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resolve conflictions
This commit is contained in:
Zhenwen Dai
parent
96901401cc
commit
b9d0ae06b3
2 changed files with 14 additions and 65 deletions
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@ -40,7 +40,7 @@ class GmmNormalPrior(VariationalPrior):
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def __init__(self, px_mu, px_var, pi, n_component, variational_pi, name="GMMNormalPrior", **kw):
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super(GmmNormalPrior, self).__init__(name=name, **kw)
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self.n_component = n_component
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self.px_mu = Param('mu_k', px_mu)
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self.px_var = Param('var_k', px_var)
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@ -48,9 +48,9 @@ class GmmNormalPrior(VariationalPrior):
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variational_pi = variational_pi / np.sum(variational_pi)
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pi = pi / np.sum(pi)
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self.pi = pi # p(x) mixing coeffients
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self.variational_pi = Param('variational_pi', variational_pi) # variational mixing coefficients
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self.pi = pi # p(x) mixing coeffients
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self.variational_pi = Param('variational_pi', variational_pi) # variational mixing coefficients
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self.check_all_weights()
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self.link_parameter(self.px_mu)
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@ -58,22 +58,15 @@ class GmmNormalPrior(VariationalPrior):
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self.link_parameter(self.variational_pi)
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self.variational_pi.constrain_bounded(0.0, 1.0)
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<<<<<<< HEAD
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# self.stop = 5
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=======
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self.stop = 5
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>>>>>>> a43d4b074832a791b7453c454f304eacd478c624
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def KL_divergence(self, variational_posterior):
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# Lagrange multiplier maybe also needed here
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<<<<<<< HEAD
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=======
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# var_mean = np.square(variational_posterior.mean).sum()
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# var_S = (variational_posterior.variance - np.log(variational_posterior.variance)).sum()
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# return 0.5 * (var_mean + var_S) - 0.5 * variational_posterior.input_dim * variational_posterior.num_data
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>>>>>>> a43d4b074832a791b7453c454f304eacd478c624
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mu = variational_posterior.mean
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S = variational_posterior.variance
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pi = self.variational_pi
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@ -88,76 +81,48 @@ class GmmNormalPrior(VariationalPrior):
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return 0.5 * (cita[0] - (np.log(S)).sum() + cita[1]) + 0.5 * (cita[2] - total_n) + cita[3]
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def update_gradients_KL(self, variational_posterior):
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<<<<<<< HEAD
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# import pdb; pdb.set_trace() # breakpoint 1
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# print("Updating Gradients")
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# if self.stop<1:
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# return
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# self.stop-=1
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=======
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import pdb; pdb.set_trace() # breakpoint 1
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print("Updating Gradients")
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if self.stop<1:
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return
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self.stop-=1
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>>>>>>> a43d4b074832a791b7453c454f304eacd478c624
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#dL:
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#variational_posterior.mean.gradient -= variational_posterior.mean
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#variational_posterior.variance.gradient -= (1. - (1. / (variational_posterior.variance))) * 0.5
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mu = variational_posterior.mean
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S = variational_posterior.variance
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pi = self.variational_pi
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<<<<<<< HEAD
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cita_0 = np.zeros(mu.shape)
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cita_1 = np.zeros(mu.shape)
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cita_2 = np.zeros(mu.shape)
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cita_3 = np.zeros(pi.shape)
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for i in range(self.n_component):
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cita_0 += pi.values[i] * (mu - self.px_mu.values[i]) / self.px_var.values[i]
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cita_1 += (pi[i] / self.px_var[i])
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cita_2 += pi[i] * (S + np.square(mu - self.px_mu[i])) / np.square(self.px_var[i])
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self.px_mu[i].gradient += pi[i] * (mu - self.px_mu[i]) / self.px_var[i]
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self.px_var[i].gradient += (pi[i] * (S + np.square(mu - self.px_mu[i])) / np.square(self.px_var[i]) - (pi[i] / self.px_var[i])) * 0.5
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cita_3[i] = (np.log(self.px_var[i]).sum() + (S / self.px_var[i]).sum()+ (np.square(mu - self.px_mu[i]) / self.px_var[i]).sum() )* (-0.5) + np.log(self.pi[i] / pi[i]) - 1
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# self.variational_pi[i].gradient += cita_3[i]
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variational_posterior.mean.gradient -= cita_0
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variational_posterior.variance.gradient += (1. / (S) - cita_1) * 0.5
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self.variational_pi.gradient +=cita_3
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=======
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cita_0 = np.zeros_like(mu)
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cita_1 = np.zeros_like(mu)
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cita_2 = np.zeros_like(mu)
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cita_3 = np.zeros_like(pi)
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for i in range(self.n_component):
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print("About to change the gradient")
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print("About to change the gradient")
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print pi.values[i]
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print mu
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print self.px_mu.values[i]
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print self.px_var.values[i]
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cita_0 += pi.values[i] * (mu - self.px_mu.values[i]) / self.px_var.values[i]
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print "Has this helped?"
