initial commit for adding boost lib #2985
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| const Float* data = s.get_data(); | ||
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| Eigen::Matrix<Float, Eigen::Dynamic, Eigen::Dynamic> eigen_matrix(row_count, column_count); | ||
| for (int i = 0; i < eigen_matrix.rows(); ++i) { |
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An Eigen matrix can also be created from a non-owned pointer for float/double data. I think something along the lines of Eigen::map< Eigen::Matrix<info, incl. row/col major> , alignment, stride >.
| const la::matrix<Float>& eigvecs, | ||
| const la::matrix<Float>& eigvals) const { | ||
| INFO("convert results to float64"); | ||
| const auto s_f64 = la::astype<double>(s); |
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As an alternative, perhaps the input values could be hard-coded along with the solutions instead of checking them against a different library.
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Yes, but with this solution there is no opportunity to use random generated data(I mean extend tests with for example row_count = GENERATE(3, 28, 125, 256);) and also it will be complicated to check results for big datasets
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How about putting them in the existing folders with .csv files that have data and expected results?
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It's possible, but let's say for pca it's necessary to contain gold eigenvectors, eigenvalues, and it will increase the total size of the repo, especially with big datasets. I see no reasons to avoid this pr tbh
Description
Add a comprehensive description of proposed changes
List associated issue number(s) if exist(s): #6 (for example)
Documentation PR (if needed): #1340 (for example)
Benchmarks PR (if needed): IntelPython/scikit-learn_bench#155 (for example)
PR should start as a draft, then move to ready for review state after CI is passed and all applicable checkboxes are closed.
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