I am trying to create a neural network for the function y=e^(-(x-u)^2)/(2*o^2)) where u = 50 and o = 15.
I must train my neural network so I can find the 2 x's for each y. I have created the folling code, it seems to learn it nicely, but once I test the outputs go I only get numbers around 0.99 to 1 where I should get 25 and 75 and I just can't see why. My best guess is that my error correction is wrong, but can't find the error. The neural network uses back-propagation.
The test code and training set
class Program
{
static void Main(string[] args)
{
args = new string[] {
"c:\\testTrain.csv",
"c:\\testValues.csv"
};
// Output File
string fileTrainPath = null;
string fileValuesPath = null;
if (args.Length > 0)
{
fileTrainPath = args[0];
if (File.Exists(fileTrainPath))
File.Delete(fileTrainPath);
fileValuesPath = args[1];
if (File.Exists(fileValuesPath))
File.Delete(fileValuesPath);
}
double learningRate = 0.1;
double u = 50;
double o = 15;
Random rand = new Random();
Network net = new Network(1, 8, 4, 2);
NetworkTrainer netTrainer = new NetworkTrainer(learningRate, net);
List<TrainerSet> TrainerSets = new List<TrainerSet>();
for(int i = 0; i <= 20; i++)
{
double random = rand.NextDouble();
TrainerSets.Add(new TrainerSet(){
Inputs = new double[] { random },
Outputs = getX(random, u, o)
});
}
// Train Network
string fileTrainValue = String.Empty;
for (int i = 0; i <= 10000; i++)
{
if (i == 5000)
{ }
double error = netTrainer.RunEpoch(TrainerSets);
Console.WriteLine("Epoch " + i + ": Error = " + error);
if(fileTrainPath != null)
fileTrainValue += i + "," + learningRate + "," + error + "\n";
}
if (fileTrainPath != null)
File.WriteAllText(fileTrainPath, fileTrainValue);
// Test Network
string fileValuesValue = String.Empty;
for (int i = 0; i <= 100; i++)
{
double y = rand.NextDouble();
double[] dOutput = getX(y, u, o);
double[] Output = net.Compute(new double[] { y });
if (fileValuesPath != null)
fileValuesValue += i + "," + y + "," + dOutput[0] + "," + dOutput[1] + "," + Output[0] + "," + Output[1] + "\n";
}
if (fileValuesPath != null)
File.WriteAllText(fileValuesPath, fileValuesValue);
}
public static double getResult(int x, double u, double o)
{
return Math.Exp(-Math.Pow(x-u,2)/(2*Math.Pow(o,2)));
}
public static double[] getX(double y, double u, double o)
{
return new double[] {
u + Math.Sqrt(2 * Math.Pow(o, 2) * Math.Log(1/y)),
u - Math.Sqrt(2 * Math.Pow(o, 2) * Math.Log(1/y)),
};
}
}
The code behind the network
public class Network
{
protected int inputsCount;
protected int layersCount;
protected NetworkLayer[] layers;
protected double[] output;
public int Count
{
get
{
return layers.Count();
}
}
public NetworkLayer this[int index]
{
get { return layers[index]; }
}
public Network(int inputsCount, params int[] neuronsCount)
{
this.inputsCount = Math.Max(1, inputsCount);
this.layersCount = Math.Max(1, neuronsCount.Length);
layers = new NetworkLayer[neuronsCount.Length];
for (int i = 0; i < layersCount; i++)
layers[i] = new NetworkLayer(neuronsCount[i],
(i == 0) ? inputsCount : neuronsCount[i - 1]);
}
public virtual double[] Compute(double[] input)
{
output = input;
foreach (NetworkLayer layer in layers)
output = layer.Compute(output);
return output;
}
}
public class NetworkLayer
{
protected int inputsCount = 0;
protected int neuronsCount = 0;
protected Neuron[] neurons;
protected double[] output;
public Neuron this[int index]
{
get { return neurons[index]; }
}
public int Count
{
get { return neurons.Length; }
}
public int Inputs
{
get { return inputsCount; }
}
public double[] Output
{
get { return output; }
}
public NetworkLayer(int neuronsCount, int inputsCount)
{
this.inputsCount = Math.Max( 1, inputsCount );
this.neuronsCount = Math.Max( 1, neuronsCount );
neurons = new Neuron[this.neuronsCount];
output = new double[this.neuronsCount];
// create each neuron
for (int i = 0; i < neuronsCount; i++)
neurons[i] = new Neuron(inputsCount);
}
public virtual double[] Compute(double[] input)
{
// compute each neuron
for (int i = 0; i < neuronsCount; i++)
output[i] = neurons[i].Compute(input);
return output;
}
}
public class Neuron
{
protected static Random rand = new Random((int)DateTime.Now.Ticks);
public int Inputs;
public double[] Input;
public double[] Weights;
public double Output = 0;
public double Threshold;
public double Error;
public Neuron(int inputs)
