static float[][] Normalize(float[][] matrix)
{
int rows = matrix.Length;
int columns = matrix[0].Length;
MT.ForEach(matrix, R => {
float av = R.Average();
double sum = 0.0;
for (int col = 0; col < columns; col++)
{
R[col] -= av;
sum += R[col] * R[col];
}
if (sum <= 0)
{
float c = (float)(1.0 / Math.Sqrt(columns));
for (int col = 0; col < columns; col++)
{
R[col] = c;
}
}
else
{
double norm = Math.Sqrt(sum);
for (int col = 0; col < columns; col++)
{
R[col] = (float)(R[col] / norm);
}
}
});
return(matrix);
}
private void App_ItemsSelected(object sender, Lib.ItemListEventArgs e)
{
List <string> idList = e.Items as List <string>;
if ((featureMap == null) || (idList == null) || (idList.Count == 0))
{
return;
}
// Calculate the squared mean of columns into m[].
int columns = NumTable.Columns;
var rowList = NumTable.IndexOfRows(idList);
double[] m = new double[columns];
MT.ForEach(rowList, row => {
var R = NumTable.Matrix[row];
for (int col = 0; col < columns; col++)
{
double v = R[col];
m[col] += v * v;
}
});
for (int col = 0; col < columns; col++)
{
m[col] = Math.Sqrt(m[col] / rowList.Count);
}
for (int i = 0; i < m.Length; i++)
{
short t = (short)((m[i] - minExpression) / stepExpression);
OrgBodies[i].Type = Math.Max((short)0, Math.Min((short)15, t));
}
featureMap.RedrawBodiesType();
}
void PropagateByNeighborsSimple(Neighbor[][] nbList, float[][] a)
{
float avAff = 0.0f;
MT.ForEach(nbList, nbs => {
float s = nbs.Sum(nb => nb.distance * nb.distance);
lock (this)
avAff += s;
});
avAff = (float)Math.Sqrt(avAff / (nbList.Length * nbList[0].Length));
int rows = a.Length;
int columns = a[0].Length;
MT.Loop(0, rows, row => {
float[] newAff = new float[columns];
float[] aR = a[row];
for (int col = 0; col < columns; col++)
{
float aff = aR[col];
foreach (var nb in nbList[col])
{
if (nb.distance >= avAff)
{
break;
}
aff += aR[nb.index];
}
newAff[col] = aff;
}
a[row] = newAff;
});
}
public static Tuple <float, float, float> MinMaxMean(float[][] matrix)
{
float minValue = float.MaxValue;
float maxValue = float.MinValue;
float meanValue = 0;
int cnt = 0;
MT.ForEach(matrix, R => {
float minV = float.MaxValue;
float maxV = float.MinValue;
float meanV = 0;
for (int j = 0; j < R.Length; j++)
{
float v = R[j];
minV = Math.Min(minV, v);
maxV = Math.Max(maxV, v);
meanV += v;
}
lock (matrix) {
minValue = Math.Min(minValue, minV);
maxValue = Math.Max(maxValue, maxV);
meanValue += meanV;
cnt += R.Length;
}
});
meanValue /= cnt;
return(Tuple.Create(minValue, maxValue, meanValue));
}
public static void ShiftMatrix(float[][] matrix, float delta)
{
if (delta == 0.0f)
{
return;
}
MT.ForEach(matrix, R => {
for (int j = 0; j < R.Length; j++)
{
R[j] = Math.Max(0, R[j] + delta);
}
});
}
public static void ScaleMatrix(float[][] matrix, double fct)
{
if (fct == 1.0)
{
return;
}
MT.ForEach(matrix, R => {
for (int j = 0; j < R.Length; j++)
{
R[j] = (float)(R[j] * fct);
}
});
}
double ExpThreshold()
{
double maxV = double.MinValue;
MT.ForEach(NumTable.Matrix, R => {
double mxV = double.MinValue;
for (int col = 0; col < NumTable.Columns; col++)
{
mxV = Math.Max(R[col], mxV);
}
lock (this)
maxV = Math.Max(maxV, mxV);
});
return((maxV < 100) ? maxV / 2.0 : Math.Sqrt(maxV));
}
public static float Average2(float[][] matrix)
{
float sum = 0.0f;
MT.ForEach(matrix, R => {
float s = 0;
for (int i = 0; i < R.Length; i++)
{
s += R[i] * R[i];
}
lock (SingleCellPlugin.App)
sum += s;
});
return((float)Math.Sqrt(sum / matrix.Sum(R => (double)R.Length)));
}
public static float AverageSqrt(float[][] matrix)
{
double sum = 0.0f;
MT.ForEach(matrix, R => {
double s = 0;
for (int i = 0; i < R.Length; i++)
{
s += Math.Sqrt(Math.Abs(R[i]));
}
lock (SingleCellPlugin.App)
sum += s;
});
double mean = sum / matrix.Sum(R => (double)R.Length);
return((float)(mean * mean));
}
void PreCalculate() {
if (dataset == null)
return;
// Extract the relevant data table.
