mirror of
https://github.com/SheffieldML/GPy.git
synced 2026-05-10 12:32:40 +02:00
[MRD] init and sim nicer now
This commit is contained in:
Max Zwiessele
parent
9b2e49c949
commit
a5c3e88f83
4 changed files with 67 additions and 66 deletions
|
|
@ -2,7 +2,7 @@
|
|||
# Licensed under the BSD 3-clause license (see LICENSE.txt)
|
||||
import numpy as _np
|
||||
|
||||
#default_seed = _np.random.seed(123344)
|
||||
# default_seed = _np.random.seed(123344)
|
||||
|
||||
def bgplvm_test_model(optimize=False, verbose=1, plot=False, output_dim=200, nan=False):
|
||||
"""
|
||||
|
|
@ -28,7 +28,7 @@ def bgplvm_test_model(optimize=False, verbose=1, plot=False, output_dim=200, nan
|
|||
Y = _np.random.multivariate_normal(_np.zeros(num_inputs), K, (output_dim,)).T
|
||||
|
||||
# k = GPy.kern.RBF_inv(input_dim, .5, _np.ones(input_dim) * 2., ARD=True) + GPy.kern.bias(input_dim) + GPy.kern.white(input_dim)
|
||||
#k = GPy.kern.linear(input_dim)# + GPy.kern.bias(input_dim) + GPy.kern.white(input_dim, 0.00001)
|
||||
# k = GPy.kern.linear(input_dim)# + GPy.kern.bias(input_dim) + GPy.kern.white(input_dim, 0.00001)
|
||||
# k = GPy.kern.RBF(input_dim, ARD = False) + GPy.kern.white(input_dim, 0.00001)
|
||||
# k = GPy.kern.RBF(input_dim, .5, _np.ones(input_dim) * 2., ARD=True) + GPy.kern.RBF(input_dim, .3, _np.ones(input_dim) * .2, ARD=True)
|
||||
# k = GPy.kern.RBF(input_dim, .5, 2., ARD=0) + GPy.kern.RBF(input_dim, .3, .2, ARD=0)
|
||||
|
|
@ -40,30 +40,30 @@ def bgplvm_test_model(optimize=False, verbose=1, plot=False, output_dim=200, nan
|
|||
|
||||
if nan:
|
||||
m.inference_method = GPy.inference.latent_function_inference.var_dtc.VarDTCMissingData()
|
||||
m.Y[_np.random.binomial(1,p,size=(Y.shape)).astype(bool)] = _np.nan
|
||||
m.Y[_np.random.binomial(1, p, size=(Y.shape)).astype(bool)] = _np.nan
|
||||
m.parameters_changed()
|
||||
|
||||
#===========================================================================
|
||||
# randomly obstruct data with percentage p
|
||||
#===========================================================================
|
||||
#m2 = GPy.models.BayesianGPLVMWithMissingData(Y_obstruct, input_dim, kernel=k, num_inducing=num_inducing)
|
||||
#m.lengthscales = lengthscales
|
||||
# m2 = GPy.models.BayesianGPLVMWithMissingData(Y_obstruct, input_dim, kernel=k, num_inducing=num_inducing)
|
||||
# m.lengthscales = lengthscales
|
||||
|
||||
if plot:
|
||||
import matplotlib.pyplot as pb
|
||||
m.plot()
|
||||
pb.title('PCA initialisation')
|
||||
#m2.plot()
|
||||
#pb.title('PCA initialisation')
|
||||
# m2.plot()
|
||||
# pb.title('PCA initialisation')
|
||||
|
||||
if optimize:
|
||||
m.optimize('scg', messages=verbose)
|
||||
#m2.optimize('scg', messages=verbose)
|
||||
# m2.optimize('scg', messages=verbose)
|
||||
if plot:
|
||||
m.plot()
|
||||
pb.title('After optimisation')
|
||||
#m2.plot()
|
||||
