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2to3 itertools fixer
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3e25098710
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1 changed files with 3 additions and 3 deletions
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@ -139,7 +139,7 @@ class MRD(BayesianGPLVMMiniBatch):
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self.bgplvms = []
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for i, n, k, l, Y, im, bs in itertools.izip(itertools.count(), Ynames, kernels, likelihoods, Ylist, self.inference_method, batchsize):
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for i, n, k, l, Y, im, bs in zip(itertools.count(), Ynames, kernels, likelihoods, Ylist, self.inference_method, batchsize):
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assert Y.shape[0] == self.num_data, "All datasets need to share the number of datapoints, and those have to correspond to one another"
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md = np.isnan(Y).any()
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spgp = BayesianGPLVMMiniBatch(Y, input_dim, X, X_variance,
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@ -166,7 +166,7 @@ class MRD(BayesianGPLVMMiniBatch):
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self._log_marginal_likelihood = 0
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self.Z.gradient[:] = 0.
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self.X.gradient[:] = 0.
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for b, i in itertools.izip(self.bgplvms, self.inference_method):
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for b, i in zip(self.bgplvms, self.inference_method):
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self._log_marginal_likelihood += b._log_marginal_likelihood
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self.logger.info('working on im <{}>'.format(hex(id(i))))
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@ -197,7 +197,7 @@ class MRD(BayesianGPLVMMiniBatch):
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elif init in "PCA_single":
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X = np.zeros((Ylist[0].shape[0], self.input_dim))
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fracs = []
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for qs, Y in itertools.izip(np.array_split(np.arange(self.input_dim), len(Ylist)), Ylist):
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for qs, Y in zip(np.array_split(np.arange(self.input_dim), len(Ylist)), Ylist):
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x,frcs = initialize_latent('PCA', len(qs), Y)
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X[:, qs] = x
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fracs.append(frcs)
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