//' @title Find the Rank Models result
//' @description Provides the core material to create an S3 object for rank.models
//' @param data A \code{vec} of data.
//' @param model_str A \code{vector<vector<string>>} that gives a list of models to test.
//' @param full_model A \code{vector<string>} that contains the largest / full model.
//' @param alpha A \code{double} that indicates the alpha level for CIs.
//' @param compute_v A \code{string} indicating the type of V matrix to generate
//' @param model_type A \code{string} that describes the model generation / transformation: 'ssm' or 'imu'
//' @param K A \code{int} that controls how many times the GMWM is run.
//' @param H A \code{int} that controls how many bootstraps occur.
//' @param G A \code{int} that controls how many guesses occur.
//' @param robust A \code{bool} that indicates whether to use classical or robust wavelet variance.
//' @param eff A \code{double} that indicates the efficiency to use.
//' @param bs_optimism A \code{bool} that indicates whether the model selection score should be calculated with bootstrap or asymptotics.
//' @param seed A \code{unsigned int} that is the seed one wishes to use.
//' @return A \code{field<field<field<mat>>>} that contains the model score matrix and the best GMWM model object.
//' @keywords internal
// [[Rcpp::export]]
arma::field< arma::field<arma::field<arma::mat> > > rank_models_cpp(arma::vec& data,
const std::vector<std::vector < std::string > >& model_str,
const std::vector< std::string >& full_model,
double alpha,
std::string compute_v, std::string model_type,
unsigned int K, unsigned int H, unsigned int G,
bool robust, double eff, bool bs_optimism, unsigned int seed){
std::set<std::vector < std::string > > models = vector_to_set(model_str);
arma::field< arma::field<arma::field<arma::mat> > > h(1);
h(0) = model_select(data,
models,
full_model,
model_type,
bs_optimism,
alpha,
compute_v,
K, H, G,
robust, eff, seed);
return h;
}
int main()
{
config_parse_remote_servers("servers.txt");
trainingdata_t* data = trainingdata_new();
int t;
for(t=0;t<256;t++) {
example_t*e = example_new(16);
int s;
for(s=0;s<16;s++) {
e->inputs[s] = variable_new_continuous((lrand48()%256)/256.0);
}
e->desired_response = variable_new_categorical(t%2);
trainingdata_add_example(data, e);
}
trainingdata_save(data, "/tmp/data.data");
trainingdata_destroy(data);
data = trainingdata_load("/tmp/data.data");
model_t*m = model_select(data);
char*code = model_generate_code(m, "python");
printf("%s\n", code);
free(code);
model_destroy(m);
trainingdata_destroy(data);
}
static PyObject* py_dataset_get_model(PyObject*_self, PyObject* args, PyObject* kwargs)
{
DataSetObject*self = (DataSetObject*)_self;
static char *kwlist[] = {"name", NULL};
const char*name = 0;
if (args && !PyArg_ParseTupleAndKeywords(args, kwargs, "|s", kwlist, &name))
return NULL;
int num_examples = self->data->num_examples;
if(!num_examples) {
return PY_ERROR("No training data given. Can't build a model from no data.");
}
if(!trainingdata_check_format(self->data)) {
return PY_ERROR("bad training data");
}
model_t*model = NULL;
if(name == NULL) {
model = model_select(self->data);
} else {
model = model_train_specific_model(self->data, name);
if(!model)
return PY_ERROR("unknown model %s", name);
}
if(!model)
return PY_NONE;
ModelObject*ret = PyObject_New(ModelObject, &ModelClass);
ret->model = model;
return (PyObject*)ret;
}
//' @title Find the auto imu result
//' @description Provides the core material to create an S3 object for auto.imu
//' @param data A \code{mat} containing multiple columns of independent data with the same number of observations.
//' @param model_str A \code{vector<vector<string>>} that gives a list of models to test.
//' @param full_model A \code{vector<string>} that contains the largest / full model.
//' @param alpha A \code{double} that indicates the alpha level for CIs.
//' @param compute_v A \code{string} indicating the type of V matrix to generate
//' @param model_type A \code{string} that describes the model generation / transformation: 'ssm' or 'imu'
//' @param K A \code{int} that controls how many times the GMWM is run.
//' @param H A \code{int} that controls how many bootstraps occur.
//' @param G A \code{int} that controls how many guesses occur.
//' @param robust A \code{bool} that indicates whether to use classical or robust wavelet variance.
//' @param eff A \code{double} that indicates the efficiency to use.
//' @param bs_optimism A \code{bool} that indicates whether the model selection score should be calculated with bootstrap or asymptotics.
//' @param seed A \code{unsigned int} that is the seed one wishes to use.
//' @return A \code{field<field<field<mat>>>} that contains the model score matrix and the best GMWM model object.
//' @keywords internal
// [[Rcpp::export]]
arma::field< arma::field<arma::field<arma::mat> > > auto_imu_cpp(arma::mat& data,
const arma::mat& combs,
const std::vector< std::string >& full_model,
double alpha,
std::string compute_v, std::string model_type,
unsigned int K, unsigned int H, unsigned int G,
bool robust, double eff, bool bs_optimism, unsigned int seed){
// Number of columns to process
unsigned int V = data.n_cols;
// Create a set of unique models.
std::set<std::vector<std::string > > models = build_model_set(combs, full_model);
arma::field< arma::field<arma::field<arma::mat> > > h(V);
for(unsigned int i = 0; i < V; i++){
Rcpp::Rcout << "Generating models for the " << i + 1 << " column in the data set " << std::endl << std::endl;
arma::vec signal_col = data.col(i);
h(i) = model_select(signal_col,
models,
full_model,
model_type,
bs_optimism,
alpha,
compute_v,
K, H, G,
robust, eff, seed);
Rcpp::Rcout << std::endl;
}
return h;
}
void SvmMachine::run( const std::string purpose, Options& options )
{
if( purpose == "model-selection" )
{
model_select( options );
}
else if( purpose == "model-validation" )
{
model_validate( options );
}
else if( purpose == "prediction" )
{
predict( options );
}
else//problem. Log and Exit.
{
std::string msg;
msg.append( "Error: Purpose is not valid: " + purpose );
msg.append( "\nType hivm --help for usage. \nExiting..." );
Log::append( msg );
std::cerr << msg;
return;
}
}