#pb.title('After optimisation')
|
||||
# m2.plot()
|
||||
# pb.title('After optimisation')
|
||||
|
||||
return m
|
||||
|
||||
|
|
@ -169,7 +169,7 @@ def bgplvm_oil(optimize=True, verbose=1, plot=True, N=200, Q=7, num_inducing=40,
|
|||
data = GPy.util.datasets.oil()
|
||||
|
||||
|
||||
kernel = GPy.kern.RBF(Q, 1., 1./_np.random.uniform(0,1,(Q,)), ARD=True)# + GPy.kern.Bias(Q, _np.exp(-2))
|
||||
kernel = GPy.kern.RBF(Q, 1., 1. / _np.random.uniform(0, 1, (Q,)), ARD=True) # + GPy.kern.Bias(Q, _np.exp(-2))
|
||||
Y = data['X'][:N]
|
||||
m = GPy.models.BayesianGPLVM(Y, Q, kernel=kernel, num_inducing=num_inducing, **k)
|
||||
m.data_labels = data['Y'][:N].argmax(axis=1)
|
||||
|
|
@ -180,8 +180,8 @@ def bgplvm_oil(optimize=True, verbose=1, plot=True, N=200, Q=7, num_inducing=40,
|
|||
if plot:
|
||||
fig, (latent_axes, sense_axes) = plt.subplots(1, 2)
|
||||
m.plot_latent(ax=latent_axes, labels=m.data_labels)
|
||||
data_show = GPy.plotting.matplot_dep.visualize.vector_show((m.Y[0,:]))
|
||||
lvm_visualizer = GPy.plotting.matplot_dep.visualize.lvm_dimselect(m.X.mean.values[0:1,:], # @UnusedVariable
|
||||
data_show = GPy.plotting.matplot_dep.visualize.vector_show((m.Y[0, :]))
|
||||
lvm_visualizer = GPy.plotting.matplot_dep.visualize.lvm_dimselect(m.X.mean.values[0:1, :], # @UnusedVariable
|
||||
m, data_show, latent_axes=latent_axes, sense_axes=sense_axes, labels=m.data_labels)
|
||||
raw_input('Press enter to finish')
|
||||
plt.close(fig)
|
||||
|
|
@ -195,7 +195,7 @@ def ssgplvm_oil(optimize=True, verbose=1, plot=True, N=200, Q=7, num_inducing=40
|
|||
_np.random.seed(0)
|
||||
data = pods.datasets.oil()
|
||||
|
||||
kernel = GPy.kern.RBF(Q, 1., 1./_np.random.uniform(0,1,(Q,)), ARD=True)# + GPy.kern.Bias(Q, _np.exp(-2))
|
||||
kernel = GPy.kern.RBF(Q, 1., 1. / _np.random.uniform(0, 1, (Q,)), ARD=True) # + GPy.kern.Bias(Q, _np.exp(-2))
|
||||
Y = data['X'][:N]
|
||||
m = GPy.models.SSGPLVM(Y, Q, kernel=kernel, num_inducing=num_inducing, **k)
|
||||
m.data_labels = data['Y'][:N].argmax(axis=1)
|
||||
|
|
@ -206,8 +206,8 @@ def ssgplvm_oil(optimize=True, verbose=1, plot=True, N=200, Q=7, num_inducing=40
|
|||
if plot:
|
||||
fig, (latent_axes, sense_axes) = plt.subplots(1, 2)
|
||||
m.plot_latent(ax=latent_axes, labels=m.data_labels)
|
||||
data_show = GPy.plotting.matplot_dep.visualize.vector_show((m.Y[0,:]))
|
||||
lvm_visualizer = GPy.plotting.matplot_dep.visualize.lvm_dimselect(m.X.mean.values[0:1,:], # @UnusedVariable
|
||||
data_show = GPy.plotting.matplot_dep.visualize.vector_show((m.Y[0, :]))
|
||||
lvm_visualizer = GPy.plotting.matplot_dep.visualize.lvm_dimselect(m.X.mean.values[0:1, :], # @UnusedVariable
|
||||
m, data_show, latent_axes=latent_axes, sense_axes=sense_axes, labels=m.data_labels)
|
||||
raw_input('Press enter to finish')
|
||||
plt.close(fig)
|
||||
|
|
@ -219,10 +219,12 @@ def _simulate_matern(D1, D2, D3, N, num_inducing, plot_sim=False):
