生成旋转森林python
时间: 2023-10-13 22:22:04 浏览: 54
Sure, I can help with generating rotation forest in Python. Rotation forest is an ensemble learning algorithm that uses the principal component analysis (PCA) technique to create rotated subsets of the feature set and then trains decision trees on each rotated subset. Here's a basic implementation:
```python
from sklearn.decomposition import PCA
from sklearn.tree import DecisionTreeClassifier
class RotationForest:
def __init__(self, n_trees):
self.n_trees = n_trees
self.trees = []
def fit(self, X, y):
n_samples, n_features = X.shape
# create and train decision trees on rotated feature subsets
for i in range(self.n_trees):
# generate random rotation matrix
pca = PCA(n_components=n_features)
pca.fit(X)
rotation = pca.components_
# apply rotation to input data
rotated_X = X.dot(rotation.T)
# create decision tree and train on rotated data subset
tree = DecisionTreeClassifier()
tree.fit(rotated_X, y)
# store trained tree
self.trees.append((tree, rotation))
def predict(self, X):
# compute predictions from all trees and average results
y_pred = []
for tree, rotation in self.trees:
rotated_X = X.dot(rotation.T)
y_pred.append(tree.predict(rotated_X))
return np.mean(y_pred, axis=0)
```
To use the rotation forest classifier, simply create an instance of the `RotationForest` class with the desired number of trees and call the `fit` method with your input data and labels. Then, you can call the `predict` method with new input data to obtain ensemble predictions.
I hope this helps! Let me know if you have any more questions.