第1关：逻辑回归算法大体思想
#encoding=utf8import numpy as np#sigmoid函数
def sigmoid(t):#输入：负无穷到正无穷的实数#输出：转换后的概率值#********** Begin **********#result = 1.0 / (1 + np.exp(-t))#********** End **********#return round(result,12)
if __name__ == '__main__':pass
第2关：逻辑回归的损失函数
第3关：梯度下降
# -*- coding: utf-8 -*-import numpy as np
import warnings
warnings.filterwarnings("ignore")#梯度下降,inital_theta为参数初始值，eta为学习率，n_iters为训练轮数，epslion为误差范围
def gradient_descent(initial_theta,eta=0.05,n_iters=1e3,epslion=1e-8):# 请在此添加实现代码 ##********** Begin *********#theta = initial_thetai_iter = 0while i_iter < n_iters:gradient = 2*(theta-3)last_theta = thetatheta = theta - eta*gradientif(abs(theta-last_theta)<epslion):breaki_iter +=1#********** End **********#return theta
第4关：逻辑回归算法流程
# -*- coding: utf-8 -*-import numpy as np
import warnings
warnings.filterwarnings("ignore")
#定义sigmoid函数
def sigmoid(x):return 1/(1+np.exp(-x))#梯度下降，x为输入数据，y为数据label，eta为学习率，n_iters为训练轮数
def fit(x,y,eta=1e-3,n_iters=1e4):# 请在此添加实现代码 ##********** Begin *********#theta = np.zeros(x.shape[1])i_iter = 0while i_iter < n_iters:gradient = (sigmoid(x.dot(theta))-y).dot(x)theta = theta -eta*gradienti_iter += 1#********** End **********#return theta
第5关：sklearn中的逻辑回归
#encoding=utf8
import warnings
warnings.filterwarnings("ignore")
from sklearn.linear_model import LogisticRegression
from sklearn import datasets
from sklearn.model_selection import train_test_split
def cancer_predict(train_sample, train_label, test_sample):'''实现功能：1.训练模型 2.预测:param train_sample: 包含多条训练样本的样本集，类型为ndarray:param train_label: 包含多条训练样本标签的标签集，类型为ndarray:param test_sample: 包含多条测试样本的测试集，类型为ndarry:return: test_sample对应的预测标签'''#********* Begin *********#cancer = datasets.load_breast_cancer()#X表示特征，y表示标签X = cancer.datay = cancer.target##划分训练集和测试集X_train,X_test,y_train,y_test=train_test_split(X,y,test_size=0.20)logreg = LogisticRegression(solver='lbfgs',max_iter =200,C=10)logreg.fit(X_train, y_train)result = logreg.predict(test_sample)# print(result)return result#********* End *********#
