public void TestInit()
{
var matrix = new Matrix<int>(5, 5);
int[] row = { 2, 2, 2, 2, 2 };
matrix.Init(2);
Assert.AreEqual(row, matrix.GetRow(2));
}
public void TestInc()
{
var matrix = new Matrix<double>(5, 5);
double[] row = { 1, 2, 3, 4, 5 };
for (int i = 0; i < 5; i++)
matrix.SetRow(i, row);
matrix.Inc(3, 4, 2.5);
Assert.AreEqual(7.5, matrix[3, 4]);
var matrix1 = new Matrix<double>(5, 5);
for (int i = 0; i < 5; i++)
matrix1.SetRow(i, row);
var matrix2 = new Matrix<double>(5, 5);
for (int i = 0; i < 5; i++)
matrix2.SetRow(i, row);
double[] testrow = { 2, 4, 6, 8, 10 };
matrix1.Inc(matrix2);
Assert.AreEqual(testrow, matrix1.GetRow(2));
var matrix3 = new Matrix<double>(5, 5);
for (int i = 0; i < 5; i++)
matrix3.SetRow(i, row);
matrix3.Inc(1.0);
for (int j = 0; j < 5; j++)
Assert.AreEqual(row[j] + 1, matrix3[1, j]);
var matrix4 = new Matrix<int>(5, 5);
int[] int_row = { 1, 2, 3, 4, 5 };
for (int i = 0; i < 5; i++)
matrix4.SetRow(i, int_row);
Assert.AreEqual(matrix4[1, 2], 3);
matrix4.Inc(1, 2);
Assert.AreEqual(matrix4[1, 2], 4);
}
public void TestGetSetRow()
{
var matrix = new Matrix<int>(5, 5);
int[] row = { 1, 2, 3, 4, 5 };
matrix.SetRow(3, row);
Assert.AreEqual(row, matrix.GetRow(3));
Assert.AreEqual(0, matrix[0, 0]);
Assert.AreEqual(1, matrix[3, 0]);
}
[Test()] public void TestSetRowToOneValue()
{
var matrix = new Matrix<int>(5, 5);
int[] row = { 1, 2, 3, 4, 5 };
for (int i = 0; i < 5; i++)
matrix.SetRow(i, row);
matrix.SetRowToOneValue(3, 10);
int[] testrow = { 10, 10, 10, 10, 10 };
Assert.AreEqual(testrow, matrix.GetRow(3));
}
[Test()] public void TestMultiply()
{
var matrix = new Matrix<float>(5, 5);
float[] row = { 1, 2, 3, 4, 5 };
for (int i = 0; i < 5; i++)
matrix.SetRow(i, row);
matrix.Multiply(2.5f);
float[] testrow = { 2.5f, 5f, 7.5f, 10f, 12.5f };
Assert.AreEqual(testrow, matrix.GetRow(3));
}
public void TestInc2()
{
var matrix1 = new Matrix<double>(5, 5);
double[] row = { 1, 2, 3, 4, 5 };
for (int i = 0; i < 5; i++)
matrix1.SetRow(i, row);
var matrix2 = new Matrix<double>(5, 5);
for (int i = 0; i < 5; i++)
matrix2.SetRow(i, row);
double[] testrow = {2, 4, 6, 8, 10};
MatrixUtils.Inc(matrix1, matrix2);
Assert.AreEqual(testrow, matrix1.GetRow(2));
}
private bool canBeThere(Matrix m, int row, int column, int digit)
{
int[] temp = m.GetRow(row);
foreach (int i in temp)
if (i == digit)
return false;
temp = m.GetColumn(column);
foreach (int i in temp)
if (i == digit)
return false;
temp = m.GetRegion(row, column);
foreach (int i in temp)
if (i == digit)
return false;
return true;
}
/*
* DIST2 Calculates squared distance between two sets of points.
*
* Description
* D = DIST2(X, C) takes two matrices of vectors and calculates the
* squared Euclidean distance between them. Both matrices must be of
* the same column dimension. If X has M rows and N columns, and C has
* L rows and N columns, then the result has M rows and L columns. The
* I, Jth entry is the squared distance from the Ith row of X to the
* Jth row of C.
