/// <summary>
/// 計画行列の識別を行う
/// </summary>
/// <param name="labelY"></param>
/// <param name="design_Matrix_without_Constant"></param>
/// <param name="iKernel"></param>
/// <param name="variance_Covariance_Matrix"></param>
/// <param name="CoefficientA"></param>
/// <param name="design_Matrix_for_Classification"></param>
/// <returns></returns>
public static double[,] Classify_Design_Matrix(double[,] labelY, double[,] design_Matrix_without_Constant, IKernel iKernel, double[,] CoefficientA, double[,] design_Matrix_for_Classification)
{
double[,] classified = new double[design_Matrix_for_Classification.GetLength(0), 1];
//カーネルのセット
iKernel.Set_Variance_Covariance_Matrix(DesignMatrix.Variance_Covariance_Matrix(design_Matrix_without_Constant));
//カーネル用の行列
double[,] kernelMatrix = new double[design_Matrix_without_Constant.GetLength(0), 1];
//識別したい計画行列を1行ずつ計算する
double[,] rowVector;
for (int n = 0; n < classified.GetLength(0); n++)
{
rowVector = Matrix.Pick_Up_Row_Vector(design_Matrix_for_Classification, n);
//カーネルを計算する
double[,] r_j = new double[1, 1];
for (int j = 0; j < design_Matrix_without_Constant.GetLength(0); j++)
{
r_j = Matrix.Pick_Up_Row_Vector(design_Matrix_without_Constant, j);
kernelMatrix[j, 0] = iKernel.Calculate(rowVector, r_j);
}
//予測値の計算
//予測値の符号 = Σ( for j ) 教師ラベル[Y]j * 係数[A]j * カーネル K( x , [X]j )
double[,] hadamard = Matrix.Hadamard_product(labelY, CoefficientA);
hadamard = Matrix.Hadamard_product(hadamard, kernelMatrix);
//Σ( for j )
double sum = 0;
foreach (double h in hadamard)
{
sum += h;
}
classified[n, 0] = sum;
}
return(classified);
}
/// <summary>
/// 行ベクトルの識別を行う .
/// Perform row vector identification .
/// </summary>
/// <param name="labelY"></param>
/// <param name="design_Matrix_without_Constant"></param>
/// <param name="iKernel"></param>
/// <param name="variance_Covariance_Matrix"></param>
/// <param name="CoefficientA"></param>
/// <param name="rowVector"></param>
/// <returns></returns>
public static double Classify(double[,] labelY, double[,] design_Matrix_without_Constant, IKernel iKernel, double[,] CoefficientA, double[,] rowVector)
{
if (rowVector.GetLength(0) > 1)
{
throw new FormatException(nameof(rowVector) + "(" + rowVector.GetLength(0) + ")" + " must be 1 .");
}
//カーネルのセット
iKernel.Set_Variance_Covariance_Matrix(DesignMatrix.Variance_Covariance_Matrix(design_Matrix_without_Constant));
//カーネル用の行列
double[,] kernelMatrix = new double[design_Matrix_without_Constant.GetLength(0), 1];
//カーネルを計算する
double[,] r_j = new double[1, 1];
for (int j = 0; j < design_Matrix_without_Constant.GetLength(0); j++)
{
r_j = Matrix.Pick_Up_Row_Vector(design_Matrix_without_Constant, j);
kernelMatrix[j, 0] = iKernel.Calculate(rowVector, r_j);
}
//予測値の計算
//予測値の符号 = Σ( for j ) 教師ラベル[Y]j * 係数[A]j * カーネル K( x , [X]j )
double[,] hadamard = Matrix.Hadamard_product(labelY, CoefficientA);
hadamard = Matrix.Hadamard_product(hadamard, kernelMatrix);
//Σ( for j )
double sum = 0;
foreach (double h in hadamard)
{
sum += h;
}
return(sum);
}
/// <summary>
/// 係数Aを学習する .
/// Learn coefficient A .
