msmbuilder.preprocessing.EWMA

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

Smooth time-series data using an exponentially-weighted moving average filter

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

[1]	“pandas.stats.moments.ewma”. Pandas Documentation. Pandas. <http://pandas.pydata.org/pandas-docs/version/0.13.1/generated/pandas.stats.moments.ewma.html>.

Methods

fit(X[, 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)	Apply preprocessing to single 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)

Initialize self. See help(type(self)) for accurate signature.

Methods

__init__([com, span, halflife, min_periods, …])	Initialize self.
fit(X[, 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)	Apply preprocessing to single 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(X, y=None)

Fit Preprocessing to X.

Parameters:

sequence : array-like, [sequence_length, n_features]

A multivariate timeseries.

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

self

partial_transform(sequence)

Apply preprocessing to single sequence

Parameters:

sequence: array like, shape (n_samples, n_features)

A single sequence to transform

Returns:

out : array like, shape (n_samples, n_features)

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)