*
* See also
* GMMACTIV, KMEANS, RBFFWD
*
*
* Copyright (c) Christopher M Bishop, Ian T Nabney (1996, 1997)
*
*/
/// <summary>
/// TODO: 还没验证对不对
/// </summary>
public Matrix Dist2(Matrix A, Matrix B)
{
int arows = A.Rows, acols = A.Columns,
brows = B.Rows, bcols = B.Columns;
if (acols != bcols)
throw new Exception("Data dimension does not match dimension of centres");
//var n2 = (ones(ncentres, 1) * sum((x.^2)', 1))' + ...
// ones(ndata, 1) * sum((c.^2)',1) - ...
// 2.*(x*(c'))
DenseMatrix result = new DenseMatrix(arows, brows);//Zeros(arows, brows);
for (int i = 0; i < arows; ++i) {
var arowi = A.GetRow(i);
for (int j = 0; j < brows; ++j) {
//result[i, j] = x.GetRow(i).Sum(xi => xi * xi)
// + c.GetRow(j).Sum(ci => ci * ci)
// - 2 * x.GetRow(i).Select((xi, jj) => xi * c.GetRow(j)[jj]).Sum();
var browj = B.GetRow(j);
double rij = 0;
for (int k = 0; k < acols; ++k) {
//double a = A[i, k], b = B[j, k];
//double a = arowi[k], b = browj[k];
//result[i, j] += (a * a + b * b - 2 * a * b);
//rij += (a * a + b * b - 2 * a * b);
double d = arowi[k] - browj[k];
rij += (d*d);
}
result[i, j] = rij;
// result[i,j] = (Xi^2+Xi^2) + (Xj^2+Xj^2) - 2(Xi*Xj + Xi*Xj)
// = (Xi-Xj)^2 + (Yi-Yj)^2
//double Xi = x[i, 0], Yi = x[i, 1], Xj = c[j, 0], Yj = c[j, 1];
//result[i, j] = (Xi - Xj) * (Xi - Xj) + (Yi - Yj) * (Yi - Yj);
}
}
return result;
}
public static DenseMatrix PointMultiply(this Matrix a, Matrix b)
{
var c = new DenseMatrix(a.Rows, a.Columns);
var rowvec = new double[c.Columns];
for (int i = 0; i < a.Rows; ++i) {
var arowi = a.GetRow(i);
var browi = b.GetRow(i);
for (int j = 0; j < a.Columns; ++j) {
rowvec[j] = arowi[j] * browi[j];
}
c.SetRow(i, rowvec);
}
return c;
}
public void TestMultiply()
{
var matrix = new Matrix<double>(5, 5);
double[] row = { 1, 2, 3, 4, 5 };
for (int i = 0; i<5; i++)
matrix.SetRow(i, row);
MatrixUtils.Multiply(matrix, 2.5);
double[] testrow = { 2.5, 5, 7.5, 10, 12.5 };
Assert.AreEqual(testrow, matrix.GetRow(3));
}
private Matrix Hminired(Matrix A)
{
//function A=hminired(A)
//%HMINIRED Initial reduction of cost matrix for the Hungarian method.
//%
//%B=assredin(A)
//%A - the unreduced cost matris.
//%B - the reduced cost matrix with linked zeros in each row.
//% v1.0 96-06-13. Niclas Borlin, [email protected].
//[m,n]=size(A);
int m = A.Rows, n = A.Columns;
//% Subtract column-minimum values from each column.
//colMin=min(A);
var colMin = new DenseVector(A.GetColumns().Select(col => col.Min()).ToArray());
//A=A-colMin(ones(n,1),:);
for (int i = 0; i < A.Rows; ++i) {
A.SetRow(i, A.GetRow(i) - colMin);
}
//% Subtract row-minimum values from each row.
//rowMin=min(A')';
var rowMin = new DenseVector(A.GetRows().Select(row => row.Min()).ToArray());
//A=A-rowMin(:,ones(1,n));
for (int j = 0; j < A.Rows; ++j) {
A.SetColumn(j, A.GetColumn(j) - rowMin);
}
//% Get positions of all zeros.
//[i,j]=find(A==0);
List<int> ilist = new List<int>();
List<int> jlist = new List<int>();
A.EachT((v, i, j) => {
if (v == 0) {
ilist.Add(i);
jlist.Add(j);
}
});
//% Extend A to give room for row zero list header column.
//A(1,n+1)=0;
Matrix tmp = Zeros(n, n + 1);
tmp.SetSubMatrix(0, n, 0, n, A);
//for k=1:n
for (int k = 0; k < n; ++k) {
// % Get all column in this row.
