compression_matrix(data, q, iterator='power', n_power_iter=0, n_oversamples=10, seed=None, compute=False)
This compression matrix returned by this algorithm can be used to compute both the QR decomposition and the Singular Value Decomposition.
Size of the desired subspace (the actual size will be bigger, because of oversampling, see da.linalg.compression_level
)
Define the technique used for iterations to cope with flat singular spectra or when the input matrix is very large.
Number of power iterations, useful when the singular values decay slowly. Error decreases exponentially as :None:None:`n_power_iter` increases. In practice, set :None:None:`n_power_iter` <= 4.
Number of oversamples used for generating the sampling matrix. This value increases the size of the subspace computed, which is more accurate at the cost of efficiency. Results are rarely sensitive to this choice though and in practice a value of 10 is very commonly high enough.
Whether or not to compute data at each use. Recomputing the input while performing several passes reduces memory pressure, but means that we have to compute the input multiple times. This is a good choice if the data is larger than memory and cheap to recreate.
Randomly sample matrix to find most active subspace
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