Knn

knn.py

__author__ = 'gavinwhyte'
from numpy import *
import operator

import matplotlib
import matplotlib.pyplot as plt

def createDataSet():
    group = array([[1.0,1.1],[1.0,1.0],[0,0],[0,0.1]])
    labels = ['A', 'A', 'B', 'B']
    return group, labels


#group,labels = createDataSet()
#print group
#print labels

def classify0(inX, dataSet, labels,k):
    dataSetSize = dataSet.shape[0]
    diffMat = tile(inX, (dataSetSize,1)) - dataSet
    sqDiffMat = diffMat ** 2
    sqDistances = sqDiffMat.sum(axis=1)
    distances = sqDistances**0.5
    sortedDistIndices = distances.argsort()
    classCount ={}
    for i in range(k):
        voteIlabel = labels[sortedDistIndices[i]]
        classCount[voteIlabel] = classCount.get(voteIlabel,0) + 1
    sortedClassCount = sorted(classCount.iteritems(),
                              key=operator.itemgetter(1),reverse=True)
    return sortedClassCount[0][0]


#results = classify0([0,0.1], group,labels,3)
#print results
def file2matrix(filename):
    fr = open(filename)
    arrayOLines = fr.readlines()

    numberOfLines = len(arrayOLines)
    returnMat = zeros((numberOfLines,3))
    classLabelVector = []
    fr = open(filename)
    index = 0
    for line in arrayOLines:
        line = line.strip()
        listFromLine = line.split('\t')
        returnMat[index,:] = listFromLine[0:3]
        classLabelVector.append(int(listFromLine[-1]))
        index += 1
    return returnMat, classLabelVector

datingDataMat, datingLabels = file2matrix('datingTestSet2.txt')

print datingDataMat
print datingLabels

def autoNorm(dataSet):
    minVals = dataSet.min(0)
    maxVals = dataSet.max(0)
    ranges = maxVals - minVals
    normDataSet = zeros(shape(dataSet))
    m = dataSet.shape[0]
    normDataSet = dataSet - tile(minVals, (m,1))
    normDataSet = normDataSet/tile(ranges, (m,1))
    return normDataSet, ranges, minVals

normMat, ranges, minVals = autoNorm(datingDataMat)

print normMat
print ranges
print minVals


def datingClassTest():
    hoRatio = 0.10
    datingDataMat,datingLabels = file2matrix('datingTestSet2.txt')
    normMat, ranges, minVals = autoNorm(datingDataMat)
    m = normMat.shape[0]
    numTestVecs = int(m*hoRatio)
    errorCount = 0.0
    for i in range(numTestVecs):
        classifierResult = classify0(normMat[i,:],normMat[numTestVecs:m,:],\
                                     datingLabels[numTestVecs:m],3)
        print "the classifier came back with: %d, the real answer is: %d"\
                             % (classifierResult, datingLabels[i])
        if (classifierResult != datingLabels[i]): errorCount += 1.0
    print "the total error rate is: %f" % (errorCount/float(numTestVecs))


def classifyPerson():
    resultList = ['not at all','in small doses', 'in large doses']
    percentTats = 10
    ffMiles = 10000
    iceCream = 0.5
    datingDataMat,datingLabels = file2matrix('datingTestSet2.txt')
    normMat, ranges, minVals = autoNorm(datingDataMat)
    inArr = array([ffMiles, percentTats, iceCream])
    classifierResult = classify0((inArr-\
                   minVals)/ranges,normMat,datingLabels,3)
    print "You will probably like this person: ",\
                   resultList[classifierResult - 1]


datingClassTest()

classifyPerson()

fig = plt.figure()
ax = fig.add_subplot(111)
ax.scatter(datingDataMat[:,1], datingDataMat[:,2],15.0*array(datingLabels),15.0*array(datingLabels))
plt.show()