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81 lines
1.8 KiB
81 lines
1.8 KiB
1 year ago
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"""
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``numpy.linalg``
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================
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The NumPy linear algebra functions rely on BLAS and LAPACK to provide efficient
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low level implementations of standard linear algebra algorithms. Those
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libraries may be provided by NumPy itself using C versions of a subset of their
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reference implementations but, when possible, highly optimized libraries that
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take advantage of specialized processor functionality are preferred. Examples
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of such libraries are OpenBLAS, MKL (TM), and ATLAS. Because those libraries
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are multithreaded and processor dependent, environmental variables and external
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packages such as threadpoolctl may be needed to control the number of threads
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or specify the processor architecture.
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- OpenBLAS: https://www.openblas.net/
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- threadpoolctl: https://github.com/joblib/threadpoolctl
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Please note that the most-used linear algebra functions in NumPy are present in
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the main ``numpy`` namespace rather than in ``numpy.linalg``. There are:
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``dot``, ``vdot``, ``inner``, ``outer``, ``matmul``, ``tensordot``, ``einsum``,
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``einsum_path`` and ``kron``.
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Functions present in numpy.linalg are listed below.
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Matrix and vector products
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--------------------------
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multi_dot
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matrix_power
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Decompositions
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--------------
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cholesky
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qr
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svd
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Matrix eigenvalues
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------------------
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eig
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eigh
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eigvals
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eigvalsh
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Norms and other numbers
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-----------------------
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norm
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cond
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det
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matrix_rank
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slogdet
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Solving equations and inverting matrices
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----------------------------------------
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solve
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tensorsolve
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lstsq
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inv
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pinv
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tensorinv
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Exceptions
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----------
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LinAlgError
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"""
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# To get sub-modules
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from . import linalg
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from .linalg import *
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__all__ = linalg.__all__.copy()
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from numpy._pytesttester import PytestTester
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test = PytestTester(__name__)
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del PytestTester
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