From 8e78c76709b20b55e63aa6e374984ba50cf2a901 Mon Sep 17 00:00:00 2001 From: libo Date: Fri, 11 Jul 2014 15:27:29 +0800 Subject: [PATCH] AutoNorm and K-means --- AutoNormal/AutoNorm.py | 84 +++ K-means/K-means/.project | 17 + K-means/K-means/.pydevproject | 10 + K-means/K-means/src/Test.py | 95 ++++ K-means/effect.png | Bin 0 -> 73327 bytes K-means/testSet.txt | 1000 +++++++++++++++++++++++++++++++++ 6 files changed, 1206 insertions(+) create mode 100644 AutoNormal/AutoNorm.py create mode 100644 K-means/K-means/.project create mode 100644 K-means/K-means/.pydevproject create mode 100644 K-means/K-means/src/Test.py create mode 100644 K-means/effect.png create mode 100644 K-means/testSet.txt diff --git a/AutoNormal/AutoNorm.py b/AutoNormal/AutoNorm.py new file mode 100644 index 0000000..4a4d767 --- /dev/null +++ b/AutoNormal/AutoNorm.py @@ -0,0 +1,84 @@ + +from __future__ import division +def GetAverage(mat): + + n=len(mat) + m= width(mat) + num = [0]*m + for j in range(0,m): + for i in mat: + num[j]=num[j]+i[j] + num[j]=num[j]/n + return num + +def width(lst): + i=0 + for j in lst[0]: + i=i+1 + return i + +def GetVar(average,mat): + ListMat=[] + for i in mat: + ListMat.append(list(map(lambda x: x[0]-x[1], zip(average, i)))) + + n=len(ListMat) + m= width(ListMat) + num = [0]*m + for j in range(0,m): + for i in ListMat: + num[j]=num[j]+(i[j]*i[j]) + num[j]=num[j]/n + return num + +def DenoisMat(mat): + average=GetAverage(mat) + variance=GetVar(average,mat) + section=list(map(lambda x: x[0]+x[1], zip(average, variance))) + + n=len(mat) + m= width(mat) + num = [0]*m + denoisMat=[] + for i in mat: + for j in range(0,m): + if i[j]>section[j]: + i[j]=section[j] + denoisMat.append(i) + return denoisMat + +def AutoNorm(mat): + n=len(mat) + m= width(mat) + MinNum=[9999999999]*m + MaxNum = [0]*m + for i in mat: + for j in range(0,m): + if i[j]>MaxNum[j]: + MaxNum[j]=i[j] + + for p in mat: + for q in range(0,m): + if p[q]<=MinNum[q]: + MinNum[q]=p[q] + + section=list(map(lambda x: x[0]-x[1], zip(MaxNum, MinNum))) + print section + NormMat=[] + + for k in mat: + + distance=list(map(lambda x: x[0]-x[1], zip(k, MinNum))) + value=list(map(lambda x: x[0]/x[1], zip(distance,section))) + NormMat.append(value) + return NormMat + +if __name__=='__main__': + mat=[[1,42,512],[4,5,6],[7,8,9],[2,2,2],[2,10,5]] + a=GetAverage(mat) + b=GetVar(a,mat) + print a, + print DenoisMat(mat) + +# print list(map(lambda x: x[0]-x[1], zip(v2, v1))) + print AutoNorm(mat) \ No newline at end of file diff --git a/K-means/K-means/.project b/K-means/K-means/.project new file mode 100644 index 0000000..896c77f --- /dev/null +++ b/K-means/K-means/.project @@ -0,0 +1,17 @@ + + + K-means + + + + + + org.python.pydev.PyDevBuilder + + + + + + org.python.pydev.pythonNature + + diff --git a/K-means/K-means/.pydevproject b/K-means/K-means/.pydevproject new file mode 100644 index 0000000..295bc82 --- /dev/null +++ b/K-means/K-means/.pydevproject @@ -0,0 +1,10 @@ + + + + +Default +python 2.7 + +/K-means/src + + diff --git a/K-means/K-means/src/Test.py b/K-means/K-means/src/Test.py new file mode 100644 index 0000000..7b2cc0b --- /dev/null +++ b/K-means/K-means/src/Test.py @@ -0,0 +1,95 @@ +''' +@author: hakuri +''' +from numpy import * +import matplotlib.pyplot as plt +def loadDataSet(fileName): #general function to parse tab -delimited floats + dataMat = [] #assume last column is target value + fr = open(fileName) + for line in fr.readlines(): + curLine = line.strip().split('\t') + fltLine = map(float,curLine) #map all elements to float() + dataMat.append(fltLine) + return dataMat + +def distEclud(vecA, vecB): + return sqrt(sum(power(vecA - vecB, 2))) #la.norm(vecA-vecB) + +def randCent(dataSet, k): + n = shape(dataSet)[1] + centroids = mat(zeros((k,n)))#create centroid mat + for j in range(n):#create random cluster centers, within bounds of each dimension + minJ = min(array(dataSet)[:,j]) + + rangeJ = float(max(array(dataSet)[:,j]) - minJ) + centroids[:,j] = mat(minJ + rangeJ * random.rand(k,1)) + + return centroids + +def kMeans(dataSet, k, distMeas=distEclud, createCent=randCent): + m = shape(dataSet)[0] + clusterAssment = mat(zeros((m,2)))#create mat to assign data points #to a centroid, also holds SE of each point + centroids = createCent(dataSet, k) + clusterChanged = True + while clusterChanged: + clusterChanged = False + for i in range(m):#for each data point assign it to the closest centroid + minDist = inf; minIndex = -1 + for j in range(k): + distJI = distMeas(array(centroids)[j,:],array(dataSet)[i,:]) + if distJI < minDist: + minDist = distJI; minIndex = j + if clusterAssment[i,0] != minIndex: clusterChanged = True + clusterAssment[i,:] = minIndex,minDist**2 + print centroids +# print nonzero(array(clusterAssment)[:,0] + for cent in range(k):#recalculate centroids + ptsInClust = dataSet[nonzero(array(clusterAssment)[:,0]==cent)[0][0]]#get all the point in this cluster + + centroids[cent,:] = mean(ptsInClust, axis=0) #assign centroid to mean + id=nonzero(array(clusterAssment)[:,0]==cent)[0] + return centroids, clusterAssment,id + +def plotBestFit(dataSet,id,centroids): + + dataArr = array(dataSet) + cent=array(centroids) + n = shape(dataArr)[0] + n1=shape(cent)[0] + xcord1 = []; ycord1 = [] + xcord2 = []; ycord2 = [] + xcord3=[];ycord3=[] + j=0 + for i in range(n): + if j in id: + xcord1.append(dataArr[i,0]); ycord1.append(dataArr[i,1]) + else: + xcord2.append(dataArr[i,0]); ycord2.append(dataArr[i,1]) + j=j+1 + for k in range(n1): + xcord3.append(cent[k,0]);ycord3.append(cent[k,1]) + + fig = plt.figure() + ax = fig.add_subplot(111) + ax.scatter(xcord1, ycord1, s=30, c='red', marker='s') + ax.scatter(xcord2, ycord2, s=30, c='green') + ax.scatter(xcord3, ycord3, s=50, c='black') + + plt.xlabel('X1'); plt.ylabel('X2'); + plt.show() + + +if __name__=='__main__': + dataSet=loadDataSet('/Users/hakuri/Desktop/testSet.txt') +# # print randCent(dataSet,2) +# print dataSet +# +# print kMeans(dataSet,2) + a=[] + b=[] + a, 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