def setModel(self, modelNo): if modelNo == 1: self.model = NaiveBayesClassifier() if modelNo == 2: print("model not yet imported") if modelNo == 3: self.model = KNearestNeighborClassifier() print("model is set to knn")
def testing(num): trainData = samples.loadImagesFile("data/facedata/facedatatrain", num, 60, 70) trainLabels = samples.loadLabelsFile("data/facedata/facedatatrainlabels", num) testData = samples.loadImagesFile("data/facedata/facedatatest", 150, 60, 70) testLabels = samples.loadLabelsFile("data/facedata/facedatatestlabels", 151) validData = samples.loadImagesFile("data/facedata/facedatavalidation", 301, 60, 70) validLabels = samples.loadLabelsFile("data/facedata/facedatavalidationlabels", 301) nb = NaiveBayesClassifier(1, 0) nb.train(trainData, trainLabels) print "===================================" print "Test Data" guess = nb.classify(testData) samples.verify(nb, guess, testLabels) print "===================================" print "Validation Data" guess = nb.classify(validData) samples.verify(nb, guess, validLabels)
def testing(num): trainData = samples.loadImagesFile("data/digitdata/trainingimages", num, 28, 28) trainLabels = samples.loadLabelsFile("data/digitdata/traininglabels", num) testData = samples.loadImagesFile("data/digitdata/testimages", 1000, 28, 28) testLabels = samples.loadLabelsFile("data/digitdata/testlabels", 1000) validData = samples.loadImagesFile("data/digitdata/validationimages", 1000, 28, 28) validLabels = samples.loadLabelsFile("data/digitdata/validationlabels", 1000) nb = NaiveBayesClassifier(1,0) nb.train(trainData, trainLabels) print "===================================" print "Test Data" guess = nb.classify(testData) samples.verify(nb,guess,testLabels) print "===================================" print "Validation Data" guess=nb.classify(validData) samples.verify(nb,guess,validLabels)
def testing(num): trainData = samples.loadImagesFile("data/digitdata/trainingimages", num, 28, 28) trainLabels = samples.loadLabelsFile("data/digitdata/traininglabels", num) testData = samples.loadImagesFile("data/digitdata/testimages", 1000, 28, 28) testLabels = samples.loadLabelsFile("data/digitdata/testlabels", 1000) validData = samples.loadImagesFile("data/digitdata/validationimages", 1000, 28, 28) validLabels = samples.loadLabelsFile("data/digitdata/validationlabels", 1000) nb = NaiveBayesClassifier(1,0) nb.train(trainData, trainLabels) print "***********************************" print "*************Test Data*************" guess = nb.classify(testData) samples.verify(nb,guess,testLabels) print "***********************************" print "************Valid Data*************" guess=nb.classify(validData) samples.verify(nb,guess,validLabels)
def testing(num): trainData = samples.loadImagesFile("data/facedata/facedatatrain", num, 60, 70) trainLabels = samples.loadLabelsFile("data/facedata/facedatatrainlabels", num) testData = samples.loadImagesFile("data/facedata/facedatatest", 150, 60, 70) testLabels = samples.loadLabelsFile("data/facedata/facedatatestlabels", 151) validData = samples.loadImagesFile("data/facedata/facedatavalidation", 301, 60, 70) validLabels = samples.loadLabelsFile( "data/facedata/facedatavalidationlabels", 301) nb = NaiveBayesClassifier(1, 0) nb.train(trainData, trainLabels) print "***********************************" print "************Test Data**************" guess = nb.classify(testData) samples.verify(nb, guess, testLabels) print "***********************************" print "***********Valid Data**************" guess = nb.classify(validData) samples.verify(nb, guess, validLabels)