DSL_network LearnParamsEM(string data_infile, string network_infile, string child_name) {
DSL_dataset ds;
if (ds.ReadFile(data_infile.c_str()) != DSL_OKAY) {
cout << "Cannot read data file... exiting." << endl;
exit(1);
}
DSL_network originalNet;
if (originalNet.ReadFile(network_infile.c_str(), DSL_XDSL_FORMAT) != DSL_OKAY) {
cout << "Cannot read network... exiting." << endl;
exit(1);
}
int childIdx = originalNet.FindNode(child_name.c_str());
originalNet.GetNode(childIdx)->ChangeType(DSL_CPT);
vector<DSL_datasetMatch> matches;
string err;
if (ds.MatchNetwork(originalNet, matches, err) != DSL_OKAY) {
cout << "Cannot match network... exiting." << endl;
exit(1);
}
DSL_em em;
em.SetUniformizeParameters(true);
em.SetRandomizeParameters(true);
em.SetSeed(0);
em.SetEquivalentSampleSize(1);
if (em.Learn(ds, originalNet, matches) != DSL_OKAY) {
cout << "Cannot learn parameters... exiting." << endl;
exit(1);
}
return originalNet;
}
LearningInfo(string data_infile, string network_infile, string child_name) {
if (dataSet.ReadFile(data_infile.c_str()) != DSL_OKAY) {
cout << "Cannot read data file... exiting." << endl;
exit(1);
}
if (originalNet.ReadFile(network_infile.c_str(), DSL_XDSL_FORMAT) != DSL_OKAY) {
cout << "Cannot read network... exiting." << endl;
exit(1);
}
string err;
if (dataSet.MatchNetwork(originalNet, matches, err) != DSL_OKAY) {
cout << "Cannot match network... exiting." << endl;
exit(1);
}
for(unsigned int i=0 ; i < matches.size() ; ++i) {
matchNetToData[matches[i].node] = matches[i].column;
matchDataToNet[matches[i].column] = matches[i].node;
}
childIdx = originalNet.FindNode(child_name.c_str());
childNode = originalNet.GetNode(childIdx);
if (childNode->Definition()->GetType() != (DSL_CHANCE | DSL_DISCRETE | DSL_NOISY_MAX) ) {
cout << "Child should be a NoisyMAX... exiting" << endl;
// ewentualnie zmienic na noisy-max ręcznie
exit(1);
}
childMAXDefinition = new DSL_noisyMAX(*(childNode->Definition()));
DSL_intArray &parents = originalNet.GetParents(childNode->Handle());
numberOfParents = parents.NumItems();
parentIndices = vector<int>(numberOfParents, 0);
for(int i=0; i<numberOfParents; ++i)
parentIndices[i] = parents[i];
childDimension = childNode->Definition()->GetNumberOfOutcomes();
parentDimensions = vector<int>(numberOfParents, 0);
sumParentDimensions = 0;
parentOutcomesStrengths = vector<DSL_intArray>(numberOfParents);
minimalNumberOfParameters = 1; // minimal number of unique parameters to calculate (count leak right away)
for(int parentIdx = 0 ; parentIdx < numberOfParents ; ++parentIdx) {
DSL_node *parentNode = originalNet.GetNode(parentIndices[parentIdx]);
sumParentDimensions += (parentDimensions[parentIdx] = parentNode->Definition()->GetNumberOfOutcomes()); //parent dimension is equal to the number of outcomes
parentOutcomesStrengths[parentIdx] = childMAXDefinition->GetParentOutcomeStrengths(parentIdx);
//for (int stateIdx=0 ; stateIdx < parentDimensions[parentIdx] ; ++stateIdx)
// cout << parentOutcomesStrengths[parentIdx][stateIdx] << " ";
//cout << endl;
minimalNumberOfParameters += parentDimensions[parentIdx] - 1; // (each parent dimension reduced by one) because we don't count distinguished states of parents
distinguishedStates[parentIdx] = parentOutcomesStrengths[parentIdx][parentDimensions[parentIdx] - 1];
}
int sumOffset = 0;
parameterRowOffset = vi1D(numberOfParents + 1, 0); // +1 so we know the offset for LEAK column
for(int parentIdx = 0; parentIdx < numberOfParents ; ++parentIdx) {
