def __init__(self, separated_input_data, separated_weights = None, approx = -1 ):
CDataset.__init__(self,separated_input_data)
self.weights = separated_weights
# If approx > 0 then approximately compute mu, cov, var, std using
# 100*approx percent of the total weights
self.approx = approx
def concat(self,j,input_data,weights = None):
"""Concatenate a few samples to class j.
Input:
j: class j
input_data: an array of samples
weights: the additional weights of the samples
if weights is None xor self.weights is None:
raise an error
else:
concatenate the new weights to the old weights list
Output:
The WeightedCDataset is updated.
"""
CDataset.concat(self,j,input_data)
if (weights is None) ^ (self.weights is None):
raise ValueError('weights and self.weights are not compatible')
if weights is not None:
self.weights[j] = concatenate([self.weights[j],weights])
def dofilter(self,j,filterarray):
"""Filter away some samples in class j.
Input:
j: class j
filterarray: a 'bool' numpy.array of size self.nspc[j]
Output:
fr: filtering rate, the new number of samples of class j divided
by the old number of samples of class j.
The WeightedCDataset is updated.
"""
if self.weights is not None:
self.weights[j] = self.weights[j][filterarray]
return CDataset.dofilter(self,j,filterarray)