pyrfm.random_feature.MB¶
-
class
pyrfm.random_feature.MB(n_components=1000, dense_output=False)[source]¶ Bases:
sklearn.base.BaseEstimator,sklearn.base.TransformerMixinApproximates feature map of the intersection (min) kernel by explicit feature map, which is proposed by S.Maji and A.C.Berg.
- Parameters
n_components (int (default=1000)) – The dimension of the computed (mapped) feature space.
dense_output (bool (default=False)) – Whether output feature matrix is dense or sparse.
References
[1] Max-Margin Additive Classifiers for Detection. Subhransu Maji and Alexander C. Berg. In ICCV 2009. (http://acberg.com/papers/mb09iccv.pdf)
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fit(X, y=None)[source]¶ Compute the number of grids 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
{np.ndarray, csr_matrix}, shape (n_samples, n_components)