def castToRegression(self, values):
"""Converts data set into a SupervisedDataSet for regression. Classes
are used as indices into the value array given."""
regDs = SupervisedDataSet(self.indim, 1)
fields = self.getFieldNames()
fields.remove('target')
for f in fields:
regDs.setField(f, self[f])
regDs.setField('target', values[self['class'].astype(int)])
return regDs
def __init__(self, inp, target, M, inp_transform=None, tgt_transform=None):
"""
Initialize an MDN dataset.
@param inp: int or np.array, input data or input dimension
@param target: int or np.array, target data or target dimension
@param M: number of Gaussian kernels #TODO: remove this from dataset!
@param inp_transform: set of input transformations that have been
applied to the dataset #TODO: remove
@param tgt_transform: #TODO: remove
"""
SupervisedDataSet.__init__(self, inp, target)
self.M = M
if np.isscalar(target):
self.c = target
else:
self.c = target.shape[1]
self.inp_transform=inp_transform
self.tgt_transform=tgt_transform
def __init__(self, inp, target=1, nb_classes=0, class_labels=None):
"""Initialize an empty dataset.
`inp` is used to specify the dimensionality of the input. While the
number of targets is given by implicitly by the training samples, it can
also be set explicity by `nb_classes`. To give the classes names, supply
an iterable of strings as `class_labels`."""
# FIXME: hard to keep nClasses synchronized if appendLinked() etc. is used.
SupervisedDataSet.__init__(self, inp, target)
self.addField('class', 1)
self.nClasses = nb_classes
if len(self) > 0:
# calculate class histogram, if we already have data
self.calculateStatistics()
self.convertField('target', int)
if class_labels is None:
self.class_labels = list(set(self.getField('target').flatten()))
else:
self.class_labels = class_labels
# copy classes (may be changed into other representation)
self.setField('class', self.getField('target'))
def __init__(self, indim, targetdim):
SupervisedDataSet.__init__(self, indim, targetdim)
# add field that stores the beginning of a new episode
self.addField('sequence_index', 1)
self.append('sequence_index', 0)
self.currentSeq = 0
def clear(self):
SupervisedDataSet.clear(self, True)
self._appendUnlinked('sequence_index', [0])
self.currentSeq = 0
def __init__(self, indim, targetdim):
SupervisedDataSet.__init__(self, indim, targetdim)
# add field that stores the beginning of a new episode
self.addField('sequence_index', 1)
self.append('sequence_index', 0)
self.currentSeq = 0
def clear(self):
SupervisedDataSet.clear(self, True)
self._appendUnlinked('sequence_index', [0])
self.currentSeq = 0
