Python PLSSVD Examples

Example #1

 def fit(self, X, y=None):
     self._sklearn_model = SKLModel(**self._hyperparams)
     if (y is not None):
         self._sklearn_model.fit(X, y)
     else:
         self._sklearn_model.fit(X)
     return self

Example #2

File: plssvd.py Project: sreev/lale

 def __init__(self, n_components=2, scale=True, copy=True):
     self._hyperparams = {
         'n_components': n_components,
         'scale': scale,
         'copy': copy
     }
     self._wrapped_model = SKLModel(**self._hyperparams)

Example #3

class PLSSVDImpl():
    def __init__(self, n_components=2, scale=True, copy=True):
        self._hyperparams = {
            'n_components': n_components,
            'scale': scale,
            'copy': copy
        }

    def fit(self, X, y=None):
        self._sklearn_model = SKLModel(**self._hyperparams)
        if (y is not None):
            self._sklearn_model.fit(X, y)
        else:
            self._sklearn_model.fit(X)
        return self

    def transform(self, X):
        return self._sklearn_model.transform(X)

Example #4

			'MultinomialNB':MultinomialNB(),
			'NMF':NMF(),
			'NearestCentroid':NearestCentroid(),
			'NearestNeighbors':NearestNeighbors(),
			'Normalizer':Normalizer(),
			'NuSVC':NuSVC(),
			'NuSVR':NuSVR(),
			'Nystroem':Nystroem(),
			'OAS':OAS(),
			'OneClassSVM':OneClassSVM(),
			'OrthogonalMatchingPursuit':OrthogonalMatchingPursuit(),
			'OrthogonalMatchingPursuitCV':OrthogonalMatchingPursuitCV(),
			'PCA':PCA(),
			'PLSCanonical':PLSCanonical(),
			'PLSRegression':PLSRegression(),
			'PLSSVD':PLSSVD(),
			'PassiveAggressiveClassifier':PassiveAggressiveClassifier(),
			'PassiveAggressiveRegressor':PassiveAggressiveRegressor(),
			'Perceptron':Perceptron(),
			'ProjectedGradientNMF':ProjectedGradientNMF(),
			'QuadraticDiscriminantAnalysis':QuadraticDiscriminantAnalysis(),
			'RANSACRegressor':RANSACRegressor(),
			'RBFSampler':RBFSampler(),
			'RadiusNeighborsClassifier':RadiusNeighborsClassifier(),
			'RadiusNeighborsRegressor':RadiusNeighborsRegressor(),
			'RandomForestClassifier':RandomForestClassifier(),
			'RandomForestRegressor':RandomForestRegressor(),
			'RandomizedLasso':RandomizedLasso(),
			'RandomizedLogisticRegression':RandomizedLogisticRegression(),
			'RandomizedPCA':RandomizedPCA(),
			'Ridge':Ridge(),