Issue/64/mp_sparse_encode - #65
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sutkss
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Jul 25, 2019
| code[idx, :] = sparse_encode(X[idx, :][mask[idx, :] == 1].reshape(1, -1), | ||
| dictionary[:, mask[idx, :] == 1], | ||
| **kwargs) | ||
| code[idx, :] = sklearn.decomposition.sparse_encode( |
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@zalgo-edu In this function, sklearn.decomposition.sparse_encode is used.
This is the function in scikit learn. However, we should use sparse_encode defined by override here.
| tol : float | ||
| Maximum norm of the residual. | ||
| """ | ||
| if tol is not None and tol < 0: |
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@zalgo-edu Please make sure each vector in dictionary has norm 1.
If this condition is not satisfied, please raise warning message.
…n_unittest Issue/66/normalize dictionaries in unittest
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Summary
Implement matching pursuit (MP) mode into sparse_encode function.
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