|
|||
import numpy as np
|
||||
np.random.seed(3000)
|
||||
|
||||
k = GPy.kern.Matern32(Q_signal, 10., lengthscale=1+(np.random.uniform(1,6,Q_signal)), ARD=1)
|
||||
t = np.c_[[np.linspace(-1,5,N) for _ in range(Q_signal)]].T
|
||||
k = GPy.kern.Matern32(Q_signal, 1., lengthscale=(np.random.uniform(1, 6, Q_signal)), ARD=1)
|
||||
for i in range(Q_signal):
|
||||
k += GPy.kern.PeriodicExponential(1, variance=1., active_dims=[i], period=3., lower=-2, upper=6)
|
||||
t = np.c_[[np.linspace(-1, 5, N) for _ in range(Q_signal)]].T
|
||||
K = k.K(t)
|
||||
s2, s1, s3, sS = np.random.multivariate_normal(np.zeros(K.shape[0]), K, size=(4))[:,:,None]
|
||||
s2, s1, s3, sS = np.random.multivariate_normal(np.zeros(K.shape[0]), K, size=(4))[:, :, None]
|
||||
|
||||
Y1, Y2, Y3, S1, S2, S3 = _generate_high_dimensional_output(D1, D2, D3, s1, s2, s3, sS)
|
||||
|
||||
|
|
@ -243,7 +245,7 @@ def _simulate_matern(D1, D2, D3, N, num_inducing, plot_sim=False):
|
|||
ax.legend()
|
||||
for i, Y in enumerate(Ylist):
|
||||
ax = fig.add_subplot(2, len(Ylist), len(Ylist) + 1 + i)
|
||||
ax.imshow(Y, aspect='auto', cmap=cm.gray) # @UndefinedVariable
|
||||
ax.imshow(Y, aspect='auto', cmap=cm.gray) # @UndefinedVariable
|
||||
ax.set_title("Y{}".format(i + 1))
|
||||
plt.draw()
|
||||
plt.tight_layout()
|
||||
|
|
@ -255,7 +257,7 @@ def _simulate_sincos(D1, D2, D3, N, num_inducing, plot_sim=False):
|
|||
|
||||
x = _np.linspace(0, 4 * _np.pi, N)[:, None]
|
||||
s1 = _np.vectorize(lambda x: _np.sin(x))
|
||||
s2 = _np.vectorize(lambda x: _np.cos(x)**2)
|
||||
s2 = _np.vectorize(lambda x: _np.cos(x) ** 2)
|
||||
s3 = _np.vectorize(lambda x:-_np.exp(-_np.cos(2 * x)))
|
||||
sS = _np.vectorize(lambda x: _np.cos(x))
|
||||
|
||||
|
|
@ -288,7 +290,7 @@ def _simulate_sincos(D1, D2, D3, N, num_inducing, plot_sim=False):
|
|||
ax.legend()
|
||||
for i, Y in enumerate(Ylist):
|
||||
ax = fig.add_subplot(2, len(Ylist), len(Ylist) + 1 + i)
|
||||
ax.imshow(Y, aspect='auto', cmap=cm.gray) # @UndefinedVariable
|
||||
ax.imshow(Y, aspect='auto', cmap=cm.gray) # @UndefinedVariable
|
||||
ax.set_title("Y{}".format(i + 1))
|
||||
plt.draw()
|
||||
plt.tight_layout()
|
||||
|
|
@ -323,10 +325,10 @@ def bgplvm_simulation(optimize=True, verbose=1,
|
|||
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.)
|
||||
k = kern.Linear(Q, ARD=True) # + kern.white(Q, _np.exp(-2)) # + kern.bias(Q)
|
||||
# k = kern.RBF(Q, ARD=True, lengthscale=10.)
|
||||
m = BayesianGPLVM(Y, Q, init="PCA", num_inducing=num_inducing, kernel=k)
|
||||
m.X.variance[:] = _np.random.uniform(0,.01,m.X.shape)
|
||||
m.X.variance[:] = _np.random.uniform(0, .01, m.X.shape)
|
||||
m.likelihood.variance = .1
|
||||
|
||||
if optimize:
|
||||
|
|
@ -348,10 +350,10 @@ def ssgplvm_simulation(optimize=True, verbose=1,
|
|||
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.)
