C++ LinearRegression::Lambda方法代码示例

int main(int argc, char* argv[])
{
  // Handle parameters.
  CLI::ParseCommandLine(argc, argv);

  const string inputModelFile = CLI::GetParam<string>("input_model_file");
  const string outputModelFile = CLI::GetParam<string>("output_model_file");
  const string outputPredictionsFile =
      CLI::GetParam<string>("output_predictions");
  const string trainingResponsesFile =
      CLI::GetParam<string>("training_responses");
  const string testFile = CLI::GetParam<string>("test_file");
  const string trainFile = CLI::GetParam<string>("training_file");
  const double lambda = CLI::GetParam<double>("lambda");

  mat regressors;
  mat responses;

  LinearRegression lr;
  lr.Lambda() = lambda;

  bool computeModel = false;

  // We want to determine if an input file XOR model file were given.
  if (!CLI::HasParam("training_file"))
  {
    if (!CLI::HasParam("input_model_file"))
      Log::Fatal << "You must specify either --input_file or --model_file."
          << endl;
    else // The model file was specified, no problems.
      computeModel = false;
  }
  // The user specified an input file but no model file, no problems.
  else if (!CLI::HasParam("input_model_file"))
    computeModel = true;
  // The user specified both an input file and model file.
  // This is ambiguous -- which model should we use? A generated one or given
  // one?  Report error and exit.
  else
  {
    Log::Fatal << "You must specify either --input_file or --model_file, not "
        << "both." << endl;
  }

  if (CLI::HasParam("test_file") && !CLI::HasParam("output_predictions"))
    Log::Warn << "--test_file (-t) specified, but --output_predictions "
        << "(-o) is not; no results will be saved." << endl;

  // If they specified a model file, we also need a test file or we
  // have nothing to do.
  if (!computeModel && !CLI::HasParam("test_file"))
  {
    Log::Fatal << "When specifying --model_file, you must also specify "
        << "--test_file." << endl;
  }

  if (!computeModel && CLI::HasParam("lambda"))
  {
    Log::Warn << "--lambda ignored because no model is being trained." << endl;
  }

  // An input file was given and we need to generate the model.
  if (computeModel)
  {
    Timer::Start("load_regressors");
    data::Load(trainFile, regressors, true);
    Timer::Stop("load_regressors");

    // Are the responses in a separate file?
    if (CLI::HasParam("training_responses"))
    {
      // The initial predictors for y, Nx1.
      responses = trans(regressors.row(regressors.n_rows - 1));
      regressors.shed_row(regressors.n_rows - 1);
    }
    else
    {
      // The initial predictors for y, Nx1.
      Timer::Start("load_responses");
      data::Load(trainingResponsesFile, responses, true);
      Timer::Stop("load_responses");

      if (responses.n_rows == 1)
        responses = trans(responses); // Probably loaded backwards.

      if (responses.n_cols > 1)
        Log::Fatal << "The responses must have one column.\n";

      if (responses.n_rows != regressors.n_cols)
        Log::Fatal << "The responses must have the same number of rows as the "
            "training file.\n";
    }

    Timer::Start("regression");
    lr = LinearRegression(regressors, responses.unsafe_col(0));
    Timer::Stop("regression");

    // Save the parameters.
    if (CLI::HasParam("output_model_file"))
      data::Save(outputModelFile, "linearRegressionModel", lr);
//.........这里部分代码省略.........