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| author | RedRuM <44142765+zoj613@users.noreply.github.com> | 2019-08-20 00:41:26 +0200 |
|---|---|---|
| committer | RedRuM <44142765+zoj613@users.noreply.github.com> | 2019-08-20 00:41:26 +0200 |
| commit | f960491d5636cbf4b4f7978c5cb009739a67b4ad (patch) | |
| tree | fe69cb121b81d0b11ae2a0e3cf72e5181f792bee | |
| parent | b665d761b98a176d0df13ee6d7da8037d007c0f7 (diff) | |
| download | numpy-f960491d5636cbf4b4f7978c5cb009739a67b4ad.tar.gz | |
DOC: Improve documentation style for matrix multiplication
| -rw-r--r-- | numpy/random/generator.pyx | 11 |
1 files changed, 5 insertions, 6 deletions
diff --git a/numpy/random/generator.pyx b/numpy/random/generator.pyx index e0fd14c0d..f7c3b7adc 100644 --- a/numpy/random/generator.pyx +++ b/numpy/random/generator.pyx @@ -3363,17 +3363,16 @@ cdef class Generator: cov is cast to double before the check. method : { 'svd', 'eigh', 'cholesky'}, optional The cov input is used to compute a factor matrix A such that - A.dot(transpose(A)) = cov. This argument is used to select the method + ``A @ A.T = cov``. This argument is used to select the method used to compute the factor matrix A. The default method 'svd' is the slowest, while 'cholesky' is the fastest but less robust than the slowest method. The method `eigh` uses eigen decomposition to compute A and is faster than svd but slower than cholesky. use_factor : bool, optional If set to True then cov argument is treated as a precomputed factor - matrix A such that A.dot(transpose(A)) = cov holds true. This - provides significant speedups because the factorization of cov is - avoided. Note that when this argument is set to True, ``method`` - is ignored. + matrix A such that `A @ A.T = cov`` holds true. This provides + significant speedups because the factorization of cov is avoided. + Note that when this argument is set to True, ``method`` is ignored. Returns ------- @@ -3423,7 +3422,7 @@ cdef class Generator: nonnegative-definite). Otherwise, the behavior of this method is undefined and backwards compatibility is not guaranteed. When supplying a factor `F` in place of the full covariance matrix the user is expected - to ensure that F.dot(transpose(F)) recovers the covariance matrix. This + to ensure that ``F @ F.T`` recovers the covariance matrix. This also implies that a factor input computed using the Cholesky method must be a lower-triangular matrix. |