diff options
Diffstat (limited to 'numpy')
| -rw-r--r-- | numpy/linalg/_gufuncs_linalg.py | 122 | ||||
| -rw-r--r-- | numpy/linalg/gufuncs_linalg_contents.rst | 104 |
2 files changed, 103 insertions, 123 deletions
diff --git a/numpy/linalg/_gufuncs_linalg.py b/numpy/linalg/_gufuncs_linalg.py index 3da399b1f..eb6335741 100644 --- a/numpy/linalg/_gufuncs_linalg.py +++ b/numpy/linalg/_gufuncs_linalg.py @@ -1,29 +1,113 @@ """Linear Algebra functions implemented as gufuncs, so they broadcast. -Notes ------ - .. warning:: This module is only for testing, the functionality will be integrated into numpy.linalg proper. -This module contains functionality that could be found in the linalg module, -but in a gufunc-like way. This allows the use of vectorization and broadcasting -on the operands. - -This module itself provides wrappers to kernels written as numpy -generalized-ufuncs that perform the heavy-lifting of computation. The wrappers -exist in order to provide a sane interface, like providing keyword arguments in -line with the ones used by linalg, or just to automatically select the -appropriate kernel depending on the parameters. All wrappers forward the -keyword parameters to the underlying generalized ufunc (the kernel). +======================= + gufuncs_linalg module +======================= + +gufuncs_linalg implements a series of linear algebra functions as gufuncs. +Most of these functions are already present in numpy.linalg, but as they +are implemented using gufunc kernels they can be broadcasting. Some parts +that are python in numpy.linalg are implemented inside C functions, as well +as the iteration used when used on vectors. This can result in faster +execution as well. + +In addition, there are some ufuncs thrown in that implement fused operations +over numpy vectors that can result in faster execution on large vector +compared to non-fused versions (for example: multiply_add, multiply3). + +In fact, gufuncs_linalg is a very thin wrapper of python code that wraps +the actual kernels (gufuncs). This wrapper was needed in order to provide +a sane interface for some functions. Mostly working around limitations on +what can be described in a gufunc signature. Things like having one dimension +of a result depending on the minimum of two dimensions of the sources (like +in svd) or passing an uniform keyword parameter to the whole operation +(like UPLO on functions over symmetric/hermitian matrices). + +The gufunc kernels are in a c module named _umath_linalg, that is imported +privately in gufuncs_linalg. + +========== + Contents +========== + +Here is an enumeration of the functions. These are the functions exported by +the module and should appear in its __all__ attribute. All the functions +contain a docstring explaining them in detail. + +General +======= +- inner1d +- innerwt +- matrix_multiply +- quadratic_form + +Lineal Algebra +============== +- det +- slogdet +- cholesky +- eig +- eigvals +- eigh +- eigvalsh +- solve +- svd +- chosolve +- inv +- poinv + +Fused Operations +================ +- add3 +- multiply3 +- multiply3_add +- multiply_add +- multiply_add2 +- multiply4 +- multiply4_add + +================ + Error Handling +================ +Unlike the numpy.linalg module, this module does not use exceptions to notify +errors in the execution of the kernels. As these functions are thougth to be +used in a vector way it didn't seem appropriate to raise exceptions on failure +of an element. So instead, when an error computing an element occurs its +associated result will be set to an invalid value (all NaNs). + +Exceptions can occur if the arguments fail to map properly to the underlying +gufunc (due to signature mismatch, for example). + +================================ + Notes about the implementation +================================ +Where possible, the wrapper functions map directly into a gufunc implementing +the computation. + +That's not always the case, as due to limitations of the gufunc interface some +functions cannot be mapped straight into a kernel. + +Two cases come to mind: +- An uniform parameter is needed to configure the way the computation is +performed (like UPLO in the functions working on symmetric/hermitian matrices) +- svd, where it was impossible to map the function to a gufunc signature. + +In the case of uniform parameters like UPLO, there are two separate entry points +in the C module that imply either 'U' or 'L'. The wrapper just selects the +kernel to use by checking the appropriate keyword parameter. This way a +function interface similar to numpy.linalg can be kept. + +In the case of SVD not only there were problems with the support of keyword +arguments. There was the added problem of the signature system not being able +to cope with the needs of this