{
this.Inputs = inputs;
Weights = new double[inputs];
for (int i = 0; i < inputs; i++)
Weights[i] = rand.NextDouble() * 0.5;
}
public double Compute(double[] inputs)
{
Input = inputs;
double e = 0.0;
for (int i = 0; i < inputs.Length; i++)
e += Weights[i] * inputs[i];
e -= Threshold;
return (Output = sigmoid(e));
}
private double sigmoid(double value)
{
return (1 / (1 + Math.Exp(-1 * value)));
//return 1 / (1 + Math.Exp(-value));
}
}
My Trainer
public class NetworkTrainer
{
private Network network;
private double learningRate = 0.1;
public NetworkTrainer(double a, Network network)
{
this.network = network;
this.learningRate = a;
}
public double Run(double[] input, double[] output)
{
network.Compute(input);
return CorrectErrors(output);
}
public double RunEpoch(List<TrainerSet> sets)
{
double error = 0.0;
for (int i = 0, n = sets.Count; i < n; i++)
error += Run(sets[i].Inputs, sets[i].Outputs);
// return summary error
return error;
}
private double CorrectErrors(double[] desiredOutput)
{
double[] errorLast = new double[desiredOutput.Length];
NetworkLayer lastLayer = network[network.Count - 1];
for (int i = 0; i < desiredOutput.Length; i++)
{
// S(p)=y(p)*[1-y(p)]*(yd(p)-y(p))
lastLayer[i].Error = lastLayer[i].Output * (1-lastLayer[i].Output)*(desiredOutput[i] - lastLayer[i].Output);
errorLast[i] = lastLayer[i].Error;
}
// Calculate errors
for (int l = network.Count - 2; l >= 0; l--)
{
for (int n = 0; n < network[l].Count; n++)
{
double newError = 0;
for (int np = 0; np < network[l + 1].Count; np++)
{
newError += network[l + 1][np].Weights[n] * network[l + 1][np].Error;
}
network[l][n].Error = newError;
}
}
// Update Weights
// w = w + (a * input * error)
for (int l = network.Count - 1; l >= 0; l--)
{
for (int n = 0; n < network[l].Count; n++)
{
for (int i = 0; i < network[l][n].Inputs; i++)
{
// deltaW = a * y(p) * s(p)
double deltaW = learningRate * network[l][n].Output * network[l][n].Error;
network[l][n].Weights[i] += deltaW;
}
}
}
double returnError = 0;
foreach (double e in errorLast)
returnError += e;
return returnError;
}
}
For regression problems your output layer should have the identity (or at least a linear) activation function. This way you don't have to scale your output. The derivative of the identity function is 1 and thus the derivative dE/da_i for the output layer is y-t (lastLayer[i].Output - desiredOutput[i]).
Related
You may think I am crazy, but I decided to write a neural network from scratch in C# for studying purposes. Please, be patient, I still have little experience)). English is not my first language, so I am sorry for it in advance.
I started with a program for handwritten digit recognizing with the MNIST database. I've read through a book about the algorithms inside the process and wrote this code.
public class NeuralNetwork
{
private List<Matrix<double>> weights = new List<Matrix<double>>();
private List<Vector<double>> biases = new List<Vector<double>>();
private Random random = new Random();
private List<Image> test_data;
private int[] layer_sizes;
public NeuralNetwork(params int[] layers)
{
layer_sizes = layers;
for (int i = 0; i < layers.Length - 1; i++)
{
var weigthLayer = Matrix<double>.Build.Dense(layers[i + 1], layers[i], (k, j) => random.NextDouble());
weights.Add(weigthLayer);
}
for (int i = 1; i < layers.Length; i++)
{
var biasesLayer = Vector<double>.Build.Dense(layers[i], (k) => random.NextDouble());
biases.Add(biasesLayer);
}
}
public Vector<double> FeedForward(Vector<double> a)
{
for (int i = 0; i < weights.Count; i++)
{
a = Sigmoid(weights[i].Multiply(a) + biases[i]);
}
return Sigmoid(a);
}
public void SGD(ITrainingDataProvider dataProvider, int epochs, int chuck_size, double eta)
{
test_data = new MnistReader().ReadTestData();
Console.WriteLine("SGD algorithm started");
var training_data = dataProvider.ReadTrainingData();
Console.WriteLine("Training data has beeen read");
Console.WriteLine($"Training data test: {Test(training_data)}%");
Console.WriteLine($"Test data test: {Test(test_data)}%");
for (int epoch = 0; epoch < epochs; epoch++)
{
training_data = training_data.OrderBy(item => random.Next()).ToList();
List<List<Image>> chunks = training_data.ChunkBy(chuck_size);
foreach (List<Image> chunk in chunks)
{
ProcessChunk(chunk, eta);
}
Console.WriteLine($"Epoch: {epoch + 1}/{epochs}");
