Package mdp :: Package nodes :: Class GaussianClassifierNode
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Class GaussianClassifierNode


Perform a supervised Gaussian classification.

Given a set of labelled data, the node fits a gaussian distribution
to each class. Note that it is written as an analysis node (i.e., the
execute function is the identity function). To perform classification,
use the 'classify' method. If instead you need the posterior
probability of the classes given the data use the 'class_probabilities'
method.

Nested Classes [hide private]
    Inherited from Node
  __metaclass__
This Metaclass is meant to overwrite doc strings of methods like execute, stop_training, inverse with the ones defined in the corresponding private methods _execute, _stop_training, _inverse, etc...
Instance Methods [hide private]
 
__init__(self, input_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.
 
_check_train_args(self, x, cl)
 
_gaussian_prob(self, x, lbl_idx)
Return the probability of the data points x with respect to a gaussian.
 
_get_supported_dtypes(self)
Return the list of dtypes supported by this node.
 
_set_input_dim(self, n)
 
_set_output_dim(self, n)
 
_stop_training(self)
 
_train(self, x, cl)
 
_update_covs(self, x, lbl)
 
class_probabilities(self, x)
Return the posterior probability of each class given the input.
 
classify(self, x)
Classify the input data using Maximum A-Posteriori.
 
is_invertible(self)
Return True if the node can be inverted, False otherwise.
 
train(self, x, cl)
Additional input arguments: cl -- 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.

Inherited from object: __delattr__, __getattribute__, __hash__, __new__, __reduce__, __reduce_ex__, __setattr__

    Inherited from Node
 
__add__(self, other)
 
__call__(self, x, *args, **kargs)
Calling an instance of Node is equivalent to call its 'execute' method.
 
__repr__(self)
repr(x)
 
__str__(self)
str(x)
 
_check_input(self, x)
 
_check_output(self, y)
 
_execute(self, x)
 
_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=-1)
Return a deep copy of the node.
 
execute(self, x, *args, **kargs)
Process the data contained in 'x'.
 
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.
 
inverse(self, y, *args, **kargs)
Invert 'y'.
 
is_trainable(self)
Return True if the node can be trained, False otherwise.
 
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'.
 
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.
Properties [hide private]

Inherited from object: __class__

    Inherited from Node
  _train_seq
List of tuples: [(training-phase1, stop-training-phase1), (training-phase2, stop_training-phase2), ...
  dtype
dtype
  input_dim
Input dimensions
  output_dim
Output dimensions
  supported_dtypes
Supported dtypes
Method Details [hide private]

__init__(self, input_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, cl)

 
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

_get_supported_dtypes(self)

 
Return the list of dtypes supported by this node.

Overrides: Node._get_supported_dtypes

_set_input_dim(self, n)

 
Overrides: Node._set_input_dim

_set_output_dim(self, n)

 
Overrides: Node._set_output_dim

_stop_training(self)

 
Overrides: Node._stop_training

_train(self, x, cl)

 
Overrides: Node._train

_update_covs(self, x, lbl)

 

class_probabilities(self, x)

 
Return the posterior probability of each class given the input.

classify(self, x)

 
Classify the input data using Maximum A-Posteriori.

is_invertible(self)

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

Overrides: Node.is_invertible
(inherited documentation)

train(self, x, cl)

 

Additional input arguments:
cl -- 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