import numpy from dispims import dispims import logreg trainimages = numpy.loadtxt("cifarmini_gray_images_train.txt") trainlabels = numpy.loadtxt("cifarmini_labels_train.txt") testimages = numpy.loadtxt("cifarmini_gray_images_test.txt") testlabels = numpy.loadtxt("cifarmini_labels_test.txt") trainimages -= trainimages.mean(1)[:, None] trainimages /= trainimages.std(1)[:, None]+0.0001 testimages -= testimages.mean(1)[:, None] testimages /= testimages.std(1)[:, None]+0.0001 lr = logreg.Logreg(numclasses=10, numdims=trainimages.shape[1]) lr.train(trainimages.T, trainlabels.T, weightcost=1.0/2000, numsteps=100) print "error rate train data: ", lr.zeroone(trainimages.T, trainlabels.T) print "error rate test data: ", lr.zeroone(testimages.T, testlabels.T) print "log likelihood train data: ", -lr.cost(trainimages.T, trainlabels.T, weightcost=0.0) dispims(lr.weights.T, 32, 32, 10)