超立方选线理论代码

以下是一个简单的超立方选线理论实现的 Python 代码示例：

import numpy as np

class Hypercube:
    def __init__(self, data):
        self.data = data
        self.label = None
        self.regression_value = None
    
class HypercubeSelection:
    def __init__(self, n_hypercubes):
        self.n_hypercubes = n_hypercubes
        self.hypercubes = []
    
    def train(self, X, y):
        min_values = np.min(X, axis=0)
        max_values = np.max(X, axis=0)
        edges = (max_values - min_values) / self.n_hypercubes
        self.hypercubes = []
        for i in range(self.n_hypercubes):
            for j in range(self.n_hypercubes):
                cube_data = []
                for k in range(X.shape[0]):
                    if (X[k][0] >= min_values[0] + i * edges[0]) and \
                       (X[k][0] <= min_values[0] + (i+1) * edges[0]) and \
                       (X[k][1] >= min_values[1] + j * edges[1]) and \
                       (X[k][1] <= min_values[1] + (j+1) * edges[1]):
                        cube_data.append(X[k])
                cube = Hypercube(np.array(cube_data))
                cube.label = np.bincount(y[np.isin(X, cube_data)].flatten()).argmax()
                cube.regression_value = np.mean(y[np.isin(X, cube_data)].flatten())
                self.hypercubes.append(cube)
    
    def predict(self, X):
        y_pred = []
        for i in range(X.shape[0]):
            for j in range(len(self.hypercubes)):
                if (X[i][0] >= np.min(self.hypercubes[j].data[:,0])) and \
                   (X[i][0] <= np.max(self.hypercubes[j].data[:,0])) and \
                   (X[i][1] >= np.min(self.hypercubes[j].data[:,1])) and \
                   (X[i][1] <= np.max(self.hypercubes[j].data[:,1])):
                    y_pred.append(self.hypercubes[j].label)
                    break
        return np.array(y_pred)

在该代码示例中，`Hypercube` 类表示一个超立方体，包含了超立方体内的数据、标签和回归值等信息。`HypercubeSelection` 类表示超立方选线理论模型，包含了超立方体的数量和超立方体列表等信息。`train` 方法用于训练模型，该方法首先计算出每个超立方体的边界，并根据超立方体内的数据计算出该超立方体的标签或回归值。`predict` 方法用于对新的数据进行预测，该方法首先找到该数据所在的超立方体，然后返回该超立方体的标签或回归值。