using Bigtree.Algorithm.SVM;

using System;

using System.Collections.Generic;

using System.Linq;

using System.Text;

using System.Threading.Tasks;

namespace Bigtree.Algorithm.SVMTest

{

public static class SVMUtilities

{

private const double SCALE = 100;

public const int TRAINING_SEED = 20080524;

public const int TESTING_SEED = 20140407;

public static Problem CreateTwoClassProblem(int count, bool isTraining = true)

{

Problem prob = new Problem();

prob.Count = count;

prob.MaxIndex = 2;

Random rand = new Random(isTraining ? TRAINING_SEED : TESTING_SEED);

// create points on either side of the vertical axis

int positive = count / 2;

List<double> labels = new List<double>();

List<Node[]> data = new List<Node[]>();

for (int i = 0; i < count; i++)

{

double x = rand.NextDouble() * SCALE + 10;

double y = rand.NextDouble() * SCALE - (SCALE * .5);

x = i < positive ? x : -x;

data.Add(new Node[] { new Node(1, x), new Node(2, y) });

labels.Add(i < positive ? 1 : -1);

}

prob.X = data.ToArray();

prob.Y = labels.ToArray();

return prob;

}

public static Problem CreateMulticlassProblem(int numberOfClasses, int count, bool isTraining = true)

{

if (numberOfClasses > 8)

throw new ArgumentException("Number of classes must be < 8");

Problem prob = new Problem();

prob.Count = count;

prob.MaxIndex = 3;

int[] samplesPerClass = new int[numberOfClasses];

double countPerClass = (double)count / numberOfClasses;

double current = countPerClass;

for (int i = 1; i < samplesPerClass.Length; i++)

{

samplesPerClass[i] = (int)current;

current += countPerClass;

samplesPerClass[i - 1] = samplesPerClass[i] - samplesPerClass[i - 1];

}

samplesPerClass[samplesPerClass.Length - 1] = count - samplesPerClass.Last();

int[] xSigns = new int[8] { -1, 1, 1, -1, -1, 1, 1, -1 };

int[] ySigns = new int[8] { 1, 1, -1, -1, 1, 1, -1, -1 };

int[] zSigns = new int[8] { 1, 1, 1, 1, -1, -1, -1, -1 };

Random rand = new Random(isTraining ? TRAINING_SEED : TESTING_SEED);

List<double> labels = new List<double>();

List<Node[]> data = new List<Node[]>();

for (int i = 0; i < numberOfClasses; i++)

{

for (int j = 0; j < samplesPerClass[i]; j++)

{

double x = rand.NextDouble() * SCALE + 10;

double y = rand.NextDouble() * SCALE + 10;

double z = rand.NextDouble() * SCALE + 10;

x *= xSigns[i];

y *= ySigns[i];

z *= zSigns[i];

data.Add(new Node[] { new Node(1, x), new Node(2, y), new Node(3, z) });

labels.Add(i);

}

}

prob.X = data.ToArray();

prob.Y = labels.ToArray();

return prob;

}

public static Problem CreateRegressionProblem(int count, bool isTraining = true)

{

Problem prob = new Problem();

prob.Count = count;

prob.MaxIndex = 2;

Random rand = new Random(isTraining ? TRAINING_SEED : TESTING_SEED);

List<double> labels = new List<double>();

List<Node[]> data = new List<Node[]>();

for (int i = 0; i < count; i++)

{

double y = rand.NextDouble() * 10 - 5;

double z = rand.NextDouble() * 10 - 5;

double x = 2 * y + z;

data.Add(new Node[] { new Node(1, y), new Node(2, z) });

labels.Add(x);

}

prob.X = data.ToArray();

prob.Y = labels.ToArray();

return prob;

}

}

}