def mrd_simulation_missing_data(optimize=True, verbose=True, plot=True, plot_sim=True, **kw):
from GPy import kern
from GPy.models import MRD
D1, D2, D3, N, num_inducing, Q = 60, 20, 36, 60, 6, 5
_, _, Ylist = _simulate_matern(D1, D2, D3, N, num_inducing, plot_sim)
# Ylist = [Ylist[0]]
k = kern.Linear(Q, ARD=True)
inanlist = []
for Y in Ylist:
inan = _np.random.binomial(1, .6, size=Y.shape).astype(bool)
inanlist.append(inan)
Y[inan] = _np.nan
m = MRD(Ylist, input_dim=Q, num_inducing=num_inducing,
kernel=k, inference_method=None,
initx="random", initz='permute', **kw)
if optimize:
print("Optimizing Model:")
m.optimize('bfgs', messages=verbose, max_iters=8e3, gtol=.1)
if plot:
m.X.plot("MRD Latent Space 1D")
m.plot_scales("MRD Scales")
return m
def mrd_simulation(optimize=True,
verbose=True,
plot=True,
plot_sim=True,
**kw):
from GPy import kern
from GPy.models import MRD
from GPy.likelihoods import Gaussian
D1, D2, D3, N, num_inducing, Q = 60, 20, 36, 60, 6, 5
_, _, Ylist = _simulate_sincos(D1, D2, D3, N, num_inducing, Q, plot_sim)
likelihood_list = [Gaussian(x, normalize=True) for x in Ylist]
k = kern.linear(
Q,
ARD=True) # + kern.bias(Q, _np.exp(-2)) + kern.white(Q, _np.exp(-2))
m = MRD(likelihood_list,
input_dim=Q,
num_inducing=num_inducing,
kernels=k,
initx="",
initz='permute',
**kw)
m.ensure_default_constraints()
for i, bgplvm in enumerate(m.bgplvms):
m['{}_noise'.format(i)] = 1 #bgplvm.likelihood.Y.var() / 500.
bgplvm.X_variance = bgplvm.X_variance #* .1
if optimize:
print "Optimizing Model:"
m.optimize(messages=verbose, max_iters=8e3, gtol=.1)
if plot:
m.plot_X_1d("MRD Latent Space 1D")
m.plot_scales("MRD Scales")
return m
def mrd_simulation(optimize=True, verbose=True, plot=True, plot_sim=True, **kw):
from GPy import kern
from GPy.models import MRD
D1, D2, D3, N, num_inducing, Q = 60, 20, 36, 60, 6, 5
_, _, Ylist = _simulate_matern(D1, D2, D3, N, num_inducing, plot_sim)
# Ylist = [Ylist[0]]
k = kern.Linear(Q, ARD=True)
m = MRD(Ylist, input_dim=Q, num_inducing=num_inducing, kernel=k, initx="PCA_concat", initz='permute', **kw)
m['.*noise'] = [Y.var() / 40. for Y in Ylist]
if optimize:
print "Optimizing Model:"
m.optimize(messages=verbose, max_iters=8e3)
if plot:
m.X.plot("MRD Latent Space 1D")
m.plot_scales("MRD Scales")
return m
def mrd_simulation(optimize=True,
verbose=True,
plot=True,
plot_sim=True,
**kw):
from GPy import kern
from GPy.models import MRD
D1, D2, D3, N, num_inducing, Q = 60, 20, 36, 60, 6, 5
_, _, Ylist = _simulate_sincos(D1, D2, D3, N, num_inducing, plot_sim)
# Ylist = [Ylist[0]]
k = kern.Linear(Q, ARD=True)
m = MRD(Ylist,
input_dim=Q,
num_inducing=num_inducing,
kernel=k,
initx="PCA_concat",
initz='permute',
**kw)
m['.*noise'] = [Y.var() / 40. for Y in Ylist]
if optimize:
print("Optimizing Model:")
m.optimize(messages=verbose, max_iters=8e3)
if plot:
m.X.plot("MRD Latent Space 1D")
m.plot_scales("MRD Scales")
return m
def mrd_simulation_missing_data(optimize=True,
verbose=True,
plot=True,
plot_sim=True,
**kw):
from GPy import kern
from GPy.models import MRD
D1, D2, D3, N, num_inducing, Q = 60, 20, 36, 60, 6, 5
_, _, Ylist = _simulate_matern(D1, D2, D3, N, num_inducing, plot_sim)
k = kern.Linear(Q, ARD=True) + kern.White(Q, variance=1e-4)
inanlist = []
for Y in Ylist:
inan = _np.random.binomial(1, .6, size=Y.shape).astype(bool)
inanlist.append(inan)
Y[inan] = _np.nan
m = MRD(Ylist,
input_dim=Q,
num_inducing=num_inducing,
kernel=k,
inference_method=None,
initx="random",
initz='permute',
**kw)
if optimize:
print("Optimizing Model:")
m.optimize('bfgs', messages=verbose, max_iters=8e3, gtol=.1)
if plot:
m.X.plot("MRD Latent Space 1D")
m.plot_scales()
return m
def optimize(self,
views,
latent_dims=7,
messages=True,
max_iters=8e3,
save_model=False):
if (self.kernel):
if (self.kernel == 'rbf'):
print("Chosen kernel: RBF")
print("Chosen lengthscale: " + self.lengthscale)
k = kern.RBF(latent_dims,
ARD=True,
lengthscale=self.lengthscale) + kern.White(
latent_dims,
variance=1e-4) + GPy.kern.Bias(latent_dims)
elif (self.kernel == 'linear'):
print("Chosen kernel: Linear")
k = kern.Linear(latent_dims, ARD=True) + kern.White(
latent_dims, variance=1e-4) + GPy.kern.Bias(latent_dims)
else:
print("No kernel or chosen - using RBF with lengthscale 10...")
k = kern.RBF(latent_dims, ARD=True, lengthscale=10) + kern.White(
latent_dims, variance=1e-4) + GPy.kern.Bias(latent_dims)
print("Number of inducing inputs: " + str(self.num_inducing))
m = MRD(views,
input_dim=latent_dims,
num_inducing=self.num_inducing,
kernel=k,
normalizer=False)
print("Optimizing Model...")
m.optimize(messages=True, max_iters=8e3)
if (save_model):
pickle.dump(m, open(save_model, "wb"), protocol=2)
self.model = m
def mrd_simulation(optimize=True, verbose=True, plot=True, plot_sim=True, **kw):
from GPy import kern
from GPy.models import MRD
from GPy.likelihoods import Gaussian
D1, D2, D3, N, num_inducing, Q = 60, 20, 36, 60, 6, 5
_, _, Ylist = _simulate_sincos(D1, D2, D3, N, num_inducing, Q, plot_sim)
likelihood_list = [Gaussian(x, normalize=True) for x in Ylist]
k = kern.linear(Q, ARD=True) + kern.bias(Q, _np.exp(-2)) + kern.white(Q, _np.exp(-2))
m = MRD(likelihood_list, input_dim=Q, num_inducing=num_inducing, kernels=k, initx="", initz='permute', **kw)
m.ensure_default_constraints()
for i, bgplvm in enumerate(m.bgplvms):
m['{}_noise'.format(i)] = bgplvm.likelihood.Y.var() / 500.
if optimize:
print "Optimizing Model:"
m.optimize(messages=verbose, max_iters=8e3, gtol=.1)
if plot:
m.plot_X_1d("MRD Latent Space 1D")
m.plot_scales("MRD Scales")
return m
