msmbuilder.preprocessing.KernelCenterer¶

class msmbuilder.preprocessing.KernelCenterer¶

Center a kernel matrix

Let K(x, z) be a kernel defined by phi(x)^T phi(z), where phi is a function mapping x to a Hilbert space. KernelCenterer centers (i.e., normalize to have zero mean) the data without explicitly computing phi(x). It is equivalent to centering phi(x) with sklearn.preprocessing.StandardScaler(with_std=False).

Read more in the User Guide.

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)	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__()¶

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

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)	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(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

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 former 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)