numpy.dual
Deprecated since version 1.20.
This module is deprecated. Instead of importing functions from numpy.dual, the functions should be imported directly from NumPy or SciPy.
Aliases for functions which may be accelerated by SciPy.
SciPy can be built to use accelerated or otherwise improved libraries for FFTs, linear algebra, and special functions. This module allows developers to transparently support these accelerated functions when SciPy is available but still support users who have only installed NumPy.
cholesky(a)
cholesky
Cholesky decomposition.
det(a)
det
Compute the determinant of an array.
eig(a)
eig
Compute the eigenvalues and right eigenvectors of a square array.
eigh(a[, UPLO])
eigh
Return the eigenvalues and eigenvectors of a complex Hermitian (conjugate symmetric) or a real symmetric matrix.
eigvals(a)
eigvals
Compute the eigenvalues of a general matrix.
eigvalsh(a[, UPLO])
eigvalsh
Compute the eigenvalues of a complex Hermitian or real symmetric matrix.
inv(a)
inv
Compute the (multiplicative) inverse of a matrix.
lstsq(a, b[, rcond])
lstsq
Return the least-squares solution to a linear matrix equation.
norm(x[, ord, axis, keepdims])
norm
Matrix or vector norm.
pinv(a[, rcond, hermitian])
pinv
Compute the (Moore-Penrose) pseudo-inverse of a matrix.
solve(a, b)
solve
Solve a linear matrix equation, or system of linear scalar equations.
svd(a[, full_matrices, compute_uv, hermitian])
svd
Singular Value Decomposition.
fft(a[, n, axis, norm])
fft
Compute the one-dimensional discrete Fourier Transform.
fft2(a[, s, axes, norm])
fft2
Compute the 2-dimensional discrete Fourier Transform.
fftn(a[, s, axes, norm])
fftn
Compute the N-dimensional discrete Fourier Transform.
ifft(a[, n, axis, norm])
ifft
Compute the one-dimensional inverse discrete Fourier Transform.
ifft2(a[, s, axes, norm])
ifft2
Compute the 2-dimensional inverse discrete Fourier Transform.
ifftn(a[, s, axes, norm])
ifftn
Compute the N-dimensional inverse discrete Fourier Transform.
i0(x)
i0
Modified Bessel function of the first kind, order 0.
numpy.emath