原文地址：https://www.jianshu.com/p/1db700f866ee

问题描述

程序实现

# kNN_RBFN.py

# coding:utf-8

import numpy as np

import matplotlib.pyplot as plt

def ReadData(dataFile):

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

        lines = f.readlines()

        data_list = []

        for line in lines:

            line = line.strip().split()

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

        dataArray = np.array(data_list)

        return dataArray

def sign(n):

    if(n>=0):

        return 1

    else:

        return -1

def kNN(k,trainArray,dataX):

    num_data=dataX.shape[0]

    predY=np.zeros((num_data,))

    for n in range(num_data):

        distArray=np.sum((trainArray[:,:-1]-dataX[n,:])**2,axis=1)

        id_list=np.argsort(distArray,axis=0).tolist()[:k]

        for i in id_list:

            predY[n]+=trainArray[i,-1]

        predY[n]=sign(predY[n])

    return predY

def GetZeroOneError(predY,dataY):

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

def plot_bar_chart(X,Y,nameX,nameY,saveName):

    plt.figure(figsize=(10,6))

    plt.bar(left=X,height=Y,width=0.8,align="center",yerr=0.000001)

    for (c,w) in zip(X,Y):

        plt.text(c,w*1.03,str(round(w,4)))

    plt.xlabel(nameX)

    plt.ylabel(nameY)

    plt.xlim(X[0]-1,X[-1]+1)

    plt.xticks(X)

    plt.ylim(0,1)

    plt.title(nameY+" versus "+nameX)

    plt.savefig(saveName)

    return

def RBFNetwork(k,gamma,trainArray,dataX):

    num_data=dataX.shape[0]

    predY=np.zeros((num_data,))

    for n in range(num_data):

        gaussianDistArray=np.exp(-gamma*np.sum((trainArray[:,:-1]-dataX[n,:])**2,axis=1))

        id_list=np.argsort(gaussianDistArray,axis=0).tolist()[:k]

        for i in id_list:

            predY[n]+=trainArray[i,-1]

        predY[n]=sign(predY[n])

    return predY

if __name__=="__main__":

    dataArray=ReadData("hw8_train.dat")

    testArray=ReadData("hw8_test.dat")

    k_list=[1,3,5,7,9]

    ein_list=[]

    eout_list=[]

    for k in k_list:

        predY=kNN(k,dataArray,dataArray[:,:-1])

        ein_list.append(GetZeroOneError(predY,dataArray[:,-1]))

        predY=kNN(k,dataArray,testArray[:,:-1])

        eout_list.append(GetZeroOneError(predY,testArray[:,-1]))

    # 12

    plot_bar_chart(k_list,ein_list,nameX="k",nameY="Ein(gk-nbor)",saveName="12.png")

    # 14

    plot_bar_chart(k_list,eout_list,nameX='k',nameY="Eout(gk-bor)",saveName="14.png")

    gamma_list=[-3,-1,0,1,2]

    ein_list=[]

    eout_list=[]

    for gamma in gamma_list:

        predY=RBFNetwork(dataArray.shape[0],10**gamma,dataArray,dataArray[:,:-1])

        ein_list.append(GetZeroOneError(predY,dataArray[:,-1]))

        predY=RBFNetwork(dataArray.shape[0],10**gamma,dataArray,testArray[:,:-1])

        eout_list.append(GetZeroOneError(predY,testArray[:,-1]))

    # 16

    plot_bar_chart(X=gamma_list,Y=ein_list,nameX="log10(gamma)",nameY="Ein(guniform)",saveName="16.png")

    # 18

    plot_bar_chart(X=gamma_list,Y=eout_list,nameX="log10(gamma)",nameY="Eout(guniform)",saveName="18.png")

# kMeans.py

# coding:utf-8

from numpy import random

from kNN_RBFN import *

def kMeans(t,k,dataArray):

    num_data=dataArray.shape[0]

    random.seed(t)

    centreIDList=random.randint(0,num_data,k).tolist()

    nowCentreArray=dataArray[centreIDList,:]

    tmpCentreArray=np.array(nowCentreArray)

    ein=1000000

    nowEin=ein-1

    dict={}

    while(nowEin<ein):

        ein=nowEin

        dict = {}

        for n in range(num_data):

            distArray=np.sum((nowCentreArray-dataArray[n,:])**2,axis=1)

            minID=np.argmin(distArray)

            tmpCentreArray[minID]=(tmpCentreArray[minID]+dataArray[n,:])/2

            try:

                dict[minID].append(dataArray[n,:])

            except:

                dict[minID]=[]

                dict[minID].append(dataArray[n,:])

        nowCentreArray=np.array(tmpCentreArray)

        nowEin=GetEin(nowCentreArray,dict)

    return nowCentreArray,dict

def GetEin(nowCentreArray,dict):

    k=nowCentreArray.shape[0]

    ein=0

    for i in range(k):

        if i not in dict.keys():

            continue

        data=np.array(dict[i])

        ein+=np.average(np.sum((data-nowCentreArray[i])**2,axis=1))

    return ein

def plot_bar_chart(X,Y,nameX,nameY,saveName):

    plt.figure(figsize=(10,6))

    plt.bar(left=X,height=Y,width=0.8,align="center",yerr=0.000001)

    for (c,w) in zip(X,Y):

        plt.text(c,w*1.03,str(round(w,4)))

    plt.xlabel(nameX)

    plt.ylabel(nameY)

    plt.xlim(X[0]-1,X[-1]+1)

    plt.xticks(X)

    plt.title(nameY+" versus "+nameX)

    plt.savefig(saveName)

    return

if __name__=="__main__":

    dataArray=ReadData("hw8_nolabel_train.dat")

    k_list=[2,4,6,8,10]

    ein_list=[]

    for k in k_list:

        ein=0

        for t in range(500):

            nowCentreArray,dict=kMeans(t,k,dataArray)

            ein+=GetEin(nowCentreArray,dict)

        ein_list.append(ein/500)

    plot_bar_chart(k_list,ein_list,nameX="k",nameY="the average Ein over 500 experiments",saveName="20.png")

巴特西

机器学习技法笔记：Homework #8 kNN&RBF&k-Means相关习题

问题描述

程序实现

运行结果

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