def cross_fold(X, Y, n, *model_args, **model_kwargs):
num_examples = len(Y)
# shuffle data first
idx = np.arange(num_examples)
np.random.shuffle(idx)
# split into n sets
splits = np.split(idx, n)
for i in range(n):
# get train/test sets
idx_test = splits[i]
tr1 = splits[:i]
tr2 = splits[i + 1:]
tr1.extend(tr2)
idx_train = np.concatenate(tr1)
X_train = X[idx_train, :]
X_test = X[idx_test, :]
Y_train = Y[idx_train]
Y_test = Y[idx_test]
# create new model
model = NeuralNet(*model_args, **model_kwargs)
# train
num_epochs = model.fit(X_train, Y_train)
# compare
out = model.predict(X_test)
print(i, model.score(X_train, Y_train), num_epochs)
def test_predict(self):
# init with preset values
nn = NeuralNet(2, 3, 2)
nn.W[0] = np.array([[.6, .2, 1], [1, .5, 0]])
nn.W[1] = np.array([[.1, 1], [1, .25], [.1, 1]])
nn.b[0] = np.array([[0., 0., 0.]])
nn.b[1] = np.array([[1., -1.]])
nn._classification = False
# input values
X = np.array([[1., .4], [0., 0.], [1., 1.], [2., .8]])
# expected output
Y = np.array([[1.6, 1.1], [1., 0.], [1.96, 1.775], [2.2, 3.2]])
# actual output
Z = nn.predict(X)
# compare
np.testing.assert_array_almost_equal(Y, Z)