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lots of bugfixes after refactoring

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This commit is contained in:
James Hensman 2013-06-05 16:14:43 +01:00
parent 3fbd7e4943
commit 527586a012
12 changed files with 53 additions and 53 deletions
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16
GPy/models/bayesian_gplvm.py
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@ -73,8 +73,8 @@ class BayesianGPLVM(SparseGP, GPLVM):
self._oldps.insert(0, p.copy())
def _get_param_names(self):
X_names = sum([['X_%i_%i' % (n, q) for q in range(self.input_dim)] for n in range(self.N)], [])
S_names = sum([['X_variance_%i_%i' % (n, q) for q in range(self.input_dim)] for n in range(self.N)], [])
X_names = sum([['X_%i_%i' % (n, q) for q in range(self.input_dim)] for n in range(self.num_data)], [])
S_names = sum([['X_variance_%i_%i' % (n, q) for q in range(self.input_dim)] for n in range(self.num_data)], [])
return (X_names + S_names + SparseGP._get_param_names(self))
def _get_params(self):
@ -96,7 +96,7 @@ class BayesianGPLVM(SparseGP, GPLVM):
def _set_params(self, x, save_old=True, save_count=0):
# try:
x = self._clipped(x)
N, input_dim = self.N, self.input_dim
N, input_dim = self.num_data, self.input_dim
self.X = x[:self.X.size].reshape(N, input_dim).copy()
self.X_variance = x[(N * input_dim):(2 * N * input_dim)].reshape(N, input_dim).copy()
SparseGP._set_params(self, x[(2 * N * input_dim):])
@ -126,7 +126,7 @@ class BayesianGPLVM(SparseGP, GPLVM):
def KL_divergence(self):
var_mean = np.square(self.X).sum()
var_S = np.sum(self.X_variance - np.log(self.X_variance))
return 0.5 * (var_mean + var_S) - 0.5 * self.input_dim * self.N
return 0.5 * (var_mean + var_S) - 0.5 * self.input_dim * self.num_data
def log_likelihood(self):
ll = SparseGP.log_likelihood(self)
@ -146,11 +146,11 @@ class BayesianGPLVM(SparseGP, GPLVM):
self._savedpsiKmm.append([self.f_call, [self.Kmm, self.dL_dKmm]])
# sf2 = self.scale_factor ** 2
if self.likelihood.is_heteroscedastic:
A = -0.5 * self.N * self.input_dim * np.log(2.*np.pi) + 0.5 * np.sum(np.log(self.likelihood.precision)) - 0.5 * np.sum(self.V * self.likelihood.Y)
A = -0.5 * self.num_data * self.input_dim * np.log(2.*np.pi) + 0.5 * np.sum(np.log(self.likelihood.precision)) - 0.5 * np.sum(self.V * self.likelihood.Y)
# B = -0.5 * self.input_dim * (np.sum(self.likelihood.precision.flatten() * self.psi0) - np.trace(self.A) * sf2)
B = -0.5 * self.input_dim * (np.sum(self.likelihood.precision.flatten() * self.psi0) - np.trace(self.A))
else:
A = -0.5 * self.N * self.input_dim * (np.log(2.*np.pi) + np.log(self.likelihood._variance)) - 0.5 * self.likelihood.precision * self.likelihood.trYYT
A = -0.5 * self.num_data * self.input_dim * (np.log(2.*np.pi) + np.log(self.likelihood._variance)) - 0.5 * self.likelihood.precision * self.likelihood.trYYT
# B = -0.5 * self.input_dim * (np.sum(self.likelihood.precision * self.psi0) - np.trace(self.A) * sf2)
B = -0.5 * self.input_dim * (np.sum(self.likelihood.precision * self.psi0) - np.trace(self.A))
C = -self.input_dim * (np.sum(np.log(np.diag(self.LB)))) # + 0.5 * self.num_inducing * np.log(sf2))
@ -266,9 +266,9 @@ class BayesianGPLVM(SparseGP, GPLVM):
def _debug_filter_params(self, x):
start, end = 0, self.X.size,
X = x[start:end].reshape(self.N, self.input_dim)
X = x[start:end].reshape(self.num_data, self.input_dim)
start, end = end, end + self.X_variance.size
X_v = x[start:end].reshape(self.N, self.input_dim)
X_v = x[start:end].reshape(self.num_data, self.input_dim)
start, end = end, end + (self.num_inducing * self.input_dim)
Z = x[start:end].reshape(self.num_inducing, self.input_dim)
start, end = end, end + self.input_dim