Package mdp :: Package nodes :: Class DiscreteHopfieldClassifier

[frames] | no frames]

Class DiscreteHopfieldClassifier

Node for simulating a simple discrete Hopfield model

Instance Methods

[hide private]

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

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

_label(self, x, threshold=0)
Retrieves patterns from the associative memory.

_label_one(self, pattern, threshold)

_stop_training(self)

_train(self, x)
Provide the hopfield net with the possible states.

_train_one(self, pattern)

label(self, x, threshold=0)
Retrieves patterns from the associative memory.

stop_training(self)
Stop the training phase.

train(self, x)
Provide the hopfield net with the possible states.

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)

_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)

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]

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]

load_parameter
Returns the load parameter of the Hopfield net. The quality of memory recall for a Hopfield net breaks down when the load parameter is larger than 0.14.

memory_size
Returns the Hopfield net's memory size

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=`'b'`)
(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)

_get_supported_dtypes(self)

Return the list of dtypes supported by this node.

The types can be specified in any format allowed by dtype.

Overrides: Node._get_supported_dtypes: (inherited documentation)

_label(self, x, threshold=0)

Retrieves patterns from the associative memory.

Overrides: ClassifierNode._label

_label_one(self, pattern, threshold)

_stop_training(self)

Overrides: Node._stop_training

_train(self, x)

Provide the hopfield net with the possible states.

x -- a matrix having different variables on different columns: and observations on rows.

Overrides: Node._train

_train_one(self, pattern)

label(self, x, threshold=0)

Retrieves patterns from the associative memory.

Overrides: ClassifierNode.label

stop_training(self)

Stop the training phase.

By default, subclasses should overwrite _stop_training to implement this functionality. The docstring of the _stop_training method overwrites this docstring.

Overrides: Node.stop_training

train(self, x)

Provide the hopfield net with the possible states.

x -- a matrix having different variables on different columns: and observations on rows.

Overrides: Node.train

Property Details

[hide private]

load_parameter

Returns the load parameter of the Hopfield net. The quality of memory recall for a Hopfield net breaks down when the load parameter is larger than 0.14.

Get Method:: unreachable.load_parameter(self) - Returns the load parameter of the Hopfield net. The quality of memory recall for a Hopfield net breaks down when the load parameter is larger than 0.14.

memory_size

Returns the Hopfield net's memory size

Get Method:: unreachable.memory_size(self) - Returns the Hopfield net's memory size

Class DiscreteHopfieldClassifier

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

_get_supported_dtypes(self)

_label(self, x, threshold=0)

_label_one(self, pattern, threshold)

_stop_training(self)

_train(self, x)

_train_one(self, pattern)

label(self, x, threshold=0)

stop_training(self)

train(self, x)

load_parameter

memory_size

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