|
||||
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="pca", num_inducing=num_inducing, kernel=k)
|
||||
m.X.variance[:] = _np.random.uniform(0,.01,m.X.shape)
|
||||
m.X.variance[:] = _np.random.uniform(0, .01, m.X.shape)
|
||||
m.likelihood.variance = .1
|
||||
|
||||
if optimize:
|
||||
|
|
@ -365,7 +367,7 @@ def ssgplvm_simulation(optimize=True, verbose=1,
|
|||
|
||||
def bgplvm_simulation_missing_data(optimize=True, verbose=1,
|
||||
plot=True, plot_sim=False,
|
||||
max_iters=2e4,
|
||||
max_iters=2e4, percent_missing=.1,
|
||||
):
|
||||
from GPy import kern
|
||||
from GPy.models.bayesian_gplvm_minibatch import BayesianGPLVMMiniBatch
|
||||
|
|
@ -373,9 +375,9 @@ def bgplvm_simulation_missing_data(optimize=True, verbose=1,
|
|||
D1, D2, D3, N, num_inducing, Q = 13, 5, 8, 400, 3, 4
|
||||
_, _, 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.Linear(Q, ARD=True) # + kern.white(Q, _np.exp(-2)) # + kern.bias(Q)
|
||||
|
||||
inan = _np.random.binomial(1, .2, size=Y.shape).astype(bool) # 80% missing data
|
||||
inan = _np.random.binomial(1, percent_missing, size=Y.shape).astype(bool) # 80% missing data
|
||||
Ymissing = Y.copy()
|
||||
Ymissing[inan] = _np.nan
|
||||
|
||||
|
|
@ -401,11 +403,11 @@ def mrd_simulation(optimize=True, verbose=True, plot=True, plot_sim=True, **kw):
|
|||
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]]
|
||||
# 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]
|
||||
m['.*noise'] = [Y.var() / 40. for Y in Ylist]
|
||||
|
||||
if optimize:
|
||||
print "Optimizing Model:"
|
||||
|
|
@ -422,7 +424,7 @@ def mrd_simulation_missing_data(optimize=True, verbose=True, plot=True, plot_sim
|
|||
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]]
|
||||
# Ylist = [Ylist[0]]
|
||||
k = kern.Linear(Q, ARD=True)
|
||||
inanlist = []
|
||||
|
||||
|
|
@ -553,7 +555,7 @@ def bcgplvm_stick(kernel=None, optimize=True, verbose=True, plot=True):
|
|||
|
||||
data = pods.datasets.osu_run1()
|
||||
# optimize
|
||||
back_kernel=GPy.kern.RBF(data['Y'].shape[1], lengthscale=5.)
|
||||
back_kernel = GPy.kern.RBF(data['Y'].shape[1], lengthscale=5.)
|
||||
mapping = GPy.mappings.Kernel(X=data['Y'], output_dim=2, kernel=back_kernel)
|
||||
m = GPy.models.BCGPLVM(data['Y'], 2, kernel=kernel, mapping=mapping)
|
||||
if optimize: m.optimize(messages=verbose, max_f_eval=10000)
|
||||
|
|
@ -563,7 +565,7 @@ def bcgplvm_stick(kernel=None, optimize=True, verbose=True, plot=True):
|
|||
y = m.likelihood.Y[0, :]
|
||||
data_show = GPy.plotting.matplot_dep.visualize.stick_show(y[None, :], connect=data['connect'])
|
||||
GPy.plotting.matplot_dep.visualize.lvm(m.X[0, :].copy(), m, data_show, ax)
|
||||
#raw_input('Press enter to finish')
|
||||
# raw_input('Press enter to finish')
|
||||
|
||||
return m
|
||||
|
||||
|
|
@ -627,7 +629,7 @@ def cmu_mocap(subject='35', motion=['01'], in_place=True, optimize=True, verbose
|
|||
Y = data['Y']
|
||||
Y_mean = Y.mean(0)
|
||||
Y_std = Y.std(0)
|
||||
m = GPy.models.GPLVM((Y-Y_mean)/Y_std, 2)
|
||||
m = GPy.models.GPLVM((Y - Y_mean) / Y_std, 2)
|
||||
|
||||
if optimize: m.optimize(messages=verbose, max_f_eval=10000)
|
||||
if plot:
|
||||
|
|
@ -651,17 +653,17 @@ def ssgplvm_simulation_linear():
|
|||
x = np.empty(Q)
|
||||
dies = np.random.rand(Q)
|
||||
for q in xrange(Q):
|
||||
if dies[q]<pi:
|
||||
if dies[q] < pi:
|
||||
x[q] = np.random.randn()
|
||||
else:
|
||||
x[q] = 0.
