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)
