Package mdp :: Package contrib :: Class LibSVMNode
[hide private]
[frames] | no frames]

Class LibSVMNode



Problems with LibSVM:
- Screen output can only be disabled when a #if 1 clause in the cpp file is disabled

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, probability=True, input_dim=None, dtype=None)
probability -- Shall the probability be computed
 
_cross_validation(self, num_parts, parameter)
 
_stop_training(self)
 
_train(self, x, cl)
Update the internal structures according to the input data 'x'.
 
_train_problem(self, labels, features, parameter)
 
classify(self, x)
 
grid_parameter_search(self, param_range)
 
probability(self, x)
 
setKernel(self, kernel)

Inherited from svm_nodes._SVMNode: is_invertible, train

Inherited from svm_nodes._SVMNode (private): _check_train_args, _normalize_labels, _set_input_dim, _set_output_dim

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_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=-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.
Class Variables [hide private]
  kernels = ['RBF', 'LINEAR', 'POLY', 'SIGMOID']
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, probability=True, input_dim=None, dtype=None)
(Constructor)

 

probability -- Shall the probability be computed

Overrides: object.__init__

_cross_validation(self, num_parts, parameter)

 

_stop_training(self)

 
Overrides: Node._stop_training

_train(self, x, cl)

 
Update the internal structures according to the input data 'x'.

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

_train_problem(self, labels, features, parameter)

 

classify(self, x)

 

grid_parameter_search(self, param_range)

 

probability(self, x)

 

setKernel(self, kernel)

 

Class Variable Details [hide private]

kernels

Value:
['RBF', 'LINEAR', 'POLY', 'SIGMOID']