slang.snippers¶
Snipping: Feature vector quantization
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class
slang.snippers.
ClassificationSnipper
(wf_to_chks=<slang.snippers.DfltWfToChk object>, chk_to_fv=<class 'slang.snippers.PcaChkToFv'>, fv_to_snip=<class 'slang.snippers.KMeansFvToSnip'>, snip_to_score=<class 'slang.snip_stats.BayesFactors'>)[source]¶
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slang.snippers.
DfltChkToFv
¶ alias of
slang.snippers.PcaChkToFv
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slang.snippers.
DfltFvToSnip
¶ alias of
slang.snippers.KMeansFvToSnip
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class
slang.snippers.
KMeansFvToSnip
(n_clusters=47, **kwargs)[source]¶ -
fit
(fvs: NewType.<locals>.new_type, y=None, sample_weight=None)[source]¶ Compute k-means clustering.
- Parameters
X ({array-like, sparse matrix} of shape (n_samples, n_features)) – Training instances to cluster. It must be noted that the data will be converted to C ordering, which will cause a memory copy if the given data is not C-contiguous. If a sparse matrix is passed, a copy will be made if it’s not in CSR format.
y (Ignored) – Not used, present here for API consistency by convention.
sample_weight (array-like of shape (n_samples,), default=None) –
The weights for each observation in X. If None, all observations are assigned equal weight.
New in version 0.20.
- Returns
self – Fitted estimator.
- Return type
object
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property
fv_of_snip
¶ array providing representative fv for each snip
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slang.snippers.
SlangClassifier
¶ alias of
slang.snippers.ClassificationSnipper
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slang.snippers.
is_iterable
(x)[source]¶ Similar in nature to
callable()
,is_iterable
returnsTrue
if an object is `iterable`_,False
if not. >>> is_iterable([]) True >>> is_iterable(1) False