Console.WriteLine($"Training data test: {Test(training_data)}%");
Console.WriteLine($"Test data test: {Test(test_data)}%");
}
Console.WriteLine("Done!");
}
private double Test(List<Image> data)
{
int count = 0;
foreach (Image im in data)
{
var output = FeedForward(im.DataToVector());
int number = output.MaximumIndex();
if (number == (int)im.Label)
{
count++;
}
}
return (double)count / data.Count * 100;
}
private void ProcessChunk(List<Image> chunk, double eta)
{
Delta[] deltas = new Delta[chunk.Count];
for (int i = 0; i < chunk.Count; i++)
{
Image image = chunk[i];
var input = image.DataToVector();
var desired_output = Vector<double>.Build.Dense(layer_sizes[layer_sizes.Length - 1]);
desired_output[(int)image.Label] = 1;
Delta delta = BackPropagation(input, desired_output);
deltas[i] = delta;
}
Delta sum = deltas[0];
for (int i = 1; i < deltas.Length; i++)
{
sum += deltas[i];
}
Delta average_delta = sum / deltas.Length;
for (int i = 0; i < layer_sizes.Length - 1; i++)
{
weights[i] += average_delta.d_weights[i].Multiply(eta);
biases[i] += average_delta.d_biases[i].Multiply(eta);
}
}
private Delta BackPropagation(Vector<double> input, Vector<double> desired_output)
{
List<Vector<double>> activations = new List<Vector<double>>();
List<Vector<double>> zs = new List<Vector<double>>();
Vector<double> a = input;
activations.Add(input);
for (int i = 0; i < layer_sizes.Length - 1; i++)
{
var z = weights[i].Multiply(a) + biases[i];
zs.Add(z);
a = Sigmoid(z);
activations.Add(a);
}
List<Vector<double>> errors = new List<Vector<double>>();
List<Matrix<double>> delta_weights = new List<Matrix<double>>();
List<Vector<double>> delta_biases = new List<Vector<double>>();
var error = CDerivative(activations[activations.Count - 1], desired_output).HProd(SigmoidDerivative(zs[^1]));
errors.Add(error);
int steps = 0;
for (int i = layer_sizes.Length - 2; i >= 1; i--)
{
var layer_error = weights[i].Transpose().Multiply(errors[steps]).HProd(SigmoidDerivative(zs[i - 1]));
errors.Add(layer_error);
steps++;
}
errors.Reverse();
for (int i = layer_sizes.Length - 1; i >= 1; i--)
{
var delta_layer_weights = (errors[i - 1].ToColumnMatrix() * activations[i - 1].ToRowMatrix()).Multiply(-1);
delta_weights.Add(delta_layer_weights);
var delta_layer_biases = errors[i - 1].Multiply(-1);
delta_biases.Add(delta_layer_biases);
}
delta_biases.Reverse();
delta_weights.Reverse();
return new Delta { d_weights = delta_weights, d_biases = delta_biases };
}
private Vector<double> CDerivative(Vector<double> x, Vector<double> y)
{
return x - y;
}
private Vector<double> Sigmoid(Vector<double> x)
{
for (int i = 0; i < x.Count; i++)
{
x[i] = 1.0 / (1.0 + Math.Exp(-x[i]));
}
return x;
}
private Vector<double> SigmoidDerivative(Vector<double> x)
{
for (int i = 0; i < x.Count; i++)
{
x[i] = Math.Exp(-x[i]) / Math.Pow(1.0 + Math.Exp(-x[i]), 2);
}
return x;
}
}
Delta class. A simple DTO to store weights and biases changes in a single object.
public class Delta
{
public List<Matrix<double>> d_weights { get; set; }
public List<Vector<double>> d_biases { get; set; }
public static Delta operator +(Delta d1, Delta d2)
{
Delta result = d1;
for (int i = 0; i < d2.d_weights.Count; i++)
{
result.d_weights[i] += d2.d_weights[i];
}
for (int i = 0; i < d2.d_biases.Count; i++)
{
result.d_biases[i] += d2.d_biases[i];
}
return result;
}
public static Delta operator /(Delta d1, double d)
{
Delta result = d1;
for (int i = 0; i < d1.d_weights.Count; i++)
{
result.d_weights[i] /= d;
}
for (int i = 0; i < d1.d_biases.Count; i++)
{
result.d_biases[i] /= d;
}
return result;
}
}
Everything ended up working fine, however complex networks with 1 or more hidden layers don't show any significant results. They are getting best 70% accuracy and then the learning curve drops. The accuracy returns to its 20-30%. Typically, the graph looks like a square root function, but in my case it is more like a turned around quadratic parabola the graph of my tries with different amounts of neurons in the first hidden layer
After a few tries, I found out, that without any hidden layers the algorithm works just fine. It learns up to 90% of accuracy and then the graph never falls down. Apparently, the bug is somewhere in back-propagation algorithm. It doesn't cause any problems with only input and output layers, but it does, when I add a hidden layer.
