Package mdp :: Class ClassifierCumulator

Class ClassifierCumulator

A ClassifierCumulator is a Node whose training phase simply collects all input data and labels. In this way it is possible to easily implement batch-mode learning.

The data is accessible in the attribute 'self.data' after the beginning of the '_stop_training' phase. 'self.tlen' contains the number of data points collected. 'self.labels' contains the assigned label to each data point.

Instance Methods

[hide private]

__init__(self, input_dim=None, output_dim=None, dtype=None)
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.

_check_train_args(self, x, labels)

_stop_training(self, *args, **kwargs)
Transform the data and labels lists to array objects and reshape them.

_train(self, x, labels)
Cumulate all input data in a one dimensional list.

stop_training(self, *args, **kwargs)
Transform the data and labels lists to array objects and reshape them.

train(self, x, labels)
Cumulate all input data in a one dimensional list.

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 ClassifierNode

_execute(self, x)

_label(self, x, *args, **kargs)

_prob(self, x, *args, **kargs)

execute(self, x)
Process the data contained in x.

label(self, x, *args, **kwargs)
Returns an array with best class labels.

prob(self, x, *args, **kwargs)
This function does classification or regression on a test vector T given a model with probability information. This node has been automatically generated by wrapping the scikits.learn.svm.classes.SVC class from the sklearn library. The wrapped instance can be accessed through the scikits_alg attribute. Parameters

rank(self, x, threshold=None)
Returns ordered list with all labels ordered according to prob(x) (e.g., [[3 1 2], [2 1 3], ...]).

Inherited from PreserveDimNode

_set_input_dim(self, n)

_set_output_dim(self, n)

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)

_get_supported_dtypes(self)
Return the list of dtypes supported by this node.

_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)

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]

Inherited from Node

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)
(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)

_check_train_args(self, x, labels)

Overrides: Node._check_train_args

_stop_training(self, *args, **kwargs)

Transform the data and labels lists to array objects and reshape them.

Overrides: Node._stop_training

_train(self, x, labels)

Cumulate all input data in a one dimensional list.

Overrides: Node._train

stop_training(self, *args, **kwargs)

Transform the data and labels lists to array objects and reshape them.

Overrides: Node.stop_training

train(self, x, labels)

Cumulate all input data in a one dimensional list.

Overrides: Node.train

Class ClassifierCumulator

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

_check_train_args(self, x, labels)

_stop_training(self, *args, **kwargs)

_train(self, x, labels)

stop_training(self, *args, **kwargs)

train(self, x, labels)

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