WIP: Sparse coder #456
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WIP: Sparse coder #456
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Please:
"""Sparse codingPEP257 :)
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With regards to precomputing wavelets dictionnaries, I don't not think that this is a good idea, because a Wavelet transform can be implemented much faster than a dot product. In addition, it is a orthogonal basis, thus sparse coding should preferably be done using soft thresholding. Finally, this would be fairly image or signal specific, and I don't like the idea of such application-specific code creeping in a general-purpose object. |
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This object should be imported in the init of decomposition.
It should also be added in docs/modules/classes.rst
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My biggest comment is that it is missing a narrative documentation. In Gael |
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I think it would be good if the SparseCoder would be referenced in the DictionaryLearning "see also" section. |
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Note that before, dict_learning and dict_learning_online would perform very different on exactly the same data and for the same alpha. This was indeed caused by sometimes forgetting to divide by n_features and this fixes it.
…into sparse-coder Conflicts: sklearn/linear_model/least_angle.py
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merged, thanks |
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# More detailed explanatory text, if necessary. Wrap it to about 72 # characters or so. In some contexts, the first line is treated as the # subject of the commit and the rest of the text as the body. The # blank line separating the summary from the body is critical (unless # you omit the body entirely); various tools like `log`, `shortlog` # and `rebase` can get confused if you run the two together. # Explain the problem that this commit is solving. Focus on why you # are making this change as opposed to how (the code explains that). # Are there side effects or other unintuitive consequences of this # change? Here's the place to explain them. # Further paragraphs come after blank lines. # - Bullet points are okay, too # - Typically a hyphen or asterisk is used for the bullet, preceded # by a single space, with blank lines in between, but conventions # vary here # If you use an issue tracker, put references to them at the bottom, # like this: # Resolves: scikit-learn#123 # See also: scikit-learn#456, scikit-learn#789
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# More detailed explanatory text, if necessary. Wrap it to about 72 # characters or so. In some contexts, the first line is treated as the # subject of the commit and the rest of the text as the body. The # blank line separating the summary from the body is critical (unless # you omit the body entirely); various tools like `log`, `shortlog` # and `rebase` can get confused if you run the two together. # Explain the problem that this commit is solving. Focus on why you # are making this change as opposed to how (the code explains that). # Are there side effects or other unintuitive consequences of this # change? Here's the place to explain them. # Further paragraphs come after blank lines. # - Bullet points are okay, too # - Typically a hyphen or asterisk is used for the bullet, preceded # by a single space, with blank lines in between, but conventions # vary here # If you use an issue tracker, put references to them at the bottom, # like this: # Resolves: scikit-learn#123 # See also: scikit-learn#456, scikit-learn#789
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# More detailed explanatory text, if necessary. Wrap it to about 72 # characters or so. In some contexts, the first line is treated as the # subject of the commit and the rest of the text as the body. The # blank line separating the summary from the body is critical (unless # you omit the body entirely); various tools like `log`, `shortlog` # and `rebase` can get confused if you run the two together. # Explain the problem that this commit is solving. Focus on why you # are making this change as opposed to how (the code explains that). # Are there side effects or other unintuitive consequences of this # change? Here's the place to explain them. # Further paragraphs come after blank lines. # - Bullet points are okay, too # - Typically a hyphen or asterisk is used for the bullet, preceded # by a single space, with blank lines in between, but conventions # vary here # If you use an issue tracker, put references to them at the bottom, # like this: # Resolves: scikit-learn#123 # See also: scikit-learn#456, scikit-learn#789
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# More detailed explanatory text, if necessary. Wrap it to about 72 # characters or so. In some contexts, the first line is treated as the # subject of the commit and the rest of the text as the body. The # blank line separating the summary from the body is critical (unless # you omit the body entirely); various tools like `log`, `shortlog` # and `rebase` can get confused if you run the two together. # Explain the problem that this commit is solving. Focus on why you # are making this change as opposed to how (the code explains that). # Are there side effects or other unintuitive consequences of this # change? Here's the place to explain them. # Further paragraphs come after blank lines. # - Bullet points are okay, too # - Typically a hyphen or asterisk is used for the bullet, preceded # by a single space, with blank lines in between, but conventions # vary here # If you use an issue tracker, put references to them at the bottom, # like this: # Resolves: scikit-learn#123 # See also: scikit-learn#456, scikit-learn#789
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# More detailed explanatory text, if necessary. Wrap it to about 72 # characters or so. In some contexts, the first line is treated as the # subject of the commit and the rest of the text as the body. The # blank line separating the summary from the body is critical (unless # you omit the body entirely); various tools like `log`, `shortlog` # and `rebase` can get confused if you run the two together. # Explain the problem that this commit is solving. Focus on why you # are making this change as opposed to how (the code explains that). # Are there side effects or other unintuitive consequences of this # change? Here's the place to explain them. # Further paragraphs come after blank lines. # - Bullet points are okay, too # - Typically a hyphen or asterisk is used for the bullet, preceded # by a single space, with blank lines in between, but conventions # vary here # If you use an issue tracker, put references to them at the bottom, # like this: # Resolves: scikit-learn#123 # See also: scikit-learn#456, scikit-learn#789
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This quick and dirty pull request aims to add an estimator (transformer) object that implements sparse coding against a fixed dictionary, in the form of the SparseCoder object.
At the same time this will address the small inconsistencies and missing info in docs that came up.