import theanoml
reload(theanoml)

## load data
import cPickle
X, y = cPickle.load(open('data/digits.pkl', 'rb'))
print X.shape, y.shape

reload(theanoml.mlp)
mlp_clf = theanoml.mlp.MLPClassifier(n_classes = 10, n_hidden = 500)
mlp_clf.partial_fit(X, y)

reload(theanoml.mlp)
#mlp_clf.predict(X)
print mlp_clf.score(X, y)

## Load blackbox data
import cPickle
import numpy as np
X, y = cPickle.load(open('data/blackbox.pkl', 'rb'))
y = y - 1
print X.shape, y.shape
classes = np.unique(y)
print classes

reload(theanoml.mlp)
mlp_clf = theanoml.mlp.MLPClassifier(n_classes = len(classes), n_hidden = 1000)
mlp_clf.partial_fit(X, y)
print mlp_clf.score(X, y)