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4 changed files with 5 additions and 8 deletions
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@ -751,8 +751,6 @@ class OptimizationHandlable(Indexable):
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Transform the gradients by multiplying the gradient factor for each
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Transform the gradients by multiplying the gradient factor for each
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constraint to it.
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constraint to it.
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"""
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"""
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if self.has_parent():
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return g
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[np.put(g, i, g[i] * c.gradfactor(self.param_array[i])) for c, i in self.constraints.iteritems() if c != __fixed__]
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[np.put(g, i, g[i] * c.gradfactor(self.param_array[i])) for c, i in self.constraints.iteritems() if c != __fixed__]
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if self._has_fixes(): return g[self._fixes_]
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if self._has_fixes(): return g[self._fixes_]
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return g
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return g
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@ -296,7 +296,7 @@ def bgplvm_simulation_missing_data(optimize=True, verbose=1,
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from GPy.models import BayesianGPLVM
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from GPy.models import BayesianGPLVM
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from GPy.inference.latent_function_inference.var_dtc import VarDTCMissingData
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from GPy.inference.latent_function_inference.var_dtc import VarDTCMissingData
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D1, D2, D3, N, num_inducing, Q = 1000, 5, 8, 400, 3, 4
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D1, D2, D3, N, num_inducing, Q = 13, 5, 8, 400, 3, 4
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_, _, Ylist = _simulate_sincos(D1, D2, D3, N, num_inducing, Q, plot_sim)
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_, _, Ylist = _simulate_sincos(D1, D2, D3, N, num_inducing, Q, plot_sim)
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Y = Ylist[0]
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Y = Ylist[0]
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k = kern.Linear(Q, ARD=True)# + kern.white(Q, _np.exp(-2)) # + kern.bias(Q)
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k = kern.Linear(Q, ARD=True)# + kern.white(Q, _np.exp(-2)) # + kern.bias(Q)
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@ -302,7 +302,6 @@ class VarDTCMissingData(LatentFunctionInference):
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#if ((i+1.)/size) >= next_ten:
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#if ((i+1.)/size) >= next_ten:
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# logger.info('preparing traces {:> 6.1%}'.format((i+1.)/size))
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# logger.info('preparing traces {:> 6.1%}'.format((i+1.)/size))
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# next_ten += .1
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# next_ten += .1
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#y = y[v]
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#y = y[v]
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if het_noise: beta = beta_all[i]
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if het_noise: beta = beta_all[i]
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else: beta = beta_all
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else: beta = beta_all
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