msmbuilder.preprocessing.DoubleEWMA¶

class msmbuilder.preprocessing.DoubleEWMA(com=None, span=None, halflife=None, min_periods=0, freq=None, adjust=True)¶

Smooth time-series data using forward and backward exponentially-weighted moving average filters

Parameters:

com : float, optional Center of mass span : float, optional Specify decay in terms of span halflife : float, optional Specify decay in terms of halflife min_periods : int, default 0 Number of observations in sample to require (only affects beginning) freq : None or string alias / date offset object, default=None Frequency to conform to before computing statistic time_rule is a legacy alias for freq adjust : boolean, default True Divide by decaying adjustment factor in beginning periods to account for imbalance in relative weightings (viewing EWMA as a moving average)

References

[R51]

“Smoothing with Exponentionally Weighted Moving Averages”. Connor Johnson. <http://connor-johnson.com/2014/02/01/smoothing-with-exponentially-weighted-moving-averages/>.

Methods

fit(sequences[, y])	Fit Preprocessing to X.
fit_transform(sequences[, y])	Fit the model and apply preprocessing
get_params([deep])	Get parameters for this estimator.
partial_fit(sequence[, y])	Fit Preprocessing to X.
partial_transform(sequence)
set_params(**params)	Set the parameters of this estimator.
summarize()	Return some diagnostic summary statistics about this Markov model
transform(sequences)	Apply preprocessing to sequences

__init__(com=None, span=None, halflife=None, min_periods=0, freq=None, adjust=True)¶

Methods

__init__([com, span, halflife, min_periods, ...])
fit(sequences[, y])	Fit Preprocessing to X.
fit_transform(sequences[, y])	Fit the model and apply preprocessing
get_params([deep])	Get parameters for this estimator.
partial_fit(sequence[, y])	Fit Preprocessing to X.
partial_transform(sequence)
set_params(**params)	Set the parameters of this estimator.
summarize()	Return some diagnostic summary statistics about this Markov model
transform(sequences)	Apply preprocessing to sequences

fit(sequences, y=None)¶

Fit Preprocessing to X.

Parameters:	sequences : list of array-like, each of shape [sequence_length, n_features] A list of multivariate timeseries. Each sequence may have a different length, but they all must have the same number of features. y : None Ignored
Returns:	self

fit_transform(sequences, y=None)¶

Fit the model and apply preprocessing

Parameters:	sequences: list of array-like, each of shape (n_samples_i, n_features) Training data, where n_samples_i in the number of samples in sequence i and n_features is the number of features. y : None Ignored
Returns:	sequence_new : list of array-like, each of shape (n_samples_i, n_components)

get_params(deep=True)¶

Get parameters for this estimator.

Parameters:	deep : boolean, optional If True, will return the parameters for this estimator and contained subobjects that are estimators.
Returns:	params : mapping of string to any Parameter names mapped to their values.

partial_fit(sequence, y=None)¶

Fit Preprocessing to X.

Parameters:	sequence : array-like, [sequence_length, n_features] A multivariate timeseries. y : None Ignored
Returns:	self

set_params(**params)¶

Set the parameters of this estimator.

The method works on simple estimators as well as on nested objects (such as pipelines). The latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.

Returns:	self

summarize()¶

Return some diagnostic summary statistics about this Markov model

transform(sequences)¶

Apply preprocessing to sequences

Parameters:	sequences: list of array-like, each of shape (n_samples_i, n_features) Sequence data to transform, where n_samples_i in the number of samples in sequence i and n_features is the number of features.
Returns:	sequence_new : list of array-like, each of shape (n_samples_i, n_components)