单木分割python代码
时间: 2023-10-13 20:21:04 浏览: 51
以下是单木分割的 Python 代码示例:
```python
import numpy as np
def decision_stump(X, y, weights):
n_samples, n_features = X.shape
m = np.inf
best_feature = None
best_threshold = None
best_direction = None
for feature in range(n_features):
feature_values = np.expand_dims(X[:, feature], axis=1)
unique_values = np.unique(feature_values)
for threshold in unique_values:
for direction in ['left', 'right']:
if direction == 'left':
y_hat = np.where(feature_values <= threshold, -1, 1)
else:
y_hat = np.where(feature_values > threshold, -1, 1)
error = np.sum(weights[y_hat != y])
if error < m:
m = error
best_feature = feature
best_threshold = threshold
best_direction = direction
return best_feature, best_threshold, best_direction, m
def adaboost(X, y, n_estimators=50, learning_rate=1.0):
n_samples = X.shape[0]
weights = np.ones(n_samples) / n_samples
models = []
for t in range(n_estimators):
feature, threshold, direction, error = decision_stump(X, y, weights)
alpha = learning_rate * 0.5 * np.log((1.0 - error) / error)
models.append((feature, threshold, direction, alpha))
y_hat = np.zeros(n_samples)
if direction == 'left':
y_hat[X[:, feature] <= threshold] = -1
else:
y_hat[X[:, feature] > threshold] = -1
weights *= np.exp(-alpha * y * y_hat)
weights /= np.sum(weights)
return models
```
其中 `decision_stump` 函数实现了单个决策树桩的训练,`adaboost` 函数则是对多个决策树桩进行集成,形成 AdaBoost 算法。在 `decision_stump` 函数中,我们使用了双重循环来遍历每个特征的每个可能的阈值和方向,从而找到最优的决策树桩。在 `adaboost` 函数中,我们在每个迭代中调用 `decision_stump` 函数来训练一个新的决策树桩,并更新样本权重。最终,我们将每个决策树桩的特征、阈值、方向和权重系数保存到 `models` 列表中,并返回该列表。
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