y_prob = np.concatenate(y_prob, axis=0)啥意思

这段代码的作用是将一个多维数组沿着指定的轴(axis)进行拼接(concatenate)。具体来说，这里的y_prob是一个列表，其中每个元素是一个numpy数组，表示模型对某个样本属于每个类别的概率。np.concatenate(y_prob, axis=0)的作用是将这个列表中的所有数组沿着第0维(axis=0)进行拼接，即将每个数组中对应位置的元素拼接成一个更长的数组。最终得到的结果是一个一维数组，表示模型对所有样本属于每个类别的概率。

相关问题

import numpy as np from sklearn.datasets import load_iris from sklearn.model_selection import train_test_split import matplotlib.pyplot as plt # 加载 iris 数据 iris = load_iris() # 只选取两个特征和两个类别进行二分类 X = iris.data[(iris.target==0)|(iris.target==1), :2] y = iris.target[(iris.target==0)|(iris.target==1)] # 将标签转化为 0 和 1 y[y==0] = -1 # 将数据集分为训练集和测试集 X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42) # 实现逻辑回归算法 class LogisticRegression: def __init__(self, lr=0.01, num_iter=100000, fit_intercept=True, verbose=False): self.lr = lr self.num_iter = num_iter self.fit_intercept = fit_intercept self.verbose = verbose def __add_intercept(self, X): intercept = np.ones((X.shape[0], 1)) return np.concatenate((intercept, X), axis=1) def __sigmoid(self, z): return 1 / (1 + np.exp(-z)) def __loss(self, h, y): return (-y * np.log(h) - (1 - y) * np.log(1 - h)).mean() def fit(self, X, y): if self.fit_intercept: X = self.__add_intercept(X) # 初始化参数 self.theta = np.zeros(X.shape[1]) for i in range(self.num_iter): # 计算梯度 z = np.dot(X, self.theta) h = self.__sigmoid(z) gradient = np.dot(X.T, (h - y)) / y.size # 更新参数 self.theta -= self.lr * gradient # 打印损失函数 if self.verbose and i % 10000 == 0: z = np.dot(X, self.theta) h = self.__sigmoid(z) loss = self.__loss(h, y) print(f"Loss: {loss} \t") def predict_prob(self, X): if self.fit_intercept: X = self.__add_intercept(X) return self.__sigmoid(np.dot(X, self.theta)) def predict(self, X, threshold=0.5): return self.predict_prob(X) >= threshold # 训练模型 model = LogisticRegressio

n()
model.fit(X_train, y_train)

# 在测试集上进行预测
y_pred = model.predict(X_test)

# 计算准确率
accuracy = np.sum(y_pred == y_test) / y_test.shape[0]
print(f"Accuracy: {accuracy}")

# 可视化
plt.scatter(X_test[:, 0], X_test[:, 1], c=y_pred)
plt.show()

请问这段代码实现了什么功能？

Traceback (most recent call last): File "D:/pycharm/projects/Pythoneeee/projects/d.py", line 65, in <module> coef_mat = np.column_stack((prob_fit.theta_.T, logit_fit.coef_, linear_fit.coef_)) # File "<__array_function__ internals>", line 200, in column_stack File "D:\pycharm\projects\venv\lib\site-packages\numpy\lib\shape_base.py", line 656, in column_stack return _nx.concatenate(arrays, 1) File "<__array_function__ internals>", line 200, in concatenate ValueError: all the input array dimensions except for the concatenation axis must match exactly, but along dimension 0, the array at index 0 has size 10 and the array at index 1 has size 1

这个错误提示是因为`prob_fit.theta_`的维度是`(1, n)`，而`logit_fit.coef_`和`linear_fit.coef_`的维度是`(1,)`和`(1, m)`，所以无法直接进行列合并。可以使用`np.squeeze`方法将`prob_fit.theta_`的维度压缩成`(n,)`，然后再进行列合并。具体代码如下：

prob_theta = np.squeeze(prob_fit.theta_)
coef_mat = np.column_stack((prob_theta, logit_fit.coef_[0], linear_fit.coef_[0]))

这里的`np.squeeze`方法可以将维度为1的维度压缩掉，即将`(1, n)`的矩阵转换成`(n,)`的向量。