def ssgplvm_simulation(optimize=True,
verbose=1,
plot=True,
plot_sim=False,
max_iters=2e4,
useGPU=False):
from GPy import kern
from GPy.models import SSGPLVM
D1, D2, D3, N, num_inducing, Q = 13, 5, 8, 45, 3, 9
_, _, Ylist = _simulate_matern(D1, D2, D3, N, num_inducing, plot_sim)
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.X.variance[:] = _np.random.uniform(0, .01, m.X.shape)
m.likelihood.variance = .01
if optimize:
print("Optimizing model:")
m.optimize('bfgs', messages=verbose, max_iters=max_iters, gtol=.05)
if plot:
m.X.plot("SSGPLVM Latent Space 1D")
m.kern.plot_ARD('SSGPLVM Simulation ARD Parameters')
return m
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 ssgplvm_simulation(optimize=True, verbose=1,
plot=True, plot_sim=False,
max_iters=2e4, useGPU=False
):
from GPy import kern
from GPy.models import SSGPLVM
D1, D2, D3, N, num_inducing, Q = 13, 5, 8, 45, 3, 9
_, _, Ylist = _simulate_matern(D1, D2, D3, N, num_inducing, plot_sim)
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.X.variance[:] = _np.random.uniform(0, .01, m.X.shape)
m.likelihood.variance = .01
if optimize:
print("Optimizing model:")
m.optimize('bfgs', messages=verbose, max_iters=max_iters,
gtol=.05)
if plot:
m.X.plot("SSGPLVM Latent Space 1D")
m.kern.plot_ARD('SSGPLVM Simulation ARD Parameters')
return m