// cols=j(k==i)';
var cols = new List<int>();
cols.Add(n);
for (int i = 0; i < ilist.Count; ++i) {
if (ilist[i] == k) {
cols.Add(jlist[i]);
}
}
cols.Add(-1);
// % Insert pointers in matrix.
// A(k,[n+1 cols])=[-cols 0];
for (int i = 0; i < cols.Count - 1; ++i) {
tmp[k, cols[i]] = -(cols[i + 1]) - 1;
} // TODO 不知道对不对了
//result[k, cols[cols.Count - 1]] = 0;
//end
}
var result = tmp.Each(v => {
if (v < 0) return v + 1;
else if (v == 0) return NoMatch;
else return v;
});
return result;
}
private void Hmflip(Matrix A, ref int[] C, ref int[] U, int[] LC, int[] LR, int l, int r)
{
//function [A,C,U]=hmflip(A,C,LC,LR,U,l,r)
//%HMFLIP Flip assignment state of all zeros along a path.
//%
//%[A,C,U]=hmflip(A,C,LC,LR,U,l,r)
//%Input:
//%A - the cost matrix.
//%C - the assignment vector.
//%LC - the column label vector.
//%LR - the row label vector.
//%U - the
//%r,l - position of last zero in path.
//%Output:
//%A - updated cost matrix.
//%C - updated assignment vector.
//%U - updated unassigned row list vector.
//% v1.0 96-06-14. Niclas Borlin, [email protected].
//n=size(A,1);
int n = A.Rows;
//while (1)
while (true) {
// % Move assignment in column l to row r.
// C(l)=r;
C[l] = r;
// % Find zero to be removed from zero list..
// % Find zero before this.
// m=find(A(r,:)==-l);
int[] m = A.GetRow(r).FindIdxBy(v => v == -l);
// % Link past this zero.
// A(r,m)=A(r,l);
for (int i = 0; i < m.Length; ++i) {
A[r, m[i]] = A[r, l];
}
// A(r,l)=0;
A[r, l] = NoMatch;
// % If this was the first zero of the path..
// if (LR(r)<0)
if (LR[r] <= 0 && isNotNoMatch(LR[r])) { // TODO <=0不确定
// ...remove row from unassigned row list and return.
// U(n+1)=U(r);
U[n] = U[r];
// U(r)=0;
U[r] = NoMatch;
// return;
return;
// else
} else {
// % Move back in this row along the path and get column of next zero.
// l=LR(r);
l = LR[r];
// % Insert zero at (r,l) first in zero list.
// A(r,l)=A(r,n+1);
A[r, l] = A[r, n];
// A(r,n+1)=-l;
A[r, n] = -l;
// % Continue back along the column to get row of next zero in path.
// r=LC(l);
r = LC[l];
// end
}
//end
}
}
public void TestSubMatrix()
{
Matrix<float> mat = new Matrix<float>(30, 40);
mat.SetRandUniform(new MCvScalar(0), new MCvScalar(255));
Matrix<float> submat = mat.GetSubRect(new Rectangle(5, 5, 15, 15));
for (int i = 0; i < 15; i++)
for (int j = 0; j < 15; j++)
EmguAssert.AreEqual(mat[i + 5, j + 5], submat[i, j]);
Matrix<float> secondRow = mat.GetRow(1);
for (int i = 0; i < mat.Cols; i++)
{
EmguAssert.AreEqual(mat[1, i], secondRow[0, i]);
}
Matrix<float> thirdCol = mat.GetCol(2);
for (int i = 0; i < mat.Rows; i++)
{
EmguAssert.AreEqual(mat[i, 2], thirdCol[i, 0]);
}
Matrix<float> diagonal = mat.GetDiag();
for (int i = 0; i < Math.Min(mat.Rows, mat.Cols); i++)
{
EmguAssert.AreEqual(diagonal[i, 0], mat[i, i]);
}
}
public void TestGetDiagColRow()
{
Matrix<double> m = new Matrix<double>(new double[,] { {1, 2}, {3, 4}});
Matrix<double> diag = m.GetDiag();
EmguAssert.IsTrue(diag[0, 0] == 1);
EmguAssert.IsTrue(diag[1, 0] == 4);
EmguAssert.IsTrue(diag.Sum == m.Trace.V0);
Matrix<double> col1 = m.GetCol(1);
EmguAssert.IsTrue(col1[0, 0] == 2);
EmguAssert.IsTrue(col1[1, 0] == 4);
EmguAssert.IsTrue(col1.Sum == 2 + 4);
Matrix<double> row1 = m.GetRow(1);
EmguAssert.IsTrue(row1[0, 0] == 3);
EmguAssert.IsTrue(row1[0, 1] == 4);
EmguAssert.IsTrue(row1.Sum == 3 + 4);
}
/*
function [BH,mean_dist]=sc_compute(Bsamp,Tsamp,mean_dist,nbins_theta,nbins_r,r_inner,r_outer,out_vec);
% [BH,mean_dist]=sc_compute(Bsamp,Tsamp,mean_dist,nbins_theta,nbins_r,r_inner,r_outer,out_vec);
%
% compute (r,theta) histograms for points along boundary
%
% Bsamp is 2 x nsamp (x and y coords.)
