二元分类逻辑回归python代码实现

import numpy as np
from math import *#sigmoid函数
def sigmoid(z):return 1/(1+exp(-z))#计算代价的函数
def get_cost_logistic(X,y,w,b):m=X.shape[0]cost=0.0for i in range(m):z_i=np.dot(X[i],w)+bf_wb_i=sigmoid(z_i)cost+=-y[i]*np.log(f_wb_i)-(1-y[i])*np.log(1-f_wb_i)cost=cost/mreturn cost#计算梯度的函数
def get_gradient(x,y,w,b):#获取数据量和特征数量m=x.shape[0]n=x.shape[1]dj_dw=np.zeros((n,))dj_db=0for i in range(m):error=sigmoid(np.dot(x[i,:],w)+b)-y[i]dj_db+=errorfor j in range(n):dj_dw[j]+=(error*x[i,j])dj_db=dj_db/mdj_dw=dj_dw/mreturn dj_dw,dj_db#梯度下降函数
def gradient_descent(x,y,w_in,b_in,alpha,iters):w=w_inb=b_incost_his=[]for i in range(iters):dj_dw,dj_db=get_gradient(x,y,w,b)w=w-dj_dw*alphab=b-dj_db*alphacost_his.append(get_cost_logistic(x,y,w,b))if (i)%(iters/10)==0:print(f'iteration:{i},cost:{cost_his[i]},w:{w},b:{b}')print(f'final w:{w},b:{b}')return w,b,cost_his