https://docs.scipy.org/doc/numpy/reference/
https://docs.scipy.org/doc/numpy/numpy-ref-1.13.0.pdf
https://docs.scipy.org/doc/numpy-dev/contents.html
help(np.linalg)
Help on package numpy.linalg in numpy:
NAME
numpy.linalg
DESCRIPTION
Core Linear Algebra Tools
-------------------------
Linear algebra basics:
- norm Vector or matrix norm
- inv Inverse of a square matrix
- solve Solve a linear system of equations
- det Determinant of a square matrix
- lstsq Solve linear least-squares problem
- pinv Pseudo-inverse (Moore-Penrose) calculated using a singular
value decomposition
- matrix_power Integer power of a square matrix
Eigenvalues and decompositions:
- eig Eigenvalues and vectors of a square matrix
- eigh Eigenvalues and eigenvectors of a Hermitian matrix
- eigvals Eigenvalues of a square matrix
- eigvalsh Eigenvalues of a Hermitian matrix
- qr QR decomposition of a matrix
- svd Singular value decomposition of a matrix
- cholesky Cholesky decomposition of a matrix
Tensor operations:
- tensorsolve Solve a linear tensor equation
- tensorinv Calculate an inverse of a tensor
Exceptions:
- LinAlgError Indicates a failed linear algebra operation
PACKAGE CONTENTS
_umath_linalg
info
lapack_lite
linalg
setup
DATA
absolute_import = _Feature((2, 5, 0, 'alpha', 1), (3, 0, 0, 'alpha', 0...
division = _Feature((2, 2, 0, 'alpha', 2), (3, 0, 0, 'alpha', 0), 8192...
print_function = _Feature((2, 6, 0, 'alpha', 2), (3, 0, 0, 'alpha', 0)...
FILE
/opt/anaconda3/lib/python3.6/site-packages/numpy/linalg/__init__.py
sorted(np.__all__)
Out[17]:
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