Package mdp :: Package nodes :: Class NearestMeanClassifier

Class NearestMeanClassifier

Nearest-Mean classifier.

Instance Methods

__init__(self, execute_method=None, input_dim=None, output_dim=None, dtype=None)
Initializes an object of type 'NearestMeanClassifier'

_check_train_args(self, x, labels)

list

_label(self, x)
Classify the data based on minimal distance to mean.

_stop_training(self)
Calculate the class means.

_train(self, x, labels)
Update the mean information for the different classes.

_update_mean(self, x, label)
Update the mean with data for a single label.

list

label(self, x)
Classify the data based on minimal distance to mean.

stop_training(self)
Calculate the class means.

train(self, x, labels)
Update the mean information for the different classes.

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

Initializes an object of type 'NearestMeanClassifier'

Parameters:

execute_method (str) - 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.
input_dim (int) - The input dimensionality.
output_dim (int) - The output dimensionality.
dtype (numpy.dtype or str) - The datatype.

Overrides: object.__init__

_check_train_args(self, x, labels)

Overrides: Node._check_train_args

_label(self, x)

Classify the data based on minimal distance to mean.

Parameters:

x (numpy.ndarray) - The data to classify.

Returns: list

The data labels.

Overrides: ClassifierNode._label

_stop_training(self)

Calculate the class means.

Overrides: Node._stop_training

_train(self, x, labels)

Update the mean information for the different classes.

Parameters:

x (numpy.ndarray) - The data.
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 (computationally this is more efficient).

Overrides: Node._train

_update_mean(self, x, label)

Update the mean with data for a single label.

Parameters:

x (numpy.ndarray) - The data.
label - The label index.

label(self, x)

Classify the data based on minimal distance to mean.

Parameters:

x (numpy.ndarray) - The data to classify.

Returns: list

The data labels.

Overrides: ClassifierNode.label

stop_training(self)

Calculate the class means.

Overrides: Node.stop_training

train(self, x, labels)

Update the mean information for the different classes.

Parameters:

x (numpy.ndarray) - The data.
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 (computationally this is more efficient).

Overrides: Node.train

Class NearestMeanClassifier

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

_check_train_args(self, x, labels)

_label(self, x)

_stop_training(self)

_train(self, x, labels)

_update_mean(self, x, label)

label(self, x)

stop_training(self)

train(self, x, labels)

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