编程实现对率回归,并给出西瓜数据集3.0、

好的，对率回归是一种二分类算法，可以用于解决分类问题。下面是对率回归的实现过程：

1. 数据预处理：读取数据集，将数据集分为训练集和测试集，对数据进行标准化处理。

2. 模型训练：使用训练集训练模型，得到模型参数。

3. 模型预测：使用测试集进行预测，计算预测准确率。

具体实现代码如下：

import numpy as np
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler

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)
        
        self.theta = np.zeros(X.shape[1])
        
        for i in range(self.num_iter):
            z = np.dot(X, self.theta)
            h = self.__sigmoid(z)
            gradient = np.dot(X.T, (h - y)) / y.size
            self.theta -= self.lr * gradient
            
            if(self.verbose == True and i % 10000 == 0):
                z = np.dot(X, self.theta)
                h = self.__sigmoid(z)
                print(f'loss: {self.__loss(h, y)} \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

# 读取数据集
data = pd.read_csv('watermelon_3.csv')

# 划分训练集和测试集
X_train, X_test, y_train, y_test = train_test_split(data.iloc[:, :-1], data.iloc[:, -1], test_size=0.2, random_state=0)

# 数据标准化处理
scaler = StandardScaler()
X_train = scaler.fit_transform(X_train)
X_test = scaler.transform(X_test)

# 训练模型
model = LogisticRegression(lr=0.1, num_iter=300000)
model.fit(X_train, y_train)

# 预测结果
y_pred = model.predict(X_test)

# 计算准确率
accuracy = np.mean(y_pred == y_test)
print(f'Accuracy: {accuracy}')