bool LoadModel(std::string filename)
{
bool success = saveRestore.ReadFile(filename);
if (success)
anInt = saveRestore.LoadParameter(anInt, "anInt");
return success;
}
int main()
{
string year = "2014";
string team = "ATL";
string col = "1";
// Load observations.
const string inputFile = "state_data/2014/2014ATL_state_col0.csv";
const string modelFile = "simulation_data/" + year + "/" + year + team + "sim_col" + col + ".xml";
mat dataSeq;
data::Load(inputFile, dataSeq, true);
// Load model, but first we have to determine its type.
SaveRestoreUtility sr;
sr.ReadFile(modelFile);
string type;
sr.LoadParameter(type, "hmm_type");
arma::Col<size_t> sequence;
if (type == "discrete")
{
HMM<DiscreteDistribution> hmm(1, DiscreteDistribution(1));
LoadHMM(hmm, sr);
// Verify only one row in observations.
if (dataSeq.n_cols == 1)
dataSeq = trans(dataSeq);
if (dataSeq.n_rows > 1)
std::cout<< "Only one-dimensional discrete observations allowed for "<< "discrete HMMs!" << endl;
hmm.Predict(dataSeq, sequence);
}
// Save output.
const string outputFile = "simulation_data/" + year + "/" + year + team + "sim_col" + col + "_data" + ".csv";
data::Save(outputFile, sequence, true);
}
int main(int argc, char** argv)
{
// Parse command line options.
CLI::ParseCommandLine(argc, argv);
// Set random seed.
if (CLI::GetParam<int>("seed") != 0)
RandomSeed((size_t) CLI::GetParam<int>("seed"));
else
RandomSeed((size_t) time(NULL));
// Load observations.
const string modelFile = CLI::GetParam<string>("model_file");
const int length = CLI::GetParam<int>("length");
const int startState = CLI::GetParam<int>("start_state");
if (length <= 0)
{
Log::Fatal << "Invalid sequence length (" << length << "); must be greater "
<< "than or equal to 0!" << endl;
}
// Load model, but first we have to determine its type.
SaveRestoreUtility sr;
sr.ReadFile(modelFile);
string emissionType;
sr.LoadParameter(emissionType, "emission_type");
mat observations;
Col<size_t> sequence;
if (emissionType == "DiscreteDistribution")
{
HMM<DiscreteDistribution> hmm(1, DiscreteDistribution(1));
hmm.Load(sr);
if (startState < 0 || startState >= (int) hmm.Transition().n_rows)
{
Log::Fatal << "Invalid start state (" << startState << "); must be "
<< "between 0 and number of states (" << hmm.Transition().n_rows
<< ")!" << endl;
}
hmm.Generate(size_t(length), observations, sequence, size_t(startState));
}
else if (emissionType == "GaussianDistribution")
{
HMM<GaussianDistribution> hmm(1, GaussianDistribution(1));
hmm.Load(sr);
if (startState < 0 || startState >= (int) hmm.Transition().n_rows)
{
Log::Fatal << "Invalid start state (" << startState << "); must be "
<< "between 0 and number of states (" << hmm.Transition().n_rows
<< ")!" << endl;
}
hmm.Generate(size_t(length), observations, sequence, size_t(startState));
}
else if (emissionType == "GMM")
{
HMM<GMM<> > hmm(1, GMM<>(1, 1));
hmm.Load(sr);
if (startState < 0 || startState >= (int) hmm.Transition().n_rows)
{
Log::Fatal << "Invalid start state (" << startState << "); must be "
<< "between 0 and number of states (" << hmm.Transition().n_rows
<< ")!" << endl;
}
hmm.Generate(size_t(length), observations, sequence, size_t(startState));
}
else
{
Log::Fatal << "Unknown HMM type '" << emissionType << "' in file '" << modelFile
<< "'!" << endl;
}
// Save observations.
const string outputFile = CLI::GetParam<string>("output_file");
data::Save(outputFile, observations, true);
// Do we want to save the hidden sequence?
const string sequenceFile = CLI::GetParam<string>("state_file");
if (sequenceFile != "")
data::Save(sequenceFile, sequence, true);
return 0;
}
int main(int argc, char** argv)
{
// Parse command line options.
CLI::ParseCommandLine(argc, argv);
// Load observations.
const string inputFile = CLI::GetParam<string>("input_file");
const string modelFile = CLI::GetParam<string>("model_file");
mat dataSeq;
data::Load(inputFile, dataSeq, true);
// Load model, but first we have to determine its type.
SaveRestoreUtility sr;
sr.ReadFile(modelFile);
string type;
sr.LoadParameter(type, "hmm_type");
double loglik = 0;
if (type == "discrete")
{
HMM<DiscreteDistribution> hmm(1, DiscreteDistribution(1));
LoadHMM(hmm, sr);
// Verify only one row in observations.
if (dataSeq.n_cols == 1)
dataSeq = trans(dataSeq);
if (dataSeq.n_rows > 1)
Log::Fatal << "Only one-dimensional discrete observations allowed for "
<< "discrete HMMs!" << endl;
loglik = hmm.LogLikelihood(dataSeq);
}
else if (type == "gaussian")
{
HMM<GaussianDistribution> hmm(1, GaussianDistribution(1));
LoadHMM(hmm, sr);
// Verify correct dimensionality.
if (dataSeq.n_rows != hmm.Emission()[0].Mean().n_elem)
Log::Fatal << "Observation dimensionality (" << dataSeq.n_rows << ") "
<< "does not match HMM Gaussian dimensionality ("
<< hmm.Emission()[0].Mean().n_elem << ")!" << endl;
loglik = hmm.LogLikelihood(dataSeq);
}
else if (type == "gmm")
{
HMM<GMM<> > hmm(1, GMM<>(1, 1));
LoadHMM(hmm, sr);
// Verify correct dimensionality.
if (dataSeq.n_rows != hmm.Emission()[0].Dimensionality())
Log::Fatal << "Observation dimensionality (" << dataSeq.n_rows << ") "
<< "does not match HMM Gaussian dimensionality ("
<< hmm.Emission()[0].Dimensionality() << ")!" << endl;
loglik = hmm.LogLikelihood(dataSeq);
}
else
{
Log::Fatal << "Unknown HMM type '" << type << "' in file '" << modelFile
<< "'!" << endl;
}
cout << loglik << endl;
}
bool SaveModel(std::string filename)
{
saveRestore.SaveParameter(anInt, "anInt");
return saveRestore.WriteFile(filename);
}