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self.px_mu[i].gradient += pi[i] * (mu - self.px_mu[i]) / self.px_var[i]
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cita_1 += (pi[i] / self.px_var[i])
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cita_2 += pi[i] * (S + np.square(mu - self.px_mu[i])) / np.square(self.px_var[i])
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self.px_var[i].gradient += (pi[i] * (S + np.square(mu - self.px_mu[i])) / np.square(self.px_var[i]) - (pi[i] / self.px_var[i])) * 0.5
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cita_3[i] = (np.log(self.px_var[i]).sum()
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cita_3[i] = (np.log(self.px_var[i]).sum()
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+ (S / self.px_var[i]).sum()
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+ (np.square(mu - self.px_mu[i]) / self.px_var[i]).sum() )* (-0.5) + np.log(self.pi[i] / pi[i]) - pi[i] * np.log(self.pi[i] / np.square(pi[i]))
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self.variational_pi[i].gradient += cita_3[i]
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variational_posterior.mean.gradient -= cita_0
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variational_posterior.variance.gradient += (1. / (S) - cita_1) * 0.5
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>>>>>>> a43d4b074832a791b7453c454f304eacd478c624
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def check_weights(self, weights):
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assert weights.min() >= 0.0
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assert weights.max() <= 1.0
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@ -167,10 +132,7 @@ class GmmNormalPrior(VariationalPrior):
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self.check_weights(self.variational_pi)
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self.check_weights(self.pi)
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<<<<<<< HEAD
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=======
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>>>>>>> a43d4b074832a791b7453c454f304eacd478c624
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class SpikeAndSlabPrior(VariationalPrior):
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def __init__(self, pi=None, learnPi=False, variance = 1.0, group_spike=False, name='SpikeAndSlabPrior', **kw):
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super(SpikeAndSlabPrior, self).__init__(name=name, **kw)
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@ -52,18 +52,6 @@ class GmmBayesianGPLVM(SparseGP_MPI):
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if likelihood is None:
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likelihood = Gaussian()
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<<<<<<< HEAD
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# Need to define what the model is initialised like
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pi = np.ones(n_component) / float(n_component) # p(k)
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variational_pi = pi.copy()
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px_mu = [[]] * n_component
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px_var = [[]] * n_component
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for i in range(n_component):
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px_mu[i] = np.zeros(X_variance.shape)
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px_var[i] = np.ones(X_variance.shape)
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=======
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# Need to define what the model is initialised like
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pi = np.ones(n_component) / float(n_component) # p(k)
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variational_pi = pi.copy()
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@ -74,7 +62,7 @@ class GmmBayesianGPLVM(SparseGP_MPI):
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for i in range(n_component):
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px_mu[i] = np.zeros_like(X_variance)
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px_var[i] = np.ones_like(X_variance)
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# print("Should print")
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# print(pi)
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@ -82,7 +70,6 @@ class GmmBayesianGPLVM(SparseGP_MPI):
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# print(px_var)
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# print(variational_pi)
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# print("Didnt print")
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>>>>>>> a43d4b074832a791b7453c454f304eacd478c624
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self.variational_prior = GmmNormalPrior(px_mu=px_mu, px_var=px_var, pi=pi,
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n_component=n_component, variational_pi=variational_pi)
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