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)¶