29 for(
unsigned int c=0;c<data.size();c++)
30 if(seq > data.at(c).size())
32 data.at(c).resize(seq);
39 if(cls >=
unsigned(numClasses()) || seq >=
unsigned(numSequences(cls)))
43 this->data[cls][seq] = new_data;
48 unsigned int seq,
EEGData &new_data)
50 if(cls >=
unsigned(numClasses()) || seq >=
unsigned(numSequences(cls)))
53 this->
set(cls,seq,new_data);
55 if(this->numSamples(cls,seq)==0)
57 this->
set(cls,seq,new_data);
61 data.at(cls).at(seq).append(new_data);
67 return data[cls][seq];
73 return data.at(c).at(s);
80 for(
unsigned int c=0; c<data.size(); c++)
82 for(
unsigned int s=0; s<data[c].size();s++)
93 ublas::vector<int> ret;
94 ret.resize(data.size() * data.at(0).size());
96 for(
unsigned int c=0; c<data.size(); c++)
98 for(
unsigned int s=0; s<data.at(c).size(); s++)
108 return sequence_order;
119 for (
unsigned cls = 0; cls < data.size(); ++cls)
122 for (
unsigned seq = 0; seq < data[cls].size(); ++seq)
125 size += data[cls][seq].size2();
130 ublas::vector<int> targets(size);
133 unsigned samp_index = 0;
136 for (
unsigned cls = 0; cls < data.size(); ++cls)
139 for (
unsigned seq = 0; seq < data[cls].size(); ++seq)
142 for (
int samp = 0; samp < data[cls][seq].size2(); ++samp)
145 targets[samp_index] = cls;
164 return data.at(0).size();
169 if(cls >=
unsigned(numClasses()))
172 return data.at(cls).size();
178 for(
unsigned int i=0; i < data.at(cls).size(); i++)
180 num += ublas::matrix<double>(data.at(cls).at(i)).size2();
187 return this->data.at(cls).at(seq).numSamples();
193 for(
unsigned int i=0; i < data.size(); i++)
195 num += samplesInClass(i);
202 if(data.size() == 0 || data.at(0).size() == 0)
205 return data.at(0).at(0).size1();
220 return channel_names;
232 this->class_labels = labels;
238 this->channel_names = names;
244 this->filtered = filtered;
251 os <<
"EEG Training Data: " << d.
numClasses() <<
" classes "