msmbuilder.preprocessing.MaxAbsScaler¶
-
class
msmbuilder.preprocessing.
MaxAbsScaler
(copy=True)¶ Scale each feature by its maximum absolute value.
This estimator scales and translates each feature individually such that the maximal absolute value of each feature in the training set will be 1.0. It does not shift/center the data, and thus does not destroy any sparsity.
This scaler can also be applied to sparse CSR or CSC matrices.
New in version 0.17.
Parameters: - copy : boolean, optional, default is True
Set to False to perform inplace scaling and avoid a copy (if the input is already a numpy array).
See also
maxabs_scale
- Equivalent function without the estimator API.
Notes
For a comparison of the different scalers, transformers, and normalizers, see examples/preprocessing/plot_all_scaling.py.
Attributes: - scale_ : ndarray, shape (n_features,)
Per feature relative scaling of the data.
New in version 0.17: scale_ attribute.
- max_abs_ : ndarray, shape (n_features,)
Per feature maximum absolute value.
- n_samples_seen_ : int
The number of samples processed by the estimator. Will be reset on new calls to fit, but increments across
partial_fit
calls.
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. inverse_transform
(X)Scale back the data to the original representation 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__
(copy=True)¶ Initialize self. See help(type(self)) for accurate signature.
Methods
__init__
([copy])Initialize self. 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. inverse_transform
(X)Scale back the data to the original representation 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)
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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.
-
inverse_transform
(X)¶ Scale back the data to the original representation
Parameters: - X : {array-like, sparse matrix}
The data that should be transformed back.
-
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)
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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)