davrot/pytutorial
1
0
Fork 0
Code Issues Pull requests Projects Releases Packages Wiki Activity Actions

Update README.md

Browse source 
Signed-off-by: David Rotermund <54365609+davrot@users.noreply.github.com>
This commit is contained in:
David Rotermund 2023-12-14 21:25:35 +01:00 committed by GitHub
parent 7aac252183
commit 752281dfe1
No known key found for this signature in database
GPG key ID: 4AEE18F83AFDEB23
1 changed files with 15 additions and 15 deletions
Show stats Download patch file Download diff file Expand all files Collapse all files

30
numpy/linear_alg/README.md
View file

@ -22,13 +22,13 @@ Introduced in NumPy 1.10.0, the @ operator is preferable to other methods when c
||| |||
|---|---| |---|---|
|[dot(a, b[, out])](https://numpy.org/doc/stable/reference/generated/numpy.dot.html#numpy.dot)|Dot product of two arrays.| |[**dot(a, b[, out])**](https://numpy.org/doc/stable/reference/generated/numpy.dot.html#numpy.dot)|**Dot product of two arrays.**|
|[linalg.multi_dot(arrays, *[, out])](https://numpy.org/doc/stable/reference/generated/numpy.linalg.multi_dot.html#numpy.linalg.multi_dot)|Compute the dot product of two or more arrays in a single function call, while automatically selecting the fastest evaluation order.| |[linalg.multi_dot(arrays, *[, out])](https://numpy.org/doc/stable/reference/generated/numpy.linalg.multi_dot.html#numpy.linalg.multi_dot)|Compute the dot product of two or more arrays in a single function call, while automatically selecting the fastest evaluation order.|
|[vdot(a, b, /)](https://numpy.org/doc/stable/reference/generated/numpy.vdot.html#numpy.vdot)|Return the dot product of two vectors.| |[**vdot(a, b, /)**](https://numpy.org/doc/stable/reference/generated/numpy.vdot.html#numpy.vdot)|**Return the dot product of two vectors.**|
|[inner(a, b, /)](https://numpy.org/doc/stable/reference/generated/numpy.inner.html#numpy.inner)|Inner product of two arrays.| |[**inner(a, b, /)**](https://numpy.org/doc/stable/reference/generated/numpy.inner.html#numpy.inner)|**Inner product of two arrays.**|
|[outer(a, b[, out])](https://numpy.org/doc/stable/reference/generated/numpy.outer.html#numpy.outer)|Compute the outer product of two vectors.| |[**outer(a, b[, out])**](https://numpy.org/doc/stable/reference/generated/numpy.outer.html#numpy.outer)|**Compute the outer product of two vectors.**|
|[matmul(x1, x2, /[, out, casting, order, ...])](https://numpy.org/doc/stable/reference/generated/numpy.matmul.html#numpy.matmul)|Matrix product of two arrays.| |[matmul(x1, x2, /[, out, casting, order, ...])](https://numpy.org/doc/stable/reference/generated/numpy.matmul.html#numpy.matmul)|Matrix product of two arrays.|
|[tensordot(a, b[, axes])](https://numpy.org/doc/stable/reference/generated/numpy.tensordot.html#numpy.tensordot)|Compute tensor dot product along specified axes.| |[**tensordot(a, b[, axes])**](https://numpy.org/doc/stable/reference/generated/numpy.tensordot.html#numpy.tensordot)|**Compute tensor dot product along specified axes.**|
|[einsum(subscripts, *operands[, out, dtype, ...])](https://numpy.org/doc/stable/reference/generated/numpy.einsum.html#numpy.einsum)|Evaluates the Einstein summation convention on the operands.| |[einsum(subscripts, *operands[, out, dtype, ...])](https://numpy.org/doc/stable/reference/generated/numpy.einsum.html#numpy.einsum)|Evaluates the Einstein summation convention on the operands.|
|[einsum_path(subscripts, *operands[, optimize])](https://numpy.org/doc/stable/reference/generated/numpy.einsum_path.html#numpy.einsum_path)|Evaluates the lowest cost contraction order for an einsum expression by considering the creation of intermediate arrays.| |[einsum_path(subscripts, *operands[, optimize])](https://numpy.org/doc/stable/reference/generated/numpy.einsum_path.html#numpy.einsum_path)|Evaluates the lowest cost contraction order for an einsum expression by considering the creation of intermediate arrays.|
