void testTraining()
{
const char * filename = "files/node.txt";
const char * inputfilepath = "files/training/da/";
const char * targetfilepath = "files/training/rt/";
const char * testinputfilepath = "files/testing/da/";
const char * testtargetfilepath = "files/testing/rt/";
const char * errorlogfile = "files/errorlog.csv";
const char * outputfile = "files/outputfie.csv";
vector<string> nodes;
get_nodes(filename, &nodes);
cout<<"Nodes to Analyze:"<<endl;
for(int i = 0, len = nodes.size();i<len; i++)
{
cout<<nodes[i]<<endl;
}
cout<<"Processing training data..."<<endl;
Dataset dataset = Dataset(nodes, inputfilepath, targetfilepath,0);
cout<<"Done"<<endl;
cout<<"Starting Neural Net..."<<endl;
Network net = Network(dataset.num_io, 10, dataset.num_io, errorlogfile);
net.add_dataset(dataset.inputs,dataset.targets);
net.train(20,0.05,true);
cout<<"Training complete."<<endl;
net.save_errorlist();
cout<<"Training error log file saved in "<<errorlogfile<<endl;
cout<<"Processing testing data..."<<endl;
Dataset testdataset = Dataset(nodes, testinputfilepath, testtargetfilepath,0);
cout<<"Done"<<endl;
cout<<"Running testing set..."<<endl;
Output output = Output(&testdataset,&net, outputfile);
output.analyze_by_node(nodes,true);
cout<<"test results saved in "<<outputfile<<endl;
}
void testNN()
{
int dataset_len = 4;
int input_len =2;
int output_len = 2;
double input[] ={0,0,0,1,1,0,1,1};
const char * errorlogfile = "D:/CS/cs3/nn/errorlog.csv";
vector<vector<double> > inputdata(dataset_len);
for(int i=0; i<dataset_len; i++)
{
//inputdata[i].reserve(input_len);
vector<double> temp(input_len);
for(int j=0; j<input_len; j++)
{
temp[j] =input[i*input_len+j];
}
inputdata[i]=temp;
}
double target[] ={1,0,1,0,1,0,0,1};
vector<vector<double> > targetdata(dataset_len);
for(int i=0; i<dataset_len; i++)
{
vector<double> temp(input_len);
for(int j=0; j<output_len; j++)
{
temp[j] = target[i*output_len+j];
}
targetdata[i] = temp;
}
Network * net = new Network(input_len, 100, output_len, errorlogfile);
net->add_dataset(inputdata,targetdata);
net->train(50,0.02,true);
net->printoutput(inputdata,targetdata);
//net->printoutputlayer();
}