Package mdp :: Package nodes :: Class GaussianClassifier

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Class GaussianClassifier

Perform a supervised Gaussian classification.

Given a set of labelled data, the node fits a gaussian distribution to each class.

Instance Methods

[hide private]

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

_check_train_args(self, x, labels)

_gaussian_prob(self, x, lbl_idx)
Return the probability of the data points x with respect to a gaussian.

_label(self, x)
Classify the input data using Maximum A-Posteriori.

_prob(self, x)
Return the posterior probability of each class given the input in a dict.

_stop_training(self)

_train(self, x, labels)

_update_covs(self, x, lbl)

class_probabilities(self, x)
Return the posterior probability of each class given the input.

label(self, x)
Classify the input data using Maximum A-Posteriori.

prob(self, x)
Return the posterior probability of each class given the input in a dict.

stop_training(self)
Stop the training phase.

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)

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

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)

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.

Inherited from Node

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=False, 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

_gaussian_prob(self, x, lbl_idx)

Return the probability of the data points x with respect to a gaussian.

Input arguments: x -- Input data S -- Covariance matrix mn -- Mean

_label(self, x)

Classify the input data using Maximum A-Posteriori.

Overrides: ClassifierNode._label

_prob(self, x)

Return the posterior probability of each class given the input in a dict.

Overrides: ClassifierNode._prob

_stop_training(self)

Overrides: Node._stop_training

_train(self, x, labels)

Parameters:

x - 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.

Overrides: Node._train

_update_covs(self, x, lbl)

class_probabilities(self, x)

Return the posterior probability of each class given the input.

is_invertible()
Static Method

Return True if the node can be inverted, False otherwise.

Overrides: Node.is_invertible: (inherited documentation)

label(self, x)

Classify the input data using Maximum A-Posteriori.

Overrides: ClassifierNode.label

prob(self, x)

Return the posterior probability of each class given the input in a dict.

Overrides: ClassifierNode.prob

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, labels)

Update the internal structures according to the input data x.

x is a matrix having different variables on different columns and observations on the rows.

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

Note: a subclass supporting multiple training phases should implement the same signature for all the training phases and document the meaning of the arguments in the _train method doc-string. Having consistent signatures is a requirement to use the node in a flow.

Parameters:

x - 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.

Overrides: Node.train

Class GaussianClassifier

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

_check_train_args(self, x, labels)

_gaussian_prob(self, x, lbl_idx)

_label(self, x)

_prob(self, x)

_stop_training(self)

_train(self, x, labels)

_update_covs(self, x, lbl)

class_probabilities(self, x)

is_invertible() Static Method

label(self, x)

prob(self, x)

stop_training(self)

train(self, x, labels)

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

is_invertible()
Static Method