Package mdp :: Package nodes :: Class PerceptronClassifier

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Class PerceptronClassifier

A simple perceptron with input_dim input nodes.

Instance Methods

[hide private]

__init__(self, execute_method=None, input_dim=None, output_dim=None, dtype=None)
Initialize classifier.

_check_train_args(self, x, labels)

_label(self, x)
Returns an array with class labels from the perceptron.

_train(self, x, labels)
Update the internal structures according to the input data 'x'.

label(self, x)
Returns an array with class labels from the perceptron.

train(self, x, labels)
Update the internal structures according to the input data 'x'.

Inherited from PreserveDimNode (private): _set_input_dim, _set_output_dim

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)

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

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

prob(self, x, *args, **kwargs)
Predict probability for each possible outcome.

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 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)

_stop_training(self, *args, **kwargs)

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.

stop_training(self, *args, **kwargs)
Stop the training phase.

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, execute_method=None, input_dim=None, output_dim=None, dtype=None)
(Constructor)

Initialize classifier.

execute_method -- Set to string value 'label', 'rank', or 'prob' to: force the corresponding classification method being used instead of the standard identity execution (which is used when execute_method has the default value None). This can be used when the node is last in a flow, the return value from Flow.execute will then consist of the classification results.

Overrides: object.__init__: (inherited documentation)

_check_train_args(self, x, labels)

Overrides: Node._check_train_args

_label(self, x)

Returns an array with class labels from the perceptron.

Overrides: ClassifierNode._label

_train(self, x, labels)

Update the internal structures according to the input data 'x'.

x -- a matrix having different variables on different columns: and observations on the rows.
labels -- can be a list, tuple or array of labels (one for each data point): or a single label, in which case all input data is assigned to the same class.

Overrides: Node._train

label(self, x)

Returns an array with class labels from the perceptron.

Overrides: ClassifierNode.label

train(self, x, labels)

Update the internal structures according to the input data 'x'.

x -- a matrix having different variables on different columns: and observations on the rows.
labels -- can be a list, tuple or array of labels (one for each data point): or a single label, in which case all input data is assigned to the same class.

Overrides: Node.train

Class PerceptronClassifier

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

_check_train_args(self, x, labels)

_label(self, x)

_train(self, x, labels)

label(self, x)

train(self, x, labels)

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