I have been trying to find the problem for a long time and I hope that someone, smarter than me, will be able to help.
Thanks in advance!
I have class library with a bunch of static methods. While trying to call one of them, I experience unhandled StackOverflowException somewhere in the ListDictionaryInternal class.
I tried enabling .Net Framework (v 4.5.2) stepping, surrounding call with try/catch block, and executing it step by step. When I place continue statement after Appendix A comment, then comment it while debugging, method works as expected. Otherwise I cannot even hit breakpoint at the start at the method. I also tried to call method with all parameters set to null, but it did not help either.
public static List<CalcSector> Split(List<CalcSector> calibration, List<ProfilePoint> profile, List<MeasurementPoint> additionalPoints)
{
double lengthCorridor = 10d;
double lengthEpsilon = 1d;
if (!(calibration?.Any() ?? false)) throw new ArgumentNullException(nameof(calibration), "Empty calibration table");
if (!(profile?.Any() ?? false)) throw new ArgumentNullException(nameof(profile), "Empty profile points collection");
for (int i = 0; i < calibration.Count - 1; i++)
if (Math.Abs(calibration[i].EndDistance - calibration[i + 1].StartDistance) > lengthEpsilon)
throw new ArgumentException($"calibration[{i}]", "Calibration table integrity is compromised");
List<CalcSector> result = new List<CalcSector>();
List<ProfilePoint> SummitPoints = new List<ProfilePoint>();
calibration.ForEach(x => result.Add(x));
profiles = profile.OrderBy(x => x.Distance).ToList();
//
if (additionalPoints?.Any() ?? false)
foreach (MeasurementPoint mp in additionalPoints.Where(x => x.Id != int.MinValue && x.Id != int.MaxValue))
for (int i = 0; i < result.Count; i++)
if (Math.Abs(mp.Distance - result[i].StartDistance) > lengthEpsilon && Math.Abs(mp.Distance - result[i].EndDistance) > lengthEpsilon && mp.Distance > result[i].StartDistance && mp.Distance < result[i].EndDistance)
{
CalcSector c = new CalcSector()
{
StartDistance = mp.Distance,
StartHeight = BinaryHeightSearch(mp.Distance),
StartPointId = mp.Id,
EndDistance = result[i].EndDistance,
EndHeight = result[i].EndHeight,
Length = result[i].EndDistance - mp.Distance,
Thickness = result[i].Thickness,
};
result[i].EndDistance = mp.Distance;
result[i].EndHeight = c.StartHeight;
result[i].EndPointId = mp.Id;
c.Volume = result[i].Volume / result[i].Length * c.Length;
result[i].Length -= c.Length;
result[i].Volume -= c.Volume;
result.Insert(i + 1, c);
break;
}
else if (Math.Abs(mp.Distance - result[i].StartDistance) < lengthEpsilon)
result[i].StartPointId = mp.Id;
else if (Math.Abs(mp.Distance - result[i].EndDistance) < lengthEpsilon)
result[i].EndPointId = mp.Id;
int start = 0;
int end = 0;
bool hasSpikes = true;
while (hasSpikes)
{
hasSpikes = false;
//Appendix A
for (int j = 0; j < result.Count; j++)
{
result[j].z = -1d * (result[j].StartHeight - result[j].EndHeight) / (result[j].EndDistance - result[j].StartDistance);
result[j].sI = start = BinaryProfileSearch(result[j].StartDistance);
result[j].eI = end = BinaryProfileSearch(result[j].EndDistance);
for (int i = start + 1; i < end; i++)
if (Math.Abs(result[j].z * (profiles[i].Distance - result[j].StartDistance) + result[j].StartHeight - profiles[i].Height) > lengthCorridor)
{
int maxIndex = -1;
double maxH = double.MinValue;
int minIndex = -1;
double minH = double.MaxValue;
for (; start < end; start++)
{
if (Math.Abs(result[j].z * (profiles[start].Distance - result[j].StartDistance) + result[j].StartHeight - profiles[start].Height) <= lengthCorridor)
continue;
if (result[j].z * (profiles[i].Distance - result[j].StartDistance) + result[j].StartHeight - profiles[i].Height > maxH)
{
maxH = profiles[start].Height;
maxIndex = start;
}
if (result[j].z * (profiles[i].Distance - result[j].StartDistance) + result[j].StartHeight - profiles[i].Height < minH)
{
minH = profiles[start].Height;
minIndex = start;
}
}
int target = Math.Min(maxIndex, minIndex);
CalcSector c = new CalcSector()
{
StartDistance = profiles[target].Distance,
StartHeight = profiles[target].Height,
sI = target,
EndDistance = result[j].EndDistance,
EndHeight = result[j].EndHeight,
