Class NystroemScikitsLearnNode

Approximate a kernel map using a subset of the training data.

This node has been automatically generated by wrapping the ``sklearn.kernel_approximation.Nystroem`` class
from the ``sklearn`` library.  The wrapped instance can be accessed
through the ``scikits_alg`` attribute.

Constructs an approximate feature map for an arbitrary kernel
using a subset of the data as basis.

Read more in the :ref:`User Guide <nystroem_kernel_approx>`.

**Parameters**

kernel : string or callable, default="rbf"
    Kernel map to be approximated. A callable should accept two arguments
    and the keyword arguments passed to this object as kernel_params, and
    should return a floating point number.

n_components : int
    Number of features to construct.
    How many data points will be used to construct the mapping.

gamma : float, default=None
    Gamma parameter for the RBF, polynomial, exponential chi2 and
    sigmoid kernels. Interpretation of the default value is left to
    the kernel; see the documentation for sklearn.metrics.pairwise.
    Ignored by other kernels.

degree : float, default=3
    Degree of the polynomial kernel. Ignored by other kernels.

coef0 : float, default=1
    Zero coefficient for polynomial and sigmoid kernels.
    Ignored by other kernels.

kernel_params : mapping of string to any, optional
    Additional parameters (keyword arguments) for kernel function passed
    as callable object.

random_state : {int, RandomState}, optional
    If int, random_state is the seed used by the random number generator;
    if RandomState instance, random_state is the random number generator.


**Attributes**

``components_`` : array, shape (n_components, n_features)
    Subset of training points used to construct the feature map.

``component_indices_`` : array, shape (n_components)
    Indices of ``components_`` in the training set.

``normalization_`` : array, shape (n_components, n_components)
    Normalization matrix needed for embedding.
    Square root of the kernel matrix on ``components_``.


**References**

* Williams, C.K.I. and Seeger, M.
  "Using the Nystroem method to speed up kernel machines",
  Advances in neural information processing systems 2001

* T. Yang, Y. Li, M. Mahdavi, R. Jin and Z. Zhou
  "Nystroem Method vs Random Fourier Features: A Theoretical and Empirical
  Comparison",
  Advances in Neural Information Processing Systems 2012


See also

RBFSampler : An approximation to the RBF kernel using random Fourier
             features.

sklearn.metrics.pairwise.kernel_metrics : List of built-in kernels.

Overrides: object.__init__

Apply feature map to X.

This node has been automatically generated by wrapping the sklearn.kernel_approximation.Nystroem class from the sklearn library. The wrapped instance can be accessed through the scikits_alg attribute.

Computes an approximate feature map using the kernel between some training points and X.

Parameters

X : array-like, shape=(n_samples, n_features)

Data to transform.

Returns

X_transformed : array, shape=(n_samples, n_components)

Transformed data.

Overrides: Node.execute

Overrides: Node.is_invertible

(inherited documentation)

Overrides: Node.is_trainable

Fit estimator to data.

This node has been automatically generated by wrapping the sklearn.kernel_approximation.Nystroem class from the sklearn library. The wrapped instance can be accessed through the scikits_alg attribute.

Samples a subset of training points, computes kernel on these and computes normalization matrix.

Parameters

X : array-like, shape=(n_samples, n_feature)

Training data.

Overrides: Node.stop_training