Logistic回归案例（数据集+python代码）.zip

# -*- coding:UTF-8 -*- import matplotlib.pyplot as plt import numpy as np import time file_path = r"你的数据路径" start = time.time() # 01 读取文件数据并预处理 dataMat = [] labelMat = [] with open(file_path) as file: data = file.readlines() for i in range(len(data)): data_list = data[i].strip().split("\t") dataMat.append([1.0,float(data_list[0]),float(data_list[1])]) labelMat.append(int(data_list[2])) # # 02 绘制数据集 # n = len(dataMat) # xcord1 = []; ycord1 = [] # xcord2 = []; ycord2 = [] # for i in range(n): # if int(labelMat[i]) == 1: # xcord1.append(float(dataMat[i][1])); ycord1.append(float(dataMat[i][2])) # else: # xcord2.append(float(dataMat[i][1])); ycord2.append(float(dataMat[i][2])) # fig = plt.figure() # ax = fig.add_subplot(111) # ax.scatter(xcord1, ycord1, s = 20, c = 'red', marker = 's',alpha=.5) # ax.scatter(xcord2, ycord2, s = 20, c = 'green',alpha=.5) # plt.title('DataSet') # plt.xlabel('x1'); plt.ylabel('x2') # plt.show() # 03 搭建回归模型 # sigmoid函数 def sigmoid(Z): return 1.0 / (1 + np.exp(-Z)) # 梯度上升算法 def gradAscent(dataMatIn, classLabels): dataMatrix = np.mat(dataMatIn)#转换成numpy的mat labelMat = np.mat(classLabels).transpose() m, n = np.shape(dataMatrix) a = 0.001 #步长 maxCycles = 500 #循环次数 weights = np.ones((n,1))#初始化回归系数为1 for k in range(maxCycles): h = sigmoid(dataMatrix * weights) error = labelMat - h weights = weights + a * dataMatrix.transpose() * error return weights.getA() # 04 绘制决策边界 def plotBestFit(weights): dataArr = np.array(dataMat) n = np.shape(dataMat)[0] xcord1 = []; ycord1 = [] xcord2 = []; ycord2 = [] for i in range(n): if int(labelMat[i]) == 1: xcord1.append(dataArr[i,1]); ycord1.append(dataArr[i,2]) else: xcord2.append(dataArr[i,1]); ycord2.append(dataArr[i,2]) fig = plt.figure() ax = fig.add_subplot(111) ax.scatter(xcord1, ycord1, s = 20, c = 'red', marker = 's',alpha=.5) ax.scatter(xcord2, ycord2, s = 20, c = 'green',alpha=.5) x = np.arange(-3.0, 3.0, 0.1) y = (-weights[0] - weights[1] * x) / weights[2] ax.plot(x, y) plt.title('BestFit') plt.xlabel('X1'); plt.ylabel('X2') plt.show() weights = gradAscent(dataMat,labelMat) plotBestFit(weights) end = time.time() print("运行时间：{}".format(end-start))