# Source code for gemseo.mlearning.transform.dimension_reduction.dimension_reduction

# -*- coding: utf-8 -*-
# Copyright 2021 IRT Saint Exupéry, https://www.irt-saintexupery.com
#
# This program is free software; you can redistribute it and/or
# modify it under the terms of the GNU Lesser General Public
#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
# Lesser General Public License for more details.
#
# You should have received a copy of the GNU Lesser General Public License
# along with this program; if not, write to the Free Software Foundation,
# Inc., 51 Franklin Street, Fifth Floor, Boston, MA  02110-1301, USA.

# Contributors:
#    INITIAL AUTHORS - initial API and implementation and/or initial
#                         documentation
#        :author: Matthias De Lozzo, Syver Doving Agdestein
#    OTHER AUTHORS   - MACROSCOPIC CHANGES
"""Dimension reduction as a generic transformer.

The :class:.DimensionReduction class implements the concept of dimension reduction.

.. seealso::

:mod:~gemseo.mlearning.transform.dimension_reduction.pca
"""
from __future__ import division, unicode_literals

from typing import NoReturn, Optional, Union

from numpy import ndarray

from gemseo.mlearning.transform.transformer import Transformer, TransformerFitOptionType

[docs]class DimensionReduction(Transformer):
"""Dimension reduction."""

def __init__(
self,
name="DimensionReduction",  # type: str
n_components=5,  # type: int
**parameters  # type: Optional[Union[float,int,str,bool]]
):  # type: (...) -> None
"""
Args:
name: A name for this transformer.
n_components: The number of components of the latent space.
**parameters: The parameters of the transformer.
"""
super(DimensionReduction, self).__init__(
name, n_components=n_components, **parameters
)

[docs]    def fit(
self,
data,  # type: ndarray
*args  # type: TransformerFitOptionType
):  # type: (...) -> NoReturn
"""Fit the transformer to the data.

Args:
data: The data to be fitted.
"""
raise NotImplementedError

@property
def n_components(self):  # type: (...) -> int
"""The number of components."""
return self.parameters["n_components"]