|
||||
return x
|
||||
|
||||
Y = np.empty((N,D))
|
||||
X = np.empty((N,Q))
|
||||
Y = np.empty((N, D))
|
||||
X = np.empty((N, Q))
|
||||
# Generate data from random sampled weight matrices
|
||||
for n in xrange(N):
|
||||
X[n] = sample_X(Q,pi)
|
||||
w = np.random.randn(D,Q)
|
||||
Y[n] = np.dot(w,X[n])
|
||||
X[n] = sample_X(Q, pi)
|
||||
w = np.random.randn(D, Q)
|
||||
Y[n] = np.dot(w, X[n])
|
||||
|
||||
|
|
|
|||
|
|
@ -81,18 +81,19 @@ class BayesianGPLVMMiniBatch(SparseGPMiniBatch):
|
|||
def _inner_parameters_changed(self, kern, X, Z, likelihood, Y, Y_metadata, Lm=None, dL_dKmm=None, subset_indices=None):
|
||||
posterior, log_marginal_likelihood, grad_dict, current_values, value_indices = super(BayesianGPLVMMiniBatch, self)._inner_parameters_changed(kern, X, Z, likelihood, Y, Y_metadata, Lm=Lm, dL_dKmm=dL_dKmm, subset_indices=subset_indices)
|
||||
|
||||
kl_fctr = 1.
|
||||
current_values['meangrad'], current_values['vargrad'] = self.kern.gradients_qX_expectations(
|
||||
variational_posterior=X,
|
||||
Z=Z, dL_dpsi0=grad_dict['dL_dpsi0'],
|
||||
dL_dpsi1=grad_dict['dL_dpsi1'],
|
||||
dL_dpsi2=grad_dict['dL_dpsi2'])
|
||||
|
||||
kl_fctr = self.kl_factr
|
||||
if self.missing_data:
|
||||
d = self.output_dim
|
||||
log_marginal_likelihood -= kl_fctr*self.variational_prior.KL_divergence(X)/d
|
||||
else:
|
||||
log_marginal_likelihood -= kl_fctr*self.variational_prior.KL_divergence(X)
|
||||
|
||||
current_values['meangrad'], current_values['vargrad'] = self.kern.gradients_qX_expectations(
|
||||
variational_posterior=X,
|
||||
Z=Z, dL_dpsi0=grad_dict['dL_dpsi0'],
|
||||
dL_dpsi1=grad_dict['dL_dpsi1'],
|
||||
dL_dpsi2=grad_dict['dL_dpsi2'])
|
||||
|
||||
# Subsetting Variational Posterior objects, makes the gradients
|
||||
# empty. We need them to be 0 though:
|
||||
|
|
|
|||
|
|
@ -105,7 +105,6 @@ class MRD(BayesianGPLVMMiniBatch):
|
|||
kernels = []
|
||||
for i in range(len(Ylist)):
|
||||
k = kernel.copy()
|
||||
print k is kernel, k.observers, k.constraints
|
||||
kernels.append(k)
|
||||
else:
|
||||
assert len(kernel) == len(Ylist), "need one kernel per output"
|
||||
|
|
@ -144,8 +143,16 @@ class MRD(BayesianGPLVMMiniBatch):
|
|||
for i, n, k, l, Y, im, bs in itertools.izip(itertools.count(), Ynames, kernels, likelihoods, Ylist, self.inference_method, batchsize):
|
||||
assert Y.shape[0] == self.num_data, "All datasets need to share the number of datapoints, and those have to correspond to one another"
|
||||
md = np.isnan(Y).any()
|
||||
spgp = SparseGPMiniBatch(self.X, Y, Z, k, l, im, n, None, normalizer, md, stochastic, bs)
