def test_gp_fit(): X = np.arange(-2, 2, .01)[:, np.newaxis] idxs = range(X.shape[0]) idxs = random.sample(idxs, 200) X = X[idxs] Z = np.sin(X) gp = GaussianProcess(1, max_iter=10) gp.fit(X, Z)
def test_gp_fit(): X = np.arange(-2, 2, .01)[:, np.newaxis].astype(theano.config.floatX) idxs = range(X.shape[0]) idxs = random.sample(idxs, 200) X = X[idxs] Z = np.sin(X) gp = GaussianProcess(1, max_iter=10, kernel='ardse') gp.fit(X, Z)
def test_gp_predict_matern52(): X = np.arange(-2, 2, .01)[:, np.newaxis] idxs = range(X.shape[0]) idxs = random.sample(idxs, 200) X = X[idxs] Z = np.sin(X) Z += np.random.normal(0, 1e-1, X.shape) gp = GaussianProcess(1, max_iter=10, kernel='matern52') gp.fit(X, Z) print gp.predict(X)
def test_gp_predict_matern52(): X = np.arange(-2, 2, .01)[:, np.newaxis].astype(theano.config.floatX) idxs = range(X.shape[0]) idxs = random.sample(idxs, 200) X = X[idxs] Z = np.sin(X) Z += np.random.normal(0, 1e-1, X.shape) gp = GaussianProcess(1, max_iter=10, kernel='matern52') gp.fit(X, Z) print gp.predict(X)
def test_gp_predict_linear(): raise SkipTest() X = np.arange(-2, 2, .01)[:, np.newaxis].astype(theano.config.floatX) idxs = range(X.shape[0]) idxs = random.sample(idxs, 200) X = X[idxs] Z = np.sin(X) Z += np.random.normal(0, 1e-1, X.shape) gp = GaussianProcess(1, max_iter=1, kernel='linear') gp.fit(X, Z) print gp.predict(X)
def test_gp_predict_matern52(): X = np.arange(-2, 2, .1)[:, np.newaxis].astype(theano.config.floatX) idxs = range(X.shape[0]) idxs = random.sample(idxs, 20) X = X[idxs] Z = np.sin(X) Z += np.random.normal(0, 1e-1, X.shape) X, Z = theano_floatx(X, Z) gp = GaussianProcess(1, max_iter=10, kernel='matern52') gp.fit(X, Z) print gp.predict(X)
def test_gp_fit_linear(): X = np.arange(-2, 2, .1)[:, np.newaxis].astype(theano.config.floatX) X, = theano_floatx(X) idxs = range(X.shape[0]) idxs = random.sample(idxs, 20) X = X[idxs] Z = np.sin(X) X, Z = theano_floatx(X, Z) gp = GaussianProcess(1, max_iter=10, kernel='linear') gp.fit(X, Z)
def test_gp_predict_linear(): raise SkipTest() X = np.arange(-2, 2, .01)[:, np.newaxis].astype(theano.config.floatX) idxs = range(X.shape[0]) idxs = random.sample(idxs, 200) X = X[idxs] Z = np.sin(X) Z += np.random.normal(0, 1e-1, X.shape) X, Z = theano_floatx(X, Z) gp = GaussianProcess(1, max_iter=1, kernel='linear') gp.fit(X, Z) print gp.predict(X)
def test_gp_predict_maxrows(): X = np.arange(-2, 2, .01)[:, np.newaxis] idxs = range(X.shape[0]) idxs = random.sample(idxs, 6) X = X[idxs] Z = np.sin(X) Z += np.random.normal(0, 1e-1, X.shape) gp = GaussianProcess(1, max_iter=10, kernel='matern52') gp.fit(X, Z) Y = gp.predict(X) Y2 = gp.predict(X, max_rows=2) assert np.allclose(Y, Y2)
def test_gp_predict_maxrows(): X = np.arange(-2, 2, .01)[:, np.newaxis].astype(theano.config.floatX) idxs = range(X.shape[0]) idxs = random.sample(idxs, 6) X = X[idxs] Z = np.sin(X) Z += np.random.normal(0, 1e-1, X.shape).astype(theano.config.floatX) gp = GaussianProcess(1, max_iter=10, kernel='matern52') gp.fit(X, Z) Y = gp.predict(X) Y2 = gp.predict(X, max_rows=2) assert np.allclose(Y, Y2)