EndPointId = result[j].EndPointId,
eI = result[j].eI,
Length = result[j].EndDistance - profiles[target].Distance,
Thickness = result[j].Thickness,
};
result[j].EndDistance = c.StartDistance;
result[j].EndHeight = c.StartHeight;
result[j].EndPointId = null;
result[j].eI = target;
result[j].z = -1d * (result[j].StartHeight - result[j].EndHeight) / (result[j].EndDistance - result[j].StartDistance);
c.Volume = result[j].Volume / result[j].Length * c.Length;
result[j].Length -= c.Length;
result[j].Volume -= c.Volume;
result.Insert(j + 1, c);
hasSpikes = true;
break;
}
}
}
for (int j = 0; j < result.Count; j++)
{
result[j].Diameter = 1000d * Math.Sqrt(4d * result[j].Volume / Constants["PI"] / result[j].Length);
result[j].OrdNum = j;
}
result.First().StartPointId = int.MinValue;
result.Last().EndPointId = int.MaxValue;
for (int i = 1; i < profiles.Count - 1; i++)
if (profiles[i - 1].Height < profiles[i].Height && profiles[i].Height > profiles[i + 1].Height)
SummitPoints.Add(profiles[i]);
return result;
}
public class CalcSector
{
public int OrdNum;
public double StartDistance;
public double StartHeight;
public int? StartPointId;
public double EndDistance;
public double EndHeight;
public int? EndPointId;
public double Length;
public double Volume;
public double Diameter;
public double Thickness;
public int sI;
public int eI;
public double z;
}
public class ProfilePoint
{
public double Distance;
public double Height;
}
public class MeasurementPoint
{
public int Id;
public double Distance;
}
I expect this method to split some of the original CalcSectors into smaller ones, but all I have is this unhandled fatal exception.
Added:
private static int BinaryProfileSearch(double distance)
{
if (profiles == null || profiles.Count == 0)
return -1;
//assuming that profile points are already ordered by distance
if (distance <= profiles.First().Distance)
return 0;
if (distance >= profiles.Last().Distance)
return profiles.Count - 1;
int first = 0;
int last = profiles.Count - 1;
while (first + 1 < last)
{
int mid = (first + last) / 2;
if (distance <= profiles[mid].Distance)
last = mid;
else
first = mid + 1;
}
if (distance - profiles[first].Distance > profiles[last].Distance - distance)
return last;
else
return first;
}
The solution came quite unexpected: invalid value of 8087 control word. Next line changes it back.
_controlfp(0x9001F, 0xFFFFF);
So the goal of my code is to pick two points in a grid of numbers and figure out with point gets the highest value of numbers when counting every other point in the grid closest to the two points. While typing this code I was trying to use private field variables to hold the current position of both pointers however I receive errors for the field variables I have and any calls that I make into this grid built.
namespace Program
{
private int xMe;
private int yMe;
private int zMe;
private int xEn;
private int yEn;
private int zEn;
private int dif = 0;
public class Castles
{
public static string Process(uint[,] grid)
{
int taxX = 0;
int taxY = 0;
for (int i = 0; i < grid.GetLength; i++)
{
for(int j = 0; j < grid[i]; j++)
{
for(int k = 0; k < grid.GetLength; k++)
{
for(int l = 0; l < grid[k]; l++)
{
if (distance(i, j, k, l) > 3)
{
if (grid[i, j] != 0 && grid[k, l] != 0)
{
for (int m = 0; grid.GetLength; m++)
{
for (int n = 0; grid[m]; n++)
{
if (grid[i, j] != grid[m, n] || grid[k, l] != grid[m, n])
{
if(distance(i,j,m,n) > distance(m, n, k, l))
{
taxX = taxX + distance[m, n];
}
else if(distance(i,j,m,n)< distance(m, n, k, l))
{
taxY = taxY + distance[m, n];
}
else
{
}
}
}
}
if(taxX - taxY > dif)
{
xMe = i;
yMe = j;
xEn = k;
yEn = l;
zMe = taxX;
zEn = taxY;
dif = taxX - taxY;
}
}
}
}
}
}
}
return "Your castle at (" + xMe + "," + yMe + ") earns " + zMe + ". Your nemesis' castle at (" + xEn + "," + yEn + ") earns " + zEn + ".";
}
public int distance(int x, int y, int a, int b)
{
int c = a - x;
int d = b - y;
return Math.Sqrt(c ^ 2 + d ^ 2);
}
static void Main(string[] args)
{
System.Console.WriteLine("");
}
}
}
This is my first exploration into c# so this could be a simple fix but any help would be useful
Just move the definitions of the fields to the inside of the class definition...
