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Modular toolkit for Data Processing MDP
Package mdp :: Package nodes :: Class OrthogonalMatchingPursuitScikitsLearnNode
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Class OrthogonalMatchingPursuitScikitsLearnNode


Orthogonal Matching Pursuit model (OMP)

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

**Parameters**

n_nonzero_coefs : int, optional
    Desired number of non-zero entries in the solution. If None (by
    default) this value is set to 10% of n_features.

tol : float, optional
    Maximum norm of the residual. If not None, overrides n_nonzero_coefs.

fit_intercept : boolean, optional
    whether to calculate the intercept for this model. If set
    to false, no intercept will be used in calculations
    (e.g. data is expected to be already centered).

normalize : boolean, optional
    If False, the regressors X are assumed to be already normalized.

precompute : {True, False, 'auto'}, default 'auto'
    Whether to use a precomputed Gram and Xy matrix to speed up
    calculations. Improves performance when `n_targets` or `n_samples` is
    very large. Note that if you already have such matrices, you can pass
    them directly to the fit method.

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

**Attributes**

``coef_`` : array, shape (n_features,) or (n_features, n_targets)
    parameter vector (w in the formula)

``intercept_`` : float or array, shape (n_targets,)
    independent term in decision function.

``n_iter_`` : int or array-like
    Number of active features across every target.

**Notes**

Orthogonal matching pursuit was introduced in G. Mallat, Z. Zhang,
Matching pursuits with time-frequency dictionaries, IEEE Transactions on
Signal Processing, Vol. 41, No. 12. (December 1993), pp. 3397-3415.
(http://blanche.polytechnique.fr/~mallat/papiers/MallatPursuit93.pdf)

This implementation is based on Rubinstein, R., Zibulevsky, M. and Elad,
M., Efficient Implementation of the K-SVD Algorithm using Batch Orthogonal
Matching Pursuit Technical Report - CS Technion, April 2008.
http://www.cs.technion.ac.il/~ronrubin/Publications/KSVD-OMP-v2.pdf

See also

orthogonal_mp
orthogonal_mp_gram
lars_path
Lars
LassoLars
decomposition.sparse_encode
Instance Methods [hide private]
 
__init__(self, input_dim=None, output_dim=None, dtype=None, **kwargs)
Orthogonal Matching Pursuit model (OMP)
 
_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)
Predict using the linear model
 
stop_training(self, **kwargs)
Fit the model using X, y as training data.

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 [hide private]
 
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 [hide private]

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 [hide private]

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

  

Orthogonal Matching Pursuit model (OMP)

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

**Parameters**

n_nonzero_coefs : int, optional
    Desired number of non-zero entries in the solution. If None (by
    default) this value is set to 10% of n_features.

tol : float, optional
    Maximum norm of the residual. If not None, overrides n_nonzero_coefs.

fit_intercept : boolean, optional
    whether to calculate the intercept for this model. If set
    to false, no intercept will be used in calculations
    (e.g. data is expected to be already centered).

normalize : boolean, optional
    If False, the regressors X are assumed to be already normalized.

precompute : {True, False, 'auto'}, default 'auto'
    Whether to use a precomputed Gram and Xy matrix to speed up
    calculations. Improves performance when `n_targets` or `n_samples` is
    very large. Note that if you already have such matrices, you can pass
    them directly to the fit method.

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

**Attributes**

``coef_`` : array, shape (n_features,) or (n_features, n_targets)
    parameter vector (w in the formula)

``intercept_`` : float or array, shape (n_targets,)
    independent term in decision function.

``n_iter_`` : int or array-like
    Number of active features across every target.

**Notes**

Orthogonal matching pursuit was introduced in G. Mallat, Z. Zhang,
Matching pursuits with time-frequency dictionaries, IEEE Transactions on
Signal Processing, Vol. 41, No. 12. (December 1993), pp. 3397-3415.
(http://blanche.polytechnique.fr/~mallat/papiers/MallatPursuit93.pdf)

This implementation is based on Rubinstein, R., Zibulevsky, M. and Elad,
M., Efficient Implementation of the K-SVD Algorithm using Batch Orthogonal
Matching Pursuit Technical Report - CS Technion, April 2008.
http://www.cs.technion.ac.il/~ronrubin/Publications/KSVD-OMP-v2.pdf

See also

orthogonal_mp
orthogonal_mp_gram
lars_path
Lars
LassoLars
decomposition.sparse_encode
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)

 

Predict using the linear model

This node has been automatically generated by wrapping the sklearn.linear_model.omp.OrthogonalMatchingPursuit 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)
Samples.

Returns

C : array, shape = (n_samples,)
Returns predicted values.
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)

 

Fit the model using X, y as training data.

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

Parameters

X : array-like, shape (n_samples, n_features)
Training data.
y : array-like, shape (n_samples,) or (n_samples, n_targets)
Target values.

Returns

self : object
returns an instance of self.
Overrides: Node.stop_training

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Modular toolkit for Data Processing MDP
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