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))