/// <summary>
/// This procedure contains the user code. Input parameters are provided as regular arguments,
/// Output parameters as ref arguments. You don't have to assign output parameters,
/// they will have a default value.
/// </summary>
private void RunScript(List <Point3d> P, Mesh M, double step, bool reset, bool go, ref object Pos, ref object status, ref object Trails)
{
if (reset || GD == null)
{
GD = new GradientDescent(M, P);
}
if (go)
{
// update live variables
// update System
// expiring solution forces the component to update
Component.ExpireSolution(true);
}
// extracts positions and trails
Pos = pts;
status = pStat;
Trails = trs;
}
/// <summary>Generate Logistic Regression model based on a set of examples.</summary>
/// <param name="x">The Matrix to process.</param>
/// <param name="y">The Vector to process.</param>
/// <returns>Model.</returns>
public override IModel Generate(Matrix x, Vector y)
{
// create initial theta
Matrix copy = x.Copy();
copy = PreProcessing.FeatureDimensions.IncreaseDimensions(copy, PolynomialFeatures);
// add intercept term
copy = copy.Insert(Vector.Ones(copy.Rows), 0, VectorType.Col);
Vector theta = Vector.Ones(copy.Cols);
var run = GradientDescent.Run(theta, copy, y, this.MaxIterations, this.LearningRate, new Functions.CostFunctions.LogisticCostFunction(),
this.Lambda, new Regularization());
LogisticRegressionModel model = new LogisticRegressionModel()
{
Descriptor = this.Descriptor,
Theta = run.Item2,
LogisticFunction = new Math.Functions.Logistic(),
PolynomialFeatures = this.PolynomialFeatures
};
return(model);
}
/// <summary>Generate Linear Regression model based on a set of examples.</summary>
/// <param name="x">The Matrix to process.</param>
/// <param name="y">The Vector to process.</param>
/// <returns>Model.</returns>
public override IModel Generate(Matrix x, Vector y)
{
// create initial theta
Vector theta = Vector.Ones(x.Cols + 1);
Matrix copy = x.Copy();
// normalise features
for (int i = 0; i < copy.Cols; i++)
{
var j = FeatureNormalizer.FeatureScale(copy[i, VectorType.Col]);
for (int k = 0; k < copy.Rows; k++)
{
copy[k, i] = j[k];
}
}
// add intercept term
copy = copy.Insert(Vector.Ones(copy.Rows), 0, VectorType.Col);
// run gradient descent
var run = GradientDescent.Run(theta, copy, y, this.MaxIterations, this.LearningRate, new LinearCostFunction(), this.Lambda, new Regularization());
// once converged create model and apply theta
LinearRegressionModel model = new LinearRegressionModel(x.Mean(VectorType.Row), x.StdDev(VectorType.Row))
{
Descriptor = this.Descriptor,
Theta = run.Item2
};
return(model);
}
protected override OptimizationAlgorithm ConstructOptimizationAlgorithm(IChannel ch)
{
Contracts.CheckValue(ch, nameof(ch));
OptimizationAlgorithm optimizationAlgorithm;
IGradientAdjuster gradientWrapper = MakeGradientWrapper(ch);
switch (Args.OptimizationAlgorithm)
{
case BoostedTreeArgs.OptimizationAlgorithmType.GradientDescent:
optimizationAlgorithm = new GradientDescent(Ensemble, TrainSet, InitTrainScores, gradientWrapper);
break;
case BoostedTreeArgs.OptimizationAlgorithmType.AcceleratedGradientDescent:
optimizationAlgorithm = new AcceleratedGradientDescent(Ensemble, TrainSet, InitTrainScores, gradientWrapper);
break;
case BoostedTreeArgs.OptimizationAlgorithmType.ConjugateGradientDescent:
optimizationAlgorithm = new ConjugateGradientDescent(Ensemble, TrainSet, InitTrainScores, gradientWrapper);
break;
default:
throw ch.Except("Unknown optimization algorithm '{0}'", Args.OptimizationAlgorithm);
}
optimizationAlgorithm.TreeLearner = ConstructTreeLearner(ch);
optimizationAlgorithm.ObjectiveFunction = ConstructObjFunc(ch);
optimizationAlgorithm.Smoothing = Args.Smoothing;
optimizationAlgorithm.DropoutRate = Args.DropoutRate;
optimizationAlgorithm.DropoutRng = new Random(Args.RngSeed);
optimizationAlgorithm.PreScoreUpdateEvent += PrintTestGraph;
return(optimizationAlgorithm);
}
[Test] public void TestFromPoints()
{
var gd = new GradientDescent(t, new [] { 0.0, 0.0 }, Regression.CostFunction, Regression.Hypothesis);