parameterRowOffset[parentIdx] = sumOffset;
sumOffset += parentDimensions[parentIdx] - 1;
}
parameterRowOffset[numberOfParents] = sumOffset;
parametersRowLength = minimalNumberOfParameters;
minimalNumberOfParameters *= (childDimension - 1); // number of unique rows, last row is always 1.0 - sum
// DEBUG(minimalNumberOfParameters);
// DEBUG(childDimension);
// DEBUGV(parentDimensions);
// DEBUGV(parameterRowOffset);
//for(int j=0; j< 7 ; ++j) {
// DSL_datasetVarInfo vi = ds.GetVariableInfo(j);
// cout << "discreete:" << vi.discrete << " id:" << vi.id << endl << " missingInt:" << vi.missingInt << " mF:" << vi.missingFloat << "snames:"<< endl;
// for(int i=0;i<vi.stateNames.size(); ++i)
// cout << vi.stateNames[i]<< " ";
// cout <<endl;
//}
//for(int i = 0; i < ds.GetNumberOfRecords(); ++i) {
// vector<int> row(ds.GetNumberOfVariables(), 0);
// int sum_ones = 0;
// for(int j = 0; j < ds.GetNumberOfVariables(); ++j) {
// sum_ones += (row[j] = ds.GetInt(j,i));
// }
//}
//vector<int> rd = ds.GetIntData(0);
//cout <<"RDSize:"<<rd.size()<< endl;
//for(int i=0;i<rd.size();++i) {
// cout << vi.stateNames[rd[i]] << endl;
//}
//
}
string dynamicEMTraining(string theNet, string trainingSet){
// open the data set:
DSL_dataset ds;
if (ds.ReadFile(trainingSet.c_str()) != DSL_OKAY) {
cout << "Cannot read data file... exiting." << endl;
exit(1);
}
// open the network:
DSL_network net;
if (net.ReadFile(theNet.c_str(), DSL_XDSL_FORMAT) != DSL_OKAY) {
cout << "Cannot read network... exiting." << endl;
exit(1);
}
// match the data set and the network (variables):
vector<DSL_datasetMatch> dsMap(ds.GetNumberOfVariables());
int varCnt = 0; // the number of variables occuring both in the data set and the network
for (int i = 0; i < ds.GetNumberOfVariables(); i++) {
string id = ds.GetId(i);
const char* idc = id.c_str();
bool done = false;
for (int j = 0; j < (int) strlen(idc) && !done; j++) {
if (idc[j] == '_') {
char* nodeId = (char*) malloc((j+1) * sizeof(char));
strncpy(nodeId, idc, j);
nodeId[j] = '\0';
int nodeHdl = net.FindNode(nodeId);
if (nodeHdl >= 0) {
DSL_intArray orders;
net.GetTemporalOrders(nodeHdl, orders);
dsMap[varCnt].node = nodeHdl;
dsMap[varCnt].slice = atoi(idc + j + 1);
dsMap[varCnt].column = i;
varCnt++;
free(nodeId);
done = true;
}
}
}
if (!done) {
int nodeHdl = net.FindNode(idc);
if (nodeHdl >= 0) {
dsMap[varCnt].node = nodeHdl;
dsMap[varCnt].slice = 0;
dsMap[varCnt].column = i;
varCnt++;
}
}
}
dsMap.resize(varCnt);
// match the data set and the network (states):
for (int i = 0; i < dsMap.size(); i++) {
DSL_datasetMatch &m = dsMap[i];
int nodeHdl = m.node;
int colIdx = m.column;
DSL_idArray* ids = net.GetNode(nodeHdl)->Definition()->GetOutcomesNames();
const DSL_datasetVarInfo &varInfo = ds.GetVariableInfo(colIdx);
const vector<string> &stateNames = varInfo.stateNames;
vector<int> map(stateNames.size(), -1);
for (int j = 0; j < (int) stateNames.size(); j++) {
const char* id = stateNames[j].c_str();
for (int k = 0; k < ids->NumItems(); k++) {
char* tmpid = (*ids)[k];
if (!strcmp(id, tmpid)) {
map[j] = k;
}
}
}
for (int k = 0; k < ds.GetNumberOfRecords(); k++) {
if (ds.GetInt(colIdx, k) >= 0) {
ds.SetInt(colIdx, k, map[ds.GetInt(colIdx, k)]);
}
}
}
// learn parameters:
DSL_em em;
if (em.Learn(ds, net, dsMap) != DSL_OKAY) {
cout << "Cannot learn parameters... exiting." << endl;
exit(1);
}
net.WriteFile((theNet.insert(theNet.find_last_of("/") + 1, "res_")).c_str(), DSL_XDSL_FORMAT);
return theNet;
}
