使用 Python 原生代码复现，多元逻辑回归,并封装成实体类调用自定义函数

当然可以！下面是一个使用Python原生代码复现多元逻辑回归的示例，并封装成实体类以便调用自定义函数的方法：

import numpy as np

class MultinomialLogisticRegression:
    def __init__(self, num_classes):
        self.num_classes = num_classes
        self.weights = None
        
    def train(self, X, y, learning_rate=0.01, num_iterations=1000):
        num_samples, num_features = X.shape
        self.weights = np.zeros((num_features, self.num_classes))
        
        for i in range(num_iterations):
            scores = np.dot(X, self.weights)
            probabilities = self.softmax(scores)
            
            error = probabilities - y
            
            gradient = np.dot(X.T, error)
            self.weights -= learning_rate * gradient
            
    def predict(self, X):
        scores = np.dot(X, self.weights)
        probabilities = self.softmax(scores)
        return np.argmax(probabilities, axis=1)
    
    def softmax(self, x):
        exps = np.exp(x - np.max(x, axis=1, keepdims=True))
        return exps / np.sum(exps, axis=1, keepdims=True)

# 示例用法
X_train = np.array([[1, 2], [2, 3], [3, 1], [4, 3], [5, 3], [6, 2]])
y_train = np.array([0, 0, 0, 1, 1, 1])

lr = MultinomialLogisticRegression(num_classes=2)
lr.train(X_train, y_train)

X_test = np.array([[1, 1], [5, 5]])
predictions = lr.predict(X_test)
print(predictions)

在上面的示例中，我们定义了一个`MultinomialLogisticRegression`类，该类包含了训练和预测函数。`train`函数用于训练模型，`predict`函数用于预测新样本的类别。

你可以根据自己的需求修改和扩展这个示例，例如添加正则化项、改变学习率等。

希望对你有所帮助！如果有任何疑问，请随时问我。