python 实现逻辑回归

import numpy as np
from matplotlib import pyplot as plt
"font.sans-serif"] = ["SimHei"]
    # 包含数据和标签的数据集
    self.data = np.loadtxt("./data2.txt", delimiter=",")
    self.data_mat = self.data[:, 0:2]
    self.label_mat = self.data[:, 2]
    self.thetas = np.zeros((self.data_mat.shape[1]))
"""
    损失函数具体实现
    :param theta: 逻辑回归系数
    :param data_mat: 带有截距项的数据集
    :param label_mat: 标签数据集
    :param reg:
    :return:
    """
    m = self.label_mat.size
    label_mat = self.label_mat.reshape(-1, 1)
    h = self.sigmoid(self.p_data_mat.dot(theta))
"""
    逻辑回归梯度下降收敛函数
    :param alpha: 学习率
    :param reg:
    :param iterations: 最大迭代次数
    :return: 逻辑回归系数组
    """
    m, n = self.p_data_mat.shape
    theta = np.zeros((n, 1))
    theta_set = []
"negative", pos_text="positive", thetas=None):
    neg = self.label_mat == 0
    pos = self.label_mat == 1
    fig1 = plt.figure(figsize=(12, 8))
    ax1 = fig1.add_subplot(111)
    ax1.scatter(self.p_data_mat[neg][:, 1], self.p_data_mat[neg][:, 2], marker="o", s=100, label=neg_text)
    ax1.scatter(self.p_data_mat[pos][:, 1], self.p_data_mat[pos][:, 2], marker="+", s=100, label=pos_text)
    ax1.set_xlabel(x_label, fontsize=14)
"线性不可分数据集")
"$\\lambda$ = {}".format(0))
"$\\lambda$ = {}".format(0))

import numpy as np
from matplotlib import pyplot as plt
"font.sans-serif"] = ["SimHei"]
    # 包含数据和标签的数据集
    self.data = np.loadtxt("./data2.txt", delimiter=",")
    self.data_mat = self.data[:, 0:2]
    self.label_mat = self.data[:, 2]
    self.thetas = np.zeros((self.data_mat.shape[1]))
"""
    损失函数具体实现
    :param theta: 逻辑回归系数
    :param data_mat: 带有截距项的数据集
    :param label_mat: 标签数据集
    :param reg:
    :return:
    """
    m = self.label_mat.size
    label_mat = self.label_mat.reshape(-1, 1)
    h = self.sigmoid(self.p_data_mat.dot(theta))
"""
    逻辑回归梯度下降收敛函数
    :param alpha: 学习率
    :param reg:
    :param iterations: 最大迭代次数
    :return: 逻辑回归系数组
    """
    m, n = self.p_data_mat.shape
    theta = np.zeros((n, 1))
    theta_set = []
"negative", pos_text="positive", thetas=None):
    neg = self.label_mat == 0
    pos = self.label_mat == 1
    fig1 = plt.figure(figsize=(12, 8))
    ax1 = fig1.add_subplot(111)
    ax1.scatter(self.p_data_mat[neg][:, 1], self.p_data_mat[neg][:, 2], marker="o", s=100, label=neg_text)
    ax1.scatter(self.p_data_mat[pos][:, 1], self.p_data_mat[pos][:, 2], marker="+", s=100, label=pos_text)
    ax1.set_xlabel(x_label, fontsize=14)
"线性不可分数据集")
"$\\lambda$ = {}".format(0))
"$\\lambda$ = {}".format(0))