I am trying to solve Gaussian elimination in c# using parallel-processing (in this case Parallel.For). My code is sometimes running fine and sometimes not.
I used as a starting point the code shown on the link: http://www.codeproject.com/Tips/388179/Linear-Equation-Solver-Gaussian-Elimination-Csharp .
I didn't use Parallel.For for back insertion just yet because there is problem with elimination.
Elimination Class:
public static bool Gauss(double[][] M)
{
int rowCount = (M.GetLength(0));
int temp = rowCount - 1;
//elimination
Parallel.For(0, temp, sourceRow =>
//for (int sourceRow = 0; sourceRow + 1 < rowCount; sourceRow++)//diagonal
{
for (int destRow = sourceRow + 1; destRow < rowCount; destRow++)//destination row
{
double df = M[sourceRow][sourceRow];
double sf = M[destRow][sourceRow];
for (int j = 0; j < rowCount + 1; j++) // line
{
M[destRow][j] = (M[destRow][j] * df - M[sourceRow][j] * sf) / df;
}
}
});
//back-insertion
for (int row = rowCount - 1; row >= 0; row--)
{
double f = M[row][row];
if (f == 0) return false;
for (int i = 0; i < rowCount + 1; i++) M[row][i] /= f;
for (int destRow = 0; destRow < row; destRow++)
{
M[destRow][rowCount] -= M[destRow][row] * M[row][rowCount];
M[destRow][row] = 0;
}
}
return true;
}
Generate Array:
public static double[][] GenerateArray2(int n)
{
Random rnd = new Random();
double[][] M = new double[n][];
for (int i = 0; i < n; i++)
{
M[i] = new double[n + 1]; // Create inner array
for (int j = 0; j < n + 1; j++)
M[i][j] = rnd.Next(-20, 20);
}
return M;
}
Print Array
public static string Print2(double[][] M)
{
string str = "";
int rowCount = M.GetUpperBound(0) + 1;
for (int row = 0; row < rowCount; row++)
{
for (int i = 0; i <= rowCount; i++)
{
str += M[row][i];
str += "\t";
}
str += Environment.NewLine;
}
return str;
}
Results are wrong in matrix 16x17 and up. At this point I don't know how to continue, I tried to lock M in elimination, didn't help.
Thanks for any help.
Related
I am working on an A* Pathfinding method that uses a custom class instead of nodes, but am having issues with my loops. The first for loop using int i is able to go up to 3 (Player1.instance.movement = 3), but I need to use an if statement inside of that loop to check if the target position has already been found. I am wondering if it is possible to break my for loop when my If statement is false.
public void GetNeighbors(Tile originTile)
{
Tile originalTile = originTile;
nextTile.Clear();
int minX = 0;
int minY = 0;
var originCostFunc = Mathf.Infinity;
for (int i = 0; i < Player1.instance.movement; i++)
{
for (int x = -1; x <= 1; x++)
{
for (int y = -1; y <= 1; y++)
{
if (x != y && y != x)
{
var costX = Mathf.Abs((originTile.transform.position.x + x) - originalTile.transform.position.x);
var costY = Mathf.Abs((originTile.transform.position.y + y) - originalTile.transform.position.y);
var distanceX = Mathf.Abs(targetPos.transform.position.x - (originTile.transform.position.x + x));
var distanceY = Mathf.Abs(targetPos.transform.position.y - (originTile.transform.position.y + y));
var costFunc = costX + costY + distanceX + distanceY;
if (costFunc <= originCostFunc)
{
originCostFunc = costFunc;
minX = x;
minY = y;
Debug.Log($"x: {x}, y: {y}");
}
}
}
}
nextTile.Add(GridManagerHandPlaced.instance.GetTileAtPosition(new Vector2(originTile.transform.position.x + minX, originTile.transform.position.y + minY)));
if (nextTile[i] != targetPos)
{
originTile = nextTile[i];
}
else
{
break;
}
}
DisplayPath();
}
You can break loop several times by condition.
bool breakLoop = false;
for (int i = 0; i < length; i++)
{
for (int j = 0; j < length; j++)
{
for (int k = 0; k < length; k++)
{
breakLoop = nextTile == target;
if (breakLoop)
break;
}
if (breakLoop)
break;
}
if (breakLoop)
break;
}
Or move search logic to separated method and return a value from any number of nested loops
string path = FindPath();
Display(path);
string FindPath()
{
for (int i = 0; i < length; i++)
{
for (int j = 0; j < length; j++)
{
for (int k = 0; k < length; k++)
{
if (nextTile == target)
return nextTile;
}
}
}
return null;
}
Never use goto operator.
