display.save(res,70,80,folder='./imgs_pie',name='out',index=i)
if(int(options.classifier)):
train = np.loadtxt("../datasets/multi_pie_train.dat")
l_train = np.loadtxt("../datasets/multi_pie_l_train.dat")
test = np.loadtxt("../datasets/multi_pie_test.dat")
l_test = np.loadtxt("../datasets/multi_pie_l_test.dat")
train = train+abs(np.min(train))
train = train/np.max(train)
train = train.astype("float32")
test = test+abs(np.min(test))
test = test/np.max(test)
test = test.astype("float32")
h = auto.get_hidden(np.asarray(train),session=session)
k = knn_classifier.knn_classifier(data=h,label=l_train,k=3)
#k = knn_classifier.knn_classifier(data=train,label=l_train,k=3)
k.learn()
t = auto.get_hidden(np.asarray(test),session=session)
print k.accuracy(t,l_test)
print zip(k.predict(t),l_test)
#print k.accuracy(test,l_test)
display.display(c,28,28)
m = np.mean(data[:500],axis=0)
c = np.matmul((b+v),w)
'''
#display.display(c,28,28)
#display.display(np.ndarray.flatten(np.array(m)),28,28)
if(int(options.save)):
for i in range(50):
display.save(data[i],28,28,folder='./imgs',name='in',index=i)
res = auto.get_output(np.asarray([data[i]]),session=session)
#if(options.normed):
# res = res + np.cumsum(data,axis=0)[-1]/data.shape[0]
display.save(res,28,28,folder='./imgs',name='out',index=i)
if(int(options.classifier)):
h = auto.get_hidden(np.asarray(data),session=session)
k = knn_classifier.knn_classifier(data=h,label=lab,k=3)
k.learn()
test, labtest = get_data_from_minst.get_test_from_mnist()
t = auto.get_hidden(np.asarray(test),session=session)
print k.accuracy(t,labtest)