C# DoubleMatrix类代码示例

 public static void MyClassInitialize(TestContext testContext) {
   random = new MersenneTwister();
   symmetricDistances = new DoubleMatrix(ProblemSize, ProblemSize);
   symmetricWeights = new DoubleMatrix(ProblemSize, ProblemSize);
   asymmetricDistances = new DoubleMatrix(ProblemSize, ProblemSize);
   asymmetricWeights = new DoubleMatrix(ProblemSize, ProblemSize);
   nonZeroDiagonalDistances = new DoubleMatrix(ProblemSize, ProblemSize);
   nonZeroDiagonalWeights = new DoubleMatrix(ProblemSize, ProblemSize);
   for (int i = 0; i < ProblemSize - 1; i++) {
     for (int j = i + 1; j < ProblemSize; j++) {
       symmetricDistances[i, j] = random.Next(ProblemSize * 100);
       symmetricDistances[j, i] = symmetricDistances[i, j];
       symmetricWeights[i, j] = random.Next(ProblemSize * 100);
       symmetricWeights[j, i] = symmetricWeights[i, j];
       asymmetricDistances[i, j] = random.Next(ProblemSize * 100);
       asymmetricDistances[j, i] = random.Next(ProblemSize * 100);
       asymmetricWeights[i, j] = random.Next(ProblemSize * 100);
       asymmetricWeights[j, i] = random.Next(ProblemSize * 100);
       nonZeroDiagonalDistances[i, j] = random.Next(ProblemSize * 100);
       nonZeroDiagonalDistances[j, i] = random.Next(ProblemSize * 100);
       nonZeroDiagonalWeights[i, j] = random.Next(ProblemSize * 100);
       nonZeroDiagonalWeights[j, i] = random.Next(ProblemSize * 100);
     }
     nonZeroDiagonalDistances[i, i] = random.Next(ProblemSize * 100);
     nonZeroDiagonalWeights[i, i] = random.Next(ProblemSize * 100);
   }
   int index = random.Next(ProblemSize);
   if (nonZeroDiagonalDistances[index, index] == 0)
     nonZeroDiagonalDistances[index, index] = random.Next(1, ProblemSize * 100);
   index = random.Next(ProblemSize);
   if (nonZeroDiagonalWeights[index, index] == 0)
     nonZeroDiagonalWeights[index, index] = random.Next(1, ProblemSize * 100);
   assignment = new Permutation(PermutationTypes.Absolute, ProblemSize, random);
 }

		public void Current()
		{
			DoubleMatrix test = new DoubleMatrix(new double[2, 2] { { 1, 2 }, { 3, 4 } });
			IEnumerator enumerator = test.GetEnumerator();
			bool movenextresult;

			movenextresult = enumerator.MoveNext();
			Assert.IsTrue(movenextresult);
			Assert.AreEqual(enumerator.Current, test[0, 0]);

			movenextresult = enumerator.MoveNext();
			Assert.IsTrue(movenextresult);
			Assert.AreEqual(enumerator.Current, test[1, 0]);

			movenextresult = enumerator.MoveNext();
			Assert.IsTrue(movenextresult);
			Assert.AreEqual(enumerator.Current, test[0, 1]);

			movenextresult = enumerator.MoveNext();
			Assert.IsTrue(movenextresult);
			Assert.AreEqual(enumerator.Current, test[1, 1]);

			movenextresult = enumerator.MoveNext();
			Assert.IsFalse(movenextresult);
		}

 /// <summary>
 /// Checks if all elements of the given <paramref name="vector"/> are inside the bounds and if not, elements are set to the respective values of the bounds.
 /// </summary>
 /// <param name="bounds">The lower and upper bound (1st and 2nd column) of the positions in the vector. If there are less rows than dimensions, the rows are cycled.</param>
 /// <param name="vector">The vector to check.</param>
 /// <returns>The corrected real vector.</returns>
 public static void Apply(RealVector vector, DoubleMatrix bounds) {
   for (int i = 0; i < vector.Length; i++) {
     double min = bounds[i % bounds.Rows, 0], max = bounds[i % bounds.Rows, 1];
     if (vector[i] < min) vector[i] = min;
     if (vector[i] > max) vector[i] = max;
   }
 }

    internal static ArrayList run(IList para)
    {
        modshogun.init_shogun_with_defaults();
        int degree = (int)((int?)para[0]);

        string[] fm_train_dna = Load.load_dna("../data/fm_train_dna.dat");
        string[] fm_test_dna = Load.load_dna("../data/fm_test_dna.dat");

