/// <summary>
/// Searches for the best combination of parameters that results in the most accurate model.
/// </summary>
///
/// <param name="bestParameters">The best combination of parameters found by the grid search.</param>
/// <param name="error">The minimum error of the best model found by the grid search.</param>
/// <returns>The best model found during the grid search.</returns>
///
public TModel Compute(out GridSearchParameterCollection bestParameters, out double error)
{
var result = Compute();
bestParameters = result.Parameter;
error = result.Error;
return(result.Model);
}
/// <summary>
/// Searches for the best combination of parameters that results in the most accurate model.
/// </summary>
///
/// <returns>The results found during the grid search.</returns>
///
public GridSearchResult <TModel> Compute()
{
// Get the total number of different parameters
var values = new GridSearchParameter[ranges.Count][];
for (int i = 0; i < values.Length; i++)
{
values[i] = ranges[i].GetParameters();
}
// Generate the Cartesian product between all parameters
GridSearchParameter[][] grid = Matrix.CartesianProduct(values);
// Initialize the search
var parameters = new GridSearchParameterCollection[grid.Length];
var models = new TModel[grid.Length];
var errors = new double[grid.Length];
var msgs = new string[grid.Length];
// Search the grid for the optimal parameters
Parallel.For(0, grid.Length, i =>
{
// Get the current parameters for the current point
parameters[i] = new GridSearchParameterCollection(grid[i]);
try
{
// Try to fit a model using the parameters
models[i] = Fitting(parameters[i], out errors[i]);
}
catch (ConvergenceException ex)
{
errors[i] = Double.PositiveInfinity;
msgs[i] = ex.Message;
}
});
// Select the minimum error
int best; errors.Min(out best);
// Return the best model found.
return(new GridSearchResult <TModel>(grid.Length,
parameters, models, errors, best));
}
