[MRG+2] Modification of GaussianMixture class. #7123
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The purpose here is to prepare the integration of BayesianGaussianMixture.
| for init in range(n_init): | ||
| self._print_verbose_msg_init_beg(init) | ||
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| if do_init: | ||
| self._initialize_parameters(X) | ||
| current_log_likelihood, resp = self._e_step(X) | ||
| self.lower_bound_ = np.infty |
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shouldn't you document the new attribute?
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Indeed, sorry for that mistake.
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I didn't know np.infty also returns np.inf... Interesting...
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Shouldn't it be self.lower_bound_ = -np.infty ?
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It has been merged but I re-ask the question: shouldn't it be -np.infty ?
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that's it for me |
tguillemot
changed the title
tguillemot
changed the title
| @@ -136,7 +136,7 @@ def _initialize_parameters(self, X): | |||
| ---------- | |||
| X : array-like, shape (n_samples, n_features) | |||
| """ | |||
| n_samples = X.shape[0] | |||
| n_samples, _ = X.shape | |||
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Sorry if this was suggested before...
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It just I prefer like this and it also check that the shape of X is a tuple.
Sorry for these useless little modifications.
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it also checks implicitly that X.ndim == 2. It do the same in my code.
| @@ -563,6 +553,9 @@ class GaussianMixture(BaseMixture): | |||
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| n_iter_ : int | |||
| Number of step used by the best fit of EM to reach the convergence. | |||
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| lower_bound_ : float | |||
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lower_bound_ -> best_log_likelihood_ ?
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In fact for GMM this is the best log likelihood but not for VBGMM which is lower bound.
I've chosen lower_bound_ because it was the most understandable.
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@ngoix This PR is related to Bayesian Gaussian Mixture, if you have some time to review it. |
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one more +1 and we're good here... |
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| for n_iter in range(self.max_iter): | ||
| prev_log_likelihood = current_log_likelihood | ||
| prev_log_likelihood = self.lower_bound_ |
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prev_log_likelihood -> prev_lower_bound ?
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By testing the code, I realized that there is a problem which was here before this PR: |
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| def _estimate_log_weights(self): | ||
| return np.log(self.weights_) | ||
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| def _compute_lower_bound(self, _, log_prob_norm): | ||
| return log_prob_norm | ||
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In _set_parameters below, compute self.covariances_ from self.precisions_.
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That's all from me! |
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@ngoix Indeed you're right. I've missed that. Thanks. |
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@agramfort merge ? |
tguillemot
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@amueller Can you merge please ? |
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Thanks @tguillemot |
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@agramfort @ngoix Thanks |
* Modification of GaussianMixture class. The purpose here is to prepare the integration of BayesianGaussianMixture. * Fix comments. * Modification of the Docstring. * Add license and author. * Fix review and add tests for init.
The PR is to simplify the integration of the
BayesianGaussianMixtureclass #6651.I've simplify the
GaussianMixtureclass by integrating a function which computes the determinant of the cholesky decomposition of the precision matrix (which will be usefull forBayesianGaussianMixture).I've also corrected a bug during the EM process: normally, the lower bound is computed after the M-step not after the E-step. It's not a problem for GMM but that creates some problem for
BayesianGaussianMixture.@agramfort @ogrisel @amueller Can you have a look ?