本文实例为大家分享了python实现梯度下降和逻辑回归的具体代码，供大家参考，具体内容如下

import numpy as npimport pandas as pdimport os data = pd.read_csv("iris.csv") # 这里的iris数据已做过处理m, n = data.shapedataMatIn = np.ones((m, n))dataMatIn[:, :-1] = data.ix[:, :-1]classLabels = data.ix[:, -1] # sigmoid函数和初始化数据def sigmoid(z): return 1 / (1 + np.exp(-z)) # 随机梯度下降def Stocgrad_descent(dataMatIn, classLabels): dataMatrix = np.mat(dataMatIn) # 训练集 labelMat = np.mat(classLabels).transpose() # y值 m, n = np.shape(dataMatrix) # m:dataMatrix的行数,n:dataMatrix的列数 weights = np.ones((n, 1)) # 初始化回归系数（n, 1) alpha = 0.001 # 步长 maxCycle = 500 # 最大循环次数 epsilon = 0.001 error = np.zeros((n,1)) for i in range(maxCycle): for j in range(m): h = sigmoid(dataMatrix * weights) # sigmoid 函数 weights = weights + alpha * dataMatrix.transpose() * (labelMat - h) # 梯度 if np.linalg.norm(weights - error) < epsilon: break else: error = weights return weights # 逻辑回归def pred_result(dataMatIn): dataMatrix = np.mat(dataMatIn) r = Stocgrad_descent(dataMatIn, classLabels) p = sigmoid(dataMatrix * r) # 根据模型预测的概率 # 预测结果二值化 pred = [] for i in range(len(data)): if p[i] > 0.5: pred.append(1) else: pred.append(0) data["pred"] = pred os.remove("data_and_pred.csv") # 删除List_lost_customers数据集 # 第一次运行此代码时此步骤不要 data.to_csv("data_and_pred.csv", index=False, encoding="utf_8_sig") # 数据集保存pred_result(dataMatIn)

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