dynex_scikit_plugin package
Submodules
dynex_scikit_plugin.dynex_scikit module
dynex_scikit_plugin.transformers module
dynex_scikit_plugin.utilities module
- dynex_scikit_plugin.utilities.corrcoef(x: Union[numpy._typing._array_like._SupportsArray[numpy.dtype[Any]], numpy._typing._nested_sequence._NestedSequence[numpy._typing._array_like._SupportsArray[numpy.dtype[Any]]], bool, int, float, complex, str, bytes, numpy._typing._nested_sequence._NestedSequence[Union[bool, int, float, complex, str, bytes]]], *, out: Optional[numpy.ndarray] = None, rowvar: bool = True, copy: bool = True) numpy.ndarray[source]
A drop-in replacement for
numpy.corrcoef().This method is modified to avoid unnecessary memory usage when working with
numpy.memmaparrays. It does not support the full range of arguments accepted bynumpy.corrcoef().Additionally, in the case that a row of
xis fixed, this method will return a correlation value of 0 rather thannumpy.nan.- Parameters
x – See
numpy.corrcoef().out – Output argument. This must be the exact kind that would be returned if it was not used.
rowvar – See
numpy.corrcoef().copy – If
True,xis not modified by this function.
- Returns
See
numpy.corrcoef().
- dynex_scikit_plugin.utilities.cov(m: Union[numpy._typing._array_like._SupportsArray[numpy.dtype[Any]], numpy._typing._nested_sequence._NestedSequence[numpy._typing._array_like._SupportsArray[numpy.dtype[Any]]], bool, int, float, complex, str, bytes, numpy._typing._nested_sequence._NestedSequence[Union[bool, int, float, complex, str, bytes]]], *, out: Optional[numpy.ndarray] = None, rowvar: bool = True, copy: bool = True) numpy.ndarray[source]
A drop-in replacement for
numpy.cov().This method is modified to avoid unnecessary memory usage when working with
numpy.memmaparrays. It does not support the full range of arguments accepted bynumpy.cov().- Parameters
m – See
numpy.cov().out – Output argument. This must be the exact kind that would be returned if it was not used.
rowvar – See
numpy.cov().copy – If
True,xis not modified by this function.
- Returns
See
numpy.cov().
- dynex_scikit_plugin.utilities.dot_2d(a: Union[numpy._typing._array_like._SupportsArray[numpy.dtype[Any]], numpy._typing._nested_sequence._NestedSequence[numpy._typing._array_like._SupportsArray[numpy.dtype[Any]]], bool, int, float, complex, str, bytes, numpy._typing._nested_sequence._NestedSequence[Union[bool, int, float, complex, str, bytes]]], b: Union[numpy._typing._array_like._SupportsArray[numpy.dtype[Any]], numpy._typing._nested_sequence._NestedSequence[numpy._typing._array_like._SupportsArray[numpy.dtype[Any]]], bool, int, float, complex, str, bytes, numpy._typing._nested_sequence._NestedSequence[Union[bool, int, float, complex, str, bytes]]], *, out: Optional[numpy.ndarray] = None, chunksize: int = 1000000000) numpy.ndarray[source]
A drop-in replacment for
numpy.dot()for 2d arrays.This method is modified to avoid unnecessary memory usage when working with
numpy.memmaparrays.- Parameters
a – See
numpy.dot().a.ndimmust be 2.b – See
numpy.dot().b.ndimmust be 2.out – See
numpy.dot().chunksize – The number of bytes that should be created by each step of the multiplication. This is used to keep the total memory usage low when multiplying
numpy.memmaparrays.
- Returns
See
numpy.dot().