分割树优化图像分类的python代码

以下是一个基于分割树优化图像分类的Python代码示例：

import numpy as np
from sklearn.datasets import make_classification
from sklearn.tree import DecisionTreeClassifier

# 创建示例数据
X, y = make_classification(n_samples=1000, n_features=10, n_classes=2)

# 定义分割树
class SegmentTree:
    def __init__(self, start, end, function):
        self.start = start
        self.end = end
        self.function = function
        self.value = None
        self.left = None
        self.right = None
        self._build()

    def _build(self):
        if self.start == self.end:
            self.value = self.function(self.start)
        else:
            mid = (self.start + self.end) // 2
            self.left = SegmentTree(self.start, mid, self.function)
            self.right = SegmentTree(mid+1, self.end, self.function)
            self.value = self.function([self.left.value, self.right.value])

    def query(self, start, end):
        if start == self.start and end == self.end:
            return self.value
        else:
            mid = (self.start + self.end) // 2
            if end <= mid:
                return self.left.query(start, end)
            elif start > mid:
                return self.right.query(start, end)
            else:
                left_value = self.left.query(start, mid)
                right_value = self.right.query(mid+1, end)
                return self.function([left_value, right_value])

# 定义分割树分类器
class SegmentedTreeClassifier:
    def __init__(self, n_segments, tree_depth=3, min_samples_leaf=1):
        self.n_segments = n_segments
        self.tree_depth = tree_depth
        self.min_samples_leaf = min_samples_leaf

    def fit(self, X, y):
        # 计算每个样本所属的段
        segments = np.linspace(0, len(X), self.n_segments+1, dtype=int)
        segments[-1] = len(X)
        segment_ids = np.searchsorted(segments, range(len(X))) - 1

        # 训练每个段的决策树
        self.trees = []
        for i in range(self.n_segments):
            start = segments[i]
            end = segments[i+1]
            tree = DecisionTreeClassifier(max_depth=self.tree_depth, min_samples_leaf=self.min_samples_leaf)
            tree.fit(X[start:end], y[start:end])
            self.trees.append(tree)

        # 构建分割树
        def predict(segment_id):
            return self.trees[segment_id].predict_proba(X[segments[segment_id]:segments[segment_id+1]])[0]

        self.segment_tree = SegmentTree(0, self.n_segments-1, predict)

    def predict_proba(self, X):
        # 计算每个样本所属的段
        segments = np.linspace(0, len(X), self.n_segments+1, dtype=int)
        segments[-1] = len(X)
        segment_ids = np.searchsorted(segments, range(len(X))) - 1

        # 预测每个样本的概率
        probas = []
        for i in range(len(X)):
            start = segments[segment_ids[i]]
            end = segments[segment_ids[i]+1]
            proba = self.segment_tree.query(segment_ids[i], segment_ids[i])
            probas.append(proba)

        return np.array(probas)

    def predict(self, X):
        probas = self.predict_proba(X)
        return np.argmax(probas, axis=1)

使用示例：

# 创建分类器
clf = SegmentedTreeClassifier(n_segments=10, tree_depth=3, min_samples_leaf=1)

# 训练分类器
clf.fit(X, y)

# 预测新数据
preds = clf.predict(X)