public void Test_factorial()
{
var result = Math_Utilities.factorial(3);
var expected = 6;
Assert.AreEqual(expected, result);
}
public override MEL_ArmRepertoire__Individual Generate_Offspring(
Random randomness_provider,
MEL_ArmRepertoire__Individual parent
)
{
List <double> genome_copy = parent.genotype.DeepCopy();
double value_range = max_gene_value - min_gene_value;
for (int i = 0; i < genome_copy.Count; i++)
{
double mutation_value = Math_Utilities.Map_Value(
randomness_provider.NextDouble(),
0.0,
1.0,
-mutation_step,
mutation_step
);
genome_copy[i] += mutation_value;
if (genome_copy[i] > max_gene_value)
{
genome_copy[i] -= value_range;
}
else if (genome_copy[i] < min_gene_value)
{
genome_copy[i] += value_range;
}
}
MEL_ArmRepertoire__Individual offspring = new MEL_ArmRepertoire__Individual(genome_copy);
return(offspring);
}
public void Test_Bernstein()
{
var result = Math_Utilities.Bernstein(0, 0.4);
var expected = 0.216;
Assert.IsTrue(Math.Abs(expected - result) < 0.0001d);
}
public void Test_Binomial_coef()
{
var result = Math_Utilities.binom_coef(1);
double expected = 3;
Assert.AreEqual(expected, result);
}
public void Cubic_curve(Point2d[] coordinates, int num_of_path_pts, Point2d[] path_pts)
{
int icount, jcount;
double step, t;
Point2d zero_point = new Point2d(0.0, 0.0);
icount = 0;
t = 0;
step = (double)1.0 / (num_of_path_pts - 1);
for (int k = 0; k != num_of_path_pts; k++)
{
if ((1.0 - t) < 0.000005)
{
t = 1.0;
}
jcount = 0;
path_pts[icount] = zero_point;
for (int i = 0; i != 4; i++)
{
double basis = Math_Utilities.Bernstein(i, t);
path_pts[icount] = path_pts[icount] + basis * coordinates[jcount];
jcount = jcount + 1;
}
icount += 1;
t += step;
}
}
public static Bitmap To_HeatMap(
this double[,] values_table,
double min_value,
double max_value,
Color out_of_range__low__color,
Color out_of_range__high__color,
Color not_a_number__color
)
{
int w = values_table.GetLength(0);
int h = values_table.GetLength(1);
Bitmap image = new Bitmap(w, h);
for (int x = 0; x < w; x++)
{
for (int y = 0; y < h; y++)
{
if (min_value == max_value)
{
double value = values_table[x, y];
if (Double.IsNaN(value))
{
image.SetPixel(x, y, not_a_number__color);
}
else
{
Color c = Color.FromArgb(0, 0, 0);
image.SetPixel(x, y, c);
}
}
else
{
double value = values_table[x, y];
if (Double.IsNaN(value))
{
image.SetPixel(x, y, not_a_number__color);
}
else if (value < min_value)
{
image.SetPixel(x, y, out_of_range__low__color);
}
else if (value > max_value)
{
image.SetPixel(x, y, out_of_range__high__color);
}
else
{
double cv = Math_Utilities.Map_Value(value, min_value, max_value, 0.0, 255.0);
int ci = (int)cv;
Color c = Color.FromArgb(ci, ci, ci);
image.SetPixel(x, y, c);
}
}
}
}
return(image);
}
public static Bitmap To_HeatMap(
this int[,] values_table,
int min_value,
int max_value,
Color out_of_range__low__color,
Color out_of_range__high__color
)
{
Bitmap image = new Bitmap(values_table.GetLength(0), values_table.GetLength(1));
for (int x = 0; x < values_table.GetLength(0); x++)
{
for (int y = 0; y < values_table.GetLength(1); y++)
{
double value = values_table[x, y];
if (value < min_value)
{
image.SetPixel(x, y, out_of_range__low__color);
}
else if (value > max_value)
{
image.SetPixel(x, y, out_of_range__high__color);
}
else
{
if (min_value == max_value)
{
Color c = Color.FromArgb(0, 0, 0);
image.SetPixel(x, y, c);
}
else
{
double cv = Math_Utilities.Map_Value(value, min_value, max_value, 0.0, 255.0);
int ci = (int)cv;
Color c = Color.FromArgb(ci, ci, ci);
image.SetPixel(x, y, c);
}
}
}
}
return(image);
}
public override MEL_Rastrigin__Individual Generate_Individual(Random randomness_provider)
{
List <double> genotype = new List <double>();
for (int i = 0; i < num_dimensions; i++)
{
double this_gene_value = Math_Utilities.Map_Value(
randomness_provider.NextDouble(),
0.0,
1.0,
min_gene_value,
max_gene_value
);
genotype.Add(this_gene_value);
}
MEL_Rastrigin__Individual individual = new MEL_Rastrigin__Individual(
genotype
);
return(individual);
}
