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

Bayesian ARD regression. This node has been automatically generated by wrapping the scikits.learn.linear_model.bayes.ARDRegression class from the sklearn library. The wrapped instance can be accessed through the scikits_alg attribute. Fit the weights of a regression model, using an ARD prior. The weights of the regression model are assumed to be in Gaussian distributions. Also estimate the parameters lambda (precisions of the distributions of the weights) and alpha (precision of the distribution of the noise). The estimation is done by an iterative procedures (Evidence Maximization)

Parameters

X : array, shape = (n_samples, n_features)
Training vectors.
y : array, shape = (n_samples)
Target values for training vectors
n_iter : int, optional
Maximum number of interations. Default is 300
eps : float, optional
Stop the algorithm if w has converged. Default is 1.e-3.
alpha_1 : float, optional
Hyper-parameter : shape parameter for the Gamma distribution prior over the alpha parameter. Default is 1.e-6.
alpha_2 : float, optional
Hyper-parameter : inverse scale parameter (rate parameter) for the Gamma distribution prior over the alpha parameter. Default is 1.e-6.
lambda_1 : float, optional
Hyper-parameter : shape parameter for the Gamma distribution prior over the lambda parameter. Default is 1.e-6.
lambda_2 : float, optional
Hyper-parameter : inverse scale parameter (rate parameter) for the Gamma distribution prior over the lambda parameter. Default is 1.e-6.
compute_score : boolean, optional
If True, compute the objective function at each step of the model. Default is False.
threshold_lambda : float, optional
threshold for removing (pruning) weights with high precision from the computation. Default is 1.e+4.
fit_intercept : boolean, optional
wether 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). Default is True.
verbose : boolean, optional
Verbose mode when fitting the model. Default is False.

Attributes

coef_ : array, shape = (n_features)
Coefficients of the regression model (mean of distribution)
alpha_ : float
estimated precision of the noise.
lambda_ : array, shape = (n_features)
estimated precisions of the weights.
sigma_ : array, shape = (n_features, n_features)
estimated variance-covariance matrix of the weights
scores_ : float
if computed, value of the objective function (to be maximized)

Methods

fit(X, y) : self
Fit the model
predict(X) : array
Predict using the model.

Examples

>>> from scikits.learn import linear_model
>>> clf = linear_model.ARDRegression()
>>> clf.fit([[0,0], [1, 1], [2, 2]], [0, 1, 2])
ARDRegression(n_iter=300, verbose=False, lambda_1=1e-06, lambda_2=1e-06,
       fit_intercept=True, eps=0.001, threshold_lambda=10000.0,
       alpha_2=1e-06, alpha_1=1e-06, compute_score=False)
>>> clf.predict([[1, 1]])
array([ 1.])

Notes

See examples/linear_model/plot_ard.py for an example.

Instance Methods [hide private]
 
__init__(self, input_dim=None, output_dim=None, dtype=None, **kwargs)
Bayesian ARD regression. This node has been automatically generated by wrapping the scikits.learn.linear_model.bayes.ARDRegression class from the sklearn library. The wrapped instance can be accessed through the scikits_alg attribute. Fit the weights of a regression model, using an ARD prior. The weights of the regression model are assumed to be in Gaussian distributions. Also estimate the parameters lambda (precisions of the distributions of the weights) and alpha (precision of the distribution of the noise). The estimation is done by an iterative procedures (Evidence Maximization)
 
_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 This node has been automatically generated by wrapping the scikits.learn.linear_model.bayes.ARDRegression class from the sklearn library. The wrapped instance can be accessed through the scikits_alg attribute. Parameters
 
stop_training(self, **kwargs)
Fit the ARDRegression model according to the given training data and parameters. This node has been automatically generated by wrapping the scikits.learn.linear_model.bayes.ARDRegression class from the sklearn library. The wrapped instance can be accessed through the scikits_alg attribute. Iterative procedure to maximize the evidence

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 numpy.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.
bool
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)

 

Bayesian ARD regression. This node has been automatically generated by wrapping the scikits.learn.linear_model.bayes.ARDRegression class from the sklearn library. The wrapped instance can be accessed through the scikits_alg attribute. Fit the weights of a regression model, using an ARD prior. The weights of the regression model are assumed to be in Gaussian distributions. Also estimate the parameters lambda (precisions of the distributions of the weights) and alpha (precision of the distribution of the noise). The estimation is done by an iterative procedures (Evidence Maximization)

Parameters

X : array, shape = (n_samples, n_features)
Training vectors.
y : array, shape = (n_samples)
Target values for training vectors
n_iter : int, optional
Maximum number of interations. Default is 300
eps : float, optional
Stop the algorithm if w has converged. Default is 1.e-3.
alpha_1 : float, optional
Hyper-parameter : shape parameter for the Gamma distribution prior over the alpha parameter. Default is 1.e-6.
alpha_2 : float, optional
Hyper-parameter : inverse scale parameter (rate parameter) for the Gamma distribution prior over the alpha parameter. Default is 1.e-6.
lambda_1 : float, optional
Hyper-parameter : shape parameter for the Gamma distribution prior over the lambda parameter. Default is 1.e-6.
lambda_2 : float, optional
Hyper-parameter : inverse scale parameter (rate parameter) for the Gamma distribution prior over the lambda parameter. Default is 1.e-6.
compute_score : boolean, optional
If True, compute the objective function at each step of the model. Default is False.
threshold_lambda : float, optional
threshold for removing (pruning) weights with high precision from the computation. Default is 1.e+4.
fit_intercept : boolean, optional
wether 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). Default is True.
verbose : boolean, optional
Verbose mode when fitting the model. Default is False.

Attributes

coef_ : array, shape = (n_features)
Coefficients of the regression model (mean of distribution)
alpha_ : float
estimated precision of the noise.
lambda_ : array, shape = (n_features)
estimated precisions of the weights.
sigma_ : array, shape = (n_features, n_features)
estimated variance-covariance matrix of the weights
scores_ : float
if computed, value of the objective function (to be maximized)

Methods

fit(X, y) : self
Fit the model
predict(X) : array
Predict using the model.

Examples

>>> from scikits.learn import linear_model
>>> clf = linear_model.ARDRegression()
>>> clf.fit([[0,0], [1, 1], [2, 2]], [0, 1, 2])
ARDRegression(n_iter=300, verbose=False, lambda_1=1e-06, lambda_2=1e-06,
       fit_intercept=True, eps=0.001, threshold_lambda=10000.0,
       alpha_2=1e-06, alpha_1=1e-06, compute_score=False)
>>> clf.predict([[1, 1]])
array([ 1.])

Notes

See examples/linear_model/plot_ard.py for an example.

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 scikits.learn.linear_model.bayes.ARDRegression class from the sklearn library. The wrapped instance can be accessed through the scikits_alg attribute. Parameters

X : numpy array of shape [n_samples, n_features]

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.
Returns: bool
A boolean indicating whether the node can be trained.
Overrides: Node.is_trainable

stop_training(self, **kwargs)

 

Fit the ARDRegression model according to the given training data and parameters. This node has been automatically generated by wrapping the scikits.learn.linear_model.bayes.ARDRegression class from the sklearn library. The wrapped instance can be accessed through the scikits_alg attribute. Iterative procedure to maximize the evidence

Parameters

X : array-like, shape = [n_samples, n_features]
Training vector, where n_samples in the number of samples and n_features is the number of features.
y : array, shape = [n_samples]
Target values (integers)

Returns

self : returns an instance of self.

Overrides: Node.stop_training

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Modular toolkit for Data Processing MDP
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