float MultilayerNN::trainOne(vector<float> tuple) {
float error;
// Set up topography & randomize weights if first run
if (!topoSet) {
setTopo(tuple);
}
// Set previous weights if size = 0
//if (previousWeights.size() == 0) previousWeights = weights;
// Set input nodes to training tuple values
for (int i = 0; i < inputNodes.size(); i++) {
inputNodes.at(i) = tuple.at(i);
}
// FIRST PASS: Feed forward through network
feedForward();
// SECOND PASS: Calculate error, propagate deltas back through network given desired output
error = addErrorForIteration(tuple.back());
backProp(tuple.back());
// THIRD PASS: Update weights
updateWeights();
return error;
}
void NeuralNetworkSimpleInternal::train(NeuralPatternArray const& patterns, const int count) throw()
{
const NeuralPattern *patternPtr = patterns.getArray();
if(patternPtr != 0)
{
for(int iteration = 0; iteration < count; iteration++)
{
const int numPatterns = patterns.size();
for(int patternIndex = 0; patternIndex < numPatterns; patternIndex++)
{
const NeuralPattern& pattern = patternPtr[patternIndex];
if(pattern.getInternal() != 0)
backProp(pattern.getInputVector(), pattern.getOutputVector());
}
}
}
}
