原文地址：https://www.jianshu.com/p/3f7d4aa6a7cf
问题描述

程序实现

# coding: utf-8

import numpy as np

import math

import matplotlib.pyplot as plt

def sign(x):

    if(x>=0):

        return 1

    else:

        return -1

def read_data(dataFile):

    with open(dataFile,'r') as f:

        lines=f.readlines()

        data_list=[]

        for line in lines:

            line=line.strip().split()

            data_list.append([1.0] + [float(l) for l in line])

        dataArray=np.array(data_list)

        num_data=dataArray.shape[0]

        num_dim=dataArray.shape[1]-1

        dataX=dataArray[:,:-1].reshape((num_data,num_dim))

        dataY=dataArray[:,-1].reshape((num_data,1))

        return dataX,dataY

def w_reg(dataX,dataY,namuta):

    num_dim=dataX.shape[1]

    dataX_T=np.transpose(dataX)

    tmp=np.dot(np.linalg.inv(np.dot(dataX_T,dataX)+namuta*np.eye(num_dim)),dataX_T)

    return np.dot(tmp,dataY)

def pred(wREG,dataX):

    pred=np.dot(dataX,wREG)

    num_data=dataX.shape[0]

    for i in range(num_data):

        pred[i][0]=sign(pred[i][0])

    return pred

def zero_one_cost(pred,dataY):

    return np.sum(pred!=dataY)/dataY.shape[0]

if __name__=="__main__":

    # train

    dataX,dataY=read_data("hw4_train.dat")

    print("\n13")

    wREG=w_reg(dataX,dataY,namuta=10)

    Ein=zero_one_cost(pred(wREG,dataX),dataY)

    print("the Ein on the train set: ",Ein)

    # test

    testX,testY=read_data("hw4_test.dat")

    Eout=zero_one_cost(pred(wREG,testX),testY)

    print("the Eout on the test set: ",Eout)

    l=[2,1,0,-1,-2,-3,-4,-5,-6,-7,-8,-9,-10]

    print("\n14")

    Ein_list=[]

    Eout_list=[]

    for i in l:

        namuta=math.pow(10,i)

        wREG=w_reg(dataX,dataY,namuta)

        Ein_list.append(zero_one_cost(pred(wREG,dataX),dataY))

        Eout_list.append(zero_one_cost(pred(wREG,testX),testY))

    id_in=Ein_list.index(min(Ein_list))

    plt.figure()

    plt.plot(np.power(np.full(shape=(len(l),),fill_value=10,dtype=np.int32),l),Ein_list)

    plt.xlabel("namuta")

    plt.xlim((math.pow(10,l[0]),math.pow(10,l[-1])))

    plt.ylabel("Ein")

    plt.savefig("14.png")

    print("the namuta with the minimun Ein: ",math.pow(10,l[id_in]))

    print("the Eout on such namuta: ", Eout_list[id_in])

    print("\n15")

    id_out = Eout_list.index(min(Eout_list))

    plt.figure()

    plt.plot(np.power(np.full(shape=(len(l),),fill_value=10,dtype=np.int32),l),Eout_list)

    plt.xlabel("namuta")

    plt.xlim((math.pow(10,l[0]),math.pow(10,l[-1])))

    plt.ylabel("Eout")

    plt.savefig("15.png")

    print("the namuta with the minimun Eout: ", math.pow(10, l[id_out]))

    trainX=dataX[:120]

    trainY=dataY[:120]

    validX=dataX[120:]

    validY=dataY[120:]

    # validation

    print("\n16")

    Ein_list.clear()

    Eout_list.clear()

    Eval_list=[]

    for i in l:

        namuta=math.pow(10,i)

        wREG=w_reg(trainX,trainY,namuta)

        Ein_list.append(zero_one_cost(pred(wREG,trainX),trainY))

        Eout_list.append(zero_one_cost(pred(wREG,testX),testY))

        Eval_list.append(zero_one_cost(pred(wREG,validX),validY))

    id_in=Ein_list.index(min(Ein_list))

    plt.figure()

    plt.plot(np.power(np.full(shape=(len(l),),fill_value=10,dtype=np.int32),l),Ein_list)

    plt.xlabel("namuta")

    plt.xlim((math.pow(10,l[0]),math.pow(10,l[-1])))

    plt.ylabel("Ein")

    plt.savefig("16.png")

    print("the namuta with the minimun Ein: ",math.pow(10,l[id_in]))

    print("the Eout on such namuta: ", Eout_list[id_in])

    print("\n17")

    id_val=Eval_list.index(min(Eval_list))

    plt.figure()

    plt.plot(np.power(np.full(shape=(len(l),),fill_value=10,dtype=np.int32),l),Eval_list)

    plt.xlabel("namuta")

    plt.xlim((math.pow(10,l[0]),math.pow(10,l[-1])))

    plt.ylabel("Eval")

    plt.savefig("17.png")

    print("the namuta with the minimun Eval: ",math.pow(10,l[id_val]))

    print("the Eout on such namuta: ", Eout_list[id_val])

    print("\n18")

    wREG=w_reg(dataX,dataY,namuta=math.pow(10,l[id_val]))

    Ein=zero_one_cost(pred(wREG,dataX),dataY)

    Eout = zero_one_cost(pred(wREG, testX), testY)

    print("Ein: ",Ein)

    print("Eout: ",Eout)

    # 5-fold cross validation

    print("\n19")

    Eval_list.clear()

    splX=np.split(dataX,5,axis=0)

    splY=np.split(dataY,5,axis=0)

    for j in l:

        Eval = 0

        namuta=math.pow(10,j)

        for i in range(5):

            li=[a for a in range(5)]

            li.pop(i)

            trainX=np.concatenate([splX[k] for k in li],axis=0)

            trainY=np.concatenate([splY[k] for k in li],axis=0)

            wREG=w_reg(trainX,trainY,namuta)

            Eval+=zero_one_cost(pred(wREG,splX[i]),splY[i])/5

        Eval_list.append(Eval)

    id_val=Eval_list.index(min(Eval_list))

    plt.figure()

    plt.plot(np.power(np.full(shape=(len(l),),fill_value=10,dtype=np.int32),l),Eval_list)

    plt.xlabel("namuta")

    plt.xlim((math.pow(10,l[0]),math.pow(10,l[-1])))

    plt.ylabel("Ecv")

    plt.savefig("19.png")

    print("the namuta with the minimun Ecv: ",math.pow(10,l[id_val]))

    print("\n20")

    wREG=w_reg(dataX,dataY,namuta=math.pow(10,l[id_val]))

    Ein=zero_one_cost(pred(wREG,dataX),dataY)

    Eout = zero_one_cost(pred(wREG, testX), testY)

    print("Ein: ",Ein)

    print("Eout: ",Eout)
巴特西

机器学习基石笔记：Homework #4 Regularization&Validation相关习题

问题描述

程序实现

运行结果

13

14

15

16

17

18

19

20

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