解析 def learning_curve(self, depth, X_train, y_train, X_test, y_test): sizes = np.round(np.linspace(1, len(X_train), 50)) train_err = np.zeros(len(sizes)) test_err = np.zeros(len(sizes)) for i, s in enumerate(sizes): s = int(s) regressor = DecisionTreeRegressor(max_depth=depth) regressor.fit(X_train[:s], y_train[:s]) train_err[i] = self.performance_metric(y_train[:s], regressor.predict(X_train[:s])) test_err[i] = self.performance_metric(y_test, regressor.predict(X_test)) self.learning_curve_graph(depth, sizes, train_err, test_err)

这段代码实现了一个决策树回归器的学习曲线的绘制。输入参数包括决策树的最大深度depth，训练集X_train和y_train，测试集X_test和y_test。在函数内部，先生成一个包含50个元素的等差数列，表示训练集大小的变化范围，然后分别初始化训练误差和测试误差的数组。接下来进入循环，每次循环中根据当前训练集大小s，创建一个最大深度为depth的决策树回归器，然后使用前s个训练样本对其进行拟合，并计算训练误差和测试误差。最后，将训练误差和测试误差存入数组中。最后一行调用了一个名为learning_curve_graph的函数，用于绘制学习曲线图。

相关问题

def __init__(self, train_x, train_y, likelihood):

这是一个类的构造函数，用于初始化类的属性。该函数有四个参数：self，train_x，train_y，和likelihood。其中self是指类自身，train_x和train_y是用于训练的数据集，likelihood是指定的似然函数。在函数中，可以将这些参数保存为类的属性，以便在类的其他方法中使用。例如：

class MyClass:
    def __init__(self, train_x, train_y, likelihood):
        self.train_x = train_x
        self.train_y = train_y
        self.likelihood = likelihood

    def train(self):
        # use train_x and train_y to train the model with the specified likelihood function
        pass

在这个例子中，train() 方法可以使用 __init__() 中保存的属性 train_x、train_y 和 likelihood 来训练模型。

def get_CIFAR10_data(num_training=5000, num_validation=500, num_test=500): cifar10_dir = r'D:\daima\cifar-10-python\cifar-10-batches-py' X_train, y_train, X_test, y_test = load_CIFAR10(cifar10_dir) print(X_train.shape) mask = range(num_training, num_training + num_validation) X_val = X_train[mask] y_val = y_train[mask] mask = range(num_training) X_train = X_train[mask] y_train = y_train[mask] mask = range(num_test) X_test = X_test[mask] y_test = y_test[mask] mean_image = np.mean(X_train, axis=0) X_train -= mean_image X_val -= mean_image X_test -= mean_image X_train = X_train.transpose(0, 3, 1, 2).copy() X_val = X_val.transpose(0, 3, 1, 2).copy() X_test = X_test.transpose(0, 3, 1, 2).copy() return { 'X_train': X_train, 'y_train': y_train, 'X_val': X_val, 'y_val': y_val, 'X_test': X_test, 'y_test': y_test, }这是一个加载cifar10数据集的函数，如何修改使其能加载mnist数据集

要加载MNIST数据集，可以使用以下代码修改上述函数：

import tensorflow as tf
from tensorflow.keras.datasets import mnist
import numpy as np

def get_MNIST_data(num_training=50000, num_validation=10000, num_test=10000):
    (X_train, y_train), (X_test, y_test) = mnist.load_data()
    X_train = np.expand_dims(X_train, axis=-1)
    X_test = np.expand_dims(X_test, axis=-1)
    X_train = X_train.astype('float32') / 255
    X_test = X_test.astype('float32') / 255
    mean_image = np.mean(X_train, axis=0)
    X_train -= mean_image
    X_test -= mean_image
    mask = range(num_training, num_training + num_validation)
    X_val = X_train[mask]
    y_val = y_train[mask]
    mask = range(num_training)
    X_train = X_train[mask]
    y_train = y_train[mask]
    mask = range(num_test)
    X_test = X_test[mask]
    y_test = y_test[mask]
    return {
        'X_train': X_train, 'y_train': y_train,
        'X_val': X_val, 'y_val': y_val,
        'X_test': X_test, 'y_test': y_test,
    }

此代码使用 TensorFlow 的 MNIST 数据集加载函数，将数据集的维度转换为 (num_samples, height, width, depth) 的形式，并对图像像素值进行归一化。