def test_gnb_check_update_with_no_data():
"""Test when the partial fit is called without any data"""
# Create an empty array
prev_points = 100
mean = 0.0
var = 1.0
x_empty = np.empty((0, X.shape[1]))
tmean, tvar = GaussianNB._update_mean_variance(prev_points, mean, var, x_empty)
assert tmean == mean
assert tvar == var
def test_check_update_with_no_data():
""" Test when the partial fit is called without any data"""
# Create an empty array
prev_points = 100
mean = 0.
var = 1.
x_empty = np.empty((0, X.shape[1]))
tmean, tvar = GaussianNB._update_mean_variance(prev_points, mean,
var, x_empty)
assert_equal(tmean, mean)
assert_equal(tvar, var)
tmp.append(testRows[i])
posNo -= 1
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))
