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 "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/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) perceptron=PerceptronClassifier(trainData, trainLabels,0) perceptron.train(trainData, trainLabels,10) print "===================================" print "Test Data" guess=perceptron.classify(testData) samples.verify(perceptron, guess, testLabels) print "===================================" print "Validation Data" guess=perceptron.classify(validData) samples.verify(perceptron,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) perceptron=PerceptronClassifier(trainData, trainLabels,0) perceptron.train(trainData, trainLabels,10) print "***********************************" print "*************Test Data*************" guess=perceptron.classify(testData) samples.verify(perceptron, guess, testLabels) print "***********************************" print "************Valid Data*************" guess=perceptron.classify(validData) samples.verify(perceptron,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) perceptron=PerceptronClassifier(trainData, trainLabels,0) perceptron.train(trainData, trainLabels,10) print "===================================" print "Test Data" guess=perceptron.classify(testData) samples.verify(perceptron, guess, testLabels) print "===================================" print "Validation Data" guess=perceptron.classify(validData) samples.verify(perceptron,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)
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) perceptron = PerceptronClassifier(trainData, trainLabels, 0) perceptron.train(trainData, trainLabels, 10) print "===================================" print "Test Data" guess = perceptron.classify(testData) samples.verify(perceptron, guess, testLabels) print "===================================" print "Validation Data" guess = perceptron.classify(validData) samples.verify(perceptron, guess, validLabels)
import samples import features import numpy as np if __name__ == "__main__": trainData = samples.loadImagesFile("data/digitdata/trainingimages", 5000, 28, 28) trainBF=features.batchExtract(trainData,0) np.save("traindigitbasic",trainBF) testData = samples.loadImagesFile("data/digitdata/testimages", 1000, 28, 28) testBF=features.batchExtract(testData,0) np.save("testdigitbasic",testBF) validData = samples.loadImagesFile("data/digitdata/validationimages", 1000, 28, 28) validBF=features.batchExtract(validData,0) np.save("validationdigitbasic",validBF) trainAF=features.batchExtract(trainData,1) np.save("traindigitadvanced",trainAF) testAF=features.batchExtract(testData,1) np.save("testdigitadvanced",testAF) validAF=features.batchExtract(validData,1) np.save("validationdigitadvanced",validAF) trainData = samples.loadImagesFile("data/facedata/facedatatrain", 451, 60, 70) trainBF=features.batchExtract(trainData,0) np.save("trainfacebasic",trainBF) testData = samples.loadImagesFile("data/facedata/facedatatest", 150, 60, 70) testBF=features.batchExtract(testData,0) np.save("testfacebasic",testBF) validData = samples.loadImagesFile("data/facedata/facedatavalidation", 301, 60, 70) validBF=features.batchExtract(validData,0) np.save("validationfacebasic",validBF)
import samples import features import numpy as np if __name__ == "__main__": trainData = samples.loadImagesFile("data/digitdata/trainingimages", 5000, 28, 28) trainBF = features.batchExtract(trainData, 0) np.save("traindigitbasic", trainBF) testData = samples.loadImagesFile("data/digitdata/testimages", 1000, 28, 28) testBF = features.batchExtract(testData, 0) np.save("testdigitbasic", testBF) validData = samples.loadImagesFile("data/digitdata/validationimages", 1000, 28, 28) validBF = features.batchExtract(validData, 0) np.save("validationdigitbasic", validBF) trainAF = features.batchExtract(trainData, 1) np.save("traindigitadvanced", trainAF) testAF = features.batchExtract(testData, 1) np.save("testdigitadvanced", testAF) validAF = features.batchExtract(validData, 1) np.save("validationdigitadvanced", validAF) trainData = samples.loadImagesFile("data/facedata/facedatatrain", 451, 60, 70) trainBF = features.batchExtract(trainData, 0) np.save("trainfacebasic", trainBF) testData = samples.loadImagesFile("data/facedata/facedatatest", 150, 60, 70)