/// </summary>
/// <param name="labelY"></param>
/// <param name="design_Matrix_without_Constant"></param>
/// <param name="iKernel"></param>
/// <param name="variance_Covariance_Matrix"></param>
/// <returns></returns>
public static double[,] Learn(double[,] labelY, double[,] design_Matrix_without_Constant, IKernel iKernel)
{
//カーネルのセット
iKernel.Set_Variance_Covariance_Matrix(DesignMatrix.Variance_Covariance_Matrix(design_Matrix_without_Constant));
//係数の最大値
double Hyper_Parameter_C = 1.0;// Math.Min(1.0, 1.0 / design_Matrix.GetLength(0));
//カーネル行列
double[,] kernel_Matrix = new double[design_Matrix_without_Constant.GetLength(0), design_Matrix_without_Constant.GetLength(0)];
//行ベクトル
double[,] r_j = new double[1, design_Matrix_without_Constant.GetLength(1)];
double[,] r_k = new double[1, design_Matrix_without_Constant.GetLength(1)];
//カーネルを計算しておく
double k_jk = 0;
for (int j = 0; j < design_Matrix_without_Constant.GetLength(0); j++)
{
r_j = Matrix.Pick_Up_Row_Vector(design_Matrix_without_Constant, j);
for (int k = j; k < design_Matrix_without_Constant.GetLength(0); k++)
{
r_k = Matrix.Pick_Up_Row_Vector(design_Matrix_without_Constant, k);
k_jk = iKernel.Calculate(r_j, r_k);
kernel_Matrix[j, k] = k_jk;
kernel_Matrix[k, j] = k_jk;
}
}
//係数の初期化
double[,] coefficient_Matrix_A = new double[design_Matrix_without_Constant.GetLength(0), 1];
double initial_a = 0.0;// 1.0 / design_Matrix.GetLength(0) / design_Matrix.GetLength(0);
for (int j = 0; j < coefficient_Matrix_A.GetLength(0); j++)
{
coefficient_Matrix_A[j, 0] = initial_a;
}
//更新用の小数のインスタンス
double a_j = 0;
double a_k = 0;
double predict_j = 0;
double predict_k = 0;
double min = 0;
double max = 0;
//学習
//2点のデータを選択する
for (int j = 0; j < design_Matrix_without_Constant.GetLength(0); j++)
{
//計画行列からj行目のベクトルを取り出す
r_j = Matrix.Pick_Up_Row_Vector(design_Matrix_without_Constant, j);
for (int k = j + 1; k < design_Matrix_without_Constant.GetLength(0); k++)
{
//計画行列からk行目のベクトルを取り出す
r_k = Matrix.Pick_Up_Row_Vector(design_Matrix_without_Constant, k);
//ベクトルr_j r_kの予測計算を行う
predict_j = 0;
for (int j2 = 0; j2 < kernel_Matrix.GetLength(1); j2++)
{
predict_j += coefficient_Matrix_A[j2, 0] * labelY[j2, 0] * kernel_Matrix[j, j2];
}
predict_k = 0;
for (int k2 = 0; k2 < kernel_Matrix.GetLength(1); k2++)
{
predict_k += coefficient_Matrix_A[k2, 0] * labelY[k2, 0] * kernel_Matrix[k, k2];
}
//ベクトルk の係数(仮)を計算する
a_k = coefficient_Matrix_A[k, 0]
+ labelY[k, 0] *
(
(predict_j - labelY[j, 0])
- (predict_k - labelY[k, 0])
)
/ (kernel_Matrix[j, j] + kernel_Matrix[k, k] - 2 * kernel_Matrix[j, k]);
//場合分け
if (labelY[j, 0] * labelY[k, 0] > 0)
{
min = Math.Min(Hyper_Parameter_C
, coefficient_Matrix_A[j, 0] + coefficient_Matrix_A[k, 0]);
max = Math.Max(0
, coefficient_Matrix_A[j, 0] + coefficient_Matrix_A[k, 0]
- Hyper_Parameter_C);
if (min < coefficient_Matrix_A[k, 0])
{
a_k = min;
}
else if (max < a_k && a_k < min)
{
//a_k = a_k;
}
else
{
a_k = max;
}
}
else
{
min = Math.Min(Hyper_Parameter_C
, Hyper_Parameter_C
- coefficient_Matrix_A[j, 0] + coefficient_Matrix_A[k, 0]);
max = Math.Max(0
, -coefficient_Matrix_A[j, 0] + coefficient_Matrix_A[k, 0]);
if (min < a_k)
{
a_k = min;
}
else if (max < a_k && a_k < min)
{
//a_k = a_k;
}
else
{
a_k = max;
}
}
a_j = coefficient_Matrix_A[j, 0]
+ labelY[j, 0]
* labelY[k, 0]
* (coefficient_Matrix_A[k, 0] - a_k);
//係数の更新
coefficient_Matrix_A[j, 0] = a_j;
coefficient_Matrix_A[k, 0] = a_k;
}
}
//学習終わり
return(coefficient_Matrix_A);
}