        StringCharFeatures feats_train = new StringCharFeatures(fm_train_dna, DNA);
        StringCharFeatures feats_test = new StringCharFeatures(fm_test_dna, DNA);

        WeightedDegreeStringKernel kernel = new WeightedDegreeStringKernel(feats_train, feats_train, degree);
        double[] w = new double[degree];
        double sum = degree * (degree + 1)/2;
        for (int i = 0; i < degree; i++)
        {
            w[i] = (degree - i)/sum;
        }

        DoubleMatrix weights = new DoubleMatrix(1, degree, w);
        kernel.set_wd_weights(weights);

        DoubleMatrix km_train = kernel.get_kernel_matrix();
        kernel.init(feats_train, feats_test);
        DoubleMatrix km_test = kernel.get_kernel_matrix();

        ArrayList result = new ArrayList();
        result.Add(km_train);
        result.Add(km_test);
        result.Add(kernel);
        modshogun.exit_shogun();
        return result;
    }

    static DoubleQRDecompTest()
    {        
      DoubleMatrix a = new DoubleMatrix(3);
      a[0,0] = -1.0;
      a[0,1] = 5.0;
      a[0,2] = 6.0;
      a[1,0] = 3.0;
      a[1,1] = -6.0;
      a[1,2] = 1.0;
      a[2,0] = 6.0;
      a[2,1] = 8.0;
      a[2,2] = 9.0;
      qr = new DoubleQRDecomp(a);

      a = new DoubleMatrix(2,3);
      a[0,0] = -1.0;
      a[0,1] = 5.0;
      a[0,2] = 6.0;
      a[1,0] = 3.0;
      a[1,1] = -6.0;
      a[1,2] = 1.0;
      wqr = new DoubleQRDecomp(a);

      a = new DoubleMatrix(3,2);
      a[0,0] = -1.0;
      a[0,1] = 5.0;
      a[1,0] = 3.0;
      a[1,1] = -6.0;
      a[2,0] = 6.0;
      a[2,1] = 8.0;
      lqr = new DoubleQRDecomp(a);
    }

 public static double Apply(Permutation assignment, TranslocationMove move, DoubleMatrix weights, DoubleMatrix distances) {
   double moveQuality = 0;
   int min = Math.Min(move.Index1, move.Index3);
   int max = Math.Max(move.Index2, move.Index3 + (move.Index2 - move.Index1));
   int iOffset, changeOffset;
   if (move.Index1 < move.Index3) {
     iOffset = move.Index2 - move.Index1 + 1;
     changeOffset = min + max - move.Index2;
   } else {
     iOffset = move.Index1 - move.Index3;
     changeOffset = min + move.Index2 - move.Index1 + 1;
   }
   for (int i = min; i <= max; i++) {
     if (i == changeOffset) iOffset -= (max - min + 1);
     int jOffset = ((move.Index1 < move.Index3) ? (move.Index2 - move.Index1 + 1) : (move.Index1 - move.Index3));
     for (int j = 0; j < assignment.Length; j++) {
       moveQuality -= weights[i, j] * distances[assignment[i], assignment[j]];
       if (j < min || j > max) {
         moveQuality -= weights[j, i] * distances[assignment[j], assignment[i]];
         moveQuality += weights[i, j] * distances[assignment[i + iOffset], assignment[j]];
         moveQuality += weights[j, i] * distances[assignment[j], assignment[i + iOffset]];
       } else {
         if (j == changeOffset) jOffset -= (max - min + 1);
         moveQuality += weights[i, j] * distances[assignment[i + iOffset], assignment[j + jOffset]];
       }
     }
   }
   return moveQuality;
 }

