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last changes for mrd stability

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Max Zwiessele 2013-05-08 16:47:56 +01:00
parent f39afb9ea5
commit 18117bba81
4 changed files with 8 additions and 7 deletions
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6
GPy/examples/dimensionality_reduction.py
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@ -312,7 +312,7 @@ def mrd_simulation(plot_sim=False):
# Y2 = np.random.multivariate_normal(np.zeros(N), k.K(X), D2).T # Y2 = np.random.multivariate_normal(np.zeros(N), k.K(X), D2).T
# Y2 -= Y2.mean(0) # Y2 -= Y2.mean(0)
# make_params = lambda ard: np.hstack([[1], ard, [1, .3]]) # make_params = lambda ard: np.hstack([[1], ard, [1, .3]])
D1, D2, D3, N, M, Q = 150, 250, 300, 700, 10, 7 D1, D2, D3, N, M, Q = 150, 250, 300, 700, 3, 7
slist, Slist, Ylist = _simulate_sincos(D1, D2, D3, N, M, Q, plot_sim) slist, Slist, Ylist = _simulate_sincos(D1, D2, D3, N, M, Q, plot_sim)
from GPy.models import mrd from GPy.models import mrd
@ -328,7 +328,7 @@ def mrd_simulation(plot_sim=False):
# k = kern.rbf(Q, ARD=True) + kern.bias(Q) + kern.white(Q) # k = kern.rbf(Q, ARD=True) + kern.bias(Q) + kern.white(Q)
k = kern.linear(Q, ARD=True) + kern.bias(Q, np.exp(-2)) + kern.white(Q, np.exp(-2)) k = kern.linear(Q, [0.01] * Q, True) + kern.bias(Q, np.exp(-2)) + kern.white(Q, np.exp(-2))
m = mrd.MRD(*Ylist, Q=Q, M=M, kernel=k, initx="concat", initz='permute', _debug=False) m = mrd.MRD(*Ylist, Q=Q, M=M, kernel=k, initx="concat", initz='permute', _debug=False)
for i, Y in enumerate(Ylist): for i, Y in enumerate(Ylist):
@ -347,7 +347,7 @@ def mrd_simulation(plot_sim=False):
print "initializing beta" print "initializing beta"
cstr = "noise" cstr = "noise"
m.unconstrain(cstr); m.constrain_fixed(cstr) m.unconstrain(cstr); m.constrain_fixed(cstr)
m.optimize('scg', messages=1, max_f_eval=200, gtol=11) m.optimize('scg', messages=1, max_f_eval=2e3, gtol=100)
print "releasing beta" print "releasing beta"
cstr = "noise" cstr = "noise"

2
GPy/inference/SCG.py
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@ -54,7 +54,7 @@ def SCG(f, gradf, x, optargs=(), maxiters=500, max_f_eval=500, display=True, xto
success = True # Force calculation of directional derivs. success = True # Force calculation of directional derivs.
nsuccess = 0 # nsuccess counts number of successes. nsuccess = 0 # nsuccess counts number of successes.
beta = 1.0 # Initial scale parameter. beta = 1.0 # Initial scale parameter.
betamin = 1.0e-25 # Lower bound on scale. betamin = 1.0e-15 # Lower bound on scale.
betamax = 1.0e100 # Upper bound on scale. betamax = 1.0e100 # Upper bound on scale.
status = "Not converged" status = "Not converged"

1
GPy/likelihoods/likelihood.py
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@ -16,6 +16,7 @@ class likelihood:
self.is_heteroscedastic : enables significant computational savings in GP self.is_heteroscedastic : enables significant computational savings in GP
self.precision : a scalar or vector representation of the effective target precision self.precision : a scalar or vector representation of the effective target precision
self.YYT : (optional) = np.dot(self.Y, self.Y.T) enables computational savings for D>N self.YYT : (optional) = np.dot(self.Y, self.Y.T) enables computational savings for D>N
self.V : self.precision * self.Y
""" """
def __init__(self,data): def __init__(self,data):
raise ValueError, "this class is not to be instantiated" raise ValueError, "this class is not to be instantiated"

6
GPy/models/sparse_GP.py
View file

@ -81,7 +81,7 @@ class sparse_GP(GP):
# psi2_beta_scaled = (self.psi2 * (self.likelihood.precision.flatten().reshape(self.N, 1, 1) / sf2)).sum(0) # psi2_beta_scaled = (self.psi2 * (self.likelihood.precision.flatten().reshape(self.N, 1, 1) / sf2)).sum(0)
psi2_beta_scaled = (self.psi2 * (self.likelihood.precision.flatten().reshape(self.N, 1, 1))).sum(0) psi2_beta_scaled = (self.psi2 * (self.likelihood.precision.flatten().reshape(self.N, 1, 1))).sum(0)
evals, evecs = linalg.eigh(psi2_beta_scaled) evals, evecs = linalg.eigh(psi2_beta_scaled)
clipped_evals = np.clip(evals, 0., 1e6) # TODO: make clipping configurable clipped_evals = np.clip(evals, 0., 1e15) # TODO: make clipping configurable
if not np.allclose(evals, clipped_evals): if not np.allclose(evals, clipped_evals):
print "Warning: clipping posterior eigenvalues" print "Warning: clipping posterior eigenvalues"
tmp = evecs * np.sqrt(clipped_evals) tmp = evecs * np.sqrt(clipped_evals)
@ -97,7 +97,7 @@ class sparse_GP(GP):
# psi2_beta_scaled = (self.psi2 * (self.likelihood.precision / sf2)).sum(0) # psi2_beta_scaled = (self.psi2 * (self.likelihood.precision / sf2)).sum(0)
psi2_beta_scaled = (self.psi2 * (self.likelihood.precision)).sum(0) psi2_beta_scaled = (self.psi2 * (self.likelihood.precision)).sum(0)
evals, evecs = linalg.eigh(psi2_beta_scaled) evals, evecs = linalg.eigh(psi2_beta_scaled)
clipped_evals = np.clip(evals, 0., 1e6) # TODO: make clipping configurable clipped_evals = np.clip(evals, 0., 1e15) # TODO: make clipping configurable
if not np.allclose(evals, clipped_evals): if not np.allclose(evals, clipped_evals):
print "Warning: clipping posterior eigenvalues" print "Warning: clipping posterior eigenvalues"
tmp = evecs * np.sqrt(clipped_evals) tmp = evecs * np.sqrt(clipped_evals)
@ -259,7 +259,7 @@ class sparse_GP(GP):
Kmmi_LmiBLmi = backsub_both_sides(self.Lm, np.eye(self.M) - Bi) Kmmi_LmiBLmi = backsub_both_sides(self.Lm, np.eye(self.M) - Bi)
Kx = self.kern.K(self.Z, Xnew, which_parts=which_parts) Kx = self.kern.K(self.Z, Xnew, which_parts=which_parts)
mu = np.dot(Kx.T, self.Cpsi1V / self.scale_factor) mu = np.dot(Kx.T, self.Cpsi1V) # / self.scale_factor)
if full_cov: if full_cov:
Kxx = self.kern.K(Xnew, which_parts=which_parts) Kxx = self.kern.K(Xnew, which_parts=which_parts)
var = Kxx - mdot(Kx.T, Kmmi_LmiBLmi, Kx) # NOTE this won't work for plotting var = Kxx - mdot(Kx.T, Kmmi_LmiBLmi, Kx) # NOTE this won't work for plotting