multitest

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Understanding the principle of logistic regression algorithm and Python implementation

, weights): prob = sigmoid (sum (inx*weights)) if prob > 0.5:return 1.0 else:return 0.0 D EF colictest (): Frtrain = open (' HorseColicTraining.txt '); frtest = open (' horseColicTest.txt ') trainingset = []; Traininglabels = [] for line in Frtrain.readlines (): Currline = Line.strip (). Split (' \ t ') Linearr =[] For I in range (21): Linearr.append (float (currline[i)) trainingset.append (Linearr) traininglabels.append (float (CU RRLINE[21]) Trainweights = stocGradAscent1 (

Use of Logistic Regression

= line.strip().split('\t')53 lineArr =[]54 for i in range(21):55 lineArr.append(float(currLine[i]))56 if int(classifyVector(array(lineArr), trainWeights))!= int(currLine[21]):57 errorCount += 158 errorRate = (float(errorCount)/numTestVec)59 print "the error rate of this test is: %f" % errorRate60 return errorRate61 62 def multiTest():63 numTests = 10; errorSum=0.064 for k in range(nu

Practical notes for machine learning 5 (Logistic regression)

('horsecolictraining.txt ') frtest = open('horsecolictest.txt') trainingset = []; traininglabels = [] for line in frtrain. readlines (): currline = line. strip (). split ('\ t') linearr = [] For I in range (21): linearr. append (float (currline [I]) trainin Gset. append (linearr) traininglabels. append (float (currline [21]) trainweights = stocgradascent1 (trainingset, traininglabels, 500) errorcount = 0; numtestvec = 0.0 for line in frtest. readlines (): numtestvec ++ = 1.0 currline = line. str

Practical notes for machine learning 5 (Logistic regression)

colictest (): frtrain = open('horsecolictraining.txt ') frtest = open('horsecolictest.txt') trainingset = []; traininglabels = [] for line in frtrain. readlines (): currline = line. strip (). split ('\ t') linearr = [] For I in range (21): linearr. append (float (currline [I]) trainin Gset. append (linearr) traininglabels. append (float (currline [21]) trainweights = stocgradascent1 (trainingset, traininglabels, 500) errorcount = 0; numtestvec = 0.0 for line in frtest. readlines (): numtestvec

Machine learning (vi)-logistic regression

= Line.strip (). Split ('\ t') Linearr=[] forIinchRange (21): Linearr.append (float (currline[i)))ifInt (classifyvector (Array (Linearr), trainweights))! = Int (currline[21]): Errorcount+ = 1errorrate= (float (errorcount)/Numtestvec)Print "the error rate is:", Errorratereturnerrorratedefmultitest (): Numtests= 10; errorsum=0.0 forKinchRange (numtests): Errorsum+=colictest ()Print "The average error rate is", (Numtests, errorsum/float (numtests)) mult

Java: Application of observer Interface Practice Observer mode

StaticInteger even = 2; Private intdata = 0; Public intGetData () {returndata; } Public voidSetData (inti) {data=i; Integer Flag=even; if(Data 0x0001) = = 1) Flag=ODD; Setchanged (); Notifyobservers (flag); }}Odd Observer Oddobserver.java Packagecom.zj.observers;Importjava.util.Observable;ImportJava.util.Observer; Public classOddobserverImplementsObserver { Public voidUpdate (Observable o, Object Arg) {if(arg = =numsobservable.odd) {numsobservable myobserable=(numsobservable) o

Machine learning python for logistic regression

), Traininglabels, #训练获得回归系数 errorcount = 0; Numtestvec = 0.0 for line in Frtest.readlines (): #测试样本的测试 Numtestvec + = 1.0 Currline = Line.strip (). Split (' \ t ') Linearr =[] for I in range: Linearr.append (float (currline[i])) if INT (classifyv Ector (Array (Linearr), trainweights))! = Int (currline[21]): Errorcount + = 1 #计算错误率 errorrate = (Float (errorcou NT)/numtestvec) print "The error rate of this test is:%f"% errorrate return errorratedef multitest

Logistic regression __logistic

') trainingset = []; Traininglabels = [] for line in Frtrain.readlines (): Currline = Line.strip (). Split (' t ') Linearr =[] for I in range: Linearr.append (float (currline[i)) Trainingset.append (Lin Earr) Traininglabels.append (float (currline[21)) Trainweights = StocGradAscent1 (Array (trainingset), Trainingla BELs, 1000) errorcount = 0; Numtestvec = 0.0 for line in Frtest.readlines (): Numtestvec + = 1.0 Currline = Line.strip (). Split (' t ') Linearr =[] for I in range: L

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