diff --git a/stumpy/aamp_mmotifs.py b/stumpy/aamp_mmotifs.py index 10b43435a..8e9307811 100644 --- a/stumpy/aamp_mmotifs.py +++ b/stumpy/aamp_mmotifs.py @@ -57,13 +57,12 @@ def aamp_mmotifs( cutoffs: numpy.ndarray or float, default None The largest matrix profile value (distance) for each dimension of the multidimensional matrix profile that a multidimenisonal candidate motif is - allowed to have. - If cutoffs is only one value, these value will be applied to every dimension. + allowed to have. If `cutoffs` is a scalar value, then this value will be + applied to every dimension. max_matches: int, default 10 - The maximum amount of similar matches (nearest neighbors) of a motif - representative to be returned. - The first match is always the self-match for each motif. + The maximum number of similar matches (nearest neighbors) to return for each + motif. The first match is always the self/trivial-match for each motif. max_motifs: int, default 1 The maximum number of motifs to return @@ -73,18 +72,14 @@ def aamp_mmotifs( when comparing distances between subsequences. k: int, default None - The number of dimensions (k + 1) in which a motif is present. - This value is available for doing guided search or - together with 'include' - - for constrained search. - The value will be applied to the discovery of all motifs. - If k is None, the value will automatically be computed for each motif using - MDL (unconstrained search). - For more informatioin on search types, see DOI: 10.1109/ICDM.2017.66s + The number of dimensions (`k + 1`) required for discovering all motifs. This + value is available for doing guided search or, together with `include`, for + constrained search. If `k is None`, then this will be automatically be computed + for each motif using MDL (unconstrained search). include: numpy.ndarray, default None A list of (zero based) indices corresponding to the dimensions in T that must be - included in the constrained multidimensional motif search. For more information, - see Section IV D in: DOI: 10.1109/ICDM.2017.66 + included in the constrained multidimensional motif search. p: float, default 2.0 The p-norm to apply for computing the Minkowski distance. @@ -104,6 +99,13 @@ def aamp_mmotifs( motif_mdls: list A list consisting of arrays that contain the mdl results for finding the dimension of each motif + + Notes + ----- + `DOI: 10.1109/ICDM.2017.66 \ + `__ + + For more information on `include` and search types, see Section IV D and IV E """ T = core._preprocess(T) m = T.shape[-1] - P.shape[-1] + 1 diff --git a/stumpy/mmotifs.py b/stumpy/mmotifs.py index 04460ae1c..63cf999c4 100644 --- a/stumpy/mmotifs.py +++ b/stumpy/mmotifs.py @@ -57,13 +57,12 @@ def mmotifs( cutoffs: numpy.ndarray or float, default None The largest matrix profile value (distance) for each dimension of the multidimensional matrix profile that a multidimenisonal candidate motif is - allowed to have. - If cutoffs is only one value, these value will be applied to every dimension. + allowed to have. If `cutoffs` is a scalar value, then this value will be + applied to every dimension. max_matches: int, default 10 - The maximum amount of similar matches (nearest neighbors) of a motif - representative to be returned. - The first match is always the self-match for each motif. + The maximum number of similar matches (nearest neighbors) to return for each + motif. The first match is always the self/trivial-match for each motif. max_motifs: int, default 1 The maximum number of motifs to return @@ -73,18 +72,14 @@ def mmotifs( when comparing distances between subsequences. k: int, default None - The number of dimensions (k + 1) in which a motif is present. - This value is available for doing guided search or - together with 'include' - - for constrained search. - The value will be applied to the discovery of all motifs. - If k is None, the value will automatically be computed for each motif using - MDL (unconstrained search). - For more informatioin on search types, see DOI: 10.1109/ICDM.2017.66s + The number of dimensions (`k + 1`) required for discovering all motifs. This + value is available for doing guided search or, together with `include`, for + constrained search. If `k is None`, then this will be automatically be computed + for each motif using MDL (unconstrained search). include: numpy.ndarray, default None A list of (zero based) indices corresponding to the dimensions in T that must be - included in the constrained multidimensional motif search. For more information, - see Section IV D in: DOI: 10.1109/ICDM.2017.66 + included in the constrained multidimensional motif search. normalize : bool, default True When set to `True`, this z-normalizes subsequences prior to computing distances. @@ -123,6 +118,12 @@ def mmotifs( stumpy.mdl : Compute the number of bits needed to compress one array with another using the minimum description length (MDL) + Notes + ----- + `DOI: 10.1109/ICDM.2017.66 \ + `__ + + For more information on `include` and search types, see Section IV D and IV E """ T = core._preprocess(T) m = T.shape[-1] - P.shape[-1] + 1