iris数据集进行KNN分类
数据集说明
Data Set Information:
This is perhaps the best known database to be found in the pattern recognition literature. Fisher’s paper is a classic in the field and is referenced frequently to this day. (See Duda & Hart, for example.) The data set contains 3 classes of 50 instances each, where each class refers to a type of iris plant. One class is linearly separable from the other 2; the latter are NOT linearly separable from each other.
Predicted attribute: class of iris plant.
This is an exceedingly simple domain.
This data differs from the data presented in Fishers article (identified by Steve Chadwick, spchadwick ‘@’ espeedaz.net ). The 35th sample should be: 4.9,3.1,1.5,0.2,”Iris-setosa” where the error is in the fourth feature. The 38th sample: 4.9,3.6,1.4,0.1,”Iris-setosa” where the errors are in the second and third features.
Attribute Information:
- sepal length in cm
- sepal width in cm
- petal length in cm
- petal width in cm
- class:
– Iris Setosa
– Iris Versicolour
– Iris Virginica
数据集来源
http://archive.ics.uci.edu/ml/
关于机器学习的数据很多的，可用来做练习
KNN分类说明
knn算法链接
1.随机的划分测试集和训练集
7:3 和6:4
2.数据是否标准化，以及选取标准化的方法
3.选取合适的k
测试结果
只对选取6:4的进行说明，训练集过多的话，预测效果比较好的
1.不标准化，k=3
>>> reload(iris)
'iris' from 'iris.py'>
>>> iris.irisClassTest()
2.0 0.966666666667
>>> iris.irisClassTest()
1.0 0.983333333333
>>> iris.irisClassTest()
2.0 0.966666666667
>>> iris.irisClassTest()
2.0 0.966666666667
>>> iris.irisClassTest()
1.0 0.983333333333 五次运行，共错了8个
2.不标准化，k=5
>>> iris.irisClassTest()
0.0 1.0
>>> iris.irisClassTest()
2.0 0.966666666667
>>> iris.irisClassTest()
2.0 0.966666666667
>>> iris.irisClassTest()
3.0 0.95
>>> iris.irisClassTest()
5.0 0.916666666667五次运行，错了12个
下面选取k=3，进行判断选取标准化的方式对预测结果的影响
3.k=3，max-min归一化
>>> iris.irisClassTest()
2.0 0.966666666667
>>> iris.irisClassTest()
4.0 0.933333333333
>>> iris.irisClassTest()
2.0 0.966666666667
>>> iris.irisClassTest()
4.0 0.933333333333
>>> iris.irisClassTest()
2.0 0.966666666667五次运行，共错了14次
4.k=3，均值方差归一化
>>> iris.irisClassTest()
4.0 0.933333333333
>>> iris.irisClassTest()
3.0 0.95
>>> iris.irisClassTest()
7.0 0.883333333333
>>> iris.irisClassTest()
2.0 0.966666666667
>>> iris.irisClassTest()
5.0 0.916666666667五次运行，错的次数太多。。。
总结
k值，是否归一化，以及归一化的方式对结果影响比较大的
由于数据集三个样本数量相等，k选的过大时候不会分到训练集某类最大的类中
由于数据中存在异常数据，使归一化过程中的某些值对某些样本影响比较大
完整Python程序
# -*- coding: utf-8 -*-
import urllib
import re
from numpy import *
import operator
def get_data():
url_data = urllib.urlopen('http://archive.ics.uci.edu/ml/machine-learning-databases/iris/bezdekIris.data').read()
return url_data
def init_data():
# 对获取的数据进行预处理 ，字符串转换成数字，特征和类别标签分类
url_data = get_data()
url_data = re.sub('Iris-setosa','1',url_data)
url_data = re.sub('Iris-versicolor','2',url_data)
url_data = re.sub('Iris-virginica','3',url_data)
lines = re.split('\n',url_data)
m =len(lines)-2
dataSet = zeros((m,5))
dataLabel = zeros((m,1))
for row in range(m):
line = re.split(',',lines[row])
col = len(line)
for i in range(col):
dataSet[row,i] = (line[i])
dataLabel = dataSet[:,-1]
dataSet = dataSet[:,(0,1,2,3)]
return dataSet,dataLabel
def classify0(intX,dataSet,labels,k):
# 产生与dataSet一样大小的矩阵，矩阵的每行数据都是intX
# 根据矩阵的减法 求聚类最近的k个类
dataSetSize=dataSet.shape[0]
diffMat=tile(intX,(dataSetSize,1))-dataSet
sqDiffMat=diffMat**2
sqDistances=sqDiffMat.sum(axis=1)
distances=sqDistances**0.5
# distances.argsort() 的结果是保存 升序后的原数据的id下标
sortedDistIndicies=distances.argsort()
classCount={}
# 取出前k个最近的类别标签，以最多的类别作为该样本的类别
for i in range(k):
voteIlabel=labels[sortedDistIndicies[i]]
classCount[voteIlabel]=classCount.get(voteIlabel,0)+1
sortedClassCount=sorted(classCount.iteritems(),
key=operator.itemgetter(1),reverse=True)
return sortedClassCount[0][0]
def norm_max_min(dataSet):
# max - min 归一化
# 求出每一类的最大值 最小值
maxVals = dataSet.max(axis=0)
minVals = dataSet.min(axis=0)
ranges = maxVals - minVals
nromDataSet = zeros(shape(dataSet))
m = dataSet.shape[0]
nromDataSet = dataSet-tile(minVals,(m,1))
nromDataSet = nromDataSet/(tile(ranges,(m,1)))
return minVals,ranges,nromDataSet
def norm_zScore(dataSet):
dataSet = array(dataSet)
# 计算每类的均值 和标准差
meanVals = dataSet.mean(0)
stdVals = dataSet.std(0)
m = dataSet.shape[0]
normDataSet = dataSet-tile(meanVals,(m,1))
normDataSet = normDataSet/tile(stdVals,(m,1))
return meanVals,stdVals,normDataSet
def irisClassTest():
dataSet,dataLabel = init_data()
trainSet = []
trainLabel = []
testSet = []
testLabel = []
predictClass = []
# k 的取值 0 -20 的奇数
k = 5
#计算预测正确率
acc = 0.0
error = 0.0
# 随机抽取40%作为测试集
testLen = int(len(dataLabel)*0.4)
#trainIndex 这里是下标，在150个下表中随机删除测试集
trainIndex = range(len(dataLabel))
testIndex = []
for index in range(testLen):
randIndex = int(random.uniform(0,len(trainIndex)))
testIndex.append(trainIndex[randIndex])
del(trainIndex[randIndex])
# 训练集
for index in trainIndex:
trainSet.append(dataSet[index])
trainLabel.append(dataLabel[index])
# 测试集
for index in testIndex:
testSet.append(dataSet[index])
testLabel.append(dataLabel[index])
# 对测试集中的每条数据判断其做在的类别，并计算准确率
# 对数据进行归一化
# minVals,ranges,normTrainSet = norm_max_min(array(trainSet))
meanVals,stdVals,normTrainSet = norm_zScore(array(trainSet))
for i in range(testLen):
intX = testSet[i]
intX = array(intX)
# normX = (intX-minVals)/ranges
normX = (intX-meanVals)/stdVals
classifierResult = classify0(normX,array(normTrainSet),trainLabel,k)
predictClass .append(classifierResult)
if(classifierResult != testLabel[i]):
error= error + 1.0
acc=1.0-error/float(testLen)
print error,acc