|
||||
spgp = BayesianGPLVMMiniBatch(Y, input_dim, X, X_variance,
|
||||
Z=Z, kernel=k, likelihood=l,
|
||||
inference_method=im, name=n,
|
||||
normalizer=normalizer,
|
||||
missing_data=md,
|
||||
stochastic=stochastic,
|
||||
batchsize=bs)
|
||||
spgp.kl_factr = 1./len(Ynames)
|
||||
spgp.unlink_parameter(spgp.Z)
|
||||
spgp.unlink_parameter(spgp.X)
|
||||
del spgp.Z
|
||||
del spgp.X
|
||||
spgp.Z = self.Z
|
||||
|
|
@ -165,20 +172,10 @@ class MRD(BayesianGPLVMMiniBatch):
|
|||
|
||||
self.logger.info('working on im <{}>'.format(hex(id(i))))
|
||||
self.Z.gradient[:] += b.full_values['Zgrad']
|
||||
grad_dict = b.grad_dict
|
||||
grad_dict = b.full_values
|
||||
|
||||
if isinstance(self.X, VariationalPosterior):
|
||||
dL_dmean, dL_dS = b.kern.gradients_qX_expectations(
|
||||
grad_dict['dL_dpsi0'],
|
||||
grad_dict['dL_dpsi1'],
|
||||
grad_dict['dL_dpsi2'],
|
||||
self.Z, self.X)
|
||||
self.X.mean.gradient += dL_dmean
|
||||
self.X.variance.gradient += dL_dS
|
||||
|
||||
else:
|
||||
#gradients wrt kernel
|
||||
self.X.gradient += self.kern.gradients_X(self.grad_dict['dL_dKnm'], self.X, self.Z)
|
||||
self.X.mean.gradient += grad_dict['meangrad']
|
||||
self.X.variance.gradient += grad_dict['vargrad']
|
||||
|
||||
if isinstance(self.X, VariationalPosterior):
|
||||
# update for the KL divergence
|
||||
|
|
@ -238,7 +235,7 @@ class MRD(BayesianGPLVMMiniBatch):
|
|||
pass
|
||||
if axes is None:
|
||||
ax = fig.add_subplot(1, len(self.bgplvms), i + 1, sharex=sharex_ax, sharey=sharey_ax)
|
||||
elif isinstance(axes, (tuple, list)):
|
||||
elif isinstance(axes, (tuple, list, np.ndarray)):
|
||||
ax = axes[i]
|
||||
else:
|
||||
raise ValueError("Need one axes per latent dimension input_dim")
|
||||
|
|
@ -286,7 +283,7 @@ class MRD(BayesianGPLVMMiniBatch):
|
|||
titles = [r'${}$'.format(name) for name in self.names]
|
||||
ymax = reduce(max, [np.ceil(max(g.kern.input_sensitivity())) for g in self.bgplvms])
|
||||
def plotf(i, g, ax):
|
||||
ax.set_ylim([0,ymax])
|
||||
#ax.set_ylim([0,ymax])
|
||||
return g.kern.plot_ARD(ax=ax, title=titles[i], *args, **kwargs)
|
||||
fig = self._handle_plotting(fignum, ax, plotf, sharex=sharex, sharey=sharey)
|
||||
return fig
|
||||
|
|
|
|||
|
|
@ -56,6 +56,7 @@ Created on 3 Nov 2014
|
|||
raise NotImplementedError, "what to do what to do?"
|
||||
print "defaulting to ", inference_method, "for latent function inference"
|
||||
|
||||
self.kl_factr = 1.
|
||||
self.Z = Param('inducing inputs', Z)
|
||||
self.num_inducing = Z.shape[0]
|
||||
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue