lale.lib.autogen.truncated_svd module¶
- class lale.lib.autogen.truncated_svd.TruncatedSVD(*, n_components=2, algorithm='randomized', n_iter=5, random_state=None, tol=0.0)¶
Bases:
PlannedIndividualOp
Combined schema for expected data and hyperparameters.
This documentation is auto-generated from JSON schemas.
- Parameters
n_components (integer, >=2 for optimizer, <='X/items/maxItems', <=256 for optimizer, uniform distribution, default 2) – Desired dimensionality of output data
algorithm (‘arpack’ or ‘randomized’, default ‘randomized’) – SVD solver to use
n_iter (integer, >=5 for optimizer, <=1000 for optimizer, uniform distribution, default 5) – Number of iterations for randomized SVD solver
random_state (union type, not for optimizer, default None) –
If int, random_state is the seed used by the random number generator; If RandomState instance, random_state is the random number generator; If None, the random number generator is the RandomState instance used by np.random.
integer
or numpy.random.RandomState
or None
tol (float, >=1e-08 for optimizer, <=0.01 for optimizer, default 0.0) – Tolerance for ARPACK
- 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 data.
y (any type) –
- 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) – New data.
- Returns
result – Reduced version of X
- Return type
array of items : array of items : float