% Bsamp是2n长的数组，提供x,y
% Tsamp is 1 x nsamp (tangent theta)
% Tsamp是n长的数组，提供tan(theta)
% out_vec is 1 x nsamp (0 for inlier, 1 for outlier)
% out_vec是n长的数组，表示是否野点
%
% mean_dist is the mean distance, used for length normalization
% mean_dist是平均距离，用来归一化
% if it is not supplied, then it is computed from the data
%
% outliers are not counted in the histograms, but they do get
% assigned a histogram
%
end
*/
public Matrix ComputeSC(Matrix Bsamp, Matrix Tsamp, double? mean_dist, out double mean_dist_out, bool[] out_vec)
{
//nsamp=size(Bsamp,2);
//% 求n长
//int nsamp = Bsamp.Columns;
//in_vec=out_vec==0;
//% 初始化野点标记数组吧？
var in_vec = Utils.InitArray<bool>(nsamp, true);
out_vec.FillArray(false);
//% compute r,theta arrays 计算r和t的数组
//r_array=real(sqrt(dist2(Bsamp',Bsamp'))); % real is needed to
// % prevent bug in Unix version
//% r_array是Bsamp的转置和Bsamp的转置之间的dist2
//% 应该是个n*n的数组了
var Bsampt = Bsamp.Transpose();
var r_array = Dist2(Bsampt, Bsampt).Each(Math.Sqrt);
//theta_array_abs=atan2(Bsamp(2,:)'*ones(1,nsamp)-ones(nsamp,1)*Bsamp(2,:),Bsamp(1,:)'*ones(1,nsamp)-ones(nsamp,1)*Bsamp(1,:))';
var theta_array_abs = new DenseMatrix(nsamp, nsamp);
var x_array = Bsamp.GetRow(0);
var y_array = Bsamp.GetRow(1);
for (int i = 0; i < nsamp; ++i) {
for (int j = 0; j < nsamp; ++j) {
double xi = x_array[i], yi = y_array[i],
xj = x_array[j], yj = y_array[j];
theta_array_abs[j, i] = Math.Atan2(yi - yj, xi - xj);
}
}
var Tsampt = Tsamp.Transpose();
//theta_array=theta_array_abs-Tsamp'*ones(1,nsamp);
//% 不知道这里是干什么，但是theta_array应该是theta的二维数组
var theta_array = theta_array_abs - Tsampt * Ones(1, nsamp);
//% create joint (r,theta) histogram by binning r_array and
//% theta_array
//% 通过对r_array和theta_array插槽来求直方图
//% normalize distance by mean, ignoring outliers
//% 通过均值来归一化距离参数，忽略野点
//if isempty(mean_dist)
if (mean_dist == null) {
// tmp=r_array(in_vec,:);
// tmp=tmp(:,in_vec);
// mean_dist=mean(tmp(:));
//end
double mean_sum = 0;
int mean_count = 0;
for (int i = 0; i < r_array.Rows; ++i) {
for (int j = 0; j < r_array.Columns; ++j) {
if (in_vec[i] && in_vec[j]) {
mean_sum += r_array[i, j];
++mean_count;
}
}
}
mean_dist_out = mean_sum / mean_count;
} else {
mean_dist_out = mean_dist.Value;
}
//% 看不懂，但是应该是求非野点的平均值吧，结果存在mean_dist中
//r_array_n=r_array/mean_dist;
//% r_array_n是规范化结果，用均值规范化，而不是用最大值，难道我错了？
var r_array_n = r_array / mean_dist_out;
//% use a log. scale for binning the distances
//r_bin_edges=logspace(log10(r_inner),log10(r_outer),5);
var r_bin_edges = Utils.LogSpace(Math.Log10(r_inner), Math.Log10(r_outer), nbins_r);
//r_array_q=zeros(nsamp,nsamp);
var r_array_q = Zeros(nsamp, nsamp);
//for m=1:nbins_r %m从1循环到r槽数
for (int i = 0; i < nbins_r; ++i) {
