max_depth=3):
+        self.max_depth = max_depth
+        self.tree = None
+    def fit(self, X, y):
+        self.tree = self._grow_tree(X, y)
+    def _grow_tree(self, X, y, depth=0):
+        n_samples, n_features = X.shape
+        if depth < self.max_depth:
+            best_feature, best_threshold = self._best_split(X, y)
+            if best_feature is not None:
+                left_indices = X[:, best_feature] < best_threshold
+                right_indices = X[:, best_feature] >= best_threshold
+                left_child = self._grow_tree(X[left_indices], y[left_indices], depth + 1)
+                right_child = self._grow_tree(X[right_indices], y[right_indices], depth + 1)
+                return (best_feature, best_threshold, left_child, right_child)
+        return np.mean(y)
+    def _best_split(self, X, y):
+        best_mse = float('inf')
+        best_feature, best_threshold = None, None
+        n_samples, n_features = X.shape
+        
+        for feature in range(n_features):
+            thresholds = np.unique(X[:, feature])
+            for threshold in thresholds:
+                left_indices = X[:, feature] < threshold
+                right_indices = X[:, feature] >= threshold
+                if len(y[left_indices]) > 0 and len(y[right_indices]) > 0:
+                    left_mse = np.mean((y[left_indices] - np.mean(y[left_indices])) ** 2)
+                    right_mse = np.mean((y[right_indices] - np.mean(y[right_indices])) ** 2)
+                    mse = (len(y[left_indices]) * left_mse + len(y[right_indices]) * right_mse) / n_samples
+                    
+                    if mse < best_mse:
+                        best_mse = mse
+                        best_feature = feature
+                        best_threshold = threshold
+        return best_feature, best_threshold
+    def predict(self, X):
+        return np.array([self._predict_sample(sample, self.tree) for sample in X])
+    def _predict_sample(self, sample, node):
+        if isinstance(node, tuple):
+            feature, threshold, left_child, right_child = node
+            if sample[feature] < threshold:
+                return self._predict_sample(sample, left_child)
+            else:
+                return self._predict_sample(sample, right_child)
+        return node
+class GradientBoostingRegressor:
+    def __init__(self, n_estimators=100, learning_rate=0.1, max_depth=3):
+        self.n_estimators = n_estimators
+        self.learning_rate = learning_rate
+        self.max_depth = max_depth
+        self.models = []
+    def fit(self, X, y):
+        y_pred = np.zeros(y.shape)
+        for _ in range(self.n_estimators):
+            residuals = y - y_pred
+            model = DecisionTreeRegressor(max_depth=self.max_depth)
+            model.fit(X, residuals)
+            y_pred += self.learning_rate * model.predict(X)
+            self.models.append(model)
+    def predict(self, X):
+        y_pred = np.zeros(X.shape[0])
+        for model in self.models:
+            y_pred += self.learning_rate * model.predict(X)
+        return y_pred
+# Example usage
+if __name__ == "__main__":
+    # Sample data
+    X = np.array([[1], [2], [3], [4], [5]])
+    y = np.array([1.5, 1.7, 3.5, 3.7, 5.0])
+    model = GradientBoostingRegressor(n_estimators=100, learning_rate=0.1, max_depth=2)
+    model.fit(X, y)
+    predictions = model.predict(X)
+    print("Predictions:", predictions)