Пример #1
0
class MiniBatchSparsePCAImpl():

    def __init__(self, n_components=None, alpha=1, ridge_alpha=0.01, n_iter=100, callback=None, batch_size=3, verbose=False, shuffle=True, n_jobs=None, method='lars', random_state=None, normalize_components=False):
        self._hyperparams = {
            'n_components': n_components,
            'alpha': alpha,
            'ridge_alpha': ridge_alpha,
            'n_iter': n_iter,
            'callback': callback,
            'batch_size': batch_size,
            'verbose': verbose,
            'shuffle': shuffle,
            'n_jobs': n_jobs,
            'method': method,
            'random_state': random_state,
            'normalize_components': normalize_components}
        self._wrapped_model = Op(**self._hyperparams)

    def fit(self, X, y=None):
        if (y is not None):
            self._wrapped_model.fit(X, y)
        else:
            self._wrapped_model.fit(X)
        return self

    def transform(self, X):
        return self._wrapped_model.transform(X)
Пример #2
0
 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
Пример #3
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 def __init__(self,
              n_components=None,
              alpha=1,
              ridge_alpha=0.01,
              n_iter=100,
              callback=None,
              batch_size=3,
              verbose=False,
              shuffle=True,
              n_jobs=None,
              method='lars',
              random_state=None,
              normalize_components=False):
     self._hyperparams = {
         'n_components': n_components,
         'alpha': alpha,
         'ridge_alpha': ridge_alpha,
         'n_iter': n_iter,
         'callback': callback,
         'batch_size': batch_size,
         'verbose': verbose,
         'shuffle': shuffle,
         'n_jobs': n_jobs,
         'method': method,
         'random_state': random_state,
         'normalize_components': normalize_components
     }
     self._wrapped_model = SKLModel(**self._hyperparams)
Пример #4
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			'LinearRegression':LinearRegression(),
			'LinearSVC':LinearSVC(),
			'LinearSVR':LinearSVR(),
			'LocallyLinearEmbedding':LocallyLinearEmbedding(),
			'LogisticRegression':LogisticRegression(),
			'LogisticRegressionCV':LogisticRegressionCV(),
			'MDS':MDS(),
			'MLPClassifier':MLPClassifier(),
			'MLPRegressor':MLPRegressor(),
			'MaxAbsScaler':MaxAbsScaler(),
			'MeanShift':MeanShift(),
			'MinCovDet':MinCovDet(),
			'MinMaxScaler':MinMaxScaler(),
			'MiniBatchDictionaryLearning':MiniBatchDictionaryLearning(),
			'MiniBatchKMeans':MiniBatchKMeans(),
			'MiniBatchSparsePCA':MiniBatchSparsePCA(),
			'MultiTaskElasticNet':MultiTaskElasticNet(),
			'MultiTaskElasticNetCV':MultiTaskElasticNetCV(),
			'MultiTaskLasso':MultiTaskLasso(),
			'MultiTaskLassoCV':MultiTaskLassoCV(),
			'MultinomialNB':MultinomialNB(),
			'NMF':NMF(),
			'NearestCentroid':NearestCentroid(),
			'NearestNeighbors':NearestNeighbors(),
			'Normalizer':Normalizer(),
			'NuSVC':NuSVC(),
			'NuSVR':NuSVR(),
			'Nystroem':Nystroem(),
			'OAS':OAS(),
			'OneClassSVM':OneClassSVM(),
			'OrthogonalMatchingPursuit':OrthogonalMatchingPursuit(),