namespace Program
{
public class Castles
{
private int xMe;
private int yMe;
private int zMe;
private int xEn;
private int yEn;
private int zEn;
private int dif = 0;
public static string Process(uint[,] grid)
{
int taxX = 0;
int taxY = 0;
for (int i = 0; i < grid.GetLength; i++)
{
for(int j = 0; j < grid[i]; j++)
{
for(int k = 0; k < grid.GetLength; k++)
{
for(int l = 0; l < grid[k]; l++)
{
if (distance(i, j, k, l) > 3)
{
if (grid[i, j] != 0 && grid[k, l] != 0)
{
for (int m = 0; grid.GetLength; m++)
{
for (int n = 0; grid[m]; n++)
{
if (grid[i, j] != grid[m, n] || grid[k, l] != grid[m, n])
{
if(distance(i,j,m,n) > distance(m, n, k, l))
{
taxX = taxX + distance[m, n];
}
else if(distance(i,j,m,n)< distance(m, n, k, l))
{
taxY = taxY + distance[m, n];
}
else
{
}
}
}
}
if(taxX - taxY > dif)
{
xMe = i;
yMe = j;
xEn = k;
yEn = l;
zMe = taxX;
zEn = taxY;
dif = taxX - taxY;
}
}
}
}
}
}
}
return "Your castle at (" + xMe + "," + yMe + ") earns " + zMe + ". Your nemesis' castle at (" + xEn + "," + yEn + ") earns " + zEn + ".";
}
public int distance(int x, int y, int a, int b)
{
int c = a - x;
int d = b - y;
return Math.Sqrt(c ^ 2 + d ^ 2);
}
static void Main(string[] args)
{
System.Console.WriteLine("");
}
}
I have two variables: long[] nextState and List<long[]> TabuList.
I use this code to add items to TabuList:
TabuList.add(nextState);
or this:
TabuList.insert(Index, nexState);
but the problem is that after either of these operations, all of TabuList’s items automatically convert to the current value of nextState.
my complete code is:
class TabuSearch
{
private long[] current { get; set; }
private double Delta;
private Random rnd = new Random();
int foundLists = 0;
public TabuSearch()
{
current = new long[Convert.ToInt32(num_list1)];
}
public long[] TabuMOSA3Objectives(long[] c)
{
assign(current, c);
long[] nextState = new long[Convert.ToInt32(num_list1)];
List<long[]> TabuList = new List<long[]>();
double proba;
double alpha = 0.969;
double temperature = 500.0;
double epsilon = 0.0001;
short domination_st;
int iter = 0;
while (temperature > epsilon)
{
iter++;
Delta = 1;
assign(nextState, GenerateNextState(primaryList, current));
domination_st = CheckDomination3Objective(nextState, current);
try { var tmp = TabuList.Find(x => x == nextState); if (tmp == null) foundLists = 0; else foundLists = tmp.Count(); }
catch { }
if (foundLists == 0)
{
if (domination_st > 0)
{
assign(current, nextState);
}
else // domination_st < 0
{
proba = rnd.NextDouble();
if (proba < 1 / (1 + Math.Exp(Delta * temperature)))
{
assign(current, nextState);
}
else
{
if (TabuList.Count == 10)
TabuList.RemoveAt(0);
assign(nextState, TabuList);
}
}
}
//cooling proces on every iteration
temperature *= alpha;
}
return current;
}
}
static void assign(long[] c, long[] n)
{
for (int i = 0; i < c.Length; i++)
c[i] = n[i];
}
static void assign(long[] item, List<long[]> list)
{
list.Add(item);
}
I'm a hobby programmer.
I tried to ask this question earlier on a very unstructured way (Sorry again), now I try to ask on the proper way.
I wrote the following code that seems to work unreliably.
The code was written like this for several reasons. I know it's messy but it should still work. To explain why I wrote it like this would mean that I need to explain several weeks' of work that is quite extensive. Please accept that this is at least the least worse option I could figure out. In the below sample I removed all sections of the code that are not needed to reproduce the error.
What this program does in a nutshell:
The purpose is to check a large number of parameter combinations for a program that receives streaming data. I simulate the original process to test parameter combinations.
First data is read from files that represents recorded streaming data.
Then the data is aggregated.
Then I build a list of parameters to test for.
Finally I run the code for each parameter combination in parallel.
Inside the parallel part I calculate a financial indicator called the bollinger bands. This is a moving average with adding +/- standard deviation. This means the upper line and the lower line should only be equal when variable bBandDelta = 0. However sometimes it happens that CandleList[slot, w][ctr].bollingerUp is equal to CandleList[slot, w][ctr].bollingerDown even when bBandDelta is not 0.
As a result I don't understand how can line 277 kick in. It seems that sometimes the program fails to write to the CandleList[slot, w][ctr]. However this should not be possible because (1) I lock the list and (2) I use ConcurrentBag. Could I have some help please?
Source files are here.