functions. Just for the singular values a +a signature like (m,n)->(min(m,n)) was needed. This has been worked around by +implementing different kernels for the cases where min(m,n) == m and where +min(m,n) == n. The wrapper code automatically selects the appropriate one. -The functions are intended to be used on arrays of matrices. Functions that in -numpy.LinAlg would generate a LinAlgError (for example, inv on a non-invertible -matrix) will just generate NaNs as result. When this happens, invalid floating -point status will be set. Error handling can be configured for this cases using -np.seterr. -Additional functions some fused arithmetic, useful for efficient operation over """ from __future__ import division, absolute_import diff --git a/numpy/linalg/gufuncs_linalg_contents.rst b/numpy/linalg/gufuncs_linalg_contents.rst deleted file mode 100644 index 424c5f214..000000000 --- a/numpy/linalg/gufuncs_linalg_contents.rst +++ /dev/null @@ -1,104 +0,0 @@ -======================= - gufuncs_linalg module -======================= - -gufuncs_linalg implements a series of linear algebra functions as gufuncs. -Most of these functions are already present in numpy.linalg, but as they -are implemented using gufunc kernels they can be broadcasting. Some parts -that are python in numpy.linalg are implemented inside C functions, as well -as the iteration used when used on vectors. This can result in faster -execution as well. - -In addition, there are some ufuncs thrown in that implement fused operations -over numpy vectors that can result in faster execution on large vector -compared to non-fused versions (for example: multiply_add, multiply3). - -In fact, gufuncs_linalg is a very thin wrapper of python code that wraps -the actual kernels (gufuncs). This wrapper was needed in order to provide -a sane interface for some functions. Mostly working around limitations on -what can be described in a gufunc signature. Things like having one dimension -of a result depending on the minimum of two dimensions of the sources (like -in svd) or passing an uniform keyword parameter to the whole operation -(like UPLO on functions over symmetric/hermitian matrices). - -The gufunc kernels are in a c module named _umath_linalg, that is imported -privately in gufuncs_linalg. - -========== - Contents -========== -Here is an enumeration of the functions. These are the functions exported by -the module and should appear in its __all__ attribute. All the functions -contain a docstring explaining them in detail. - -General -======= -- inner1d -- innerwt -- matrix_multiply -- quadratic_form - -Lineal Algebra -============== -- det -- slogdet -- cholesky -- eig -- eigvals -- eigh -- eigvalsh -- solve -- svd -- chosolve -- inv -- poinv - -Fused Operations -================ -- add3 -- multiply3 -- multiply3_add -- multiply_add -- multiply_add2 -- multiply4 -- multiply4_add - -================ - Error Handling -================ -Unlike the numpy.linalg module, this module does not use exceptions to notify -errors in the execution of the kernels. As these functions are thougth to be -used in a vector way it didn't seem appropriate to raise exceptions on failure -of an element. So instead, when an error computing an element occurs its -associated result will be set to an invalid value (all NaNs). - -Exceptions can occur if the arguments fail to map properly to the underlying -gufunc (due to signature mismatch, for example). - -================================ - Notes about the implementation -================================ -Where possible, the wrapper functions map directly into a gufunc implementing -the computation. - -That's not always the case, as due to limitations of the gufunc interface some -functions cannot be mapped straight into a kernel. - -Two cases come to mind: -- An uniform parameter is needed to configure the way the computation is -performed (like UPLO in the functions working on symmetric/hermitian matrices) -- svd, where it was impossible to map the function to a gufunc signature. - -In the case of uniform parameters like UPLO, there are two separate entry points -in the C module that imply either 'U' or 'L'. The wrapper just selects the -kernel to use by checking the appropriate keyword parameter. This way a -function interface similar to numpy.linalg can be kept. - -In the case of SVD not only there were problems with the support of keyword -arguments. There was the added problem of the signature system not being able -to cope with the needs of this functions. Just for the singular values a -a signature like (m,n)->(min(m,n)) was needed. This has been worked around by -implementing different kernels for the cases where min(m,n) == m and where -min(m,n) == n. The wrapper code automatically selects the appropriate one. - - |