|[linalg.matrix_power(a, n)](https://numpy.org/doc/stable/reference/generated/numpy.linalg.matrix_power.html#numpy.linalg.matrix_power)|Raise a square matrix to the (integer) power n.| |[linalg.matrix_power(a, n)](https://numpy.org/doc/stable/reference/generated/numpy.linalg.matrix_power.html#numpy.linalg.matrix_power)|Raise a square matrix to the (integer) power n.|
@ -40,13 +40,13 @@ Introduced in NumPy 1.10.0, the @ operator is preferable to other methods when c
|---|---| |---|---|
|[linalg.cholesky(a)](https://numpy.org/doc/stable/reference/generated/numpy.linalg.cholesky.html#numpy.linalg.cholesky)|Cholesky decomposition.| |[linalg.cholesky(a)](https://numpy.org/doc/stable/reference/generated/numpy.linalg.cholesky.html#numpy.linalg.cholesky)|Cholesky decomposition.|
|[linalg.qr(a[, mode])](https://numpy.org/doc/stable/reference/generated/numpy.linalg.qr.html#numpy.linalg.qr)|Compute the qr factorization of a matrix.| |[linalg.qr(a[, mode])](https://numpy.org/doc/stable/reference/generated/numpy.linalg.qr.html#numpy.linalg.qr)|Compute the qr factorization of a matrix.|
|[linalg.svd(a[, full_matrices, compute_uv, ...])](https://numpy.org/doc/stable/reference/generated/numpy.linalg.svd.html#numpy.linalg.svd)|Singular Value Decomposition.| |[**linalg.svd(a[, full_matrices, compute_uv, ...])**](https://numpy.org/doc/stable/reference/generated/numpy.linalg.svd.html#numpy.linalg.svd)|**Singular Value Decomposition.**|
### Matrix eigenvalues ### Matrix eigenvalues
||| |||
|---|---| |---|---|
|[linalg.eig(a)](https://numpy.org/doc/stable/reference/generated/numpy.linalg.eig.html#numpy.linalg.eig)|Compute the eigenvalues and right eigenvectors of a square array.| |[**linalg.eig(a)**](https://numpy.org/doc/stable/reference/generated/numpy.linalg.eig.html#numpy.linalg.eig)|**Compute the eigenvalues and right eigenvectors of a square array.**|
|[linalg.eigh(a[, UPLO])](https://numpy.org/doc/stable/reference/generated/numpy.linalg.eigh.html#numpy.linalg.eigh)|Return the eigenvalues and eigenvectors of a complex Hermitian (conjugate symmetric) or a real symmetric matrix.| |[linalg.eigh(a[, UPLO])](https://numpy.org/doc/stable/reference/generated/numpy.linalg.eigh.html#numpy.linalg.eigh)|Return the eigenvalues and eigenvectors of a complex Hermitian (conjugate symmetric) or a real symmetric matrix.|
|[linalg.eigvals(a)](https://numpy.org/doc/stable/reference/generated/numpy.linalg.eigvals.html#numpy.linalg.eigvals)|Compute the eigenvalues of a general matrix.| |[linalg.eigvals(a)](https://numpy.org/doc/stable/reference/generated/numpy.linalg.eigvals.html#numpy.linalg.eigvals)|Compute the eigenvalues of a general matrix.|
|[linalg.eigvalsh(a[, UPLO])](https://numpy.org/doc/stable/reference/generated/numpy.linalg.eigvalsh.html#numpy.linalg.eigvalsh)|Compute the eigenvalues of a complex Hermitian or real symmetric matrix.| |[linalg.eigvalsh(a[, UPLO])](https://numpy.org/doc/stable/reference/generated/numpy.linalg.eigvalsh.html#numpy.linalg.eigvalsh)|Compute the eigenvalues of a complex Hermitian or real symmetric matrix.|
@ -55,22 +55,22 @@ Introduced in NumPy 1.10.0, the @ operator is preferable to other methods when c
||| |||
|---|---| |---|---|
|[linalg.norm(x[, ord, axis, keepdims])](https://numpy.org/doc/stable/reference/generated/numpy.linalg.norm.html#numpy.linalg.norm)|Matrix or vector norm.| |[**linalg.norm(x[, ord, axis, keepdims])**](https://numpy.org/doc/stable/reference/generated/numpy.linalg.norm.html#numpy.linalg.norm)|**Matrix or vector norm.**|
|[linalg.cond(x[, p])](https://numpy.org/doc/stable/reference/generated/numpy.linalg.cond.html#numpy.linalg.cond)|Compute the condition number of a matrix.| |[linalg.cond(x[, p])](https://numpy.org/doc/stable/reference/generated/numpy.linalg.cond.html#numpy.linalg.cond)|Compute the condition number of a matrix.|
