pyrfm.random_feature.SubsampledRandomHadamard¶
-
class
pyrfm.random_feature.SubsampledRandomHadamard(n_components=100, distribution='rademacher', random_state=None)[source]¶ Bases:
sklearn.base.BaseEstimator,sklearn.base.TransformerMixinApproximates feature maps of the product between random matrix and feature vectors by Subsampled Randomized Hadamard Transform.
This class can be used as a sub-routine for approximating the product between random matrix and feature vectors in some random features. Subsampled Randomized Hadamard Transform uses diagonal matrices, the Walsh-Hadamard matrix, and submatrix of the identity matrix for approximating the matrix-vector product.
- Parameters
n_components (int (default=100)) – Number of Monte Carlo samples per original features. Equals the dimensionality of the computed (mapped) feature space.
distribution (str or function (default="rademacher")) – A function for sampling random bases. Its arguments must be random_state and size. For str, “gaussian” (or “normal”), “rademacher”, “laplace”, or “uniform” can be used.
random_state (int, RandomState instance or None, optional (default=None)) – If int, random_state is the seed used by the random number generator; If np.RandomState instance, random_state is the random number generator; If None, the random number generator is the RandomState instance used by np.random.
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random_weights_¶ The sampled basis. It is sampled by using self.distribution, which is the rademacher distribution default.
- Type
array, shape (n_features)
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random_indices_rows_¶ The indices of rows sampled from [0, ldots, n_features_padded-1] uniformly, where n_features_padded is the smallest power of two number that is bigger than n_features.
- Type
array, shape (n_components)
References
[1] Improved Analysis of the Subsampled Randomized Hadamard Transform. Joel A Tropp. Advances in Adaptive Data Analysis, (https://arxiv.org/pdf/1011.1595.pdf)
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fit(X, y=None)[source]¶ Generate random weights according to n_features.
- Parameters
X ({array-like, sparse matrix}, shape (n_samples, n_features)) – Training data, where n_samples is the number of samples and n_features is the number of features.
- Returns
self – Returns the transformer.
- Return type
object
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fit_transform(X, y=None, **fit_params)¶ Fit to data, then transform it.
Fits transformer to X and y with optional parameters fit_params and returns a transformed version of X.
- Parameters
X (numpy array of shape [n_samples, n_features]) – Training set.
y (numpy array of shape [n_samples]) – Target values.
- Returns
X_new – Transformed array.
- Return type
numpy array of shape [n_samples, n_features_new]
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get_params(deep=True)¶ Get parameters for this estimator.
- Parameters
deep (boolean, optional) – If True, will return the parameters for this estimator and contained subobjects that are estimators.
- Returns
params – Parameter names mapped to their values.
- Return type
mapping of string to any
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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
- Return type
self
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transform(X)[source]¶ Apply the approximate feature map to X.
- Parameters
X ({array-like, sparse matrix}, shape (n_samples, n_features)) – New data, where n_samples is the number of samples and n_features is the number of features.
- Returns
X_new
- Return type
array-like, shape (n_samples, n_components)