// r_array_q=r_array_q+(r_array_n<r_bin_edges(m)); % r_array_q = r_array_q + (r_array_n < r_bin_edges(m))
r_array_q = r_array_q + r_array_n.Each(v => v < r_bin_edges[i] ? 1 : 0);
// % 这个代码的意思应该是说r_array_q中下标小于m的项统统加上r_array_n中小于r_bin_edges(m)的项目数
//end
}
//fz=r_array_q>0; % flag all points inside outer boundary
//% fz为r_array_q中大于0的项目标记（这应该是个数组）
var fz = r_array_q.Each(v => v > 0 ? 1 : 0);
//% put all angles in [0,2pi) range
//theta_array_2 = rem(rem(theta_array,2*pi)+2*pi,2*pi);
//% 将所有方向角都弄到[0,2pi)范围内，看不懂
//% 猜测theta_array_2 = theta_array.Select(t => (t % (2*pi) + (2*pi)) % (2*pi))
var theta_array_2 = theta_array.Each(t => (t % (2 * Math.PI) + 2 * Math.PI) % (2 * Math.PI));
//% quantize to a fixed set of angles (bin edges lie on 0,(2*pi)/k,...2*pi
//% 将方向角量化到固定的角度
//theta_array_q = 1+floor(theta_array_2/(2*pi/nbins_theta));
//% theta_array_q = theta_array_2.Select(t => floor(t / (2*pi/nbins_theta)) + 1)
//% 这是以1为起点的吧
var theta_array_q = theta_array_2.Each(t => (int)(t / (2 * Math.PI / nbins_theta)));
//nbins=nbins_theta*nbins_r;
int nbins = nbins_theta * nbins_r;
//% 不用说
//BH=zeros(nsamp,nbins);
var BH = Zeros(nsamp, nbins);
//% BH应该就是最后的直方图
//for n=1:nsamp % n从1循环到nsamp
for (int i = 0; i < nsamp; ++i) {
// fzn=fz(n,:)&in_vec;
// Sn=sparse(theta_array_q(n,fzn), r_array_q(n,fzn), 1, nbins_theta, nbins_r);
// % sparse这里貌似是生成稀疏矩阵的，Sn应该是用于计算匹配代价的直方图，而BH是包含野点的
// BH(n,:)=Sn(:)'; % 转置
for (int j = 0; j < nsamp; ++j) {
int tq = (int)theta_array_q[i, j], tr = (int)r_array_q[i, j];
if (tr > 0) {
tr--;
++BH[i, tr * nbins_theta + tq];
}
}
}
return BH;
}
public void GetRows()
{
var matrix1 = new Matrix(0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15);
Assert.AreEqual(new Vector(0, 1, 2), matrix1.GetRow(0));
Assert.AreEqual(new Vector(4, 5, 6), matrix1.GetRow(1));
Assert.AreEqual(new Vector(8, 9, 10), matrix1.GetRow(2));
Assert.AreEqual(new Vector(12, 13, 14), matrix1.GetRow(3));
Assert.Throws<IndexOutOfRangeException>(() => matrix1.GetRow(-1));
}
private void Hmreduce(Matrix A, ref int[] CH, ref int[] RH, int[] LC, int[] LR, List<int> SLC, List<int> SLR)
{
//function [A,CH,RH]=hmreduce(A,CH,RH,LC,LR,SLC,SLR)
//%HMREDUCE Reduce parts of cost matrix in the Hungerian method.
//%
//%[A,CH,RH]=hmreduce(A,CH,RH,LC,LR,SLC,SLR)
//%Input:
//%A - Cost matrix.
//%CH - vector of column of 'next zeros' in each row.
//%RH - vector with list of unexplored rows.
//%LC - column labels.
//%RC - row labels.
//%SLC - set of column labels.
//%SLR - set of row labels.
//%
//%Output:
//%A - Reduced cost matrix.
//%CH - Updated vector of 'next zeros' in each row.
//%RH - Updated vector of unexplored rows.
//% v1.0 96-06-14. Niclas Borlin, [email protected].
