def test_parameterized_regressor():
mu = theano.shared(0)
p = Normal(mu=mu)
X = p.rvs(100)
y = p.pdf(X).astype(np.float32)
tf = ParameterStacker(params=[mu])
clf = ParameterizedRegressor(DecisionTreeRegressor(), params=[mu])
clf.fit(tf.transform(X), y)
assert clf.n_features_ == 1
assert_array_almost_equal(y, clf.predict(tf.transform(X)), decimal=3)
def test_parameter_stacker():
mu = theano.shared(0)
sigma = theano.shared(1)
p = Normal(mu=mu, sigma=sigma)
X = p.rvs(10)
tf = ParameterStacker(params=[mu, sigma])
Xt = tf.transform(X)
assert Xt.shape == (10, 1+2)
assert_array_almost_equal(Xt[:, 1], np.zeros(10))
assert_array_almost_equal(Xt[:, 2], np.ones(10))
mu.set_value(1)
Xt = tf.transform(X)
assert_array_almost_equal(Xt[:, 1], np.ones(10))
