Package mdp :: Package nodes :: Class AdditiveChi2SamplerScikitsLearnNode

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Class AdditiveChi2SamplerScikitsLearnNode

Approximate feature map for additive chi2 kernel.

This node has been automatically generated by wrapping the ``sklearn.kernel_approximation.AdditiveChi2Sampler`` class
from the ``sklearn`` library. The wrapped instance can be accessed
through the ``scikits_alg`` attribute.

Uses sampling the fourier transform of the kernel characteristic
at regular intervals.

Since the kernel that is to be approximated is additive, the components of
the input vectors can be treated separately. Each entry in the original
space is transformed into 2*sample_steps+1 features, where sample_steps is
a parameter of the method. Typical values of sample_steps include 1, 2 and
3.

Optimal choices for the sampling interval for certain data ranges can be
computed (see the reference). The default values should be reasonable.

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

**Parameters**

sample_steps : int, optional
Gives the number of (complex) sampling points.
sample_interval : float, optional
Sampling interval. Must be specified when sample_steps not in {1,2,3}.

**Notes**

This estimator approximates a slightly different version of the additive
chi squared kernel then ``metric.additive_chi2`` computes.

See also

SkewedChi2Sampler : A Fourier-approximation to a non-additive variant of
the chi squared kernel.

sklearn.metrics.pairwise.chi2_kernel : The exact chi squared kernel.

sklearn.metrics.pairwise.additive_chi2_kernel : The exact additive chi
squared kernel.

**References**

See `"Efficient additive kernels via explicit feature maps"
<http://www.robots.ox.ac.uk/~vedaldi/assets/pubs/vedaldi11efficient.pdf>`_
A. Vedaldi and A. Zisserman, Pattern Analysis and Machine Intelligence,
2011

Instance Methods

__init__(self, input_dim=None, output_dim=None, dtype=None, **kwargs)
Approximate feature map for additive chi2 kernel.

_execute(self, x)

_get_supported_dtypes(self)
Return the list of dtypes supported by this node. The types can be specified in any format allowed by numpy.dtype.

_stop_training(self, **kwargs)
Concatenate the collected data in a single array.

execute(self, x)
Apply approximate feature map to X.

stop_training(self, **kwargs)
This node has been automatically generated by wrapping the sklearn.kernel_approximation.AdditiveChi2Sampler class from the sklearn library. The wrapped instance can be accessed through the scikits_alg attribute.

Inherited from unreachable.newobject: __long__, __native__, __nonzero__, __unicode__, next

Inherited from object: __delattr__, __format__, __getattribute__, __hash__, __new__, __reduce__, __reduce_ex__, __setattr__, __sizeof__, __subclasshook__

Inherited from Cumulator

_train(self, *args)
Collect all input data in a list.

train(self, *args)
Collect all input data in a list.

Inherited from Node

__add__(self, other)

__call__(self, x, *args, **kwargs)
Calling an instance of Node is equivalent to calling its execute method.

__repr__(self)
repr(x)

__str__(self)
str(x)

_check_input(self, x)

_check_output(self, y)

_check_train_args(self, x, *args, **kwargs)

_get_train_seq(self)

_if_training_stop_training(self)

_inverse(self, x)

_pre_execution_checks(self, x)
This method contains all pre-execution checks.

_pre_inversion_checks(self, y)
This method contains all pre-inversion checks.

_refcast(self, x)
Helper function to cast arrays to the internal dtype.

_set_dtype(self, t)

_set_input_dim(self, n)

_set_output_dim(self, n)

copy(self, protocol=None)
Return a deep copy of the node.

get_current_train_phase(self)
Return the index of the current training phase.

get_dtype(self)
Return dtype.

get_input_dim(self)
Return input dimensions.

get_output_dim(self)
Return output dimensions.

get_remaining_train_phase(self)
Return the number of training phases still to accomplish.

get_supported_dtypes(self)
Return dtypes supported by the node as a list of dtype objects.

has_multiple_training_phases(self)
Return True if the node has multiple training phases.

inverse(self, y, *args, **kwargs)
Invert y.

is_training(self)
Return True if the node is in the training phase, False otherwise.

save(self, filename, protocol=-1)
Save a pickled serialization of the node to filename. If filename is None, return a string.

set_dtype(self, t)
Set internal structures' dtype.

set_input_dim(self, n)
Set input dimensions.

set_output_dim(self, n)
Set output dimensions.