The code is:
using System;
using System.IO;
using System.Collections.Generic;
using System.Linq;
using System.Text;
using System.Threading;
using System.Threading.Tasks;
using System.Collections.Concurrent;
namespace Justfortest
{
class tick : IComparable<tick> //Data element to represent a tick
{
public string disp_name; //ticker ID
public DateTime? trd_date; //trade date
public TimeSpan? trdtim_1; //trade time
public decimal trdprc_1; //price
public int? trdvol_1; //tick volume
public int CompareTo(tick other)
{
if (this.trdprc_1 == other.trdprc_1)
{
return other.trdprc_1.CompareTo(this.trdprc_1); //Return the later item
}
return this.trdprc_1.CompareTo(other.trdprc_1); //Return the earlier item
}
}
class candle : IComparable<candle> //Data element to represent a candle and all chart data calculated on candle level
{
public int id = 0;
public DateTime? openDate;
public TimeSpan? openTime;
public DateTime? closeDate;
public TimeSpan? closeTime;
public decimal open = 0;
public decimal high = 0;
public decimal low = 0;
public decimal close = 0;
public int? volume = 0;
public decimal totalPrice = 0;
public decimal bollingerUp = 0; //Bollinger upper line
public decimal bollingerDown = 0; //Bollinger below line
public int CompareTo(candle other)
{
if (totalPrice == other.totalPrice)
{
return other.totalPrice.CompareTo(totalPrice); //Return the later item
}
return totalPrice.CompareTo(other.totalPrice); //Return the earlier item
}
}
class param : IComparable<param> //Data element represent a trade event signal
{
public int par1;
public int bollPar;
public int par2;
public int par3;
public int par4;
public int par5;
public int par6;
public decimal par7;
public decimal par8;
public decimal par9;
public decimal par10;
int IComparable<param>.CompareTo(param other)
{
throw new NotImplementedException();
}
}
class programCLass
{
void myProgram()
{
Console.WriteLine("Hello");
Console.WindowWidth = 180;
string[] sources = new string[]
{
#"C:\test\source\sourceW1.csv",
#"C:\test\source\sourceW2.csv",
};
List<candle>[] sourceCandleList = new List<candle>[sources.Count()];
List<param> paramList = new List<param>(10000000);
var csvAnalyzer = new StringBuilder();
{
List<tick>[] updatelist = new List<tick>[sources.Count()];
Console.WriteLine("START LOAD");
for (var i = 0; i < sources.Count(); i++)
{
var file = sources[i];
updatelist[i] = new List<tick>();
// ---------- Read CSV file ----------
var reader = new StreamReader(File.OpenRead(file));
while (!reader.EndOfStream)
{
var line = reader.ReadLine();
var values = line.Split(',');
tick update = new tick();
update.disp_name = values[0].ToString();
update.trd_date = Convert.ToDateTime(values[1]);
update.trdtim_1 = TimeSpan.Parse(values[2]);
update.trdprc_1 = Convert.ToDecimal(values[3]);
update.trdvol_1 = Convert.ToInt32(values[4]);
updatelist[i].Add(update);
}
Console.WriteLine(i);
}
Console.WriteLine("END LOAD"); // All files are in the memory
// Aggreagate
Console.WriteLine("AGGREGATE START");
int tickAggr = 500;
for (var w = 0; w < sources.Count(); w++)
{
sourceCandleList[w] = new List<candle>();
List<tick> FuturesList = new List<tick>();
foreach (var update in updatelist[w])
{
tick t = new tick();
t.disp_name = update.disp_name.ToString();
t.trd_date = update.trd_date;
t.trdtim_1 = update.trdtim_1;
t.trdprc_1 = Convert.ToDecimal(update.trdprc_1);
t.trdvol_1 = update.trdvol_1;
// Add new tick to the list
FuturesList.Add(t);
if (FuturesList.Count == Math.Truncate(FuturesList.Count / (decimal)tickAggr) * tickAggr)
{
candle c = new candle();
c.openDate = FuturesList[FuturesList.Count - tickAggr].trd_date;
c.openTime = FuturesList[FuturesList.Count - tickAggr].trdtim_1;
c.closeDate = FuturesList.Last().trd_date;
c.closeTime = FuturesList.Last().trdtim_1;
c.open = FuturesList[FuturesList.Count - tickAggr].trdprc_1;
c.high = FuturesList.GetRange(FuturesList.Count - tickAggr, tickAggr).Max().trdprc_1;
c.low = FuturesList.GetRange(FuturesList.Count - tickAggr, tickAggr).Min().trdprc_1;
