lale.lib.autogen.cca module¶
- class lale.lib.autogen.cca.CCA(*, n_components=2, scale=True, max_iter=500, tol=1e-06, copy=True)¶
Bases:
PlannedIndividualOp
Combined schema for expected data and hyperparameters.
This documentation is auto-generated from JSON schemas.
- Parameters
n_components (integer, >=2 for optimizer, <=256 for optimizer, uniform distribution, default 2) – number of components to keep.
scale (boolean, default True) – whether to scale the data?
max_iter (integer, >=10 for optimizer, <=1000 for optimizer, uniform distribution, default 500) – the maximum number of iterations of the NIPALS inner loop
tol (float, >=1e-08 for optimizer, <=0.01 for optimizer, default 1e-06) – the tolerance used in the iterative algorithm
copy (boolean, default True) – Whether the deflation be done on a copy
- fit(X, y=None, **fit_params)¶
Train the operator.
Note: The fit method is not available until this operator is trainable.
Once this method is available, it will have the following signature:
- Parameters
X (array of items : array of items : float) – Training vectors, where n_samples is the number of samples and n_features is the number of predictors.
Y (array, optional of items : array of items : float) – Target vectors, where n_samples is the number of samples and n_targets is the number of response variables.
- predict(X, **predict_params)¶
Make predictions.
Note: The predict method is not available until this operator is trained.
Once this method is available, it will have the following signature:
- Parameters
X (array of items : array of items : float) – Training vectors, where n_samples is the number of samples and n_features is the number of predictors.
copy (boolean, optional, default True) – Whether to copy X and Y, or perform in-place normalization.
- Returns
result – Apply the dimension reduction learned on the train data.
- Return type
Any
- transform(X, y=None)¶
Transform the data.
Note: The transform method is not available until this operator is trained.
Once this method is available, it will have the following signature:
- Parameters
X (array of items : array of items : float) – Training vectors, where n_samples is the number of samples and n_features is the number of predictors.
Y (array, optional of items : array of items : float) – Target vectors, where n_samples is the number of samples and n_targets is the number of response variables.
copy (boolean, optional, default True) – Whether to copy X and Y, or perform in-place normalization.
- Returns
result – Apply the dimension reduction learned on the train data.
- Return type
Any