    public static double EvaluateByCoordinates(Permutation permutation, TranslocationMove move, DoubleMatrix coordinates, TSPTranslocationMovePathEvaluator evaluator) {
      if (move.Index1 == move.Index3
        || move.Index2 == permutation.Length - 1 && move.Index3 == 0
        || move.Index1 == 0 && move.Index3 == permutation.Length - 1 - move.Index2) return 0;

      int edge1source = permutation.GetCircular(move.Index1 - 1);
      int edge1target = permutation[move.Index1];
      int edge2source = permutation[move.Index2];
      int edge2target = permutation.GetCircular(move.Index2 + 1);
      int edge3source, edge3target;
      if (move.Index3 > move.Index1) {
        edge3source = permutation.GetCircular(move.Index3 + move.Index2 - move.Index1);
        edge3target = permutation.GetCircular(move.Index3 + move.Index2 - move.Index1 + 1);
      } else {
        edge3source = permutation.GetCircular(move.Index3 - 1);
        edge3target = permutation[move.Index3];
      }
      double moveQuality = 0;
      // remove three edges
      moveQuality -= evaluator.CalculateDistance(coordinates[edge1source, 0], coordinates[edge1source, 1],
        coordinates[edge1target, 0], coordinates[edge1target, 1]);
      moveQuality -= evaluator.CalculateDistance(coordinates[edge2source, 0], coordinates[edge2source, 1],
        coordinates[edge2target, 0], coordinates[edge2target, 1]);
      moveQuality -= evaluator.CalculateDistance(coordinates[edge3source, 0], coordinates[edge3source, 1],
        coordinates[edge3target, 0], coordinates[edge3target, 1]);
      // add three edges
      moveQuality += evaluator.CalculateDistance(coordinates[edge3source, 0], coordinates[edge3source, 1],
        coordinates[edge1target, 0], coordinates[edge1target, 1]);
      moveQuality += evaluator.CalculateDistance(coordinates[edge2source, 0], coordinates[edge2source, 1],
        coordinates[edge3target, 0], coordinates[edge3target, 1]);
      moveQuality += evaluator.CalculateDistance(coordinates[edge1source, 0], coordinates[edge1source, 1],
        coordinates[edge2target, 0], coordinates[edge2target, 1]);
      return moveQuality;
    }

    /// <summary>
    /// Performs a breeder genetic algorithm manipulation on the given <paramref name="vector"/>.
    /// </summary>
    /// <param name="random">A random number generator.</param>
    /// <param name="vector">The real vector to manipulate.</param>
    /// <param name="bounds">The lower and upper bound (1st and 2nd column) of the positions in the vector. If there are less rows than dimensions, the rows are cycled.</param>
    /// <param name="searchIntervalFactor">The factor determining the size of the search interval.</param>
    public static void Apply(IRandom random, RealVector vector, DoubleMatrix bounds, DoubleValue searchIntervalFactor) {
      int length = vector.Length;
      double prob, value;
      do {
        value = Sigma(random);
      } while (value == 0);

      prob = 1.0 / (double)length;
      bool wasMutated = false;

      for (int i = 0; i < length; i++) {
        if (random.NextDouble() < prob) {
          double range = bounds[i % bounds.Rows, 1] - bounds[i % bounds.Rows, 0];
          if (random.NextDouble() < 0.5) {
            vector[i] = vector[i] + value * searchIntervalFactor.Value * range;
          } else {
            vector[i] = vector[i] - value * searchIntervalFactor.Value * range;
          }
          wasMutated = true;
        }
      }

