def main():
images, labels = load_labeled_training(flatten=True)
images = standardize(images)
unl = load_unlabeled_training(flatten=True)
unl = standardize(unl)
test = load_public_test(flatten=True)
test = standardize(test)
shuffle_in_unison(images, labels)
#d = DictionaryLearning().fit(images)
d = MiniBatchDictionaryLearning(n_components=500, n_iter=500, verbose=True).fit(images)
s = SparseCoder(d.components_)
proj_test = s.transform(images)
pt = s.transform(test)
#kpca = KernelPCA(kernel="rbf")
#kpca.fit(unl)
#test_proj = kpca.transform(images)
#pt = kpca.transform(test)
#spca = SparsePCA().fit(unl)
#test_proj = spca.transform(images)
#pt = spca.transform(test)
svc = SVC()
scores = cross_validation.cross_val_score(svc, proj_test, labels, cv=10)
print scores
print np.mean(scores)
print np.var(scores)
svc.fit(proj_test, labels)
pred = svc.predict(pt)
write_results(pred, '../svm_res.csv')
def main():
images, labels = load_labeled_training(flatten=True)
public_test = load_public_test(flatten=True)
images = standardize(images)
#images, labels = load_pca_proj(K=100)
shuffle_in_unison(images, labels)
ds = ClassificationDataSet(images.shape[1],1, nb_classes=7)
testset = ClassificationDataSet(public_test.shape[1])
public_test=standardize(public_test)
for i in public_test:
testset.addSample(i,[0])
for i,l in zip(images, labels):
ds.addSample(i,[l-1])
#ds._convertToOneOfMany()
test, train = ds.splitWithProportion(0.2)
test._convertToOneOfMany()
train._convertToOneOfMany()
net=shortcuts.buildNetwork(train.indim, 500, 1000,train.outdim, outclass=SoftmaxLayer)
trainer = BackpropTrainer(net, dataset=train, learningrate=0.005, weightdecay=0.01)
#trainer = RPropMinusTrainer(net, dataset=train)
#cv = validation.CrossValidator(trainer, ds)
#print cv.validate()
net.randomize()
tr_labels_2 = net.activateOnDataset(train).argmax(axis=1)
trnres = percentError(tr_labels_2, train['class'])
#trnres = percentError(trainer.testOnClassData(dataset=train), train['class'])
testres = percentError(trainer.testOnClassData(dataset=test), test['class'])
print "Training error: %.10f, Test error: %.10f" % (trnres, testres)
print "Iters: %d" % trainer.totalepochs
for i in range(10):
trainer.trainEpochs(10)
trnres = percentError(trainer.testOnClassData(dataset=train), train['class'])
testres = percentError(trainer.testOnClassData(dataset=test), test['class'])
trnmse = trainer.testOnData(dataset=train)
testmse = trainer.testOnData(dataset=test)
print "Iteration: %d, Training error: %.5f, Test error: %.5f" % (trainer.totalepochs, trnres, testres)
print "Training MSE: %.5f, Test MSE: %.5f" % (trnmse, testmse)
out=trainer.testOnClassData(dataset=testset)
for i in range(len(out)):
out[i] += 1
write_results(out, 'nn_predictions.csv')