CopyTransformer
CopyTransformer()
Transformer that returns a copy of the input array
For usage examples, please see http://rasbt.github.io/mlxtend/user_guide/preprocessing/CopyTransformer/
Methods
fit(X, y=None)
Mock method. Does nothing.
Parameters
-
X
: {array-like, sparse matrix}, shape = [n_samples, n_features]Training vectors, where n_samples is the number of samples and n_features is the number of features.
-
y
: array-like, shape = [n_samples] (default: None)
Returns
self
fit_transform(X, y=None)
Return a copy of the input array.
Parameters
-
X
: {array-like, sparse matrix}, shape = [n_samples, n_features]Training vectors, where n_samples is the number of samples and n_features is the number of features.
-
y
: array-like, shape = [n_samples] (default: None)
Returns
X_copy
: copy of the input X array.
get_params(deep=True)
Get parameters for this estimator.
Parameters
-
deep
: boolean, optionalIf True, will return the parameters for this estimator and contained subobjects that are estimators.
Returns
-
params
: mapping of string to anyParameter names mapped to their values.
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
transform(X, y=None)
Return a copy of the input array.
Parameters
-
X
: {array-like, sparse matrix}, shape = [n_samples, n_features]Training vectors, where n_samples is the number of samples and n_features is the number of features.
-
y
: array-like, shape = [n_samples] (default: None)
Returns
X_copy
: copy of the input X array.