Package mdp :: Package nodes :: Class PriorProbabilityEstimatorScikitsLearnNode

Class PriorProbabilityEstimatorScikitsLearnNode

An estimator predicting the probability of each

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

Instance Methods

[hide private]

__init__(self, input_dim=None, output_dim=None, dtype=None, **kwargs)
If the input dimension and the output dimension are unspecified, they will be set when the train or execute method is called for the first time. If dtype is unspecified, it will be inherited from the data it receives at the first call of train or execute.

_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)
Process the data contained in x.

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

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)

If the input dimension and the output dimension are unspecified, they will be set when the train or execute method is called for the first time. If dtype is unspecified, it will be inherited from the data it receives at the first call of train or execute.

Every subclass must take care of up- or down-casting the internal structures to match this argument (use _refcast private method when possible).

Overrides: object.__init__: (inherited documentation)

_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)

Process the data contained in x.

If the object is still in the training phase, the function stop_training will be called. x is a matrix having different variables on different columns and observations on the rows.

By default, subclasses should overwrite _execute to implement their execution phase. The docstring of the _execute method overwrites this docstring.

Overrides: Node.execute: (inherited documentation)

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)

Concatenate the collected data in a single array.

Overrides: Node.stop_training: (inherited documentation)

Class PriorProbabilityEstimatorScikitsLearnNode

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

_execute(self, x)

_get_supported_dtypes(self)

_stop_training(self, **kwargs)

execute(self, x)

is_invertible() Static Method

is_trainable() Static Method

stop_training(self, **kwargs)

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

is_invertible()
Static Method

is_trainable()
Static Method