This is one of the few valid cases where I'd use goto. In-fact, this is the example given in the docs for when it should be used.
void CheckMatrices(Dictionary<string, int[][]> matrixLookup, int target)
{
foreach (var (key, matrix) in matrixLookup)
{
for (int row = 0; row < matrix.Length; row++)
{
for (int col = 0; col < matrix[row].Length; col++)
{
if (matrix[row][col] == target)
{
goto Found;
}
}
}
Console.WriteLine($"Not found {target} in matrix {key}.");
continue;
Found:
Console.WriteLine($"Found {target} in matrix {key}.");
}
}
Note the syntax for the label is simply myLabel: and you can place it anywhere in procedurally executable code.
For sake of covering other ways of handling this situation, here is the boolean solution.
bool breakLoops = false;
for (int i = 0; i < length1; i++)
{
for (int ii = 0; ii < length2; ii++)
{
for (int iii = 0; iii < length3; iii++)
{
if (breakingCondition)
{
breakLoops = true;
break;
}
}
if (breakLoops) break;
}
if (breakLoops) break;
}
Simple and straightforward, but requires a break condition check at the end of each loop that you want to break out of.
I have a neural network in visual studio. for the loss function I am using a basic cost function (pred-target)**2 and after I finish an epoch I optimize the parameter functions afterwards, but the algorithm doesn't work.
No matter what is my network configuration, the predictions are not write (it is the same output for all the inputs) and the loss function is not optimized. It stays the same through all the epochs.
void calc_lyr(int x, int y, int idx, float target) // thus function calculates the neuron value based on the previous layer
{
if (x == -1 || y == 0) // if its the first layer, get the data from input nodes
{
for (int i = 0; i < neurons[y]; i++)
{
float sum = 0;
for (int j = 0; j < inputTypes.Count; j++)
{
sum += weights[x+1][j][i] * training_test[idx][j];
}
sum = relu(sum);
vals[y+1][i] = sum;
}
}
else
{
for(int i = 0; i < neurons[y]; i++)
{
float sum = 0;
for(int j = 0; j < neurons[x]; j++)
{
sum += weights[x+1][j][i] * vals[x+1][j] + biases[y][i];
}
sum = relu(sum);
vals[y+1][i] = sum;
}
}
}
void train()
{
log("Proces de antrenare inceput ----------------- " + DateTime.Now.ToString());
vals = new List<List<float>>();
weights = new List<List<List<float>>>();
biases = new List<List<float>>();
Random randB = new Random(DateTime.Now.Millisecond);
Random randW = new Random(DateTime.Now.Millisecond);
for (int i = 0; i <= nrLayers; i++)
{
progressEpochs.Value =(int)(((float)i * (float)nrLayers) / 100.0f);
vals.Add(new List<float>());
weights.Add(new List<List<float>>());
if (i == 0)
{
for (int j = 0; j < inputTypes.Count; j++)
{
vals[i].Add(0);
}
}
else
{
biases.Add(new List<float>());
for (int j = 0; j < neurons[i-1]; j++)
{
vals[i].Add(0);
float valB = (float)randB.NextDouble();
biases[i-1].Add(valB - ((int)valB));
}
}
}
float valLB = (float)randB.NextDouble();
biases.Add(new List<float>());
biases[nrLayers].Add(valLB - ((int)valLB));
for (int i = 0; i <= nrLayers; i++)
{
if (i == 0)
{
for (int j = 0; j < inputTypes.Count; j++)
{
weights[i].Add(new List<float>());
for (int x = 0; x < neurons[i]; x++)
{
float valW = (float)randW.NextDouble();
weights[i][j].Add(valW);
}
}
}
else if (i == nrLayers)
{
for (int j = 0; j < neurons[i-1]; j++) {
weights[i].Add(new List<float>());
weights[i][j].Add(0);
}
}
else
{
for (int j = 0; j < neurons[i - 1]; j++)
{
weights[i].Add(new List<float>());
for (int x = 0; x < neurons[i]; x++)
{
float valW = (float)randW.NextDouble();
weights[i][j].Add(valW);
}
}
}
}
Random rand = new Random(DateTime.Now.Millisecond);
log("\n\n");
for (int i = 0; i < epochs; i++)
{
log("Epoch " + (i + 1).ToString() + " inceput ---> " + DateTime.Now.ToString());
int idx = rand.Next() % training_test.Count;
float target = outputsPossible.IndexOf(training_labels[idx]);
for (int j = 0; j < nrLayers; j++)
{
calc_lyr(j - 1, j, idx, target);
}
float total_val = 0;
for(int x = 0; x < neurons[nrLayers - 1]; x++)
{
float val = relu(weights[nrLayers][x][0] * vals[nrLayers][x] + biases[nrLayers][0]);
total_val += val;
}
total_val = sigmoid(total_val);
float cost_res = cost(total_val, target);
log("Epoch " + (i+1).ToString() + " terminat ----- " + DateTime.Now.ToString() + "\n");
log("Eroare epoch ---> " + (cost_res<1?"0":"") + cost_res.ToString("##.##") + "\n\n\n");
float cost_der = cost_d(total_val, target);
for (int a = 0; a < weights.Count; a++)
{
for (int b = 0; b < weights[a].Count; b++)
{
for (int c = 0; c < weights[a][b].Count; c++)
{
weights[a][b][c]-=cost_der*learning_rate * sigmoid_d(weights[a][b][c]);
}
}
}
for (int a = 0; a < nrLayers; a++)
{
for (int b = 0; b < neurons[a]; b++)
{
biases[a][b] -= cost_der * learning_rate;
}
}
}
hasTrained = true;
testBut.Enabled = hasTrained;
MessageBox.Show("Antrenament complet!");
SavePrompt sp = new SavePrompt();
sp.Show();
}
How can it be changed to optimize the weights, biases and loss function? For now, when I try to debug, the weights are changing, but it is the same value for the loss function.