|[linalg.det(a)](https://numpy.org/doc/stable/reference/generated/numpy.linalg.det.html#numpy.linalg.det)|Compute the determinant of an array.| |[**linalg.det(a)**](https://numpy.org/doc/stable/reference/generated/numpy.linalg.det.html#numpy.linalg.det)|**Compute the determinant of an array.**|
|[linalg.matrix_rank(A[, tol, hermitian])](https://numpy.org/doc/stable/reference/generated/numpy.linalg.matrix_rank.html#numpy.linalg.matrix_rank)|Return matrix rank of array using SVD method| |[linalg.matrix_rank(A[, tol, hermitian])](https://numpy.org/doc/stable/reference/generated/numpy.linalg.matrix_rank.html#numpy.linalg.matrix_rank)|Return matrix rank of array using SVD method|
|[linalg.slogdet(a)](https://numpy.org/doc/stable/reference/generated/numpy.linalg.slogdet.html#numpy.linalg.slogdet)|Compute the sign and (natural) logarithm of the determinant of an array.| |[linalg.slogdet(a)](https://numpy.org/doc/stable/reference/generated/numpy.linalg.slogdet.html#numpy.linalg.slogdet)|Compute the sign and (natural) logarithm of the determinant of an array.|
|[trace(a[, offset, axis1, axis2, dtype, out])](https://numpy.org/doc/stable/reference/generated/numpy.trace.html#numpy.trace)|Return the sum along diagonals of the array.| |[**trace(a[, offset, axis1, axis2, dtype, out])**](https://numpy.org/doc/stable/reference/generated/numpy.trace.html#numpy.trace)|**Return the sum along diagonals of the array.**|
### Solving equations and inverting matrices ### Solving equations and inverting matrices
||| |||
|---|---| |---|---|
|[linalg.solve(a, b)](https://numpy.org/doc/stable/reference/generated/numpy.linalg.solve.html#numpy.linalg.solve)|Solve a linear matrix equation, or system of linear scalar equations.| |[**linalg.solve(a, b)**](https://numpy.org/doc/stable/reference/generated/numpy.linalg.solve.html#numpy.linalg.solve)|**Solve a linear matrix equation, or system of linear scalar equations.**|
|[linalg.tensorsolve(a, b[, axes])](https://numpy.org/doc/stable/reference/generated/numpy.linalg.tensorsolve.html#numpy.linalg.tensorsolve)|Solve the tensor equation a x = b for x.| |[**linalg.tensorsolve(a, b[, axes])**](https://numpy.org/doc/stable/reference/generated/numpy.linalg.tensorsolve.html#numpy.linalg.tensorsolve)|**Solve the tensor equation a x = b for x.**|
|[linalg.lstsq(a, b[, rcond])](https://numpy.org/doc/stable/reference/generated/numpy.linalg.lstsq.html#numpy.linalg.lstsq)|Return the least-squares solution to a linear matrix equation.| |[linalg.lstsq(a, b[, rcond])](https://numpy.org/doc/stable/reference/generated/numpy.linalg.lstsq.html#numpy.linalg.lstsq)|Return the least-squares solution to a linear matrix equation.|
|[linalg.inv(a)](https://numpy.org/doc/stable/reference/generated/numpy.linalg.inv.html#numpy.linalg.inv)|Compute the (multiplicative) inverse of a matrix.| |[**linalg.inv(a)**](https://numpy.org/doc/stable/reference/generated/numpy.linalg.inv.html#numpy.linalg.inv)|**Compute the (multiplicative) inverse of a matrix.**|
|[linalg.pinv(a[, rcond, hermitian])](https://numpy.org/doc/stable/reference/generated/numpy.linalg.pinv.html#numpy.linalg.pinv)|Compute the (Moore-Penrose) pseudo-inverse of a matrix.| |[**linalg.pinv(a[, rcond, hermitian])**](https://numpy.org/doc/stable/reference/generated/numpy.linalg.pinv.html#numpy.linalg.pinv)|**Compute the (Moore-Penrose) pseudo-inverse of a matrix.**|
|[linalg.tensorinv(a[, ind])](https://numpy.org/doc/stable/reference/generated/numpy.linalg.tensorinv.html#numpy.linalg.tensorinv)|Compute the 'inverse' of an N-dimensional array.| |[**linalg.tensorinv(a[, ind])**](https://numpy.org/doc/stable/reference/generated/numpy.linalg.tensorinv.html#numpy.linalg.tensorinv)|**Compute the 'inverse' of an N-dimensional array.**|