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_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_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_iter_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)
for i, info in enumerate(gp.iter_fit(X, Z)):
if i >= 10:
break
def test_gp_iter_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')
for i, info in enumerate(gp.iter_fit(X, Z)):
if i >= 10:
break
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_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_sample_parameters():
X = np.arange(-2, 2, .01)[:, 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).astype(theano.config.floatX)
gp = GaussianProcess(1, max_iter=1, kernel='ardse')
gp.store_dataset(X, Z)
gp.sample_parameters()
print gp.predict(X, True)
def test_gp_sample_parameters():
X = np.arange(-2, 2, .01)[:, np.newaxis]
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)
gp = GaussianProcess(1, max_iter=1, kernel='linear')
gp.store_dataset(X, Z)
gp.sample_parameters()
print gp.predict(X, True)
def test_gp_sample_parameters():
X = np.arange(-2, 2, .1)[:, np.newaxis].astype(theano.config.floatX)
idxs = range(X.shape[0])
idxs = random.sample(idxs, 10)
X = X[idxs]
Z = np.sin(X)
Z += np.random.normal(0, 1e-1, X.shape).astype(theano.config.floatX)
X, Z = theano_floatx(X, Z)
gp = GaussianProcess(1, max_iter=1, kernel='ardse')
gp.store_dataset(X, Z)
gp.sample_parameters()
print gp.predict(X, True)
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)
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].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)