Static Methods

is_invertible()
Return True if the node can be inverted, False otherwise.

is_trainable()
Return True if the node can be trained, False otherwise.

Properties

Inherited from object: __class__

Inherited from Node

_train_seq
List of tuples:

dtype
dtype

input_dim
Input dimensions

output_dim
Output dimensions

supported_dtypes
Supported dtypes

Method Details

init(self, input_dim=None, output_dim=None, dtype=None, **kwargs)
(Constructor)

Approximate feature map for additive chi2 kernel.

This node has been automatically generated by wrapping the ``sklearn.kernel_approximation.AdditiveChi2Sampler`` class
from the ``sklearn`` library. The wrapped instance can be accessed
through the ``scikits_alg`` attribute.

Uses sampling the fourier transform of the kernel characteristic
at regular intervals.

Since the kernel that is to be approximated is additive, the components of
the input vectors can be treated separately. Each entry in the original
space is transformed into 2*sample_steps+1 features, where sample_steps is
a parameter of the method. Typical values of sample_steps include 1, 2 and
3.

Optimal choices for the sampling interval for certain data ranges can be
computed (see the reference). The default values should be reasonable.

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

**Parameters**

sample_steps : int, optional
Gives the number of (complex) sampling points.
sample_interval : float, optional
Sampling interval. Must be specified when sample_steps not in {1,2,3}.

**Notes**

This estimator approximates a slightly different version of the additive
chi squared kernel then ``metric.additive_chi2`` computes.

See also

SkewedChi2Sampler : A Fourier-approximation to a non-additive variant of
the chi squared kernel.

sklearn.metrics.pairwise.chi2_kernel : The exact chi squared kernel.

sklearn.metrics.pairwise.additive_chi2_kernel : The exact additive chi
squared kernel.

**References**

See `"Efficient additive kernels via explicit feature maps"
<http://www.robots.ox.ac.uk/~vedaldi/assets/pubs/vedaldi11efficient.pdf>`_
A. Vedaldi and A. Zisserman, Pattern Analysis and Machine Intelligence,
2011

Overrides: object.__init__

_execute(self, x)

Overrides: Node._execute

_get_supported_dtypes(self)

Return the list of dtypes supported by this node. The types can be specified in any format allowed by numpy.dtype.

Overrides: Node._get_supported_dtypes

_stop_training(self, **kwargs)

Concatenate the collected data in a single array.

Overrides: Node._stop_training

execute(self, x)

Apply approximate feature map to X.

This node has been automatically generated by wrapping the sklearn.kernel_approximation.AdditiveChi2Sampler class from the sklearn library. The wrapped instance can be accessed through the scikits_alg attribute.

Parameters

X : {array-like, sparse matrix}, shape = (n_samples, n_features)

Returns

X_new : {array, sparse matrix}, shape = (n_samples, n_features * (2*sample_steps + 1)): Whether the return value is an array of sparse matrix depends on the type of the input X.

Overrides: Node.execute

is_invertible()
Static Method

Return True if the node can be inverted, False otherwise.

Overrides: Node.is_invertible: (inherited documentation)

is_trainable()
Static Method

Return True if the node can be trained, False otherwise.

Overrides: Node.is_trainable

stop_training(self, **kwargs)

This node has been automatically generated by wrapping the sklearn.kernel_approximation.AdditiveChi2Sampler class from the sklearn library. The wrapped instance can be accessed through the scikits_alg attribute.

Overrides: Node.stop_training