机器学习基石—作业2（16-20题Python实现）

import numpy as npfrom numpy import randomdef sign(x):#自定义符号函数，只返回-1，+1    ret=np.ones(x.shape)    for i,each in enumerate(x):        if each<0: ret[i]=-1    return retdef getTheta(x):#由输入的x生成假设空间的所有theta的序列    n=len(x)    l1=sorted(x)    theta=np.zeros(n)    for i in range(n-1):        theta[i]=(l1[i]+l1[i+1])/2    theta[-1]=1    return theta    def q17_18():    data_size=20    expes=5000    E_in=0    E_out=0    for i in range(expes):        x=random.uniform(-1,1,data_size)        noise_rate=0.2        #生成[-0.2,0.8]范围内的随机数组，取sign()即变为有20%的-1的随机数组        noise=sign(random.uniform(size=data_size)-noise_rate)        y=sign(x)*noise   #为y加上20%的噪声        theta=getTheta(x)        e_in=np.zeros((2,data_size))#对每个theta求出一个error_in,第一行是s=1，第2行是s=-1.        for i in range(len(theta)):            a1=y*sign(x-theta[i])            e_in[0][i]=(data_size-np.sum(a1))/(2*data_size)#数组只有-1和+1，可直接计算出-1所占比例            e_in[1][i]=(data_size-np.sum(-a1))/(2*data_size)         s=0;theta_best=0        min0, min1 = np.min(e_in[0]), np.min(e_in[1])        if min0<min1:            s=1            theta_best=theta[np.argmin(e_in[0])]        else:            s=-1            theta_best=theta[np.argmin(e_in[1])]        e_out=0.5+0.3*s*(np.abs(theta_best)-1)        E_in+=np.min(e_in)        E_out+=np.min(e_out)    ave_in=E_in/expes    ave_out=E_out/expes    print(ave_in,ave_out)def deciStump(x,y):#d:第d维,x:第x维数据 y：标签    data_size=len(x)    theta=getTheta(x)    e_in=np.zeros((2,data_size))    for i in range(len(theta)):        a1=y*sign(x-theta[i])        e_in[0][i]=(data_size-np.sum(a1))/(2*data_size)        e_in[1][i]=(data_size-np.sum(-a1))/(2*data_size)            s=0    min0, min1 = np.min(e_in[0]), np.min(e_in[1])    if min0<min1:        s=1        theta_best=theta[np.argmin(e_in[0])]    else:        s=-1        theta_best=theta[np.argmin(e_in[1])       E_in=np.min(np.min(e_in))    return s,theta_best,E_indef mkDateSet(datPath):    dataSet = open(datPath, 'r').readlines()    m = len(dataSet)    X_train = np.zeros((m, 9))    Y_train = np.zeros(m)    for i, item in enumerate(dataSet):        each = item.strip().split()        X_train[i] = [float(a) for a in each[:-1]]        Y_train[i] = int(each[-1])    return (X_train, Y_train)def getData_i(X_train,i):#获取第d维数据    return np.reshape(X_train[:,i],len(X_train))#从ndarray二维数组转为array一维数组def q19_20():    (X_train, Y_train)=mkDateSet('hw2_train.dat')    e_in=np.zeros(9)    s=np.zeros(9)    theta=np.zeros(9)    for i in range(9):        s[i],theta[i],e_in[i]=deciStump(getData_i(X_train,i),Y_train)            E_in=np.min(e_in)    dimension=np.argmin(e_in)    theta_best=theta[dimension]    s_best=s[dimension]        (X_test, Y_test)=mkDateSet('hw2_test.dat')    test_len=len(Y_test)    X_i=getData_i(X_test,dimension)    q=Y_test*s_best*sign(X_i-theta_best)    E_out=(test_len-np.sum(q))/(2*test_len)    print(E_in,E_out)    if __name__=='__main__':    q17_18()#0.16737 0.257180978031    q19_20()#0.25 0.355