aopp_deconv_tool.py_ssa

module documentation

Implementation of Singular Spectrum Analysis.

See https://arxiv.org/pdf/1309.5050.pdf for details.

Class SSA No class docstring; 0/17 instance variable, 15/15 methods, 2/2 static methods documented
Function frobenius_inner_prod Sum of the elementwise multiplication
Function frobenius_norm Root of the sum of the elementwise squares
Function unvectorise_mat Takes a vector (that is a flattened matrix) and turns it back into a matrix of shape m
Function vectorise_mat Takes a matrix and flattens it into a vector
Type Variable L Undocumented
Type Variable M Undocumented
Type Variable N Undocumented
Type Variable T Undocumented
Variable Array_1D Undocumented
Variable Array_2D Undocumented
Variable Array_3D Undocumented
Variable Convolution_Mode_literals Undocumented
Variable Grouping_Mode_Literals Undocumented
Variable Reverse_Mapping_Strategy_Literals Undocumented
Variable SVD_Strategy_Literals Undocumented
Variable _lgr Undocumented
def frobenius_inner_prod(A: np.ndarray, B: np.ndarray) -> float:

Sum of the elementwise multiplication

def frobenius_norm(A: np.ndarray) -> float:

Root of the sum of the elementwise squares

def unvectorise_mat(a: Array_1D, m: tuple[int]) -> Array_2D | Array_3D:

Takes a vector (that is a flattened matrix) and turns it back into a matrix of shape m

def vectorise_mat(a: Array_2D) -> Array_1D:

Takes a matrix and flattens it into a vector

L =

Undocumented

Value
TypeVar('L',
        bound=int)
M =

Undocumented

Value
TypeVar('M',
        bound=int)
N =

Undocumented

Value
TypeVar('N',
        bound=int)
T =

Undocumented

Value
TypeVar('T')
Array_1D =

Undocumented

Array_2D =

Undocumented

Array_3D =

Undocumented

Convolution_Mode_literals =

Undocumented

Grouping_Mode_Literals =

Undocumented

Reverse_Mapping_Strategy_Literals =

Undocumented

SVD_Strategy_Literals =

Undocumented

_lgr =

Undocumented