testRows = tmp
print 'test length = ', len(testRows)
classes = [
'Nominated Best Picture',
'Won Best Picture',
]
clf=GaussianNB()
clf.fit(features[trainRows, :])[:, favoriteCols], labels[trainRows, 0], sample_weight=None)
clf._update_mean_variance(n_past, mu, var, X, sample_weight=None)
clf.partial_fit(features[trainRows, :])[:, favoriteCols], labels[trainRows, 0], classes=classes, sample_weight=None)
clf._partial_fit(features[trainRows, :])[:, favoriteCols], labels[trainRows, 0], classes=classes, _refit=False,sample_weight=None)
clf._joint_log_likelihood(features[trainRows, :])[:, favoriteCols])
print 'accuracy = %f' %(np.mean((y_test-y_pred)==0))
