public static IList <int> Minimize(bool[] labels, IFunction <ISet <int>, double> to_minimize, IFunction <ISet <int>, bool> constraints)
{
bool has_new = true;
ISet <int> all = new HashSet <int>(ToolsMathSeries.RangeInt32(0, labels.Length));
ISet <int> current = new HashSet <int>(ToolsMathSeries.RangeInt32(0, labels.Length));
while (has_new)
{
List <ISet <int> > options = new List <ISet <int> >();
GenerateOptionsRemove(all, current, options, constraints);
GenerateOptionsAdd(all, current, options, constraints);
Tuple <ISet <int>, double, bool> best = PickMinimal(current, options, to_minimize);
current = best.Item1;
double current_p = best.Item2;
has_new = best.Item3;
//Comment
Console.WriteLine(current_p);
}
List <int> current_list = new List <int>(current);
current_list.Sort();
return(current_list);
}
public static void PlotLinesXY(string file_path, double[] y_values_0)
{
PlotLine2D plot = new PlotLine2D();
double[] x_values = ToolsMathSeries.RangeFloat64(y_values_0.Length);
plot.AddSeries(x_values, y_values_0);
WriteToFile(file_path, plot.PlotModel, 800, 800);
}
public static void PlotLinesXY(string file_path, List <double[]> y_values_set)
{
PlotLine2D plot = new PlotLine2D();
foreach (double[] y_values in y_values_set)
{
double[] x_values = ToolsMathSeries.RangeFloat64(y_values.Length);
plot.AddSeries(x_values, y_values);
}
WriteToFile(file_path, plot.PlotModel, 800, 800);
}
public Tuple <IDataSet <FeatureType, LabelType>, IDataSet <FeatureType, LabelType> > Split(double fraction)
{
List <int> instances = new List <int>(ToolsMathSeries.RangeInt32(InstanceCount));
ToolsMathCollection.ShuffleIP(instances);
int set_0_size = (int)(instances.Count * fraction);
List <int> selected_instance_indexes_0 = ToolsCollection.Crop(instances, 0, set_0_size);
List <int> selected_instance_indexes_1 = ToolsCollection.Crop(instances, set_0_size, instances.Count);
IDataSet <FeatureType, LabelType> data_set_0 = SelectInstances(selected_instance_indexes_0);
IDataSet <FeatureType, LabelType> data_set_1 = SelectInstances(selected_instance_indexes_1);
return(new Tuple <IDataSet <FeatureType, LabelType>, IDataSet <FeatureType, LabelType> >(data_set_0, data_set_1));
}
public ToneSystem(double base_frequency, int tone_count, string [] all_tone_names, int[] selected_tone_indexes)
{
this.BaseFrequency = base_frequency;
this.ToneCount = tone_count;
this.ScaleToneCount = selected_tone_indexes.Length;
this.frequency_multiplyers = new double[tone_count];
this.notes_to_tones = ToolsMathSeries.RangeInt32(tone_count).Select(selected_tone_indexes);
this.tone_names = ToolsCollection.Copy(all_tone_names);
for (int tone_index = 0; tone_index < tone_count; tone_index++)
{
frequency_multiplyers[tone_index] = Math.Pow(2, tone_index / (double)tone_count);
}
}
private double[,] CreateRombergMatrix(IFunction <double, double> function, double lower_bound, double upper_bound,
int evaluation_levels)
{
double[] trapezoid_estimates = interal_evaluator.evaluate_multi_scale(function, lower_bound, upper_bound,
evaluation_levels);
double[,] romberg_matrix = new double[evaluation_levels, evaluation_levels];
for (int row_index = 0; row_index < evaluation_levels; row_index++)
{
romberg_matrix[row_index, 0] = trapezoid_estimates[row_index];
for (int col_index = 0; col_index < row_index; col_index++)
{
romberg_matrix[row_index, col_index + 1] = ToolsMathSeries.RichardsonExtrapolation(romberg_matrix[row_index - 1, col_index],
romberg_matrix[row_index, col_index], interal_evaluator.get_refinenment_scale(),
interal_evaluator.get_refinenment_scale() * (col_index + 1));
}
}
return(romberg_matrix);
}
public IDataContext SelectLabels(IList <int> selected_label_indexes)
{
return(SelectLabelsAndFeatures(ToolsMathSeries.RangeInt32(FeatureCount), selected_label_indexes));
}
public IDataContext SelectFeatures(IList <int> selected_feature_indexes)
{
return(SelectLabelsAndFeatures(selected_feature_indexes, ToolsMathSeries.RangeInt32(LabelCount)));
}
public static int[] RandomPermutation(this RandomNumberGenerator random, int count)
{
int[] numbers = ToolsMathSeries.RangeInt32(count);
return(numbers.OrderBy(x => random.RandomFloat32()).ToArray()); //TODO is a bit expencive
}
public IDataSet <FeatureType, LabelType> SelectLabels(IList <int> selected_label_indexes)
{
return(SelectInstancesFeaturesLabels(ToolsMathSeries.RangeInt32(InstanceCount), ToolsMathSeries.RangeInt32(FeatureCount), selected_label_indexes));
}
