mlxtend version: 0.15.0dev

LinearDiscriminantAnalysis

LinearDiscriminantAnalysis(n_discriminants=None)

Linear Discriminant Analysis Class

Parameters

Attributes

Examples

For usage examples, please see http://rasbt.github.io/mlxtend/user_guide/feature_extraction/LinearDiscriminantAnalysis/

Methods


fit(X, y, n_classes=None)

Fit the LDA model with X.

Parameters

Returns


get_params(deep=True)

Get parameters for this estimator.

Parameters

Returns


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

adapted from https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/base.py

Author: Gael Varoquaux gael.varoquaux@normalesup.org

License: BSD 3 clause


transform(X)

Apply the linear transformation on X.

Parameters

Returns

PrincipalComponentAnalysis

PrincipalComponentAnalysis(n_components=None, solver='svd', whitening=False)

Principal Component Analysis Class

Parameters

Attributes

Examples

For usage examples, please see http://rasbt.github.io/mlxtend/user_guide/feature_extraction/PrincipalComponentAnalysis/

Methods


fit(X, y=None)

Learn model from training data.

Parameters

Returns


get_params(deep=True)

Get parameters for this estimator.

Parameters

Returns


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

adapted from https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/base.py

Author: Gael Varoquaux gael.varoquaux@normalesup.org

License: BSD 3 clause


transform(X)

Apply the linear transformation on X.

Parameters

Returns

RBFKernelPCA

RBFKernelPCA(gamma=15.0, n_components=None, copy_X=True)

RBF Kernel Principal Component Analysis for dimensionality reduction.

Parameters

Attributes

Examples

For usage examples, please see http://rasbt.github.io/mlxtend/user_guide/feature_extraction/RBFKernelPCA/

Methods


fit(X)

Learn model from training data.

Parameters

Returns


get_params(deep=True)

Get parameters for this estimator.

Parameters

Returns


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

adapted from https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/base.py

Author: Gael Varoquaux gael.varoquaux@normalesup.org

License: BSD 3 clause


transform(X)

Apply the non-linear transformation on X.

Parameters

Returns