adobo.irlbpy package

Submodules

adobo.irlbpy.irlb module

This code comes from: https://github.com/airysen/irlbpy/

Above is forked from: https://github.com/mattsooknah/irlbpy

Which is forked from: https://github.com/bwlewis/irlbpy

Credits to the authors above.

class adobo.irlbpy.irlb.LanczosResult(**kwargs)

Bases: object

exception adobo.irlbpy.irlb.MatrixShapeException

Bases: Exception

exception adobo.irlbpy.irlb.VectorLengthException

Bases: Exception

adobo.irlbpy.irlb.invcheck(x)
adobo.irlbpy.irlb.lanczos(A, nval, tol=0.0001, maxit=50, center=None, scale=None, L=None, seed=None)

Estimate a few of the largest singular values and corresponding singular vectors of matrix using the implicitly restarted Lanczos bidiagonalization method of Baglama and Reichel, see: Augmented Implicitly Restarted Lanczos Bidiagonalization Methods, J. Baglama and L. Reichel, SIAM J. Sci. Comput. 2005 Keyword arguments: tol – An estimation tolerance. Smaller means more accurate estimates. maxit – Maximum number of Lanczos iterations allowed. Given an input matrix A of dimension j * k, and an input desired number of singular values n, the function returns a tuple X with five entries: X[0] A j * nu matrix of estimated left singular vectors. X[1] A vector of length nu of estimated singular values. X[2] A k * nu matrix of estimated right singular vectors. X[3] The number of Lanczos iterations run. X[4] The number of matrix-vector products run. The algorithm estimates the truncated singular value decomposition: A.dot(X[2]) = X[0]*X[1].

adobo.irlbpy.irlb.multA(A, x, TP=False, L=None)
adobo.irlbpy.irlb.multS(s, v, L, TP=False)
adobo.irlbpy.irlb.orthog(Y, X)

Orthogonalize a vector or matrix Y against the columns of the matrix X. This function requires that the column dimension of Y is less than X and that Y and X have the same number of rows.

adobo.irlbpy.irlb.prepare_s(s, L=None)
adobo.irlbpy.irlb.prepare_v(v, N, L, TP=False)

Module contents