def test_missing_data(self):
from GPy import kern
from GPy.models.bayesian_gplvm_minibatch import BayesianGPLVMMiniBatch
from GPy.examples.dimensionality_reduction import _simulate_matern
D1, D2, D3, N, num_inducing, Q = 13, 5, 8, 400, 3, 4
_, _, Ylist = _simulate_matern(D1, D2, D3, N, num_inducing, False)
Y = Ylist[0]
inan = np.random.binomial(1, .9, size=Y.shape).astype(bool) # 80% missing data
Ymissing = Y.copy()
Ymissing[inan] = np.nan
k = kern.Linear(Q, ARD=True) + kern.White(Q, np.exp(-2)) # + kern.bias(Q)
m = BayesianGPLVMMiniBatch(Ymissing, Q, init="random", num_inducing=num_inducing,
kernel=k, missing_data=True)
assert(m.checkgrad())
mul, varl = m.predict(m.X)
k = kern.RBF(Q, ARD=True) + kern.White(Q, np.exp(-2)) # + kern.bias(Q)
m2 = BayesianGPLVMMiniBatch(Ymissing, Q, init="random", num_inducing=num_inducing,
kernel=k, missing_data=True)
assert(m.checkgrad())
m2.kern.rbf.lengthscale[:] = 1e6
m2.X[:] = m.X.param_array
m2.likelihood[:] = m.likelihood[:]
m2.kern.white[:] = m.kern.white[:]
mu, var = m.predict(m.X)
np.testing.assert_allclose(mul, mu)
np.testing.assert_allclose(varl, var)
q50 = m.predict_quantiles(m.X, (50,))
np.testing.assert_allclose(mul, q50[0])
def test_missing_data(self):
from GPy import kern
from GPy.models.bayesian_gplvm_minibatch import BayesianGPLVMMiniBatch
from GPy.examples.dimensionality_reduction import _simulate_matern
D1, D2, D3, N, num_inducing, Q = 13, 5, 8, 400, 3, 4
_, _, Ylist = _simulate_matern(D1, D2, D3, N, num_inducing, False)
Y = Ylist[0]
inan = np.random.binomial(1, .9, size=Y.shape).astype(
bool) # 80% missing data
Ymissing = Y.copy()
Ymissing[inan] = np.nan
k = kern.Linear(Q, ARD=True) + kern.White(Q,
np.exp(-2)) # + kern.bias(Q)
m = BayesianGPLVMMiniBatch(Ymissing,
Q,
init="random",
num_inducing=num_inducing,
kernel=k,
missing_data=True)
assert (m.checkgrad())
k = kern.RBF(Q, ARD=True) + kern.White(Q, np.exp(-2)) # + kern.bias(Q)
m = BayesianGPLVMMiniBatch(Ymissing,
Q,
init="random",
num_inducing=num_inducing,
kernel=k,
missing_data=True)
assert (m.checkgrad())
def test_missing_data(self):
from GPy import kern
from GPy.models.bayesian_gplvm_minibatch import BayesianGPLVMMiniBatch
from GPy.examples.dimensionality_reduction import _simulate_matern
D1, D2, D3, N, num_inducing, Q = 13, 5, 8, 400, 3, 4
_, _, Ylist = _simulate_matern(D1, D2, D3, N, num_inducing, False)
Y = Ylist[0]
inan = np.random.binomial(1, .9, size=Y.shape).astype(bool) # 80% missing data
Ymissing = Y.copy()
Ymissing[inan] = np.nan
k = kern.Linear(Q, ARD=True) + kern.White(Q, np.exp(-2)) # + kern.bias(Q)
m = BayesianGPLVMMiniBatch(Ymissing, Q, init="random", num_inducing=num_inducing,
kernel=k, missing_data=True)
assert(m.checkgrad())
mul, varl = m.predict(m.X)
k = kern.RBF(Q, ARD=True) + kern.White(Q, np.exp(-2)) # + kern.bias(Q)
m2 = BayesianGPLVMMiniBatch(Ymissing, Q, init="random", num_inducing=num_inducing,
kernel=k, missing_data=True)
assert(m.checkgrad())
m2.kern.rbf.lengthscale[:] = 1e6
m2.X[:] = m.X.param_array
m2.likelihood[:] = m.likelihood[:]
m2.kern.white[:] = m.kern.white[:]
mu, var = m.predict(m.X)
np.testing.assert_allclose(mul, mu)
np.testing.assert_allclose(varl, var)
q50 = m.predict_quantiles(m.X, (50,))
np.testing.assert_allclose(mul, q50[0])
def test_ssgplvm(self):
from GPy import kern
from GPy.models import SSGPLVM
from GPy.examples.dimensionality_reduction import _simulate_matern
np.random.seed(10)
D1, D2, D3, N, num_inducing, Q = 13, 5, 8, 45, 3, 9
_, _, Ylist = _simulate_matern(D1, D2, D3, N, num_inducing, False)
Y = Ylist[0]
k = kern.Linear(Q, ARD=True) # + kern.white(Q, _np.exp(-2)) # + kern.bias(Q)
# k = kern.RBF(Q, ARD=True, lengthscale=10.)
m = SSGPLVM(Y, Q, init="rand", num_inducing=num_inducing, kernel=k, group_spike=True)
m.randomize()
self.assertTrue(m.checkgrad())
def test_missing_data(self):
from GPy import kern
from GPy.models.bayesian_gplvm_minibatch import BayesianGPLVMMiniBatch
from GPy.examples.dimensionality_reduction import _simulate_matern
D1, D2, D3, N, num_inducing, Q = 13, 5, 8, 400, 3, 4
_, _, Ylist = _simulate_matern(D1, D2, D3, N, num_inducing, False)
Y = Ylist[0]
inan = np.random.binomial(1, .9, size=Y.shape).astype(bool) # 80% missing data
Ymissing = Y.copy()
Ymissing[inan] = np.nan
k = kern.Linear(Q, ARD=True) + kern.White(Q, np.exp(-2)) # + kern.bias(Q)
m = BayesianGPLVMMiniBatch(Ymissing, Q, init="random", num_inducing=num_inducing,
kernel=k, missing_data=True)
assert(m.checkgrad())
k = kern.RBF(Q, ARD=True) + kern.White(Q, np.exp(-2)) # + kern.bias(Q)
m = BayesianGPLVMMiniBatch(Ymissing, Q, init="random", num_inducing=num_inducing,
kernel=k, missing_data=True)
assert(m.checkgrad())
