/// <summary>
/// Gets the array of GridSearchParameters to try.
/// </summary>
///
public GridSearchParameter[] GetParameters()
{
GridSearchParameter[] parameters = new GridSearchParameter[Values.Length];
for (int i = 0; i < Values.Length; i++)
{
parameters[i] = new GridSearchParameter(Name, Values[i]);
}
return(parameters);
}
/// <summary>
/// Determines whether the specified object is equal
/// to the current GridSearchParameter object.
/// </summary>
///
public override bool Equals(object obj)
{
if (obj is GridSearchParameter)
{
GridSearchParameter g = (GridSearchParameter)obj;
if (g.name != name || g.value != value)
{
return(false);
}
return(true);
}
return(false);
}
/// <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)
{
// 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];
int best;
// Search the grid for the optimal parameters
GABIZ.Base.Parallel.For(0, grid.Length, i =>
{
// Get the current parameters for the current point
parameters[i] = new GridSearchParameterCollection(grid[i]);
// Try to fit a model using the parameters
models[i] = Fitting(parameters[i], out errors[i]);
});
// Select the minimum error
error = errors.Min(out best);
bestParameters = parameters[best];
// Return the best model found.
return(models[best]);
}