//n=size(A,1);
int n = A.Rows;
//% Find which rows are covered, i.e. unlabelled.
//coveredRows=LR==0;
var coveredRows = LR.Select(v => isNoMatch(v)).ToArray();
//% Find which columns are covered, i.e. labelled.
//coveredCols=LC~=0;
var coveredCols = LC.Select(v => isNotNoMatch(v)).ToArray();
//r=find(~coveredRows);
var r = coveredRows.Select((v, i) => new { Val = v, Idx = i })
.Where(v => !v.Val)
.Select(v => v.Idx)
.ToArray();
//c=find(~coveredCols);
var c = coveredCols.Select((v, i) => new { Val = v, Idx = i })
.Where(v => !v.Val)
.Select(v => v.Idx)
.ToArray();
//% Get minimum of uncovered elements.
//m=min(min(A(r,c)));
double m = double.MaxValue;
foreach (int i in r) {
foreach (int j in c) {
var val = A[i, j];
if (val < m) m = val;
}
}
//% Subtract minimum from all uncovered elements.
//A(r,c)=A(r,c)-m;
foreach (int i in r) {
foreach (int j in c) {
A[i, j] -= m;
}
}
//% Check all uncovered columns..
//for j=c
foreach (int j in c) {
// % ...and uncovered rows in path order..
// for i=SLR
foreach (int i in SLR) {
// % If this is a (new) zero..
// if (A(i,j)==0)
if (isNoMatch(A[i, j])) {
// % If the row is not in unexplored list..
// if (RH(i)==0)
if (isNoMatch(RH[i])) {
// % ...insert it first in unexplored list.
// RH(i)=RH(n+1);
RH[i] = RH[n];
// RH(n+1)=i;
RH[n] = i;
// % Mark this zero as "next free" in this row.
// CH(i)=j;
CH[i] = j;
// end
}
// % Find last unassigned zero on row I.
// row=A(i,:);
var row = A.GetRow(i);
// colsInList=-row(row<0);
var colsInList = row.Where(v => v <= 0 && isNotNoMatch(v)).Select(v => -v).ToArray(); // TODO <=0不确定
int l = NoMatch;
// if (length(colsInList)==0)
if (colsInList.Length == 0) {
// % No zeros in the list.
// l=n+1;
l = n;
} else {
// else
// l=colsInList(row(colsInList)==0);
/*
* row=[2 -5 0 -3 6 -1];
* cil=-row(row<0); 5 3 1
* row(cil)==0; 0 1 0
* cil(row(cil)==0); 3
* */
foreach (int col in colsInList) {
if (isNoMatch(row[col])) {
l = col;
A[i, l] = -j; // TODO:这样不知道对不对
}
}
// end
}
// % Append this zero to end of list.
// A(i,l)=-j;
A[i, l] = -j;
// end
}
// end
}
//end
}
//% Add minimum to all doubly covered elements.
//r=find(coveredRows);
r = coveredRows.Select((v, i) => new { Val = v, Idx = i })
.Where(v => v.Val)
.Select(v => v.Idx)
.ToArray();
//c=find(coveredCols);
c = coveredCols.Select((v, i) => new { Val = v, Idx = i })
.Where(v => v.Val)
.Select(v => v.Idx)
.ToArray();
//% Take care of the zeros we will remove.
//[i,j]=find(A(r,c)<=0);
var ilist = new List<int>();
var jlist = new List<int>();
foreach (int i in r) {
foreach (int j in c) {
if (A[i, j] <= 0) { // TODO <=0不知道对不对
ilist.Add(i);
jlist.Add(j);
}
}
}
//i=r(i);
//j=c(j);
// TODO 不知道干嘛，似乎我上面的循环已经包含了这个功能
//for k=1:length(i)
for (int k = 0; k < ilist.Count; ++k) {
// % Find zero before this in this row.
// lj=find(A(i(k),:)==-j(k));
var ljlist = A.GetRow(ilist[k]).FindIdxBy(d => d != -jlist[k]);
// % Link past it.
// A(i(k),lj)=A(i(k),j(k));
foreach (var lj in ljlist) {
A[ilist[k], lj] = A[ilist[k], jlist[k]];
}
// % Mark it as assigned.
// A(i(k),j(k))=0;
A[ilist[k], jlist[k]] = NoMatch;
//end
}
//A(r,c)=A(r,c)+m;
foreach (int i in r) {
foreach (int j in c) {
A[i, j] += m;
}
}
}
