Package mdp :: Package nodes :: Class NearestMeanClassifier
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Class NearestMeanClassifier


Nearest-Mean classifier.
Instance Methods [hide private]
 
__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