I solved it by using AForge.NET: link
I am trying to convert this code:
function [C] = cumulativeMaxV2(A)
cols = size(A,2);
bscans = size(A,3);
C = zeros(size(A));
for col = 1:cols
for bscan = 1:bscans
aline = A(:,col,bscan);
for i = 1:length(aline)
if i == 1
C(i,col,bscan)=0;
else
C(i,col,bscan) = max(A(1:i-1, col,bscan));
end
end
end
end
My C# code is below:
static double[,,] CumulativeMax(double[,,] A)
{
int cols = 304; //A.GetLength(1);
int bscans = 304; //A.GetLength(2);
double[,,] C = new double[160, 304, 304];
Console.Write("Processing... ");
using (var progress = new ProgressBar())
{
for (int col = 0; col < cols; col++)
{
for (int bscan = 0; bscan < bscans; bscan++)
{
double[] aline = new double[160];
for (int i = 0; i < 160; i++)
aline[i] = A[i,col,bscan];
for (int i = 0; i < aline.GetLength(0); i++)
{
if (i == 0)
C[i,col,bscan] = 0d;
else if (i == 1)
{
double[] temp = new double[i];
for (int x = 0; x < i; x++)
temp[x] = A[x,col,bscan];
C[i,col,bscan] = temp.Max();
}
else
{
double[] temp = new double[i - 1];
for (int x = 0; x < i - 1; x++)
temp[x] = A[x,col,bscan];
C[i,col,bscan] = temp.Max();
}
}
}
progress.Report((double)col/cols);
}
}
Console.WriteLine("Done.");
return C;
}
Outputs from MatLab do not match those from the C# code.
Any pointers to where the bugs are in my C# code would be great. I'm not very good with MatLab.
I think this may be due to how MatLab's max function deals with infinity and NaNs.
Below is the code I wrote for clustering using genetic algorithm. Points are from a picturebox, generated randomly (X,Y) before calling this class. However, the result of this algorithm is much worse than k-means or lbg I'm comparing it to. Can someone take a look for any errors in the algorithm, maybe I omitted something. Thanks.
I did this using arrays, the 2 other I did using lists, but I don't think that should have any impact on result.