      // make sure at least one gene was mutated
      if (!wasMutated) {
        int pos = random.Next(length);
        double range = bounds[pos % bounds.Rows, 1] - bounds[pos % bounds.Rows, 0];
        if (random.NextDouble() < 0.5) {
          vector[pos] = vector[pos] + value * searchIntervalFactor.Value * range;
        } else {
          vector[pos] = vector[pos] - value * searchIntervalFactor.Value * range;
        }
      }
    }

        public static DoubleMatrix GetChannel(this MatrixBase<YCbCrColor> matrix,  ChannelType channelType)
        {
            var ret = new DoubleMatrix(matrix.RowCount, matrix.ColumnCount);

            for (int i = 0; i < matrix.RowCount; i++)
            {
                for (int j = 0; j < matrix.ColumnCount; j++)
                {
                    switch (channelType)
                    {
                        case ChannelType.Y:
                            ret[i, j] = matrix[i, j].Y;
                            break;
                        case ChannelType.Cr:
                            ret[i, j] = matrix[i, j].Cr;
                            break;
                        case ChannelType.Cb:
                            ret[i, j] = matrix[i, j].Cb;
                            break;
                        default:
                            throw new ArgumentOutOfRangeException("channelType");
                    }
                }
            }

            return ret;
        }

 public void Calculate(DoubleMatrix i_TargetMapping)
 {
     if (m_SourceMapping != null)
     {
         m_PixelMapping = pixelMapping(m_SourceMapping, i_TargetMapping, m_MeshSize);
     }
 }

 /***********************/
 /* PUBLIC CONSTRUCTORS */
 /***********************/
 /// <summary>Instantiates a new 2d point with the specified x and y 
 /// coordinates. Creates a new underlying matrix data source</summary>
 /// <param name="x">x-coordinate of this 2d point</param>
 /// <param name="y">y-coordinate of this 2d point</param>
 public Point2D(Double x, Double y)
 {
     _matrix = new DoubleMatrix(1, 3);
     _matrix[0, 0] = x;
     _matrix[0, 1] = y;
     _matrix[0, 2] = 1F;
 }

 private DoubleArray Randomize(IRandom random, int length, DoubleMatrix bounds) {
   var result = new DoubleArray(length);
   for (int i = 0; i < length; i++) {
     result[i] = random.NextDouble() * bounds[i % bounds.Rows, 1] - bounds[i % bounds.Rows, 0];
   }
   return result;
 }

    /// <summary>
    /// Performs the Kruskal-Shepard algorithm and applies a gradient descent method
    /// to fit the coordinates such that the difference between the fit distances
    /// and the dissimilarities is minimal.
    /// </summary>
    /// <remarks>
    /// It will use a pre-initialized x,y-coordinates matrix as a starting point of the gradient descent.
    /// </remarks>
    /// <param name="dissimilarities">A symmetric NxN matrix that specifies the dissimilarities between each element i and j. Diagonal elements are ignored.</param>
    /// <param name="coordinates">The Nx2 matrix of initial coordinates.</param>
    /// <param name="maximumIterations">The number of iterations for which the algorithm should run.
    /// In every iteration it tries to find the best location for every item.</param>
    /// <returns>A Nx2 matrix where the first column represents the x- and the second column the y coordinates.</returns>
    public static DoubleMatrix KruskalShepard(DoubleMatrix dissimilarities, DoubleMatrix coordinates, int maximumIterations = 10) {
      int dimension = dissimilarities.Rows;
      if (dimension != dissimilarities.Columns || coordinates.Rows != dimension) throw new ArgumentException("The number of coordinates and the number of rows and columns in the dissimilarities matrix do not match.");

      double epsg = 1e-7;
      double epsf = 0;
      double epsx = 0;
      int maxits = 0;

      alglib.minlmstate state;
      alglib.minlmreport rep;
      for (int iterations = 0; iterations < maximumIterations; iterations++) {
        bool changed = false;
        for (int i = 0; i < dimension; i++) {
          double[] c = new double[] { coordinates[i, 0], coordinates[i, 1] };