var bs = dataset.BodyList;
INumberTable nt = dataset.GetNumberTableView();
toIdx = Enumerable.Range(0, bs.Count).Where(i => !bs[i].Disabled).ToArray(); // Indexes of enabled bodies.
int N = nt.Rows - pcaSamples;
int[] enabledRows = toIdx.Where(i => i < N).ToArray();
if (enabledRows.Length == 0)
throw new TException("No data available!");
P = new float[enabledRows.Length][];
MT.Loop(0, P.Length, row => {
float[] R = P[row] = new float[nt.Columns];
double[] dsR = nt.Matrix[enabledRows[row]] as double[];
for (int col = 0; col < nt.Columns; col++)
R[col] = (float)dsR[col];
});
// Reverse toIdx;
int[] rIdx = Enumerable.Repeat(-1, bs.Count).ToArray();
for (int i = 0; i < toIdx.Length; i++) rIdx[toIdx[i]] = i;
toIdx = rIdx;
using (var gpu = new VisuMap.DxShader.GpuDevice())
dtP = DualAffinity.DotProduct(gpu, P, false);
float[] singValues = new float[pcaMax];
float[][] PC = FastPca.DoFastReduction(P, pcaMax, singValues, true);
P = VisuMap.MathUtil.NewMatrix<float>(PC.Length, pcaSamples); // P now links data points with the injected points on the main PCA axis.
float span = 4.0f * singValues[0];
stepSize = span / (pcaSamples - 1);
float x0 = - 0.5f * span;
MT.ForEach(PC, (R, row) => {
double yy = R.Skip(1).Sum(v => v * v);
for (int col = 0; col < pcaSamples; col++) {
double x = R[0] - (x0 + col * stepSize);
P[row][col] = (float)Math.Sqrt(x * x + yy);
}
});
}
void PropagateByNeighbors(Neighbor[][] nbList, float[][] a)
{
int kNN = nbList[0].Length;
int rows = a.Length;
int columns = a[0].Length;
// Calculate the average distance to kNN neighbors.
float avAff = 0.0f;
MT.ForEach(nbList, nbs => {
float s = nbs.Sum(nb => nb.distance);
lock (this)
avAff += s;
});
avAff /= nbList.Length * kNN;
// Prepare the exponentially descreasing coefficent to propgate neighboring affinity.
MT.ForEach(nbList, nbs => {
for (int k = 0; k < kNN; k++)
{
double e = nbs[k].distance / avAff;
nbs[k].distance = (float)Math.Exp(-0.5 * e * e);
}
});
// Propagate affinity from neibhoring columns.