public class geneticAlgorithm
{
static int pom = 0;
static PictureBox pb1;
public geneticAlgorithm(PictureBox pb)
{
pb1 = pb;
}
public static void doGA(PointCollection points, int clusterCounter)
//points is a list of points,
//those point have (X,Y) coordinates generated randomly from pictureBox
//coordinates. clusterCounter is how many clusters I want to divide the points into
{
//this part converts list of points into array testtab,
//where each array field hold X,Y of a point
Point[] test = new Point[points.Count];
test = points.ToArray();
double[][] testtab = new double[test.Length][];
for (int i = 0; i < testtab.GetLength(0); i++)
{
testtab[i] = new double[2];
testtab[i][0] = test[i].X;
testtab[i][1] = test[i].Y;
}
//end of converting
int n = testtab.GetLength(0);
int k = clusterCounter;
int chromosomeCount = 500;
int dimensions = 2;
double[][] testClusters = new double[k][];
for (int i = 0; i < k; i++)
{
testClusters[i] = new double[dimensions];
}
double[][] testChromosomes = new double[chromosomeCount][];
for (int i = 0; i < chromosomeCount; i++)
{
testChromosomes[i] = new double[2 * k];
}
int[][] testChromosomesInt = new int[chromosomeCount][];
for (int i = 0; i < chromosomeCount; i++)
{
testChromosomesInt[i] = new int[2 * k];
}
double[] partner = new double[chromosomeCount];
double[][] roulette = new double[chromosomeCount][];
for (int i = 0; i < chromosomeCount; i++)
{
roulette[i] = new double[1];
}
double[][] errors = new double[chromosomeCount][];
for (int i = 0; i < chromosomeCount; i++)
{
errors[i] = new double[1];
}
double crossingPossibility = 0.01;
double mutationPossibility = 0.0001;
int maxIterations = 10000;
//here I create chromosomes and initial clusters
for (int j = 0; j < chromosomeCount; j++)
{
for (int i = 0; i < k; i++)
{
Random rnd = new Random();
int r = rnd.Next(n);
for (int q = 0; q < dimensions; q++)
{
testClusters[i][q] = testtab[r][q];
}
}
int help = 0;
for (int i = 0; i < k; i++)
for (int l = 0; l < dimensions; l++) // here is creation of chromosome
{
testChromosomes[j][help] = testClusters[i][l];
help++;
}
//end
//here I call accomodation function to see which of them are good
errors[j][0] = accomodationFunction(testClusters, testtab, n, k);
//end
//cleaning of the cluster table
testClusters = new double[k][];
for (int i = 0; i < k; i++)
{
testClusters[i] = new double[dimensions];
}
}
//end
for (int counter = 0; counter < maxIterations; counter++)
{
//start of the roulette
double s = 0.0;
for (int i = 0; i < chromosomeCount; i++)
s += errors[i][0];
for (int i = 0; i < chromosomeCount; i++)
errors[i][0] = chromosomeCount * errors[i][0] / s;
int idx = 0;
for (int i = 0; i < chromosomeCount; i++)
for (int j = 0; i < errors[i][0] && idx < chromosomeCount; j++)
{
roulette[idx++][0] = i;
}
double[][] newTab = new double[chromosomeCount][];
for (int i = 0; i < chromosomeCount; i++)
{
newTab[i] = new double[2 * k];
}
Random rnd = new Random();
for (int i = 0; i < chromosomeCount; i++)
{
int q = rnd.Next(chromosomeCount);
newTab[i] = testChromosomes[(int)roulette[q][0]];
}
testChromosomes = newTab;
//end of roulette
//start of crossing chromosomes
for (int i = 0; i < chromosomeCount; i++)
partner[i] = (rnd.NextDouble() < crossingPossibility + 1) ? rnd.Next(chromosomeCount) : -1;
for (int i = 0; i < chromosomeCount; i++)
if (partner[i] != -1)
testChromosomes[i] = crossing(testChromosomes[i], testChromosomes[(int)partner[i]], rnd.Next(2 * k), k);
//end of crossing
//converting double to int
for (int i = 0; i < chromosomeCount; i++)
for (int j = 0; j < 2 * k; j++)
testChromosomes[i][j] = (int)Math.Round(testChromosomes[i][j]);
//end of conversion
//start of mutation
for (int i = 0; i < chromosomeCount; i++)
if (rnd.NextDouble() < mutationPossibility + 1)
testChromosomesInt[i] = mutation(testChromosomesInt[i], rnd.Next(k * 2), rnd.Next(10));
//end of mutation
}
//painting of the found centre on the picture box
int centrum = max(errors, chromosomeCount);
Graphics g = pb1.CreateGraphics();
SolidBrush brush = new SolidBrush(Color.Red);
for (int i = 0; i < 2 * k - 1; i += 2)
{
g.FillRectangle(brush, testChromosomesInt[centrum][i], testChromosomesInt[centrum][i + 1], 20, 20);
}
return;
}
//end of painting
public static int max(double[][] tab, int chromosomeCount)
{
double max = 0;
int k = 0;
for (int i = 0; i < chromosomeCount; i++)
{
if (max < tab[i][0])
{
max = tab[i][0];
k = i;
}
}
return k;
}
public static int[] mutation(int[] tab, int elem, int bit)
{
int mask = 1;
mask <<= bit;
tab[elem] = tab[elem] ^ mask;
return tab;
}
public static double[] crossing(double[] tab, double[] tab2, int p, int k)
{
double[] hold = new double[2 * k];
for (int i = 0; i < p; i++)
hold[i] = tab[i];
for (int i = p; i < 2 * k; i++)
hold[i] = tab2[i];
return hold;
}
//accomodation function, checks to which centre which point belongs based on distance
private static double accomodationFunction(double[][] klastry, double[][] testtab, int n, int k)
{
double Error = 0;
for (int i = 0; i < n; i++)
{
double min = 0;
int ktory = 0;
for (int j = 0; j < k; j++)
{
double dist = distance(klastry[j], testtab[i]);
if (j == 0)
{
min = dist;
}
if (min > dist)
{
min = dist;
ktory = j;
}
}
Error += min;
}
pom++;
return 1 / Error;
}
public static double distance(double[] tab, double[] tab2)
{
double dist = 0.0;
for (int i = 0; i < tab.GetLength(0); i++)
dist += Math.Pow(tab[i] - tab2[i], 2);
return dist;
}
}
The algorithm should work like so: (excuse me if not the best english)
1. Get random points (let's say 100)
2. Check into how many clusters we want to split them (the same thing I would do using k-means for example
3. Get starting population of chromosomes
4. Throu cutting on the roulette, crossing and mutation pick the best x centres, where x is the amount of clusters we want