          try {
            alglib.minlmcreatevj(dimension - 1, c, out state);
            alglib.minlmsetcond(state, epsg, epsf, epsx, maxits);
            alglib.minlmoptimize(state, StressFitness, StressJacobian, null, new Info(coordinates, dissimilarities, i));
            alglib.minlmresults(state, out c, out rep);
          } catch (alglib.alglibexception) { }
          if (!double.IsNaN(c[0]) && !double.IsNaN(c[1])) {
            changed = changed || (coordinates[i, 0] != c[0]) || (coordinates[i, 1] != c[1]);
            coordinates[i, 0] = c[0];
            coordinates[i, 1] = c[1];
          }
        }
        if (!changed) break;
      }
      return coordinates;
    }

 public void MichalewiczNonUniformAllPositionsManipulatorApplyTest() {
   TestRandom random = new TestRandom();
   RealVector parent, expected;
   DoubleValue generationsDependency;
   DoubleMatrix bounds;
   IntValue currentGeneration, maximumGenerations;
   bool exceptionFired;
   // The following test is not based on published examples
   random.Reset();
   random.DoubleNumbers = new double[] { 0.2, 0.5, 0.7, 0.8, 0.9, 0.5, 0.2, 0.5, 0.7, 0.8 };
   parent = new RealVector(new double[] { 0.2, 0.2, 0.3, 0.5, 0.1 });
   expected = new RealVector(new double[] { 0.45, 0.22, 0.3, 0.6, 0.14 });
   bounds = new DoubleMatrix(new double[,] { { 0.3, 0.7 } });
   generationsDependency = new DoubleValue(0.1);
   currentGeneration = new IntValue(1);
   maximumGenerations = new IntValue(4);
   MichalewiczNonUniformAllPositionsManipulator.Apply(random, parent, bounds, currentGeneration, maximumGenerations, generationsDependency);
   Assert.IsTrue(Auxiliary.RealVectorIsAlmostEqualByPosition(expected, parent));
   // The following test is not based on published examples
   exceptionFired = false;
   random.Reset();
   random.DoubleNumbers = new double[] { 0.2, 0.5, 0.7, 0.8, 0.9, 0.5, 0.2, 0.5, 0.7, 0.8 };
   parent = new RealVector(new double[] { 0.2, 0.2, 0.3, 0.5, 0.1 });
   bounds = new DoubleMatrix(new double[,] { { 0.3, 0.7 } });
   generationsDependency = new DoubleValue(0.1);
   currentGeneration = new IntValue(5); //current generation > max generation
   maximumGenerations = new IntValue(4);
   try {
     MichalewiczNonUniformAllPositionsManipulator.Apply(random, parent, bounds, currentGeneration, maximumGenerations, generationsDependency);
   } catch (System.ArgumentException) {
     exceptionFired = true;
   }
   Assert.IsTrue(exceptionFired);
 }

    public static double CalculatePhenotypeDistance(Permutation a, Permutation b, DoubleMatrix weights, DoubleMatrix distances) {
      Dictionary<double, Dictionary<double, int>> alleles = new Dictionary<double, Dictionary<double, int>>();
      int distance = 0, len = a.Length;
      for (int x = 0; x < len; x++) {
        for (int y = 0; y < len; y++) {
          // there's a limited universe of double values as they're all drawn from the same matrix
          double dA = distances[a[x], a[y]], dB = distances[b[x], b[y]];
          if (dA == dB) continue;

          Dictionary<double, int> dAlleles;
          if (!alleles.ContainsKey(weights[x, y])) {
            dAlleles = new Dictionary<double, int>();
            alleles.Add(weights[x, y], dAlleles);
          } else dAlleles = alleles[weights[x, y]];

          int countA = 1, countB = -1;

          if (dAlleles.ContainsKey(dA)) countA += dAlleles[dA];
          if (dAlleles.ContainsKey(dB)) countB += dAlleles[dB];

          if (countA <= 0) distance--; // we've found in A an allele that was present in B
          else distance++; // we've found in A a new allele
          dAlleles[dA] = countA;

          if (countB >= 0) distance--; // we've found in B an allele that was present in A
          else distance++; // we've found in B a new allele
          dAlleles[dB] = countB;
        }
      }
      return distance / (double)(2 * len * len);
    }