MT.Loop(0, rows, row => {
float[] newAff = new float[columns];
float[] aR = a[row];
for (int col = 0; col < columns; col++)
{
var nbs = nbList[col];
float aff = aR[col];
foreach (var nb in nbs)
{
aff += aR[nb.index] * nb.distance;
}
newAff[col] = aff;
}
a[row] = newAff;
});
}
public void SetExpressionTable(INumberTable numTable, IForm featureView)
{
if ((featureView is IMapSnapshot) || (featureView is IMdsCluster))
{
featureMap = featureView;
}
else
{
MsgBox.Alert("Only parnet window is not a map-snapshot or mds-cluster view.");
return;
}
NumTable = numTable;
OrgBodies = featureMap.BodyList;
featureMap.GlyphSet = "Ordered Glyphs";
featureMap.Redraw();
minExpression = double.MaxValue;
double maxExpression = double.MinValue;
MT.ForEach(NumTable.Matrix, R => {
double minV = double.MaxValue;
double maxV = double.MinValue;
foreach (var v in R)
{
minV = Math.Min(minV, v);
maxV = Math.Max(maxV, v);
}
lock (this) {
minExpression = Math.Min(minExpression, minV);
maxExpression = Math.Max(maxExpression, maxV);
}
});
stepExpression = (maxExpression - minExpression) / 16;
SingleCellPlugin.App.ItemsSelected += App_ItemsSelected;
featureView.TheForm.FormClosing += (s, e) => {
SingleCellPlugin.App.ItemsSelected -= App_ItemsSelected;
};
}
public static void PowerMatrix(float[][] matrix, double exp)
{
if (exp == 1.0)
{
return;
}
if (exp == 0.0)
{
foreach (var R in matrix)
{
for (int j = 0; j < R.Length; j++)
{
R[j] = 1.0f;
}
}
return;
}
MT.ForEach(matrix, R => {
for (int j = 0; j < R.Length; j++)
{
R[j] = (float)Math.Pow(R[j], exp);
}
});
}
static float[][] CallShadere(DxShader.GpuDevice gpu, CBuf cc, ComputeShader sd, float[][] M, bool isCorrelation)
{
cc.c.N = M.Length;
int columns = M[0].Length;
const int groupSize = 256;
int distSize = groupSize * cc.c.N;
float[][] dMatrix = new float[M.Length][];
for (int row = 0; row < M.Length; row++)
{
dMatrix[row] = new float[row];
}
int maxColumns = MaxGpuFloats / cc.c.N;
int secSize = Math.Min(columns, (maxColumns > 4096) ? 4096 : (maxColumns - 32));
float[] uploadBuf = new float[cc.c.N * secSize];
using (var dataBuf = gpu.CreateBufferRO(cc.c.N * secSize, 4, 0))
using (var distBuf = gpu.CreateBufferRW(distSize, 4, 0))
using (var distStaging = gpu.CreateStagingBuffer(distBuf)) {
gpu.SetShader(sd);
for (int s0 = 0; s0 < columns; s0 += secSize)
{
int s1 = Math.Min(s0 + secSize, columns);
WriteMarix(gpu, dataBuf, M, s0, s1, uploadBuf);
float[] blockDist = new float[distSize];
for (cc.c.iBlock = 1; cc.c.iBlock < cc.c.N; cc.c.iBlock += groupSize)
{
cc.c.columns = s1 - s0;
cc.Upload();
gpu.Run(groupSize);
int iBlock2 = Math.Min(cc.c.iBlock + groupSize, cc.c.N);
int bSize = (iBlock2 - cc.c.iBlock) * (iBlock2 + cc.c.iBlock - 1) / 2;
gpu.ReadRange <float>(blockDist, 0, distStaging, distBuf, bSize);
int offset = 0;
for (int row = cc.c.iBlock; row < iBlock2; row++)
{
float[] R = dMatrix[row];
for (int k = 0; k < row; k++)
{
R[k] += blockDist[offset + k];
}
offset += row;
}
Application.DoEvents();
}
}
}
if (isCorrelation)
{
MT.ForEach(dMatrix, R => {
for (int col = 0; col < R.Length; col++)
{
R[col] = 1.0f - R[col];
}
});
}
else // Euclidean distance is wanted.
{
float[] norm2 = new float[M.Length];
Array.Clear(norm2, 0, M.Length);
int L = M[0].Length;
MT.ForEach(M, (R, row) => {
float sumSquared = 0.0f;
for (int col = 0; col < L; col++)
{
sumSquared += R[col] * R[col];
}
norm2[row] = sumSquared;
});
MT.Loop(1, M.Length, row => {
float[] R = dMatrix[row];
for (int col = 0; col < row; col++)
{
R[col] = (float)Math.Sqrt(Math.Abs(norm2[row] + norm2[col] - 2 * R[col]));
}
});
}
return(dMatrix);
}