5. Paint them.
Here are some results, and why I think it's wrong: (it's using 100 points, 5 clusters)
k-means:
lbg:
genetic(without colors now):
I hope this clarifies a bit.
I have a non-linear optimization problem with constraints. I solved it with Excel Solver (GRG)
but when I tried to translate it to C# with Microsoft Solver Foundation it gets solution.Quality = SolverQuality.Unknown.
The problem is this: I have some decisions and limit constraints (upper and lower) for every one of them. And two other constraints (this ones are the problematic constraints):
the sum of the Decision need to be 1 (100%)
(MMULT(TRANSPOSE(Decision-Array),MMULT(Tabel-with-values,Decision-Array)))^0.5 needs to be equal to another value
The goal is sum(Decision * value). I write it by Microsoft Solver Foundation like this:
SolverContext solverData = SolverContext.GetContext();
context.ClearModel();
model = context.CreateModel();
for (i=0 ; i< decisionCount; i++)
{
var decision = new Decision(SolverDomain.RealNonnegative, "decisions_" + i);
model.AddDecision(decision);
model.AddConstraint("limits_of_" + i, downConstraint(i) <= decision <= upConstraint(i));
}
var fdecision = new Decision(SolverDomain.RealNonnegative, "formula");
//mymatrix is double[,]
model.AddConstraint("f_constraint_1", fdecision == matMult(transpose(model.Decisions.ToList()),
matMult(matrix(mymatrix), transpose(model.Decisions.ToList()))[0, 0]);
//myAalue is double = givvenValue^2
solverData.model.AddConstraint("f_constraint_2", fdecision == myAalue);
var udecision = new Decision(SolverDomain.RealNonnegative, "upto100");
model.AddConstraint("upto1_constraint_1", udecision == UpTo100Precent(model.Decisions.ToList()));
model.AddConstraint("upto1_constraint_2", udecision == 1);
solverData.model.AddGoal("goal", GoalKind.Maximize, CalcGoal(model.Decisions.ToList()));
model.AddDecision(udecision);
model.AddDecision(fdecision);
Solution solution = context.Solve(new HybridLocalSearchDirective() { TimeLimit = 60000 });
//add here I get solution.Quality == SolverQuality.Unknown
These are the functions I use in above code:
private Term[,] matrix(Double[,] m)
{
int rows = m.GetLength(0);
int cols = m.GetLength(1);
Term[,] r = new Term[rows, cols];
for (int row = 0; row < rows; row++)
for (int col = 0; col < cols; col++)
r[row, col] = m[row, col];
return r;
}
private Term[,] matMult(Term[,] a, Term[,] b)
{
int rows = a.GetLength(0);
int cols = b.GetLength(1);
Term[,] r = new Term[rows, cols];
for (int row = 0; row < rows; row++)
for (int col = 0; col < cols; col++)
{
r[row, col] = 0;
for (int k = 0; k < a.GetLength(1); k++)
{
r[row, col] += a[row, k] * b[k, col];
}
}
return r;
}
private Term[,] transpose(Term[,] m)
{
int rows = m.GetLength(0);
int cols = m.GetLength(1);
Term[,] r = new Term[cols, rows];
for (int row = 0; row < rows; row++)
for (int col = 0; col < cols; col++)
{
r[col, row] = m[row, col];
}
return r;
}
private Term UpTo100Precent(List<Decision> decisions)
{
Term to100 = 0;
foreach (var decision in decisions)
{
to100 += decision;
}
return to100;
}
private Term CalcGoal(List<Decision> decisions)
{
Term x = 0;
//A is double[]
for (i = 0; i < decisions.Count ; i++)
{
x += decisions[i] * A[1]
}
return x;
}
Does someone have a solution?