c.close = FuturesList.Last().trdprc_1;
c.volume = FuturesList.GetRange(FuturesList.Count - tickAggr, tickAggr).Sum(tick => tick.trdvol_1);
c.totalPrice = (c.open + c.high + c.low + c.close) / 4;
sourceCandleList[w].Add(c);
if (sourceCandleList[w].Count == 1)
{
c.id = 0;
}
else
{
c.id = sourceCandleList[w][sourceCandleList[w].Count - 2].id + 1;
}
}
}
FuturesList.Clear();
}
Console.WriteLine("AGGREGATE END");
for (var i = 0; i < sources.Count(); i++)
{
updatelist[i].Clear();
}
}
Console.WriteLine("BUILD PARAMLIST");
for (int par1 = 8; par1 <= 20; par1 += 4) // parameter deployed
{
for (int bollPar = 10; bollPar <= 25; bollPar += 5) // parameter deployed
{
for (int par2 = 6; par2 <= 18; par2 += 4) // parameter deployed
{
for (int par3 = 14; par3 <= 20; par3 += 3) // parameter deployed
{
for (int par4 = 10; par4 <= 20; par4 += 5) // parameter deployed
{
for (int par5 = 4; par5 <= 10; par5 += 2) // parameter deployed
{
for (int par6 = 5; par6 <= 30; par6 += 5)
{
for (decimal par7 = 1.0005M; par7 <= 1.002M; par7 += 0.0005M)
{
for (decimal par8 = 1.002M; par8 <= 1.0048M; par8 += 0.0007M)
{
for (decimal par9 = 0.2M; par9 <= 0.5M; par9 += 0.1M)
{
for (decimal par10 = 0.5M; par10 <= 2; par10 += 0.5M)
{
param p = new param();
p.par1 = par1;
p.bollPar = bollPar;
p.par2 = par2;
p.par3 = par3;
p.par4 = par4;
p.par5 = par5;
p.par6 = par6;
p.par7 = par7;
p.par8 = par8;
p.par9 = par9;
p.par10 = par10;
paramList.Add(p);
}
}
}
}
}
}
}
}
}
}
}
Console.WriteLine("END BUILD PARAMLIST, scenarios to test:{0}", paramList.Count);
var sourceCount = sources.Count();
sources = null;
Console.WriteLine("Start building pools");
int maxThreads = 64;
ConcurrentBag<int> pool = new ConcurrentBag<int>();
List<candle>[,] CandleList = new List<candle>[maxThreads, sourceCount];
for (int i = 0; i <= maxThreads - 1; i++)
{
pool.Add(i);
for (int w = 0; w <= sourceCount - 1; w++)
{
CandleList[i, w] = sourceCandleList[w].ConvertAll(p => p);
}
}
Console.WriteLine("End building pools");
int pItemsProcessed = 0;
Parallel.ForEach(paramList,
new ParallelOptions { MaxDegreeOfParallelism = maxThreads },
p =>
{
int slot = 1000;
while (!pool.TryTake(out slot));
var bollPar = p.bollPar;
decimal bollingerMiddle = 0;
double bBandDeltaX = 0;
for (var w = 0; w < sourceCount; w++)
{
lock (CandleList[slot, w])
{
for (var ctr = 0; ctr < CandleList[slot, w].Count; ctr++)
{
CandleList[slot, w][ctr].bollingerUp = 0; //Bollinger upper line
CandleList[slot, w][ctr].bollingerDown = 0; //Bollinger below line
//Bollinger Bands Calculation
if (ctr + 1 >= bollPar)
{
bollingerMiddle = 0;
bBandDeltaX = 0;
for (int i = 0; i <= bollPar - 1; i++)
{
bollingerMiddle = bollingerMiddle + CandleList[slot, w][ctr - i].totalPrice;
}
bollingerMiddle = bollingerMiddle / bollPar; //average
for (int i = 0; i <= bollPar - 1; i++)
{
bBandDeltaX = bBandDeltaX + (double)Math.Pow(System.Convert.ToDouble(CandleList[slot, w][ctr - i].totalPrice) - System.Convert.ToDouble(bollingerMiddle), 2);
}
bBandDeltaX = bBandDeltaX / bollPar;
decimal bBandDelta = (decimal)Math.Sqrt(System.Convert.ToDouble(bBandDeltaX));
CandleList[slot, w][ctr].bollingerUp = bollingerMiddle + 2 * bBandDelta;
CandleList[slot, w][ctr].bollingerDown = bollingerMiddle - 2 * bBandDelta;
if (CandleList[slot, w][ctr].bollingerUp == CandleList[slot, w][ctr].bollingerDown)
{
Console.WriteLine("?! Items processed=" + pItemsProcessed + " bollPar=" + bollPar + " ctr=" + ctr + " bollingerMiddle=" + bollingerMiddle + " bBandDeltaX=" + bBandDeltaX + " bBandDelta=" + bBandDelta + " bollingerUp=" + CandleList[slot, w][ctr].bollingerUp + " bollingerDown=" + CandleList[slot, w][ctr].bollingerDown);
}
}
// REMOVED Further calculations happen here
}
// REMOVED Some evaluations happen here
}
}
// REMOVED Some more evaluations happen here
Interlocked.Increment(ref pItemsProcessed);
pool.Add(slot);
});
}
static void Main(string[] args)
{
var P = new programCLass();
P.myProgram();
}
}
}