gd.Alpha = 1e-5;
gd.Precision = 1e-4;
ValidateParameters(gd.Optimize());
}
public void CanGetBiasesUpdate()
{
GradientDescent gd = new GradientDescent(0.5, 1);
NetworkVector testVector = new NetworkVector(new double[] { 1, 2, 3 });
NetworkVector result = gd.BiasesUpdate(testVector);
NetworkVector resultCheck = new NetworkVector(new double[] { -0.5, -1.0, -1.5 });
Assert.AreEqual(resultCheck, result);
}
// constructor (public if we use it from RunScript)
public Particle(GradientDescent GraDesc, Point3d pos)
{
this.GraDesc = GraDesc;
this.pos = pos;
vel = Vector3d.Zero;
trail = new Polyline();
trail.Add(new Point3d(pos));
alive = true;
}
public void OptimizeTest()
{
var target = new TensorOld(new double[] { 1, 2, 3, 4, 5, 6, 7, 8, 9 }, 3, 3);
var gradient = new TensorOld(new double[] { 1, 2, 3, 4, 5, 6, 7, 8, 9 }, 3, 3);
var expected = new TensorOld(new double[] { 0.9, 1.8, 2.7, 3.6, 4.5, 5.4, 6.3, 7.2, 8.1 }, 3, 3);
var gd = new GradientDescent(0.1);
gd.Optimize(target, gradient);
Assert.Equal(expected, target);
}
public void CanMakeWithStepSize()
{
try
{
GradientDescent gd = new GradientDescent(0.5, 1);
}
catch (Exception e)
{
Assert.Fail(string.Format("GradientDescent constructor threw exception: {0}", e));
}
}
public void CanGetWeightsUpdate()
{
GradientDescent gd = new GradientDescent(0.5, 1);
WeightsMatrix testMatrix = new WeightsMatrix(new double[, ] {
{ 1, 2, 3 }, { 2, 3, 4 }
});
WeightsMatrix result = gd.WeightsUpdate(testMatrix);
WeightsMatrix resultCheck = new WeightsMatrix(new double[, ] {
{ -0.5, -1.0, -1.5 }, { -1.0, -1.5, -2.0 }
});
Assert.AreEqual(resultCheck, result);
}
public void TestGradientDescent()
{
Vector3[] vectors = new Vector3[2];
Vector3[] vectors2 = new Vector3[2];
var rand = new Random();
for (int i = 0; i < 2; i++)
{
vectors[i] = new Vector3(rand.Next(10, 50), rand.Next(10, 50), rand.Next(10, 50));
vectors2[i] = new Vector3(rand.Next(50, 100), rand.Next(50, 100), rand.Next(50, 100));
}
GradientDescent gradient = new GradientDescent(vectors, vectors2);
//var values = gradient.calculateGradientDescent(0.0001f,1000000);
//Assert.IsNotNull(values);
}
public void CanUpdate()
{
AdaptationStrategy strategy = new GradientDescent(1.0, 1);
wc_2.Run(vector_3);
wc_2.BackPropagate(vector_2);
wc_2.Update(strategy);
NetworkVector biasesCheck = new NetworkVector(new double[] { 0, 0 });
WeightsMatrix weightsCheck = new WeightsMatrix(new double[, ] {
{ 1 - (11 * 111), 2 - (11 * 112), 3 - (11 * 113) }, { 2 - (12 * 111), 3 - (12 * 112), 4 - (12 * 113) }
});
Assert.AreEqual(biasesCheck, wc_2.Biases);
Assert.AreEqual(weightsCheck, wc_2.Weights);
}
public void TestGradientDescent()
{
Func <double, double> function = x => x * x;
double expectedPointValue = 2;
double epsilon = 0.0001;
var minimumPoint = new GradientDescent().Calculate(new List <double>()
{
0, 0, 0, 0, 0
}, TestFunction);
Assert.Equal(minimumPoint.Count, 5);
Assert.InRange(minimumPoint[0] - expectedPointValue, -epsilon, epsilon);
Assert.InRange(minimumPoint[1] - expectedPointValue, -epsilon, epsilon);
Assert.InRange(minimumPoint[2] - expectedPointValue, -epsilon, epsilon);
Assert.InRange(minimumPoint[3] - expectedPointValue, -epsilon, epsilon);
Assert.InRange(minimumPoint[4] - expectedPointValue, -epsilon, epsilon);
}
public void GetResult_With_Success_Inside_Anchor_Bounds_Three_Anchors()
{
var gradientDescent = new GradientDescent();
gradientDescent.SetAnchors(_anchors);
IDistance[] node =
{
new DistanceBase(1, 41.4),
new DistanceBase(3, 37.58),
new DistanceBase(4, 56.79)
};
ICoordinate result = gradientDescent.GetResult(0, node);
Assert.That(result.X, Is.EqualTo(26).Within(1.5));
Assert.That(result.Y, Is.EqualTo(0).Within(3));
Assert.That(result.Z, Is.EqualTo(34).Within(1.5));
}
public static void Test(string trainImagesPath, string trainLabelsPath, string testImagesPath, string testLabelsPath)
{
LoadDataSet(trainImagesPath, trainLabelsPath, testImagesPath, testLabelsPath);
GC.Collect();
var network = NetworkBuilder.CreateMnist(trainDataSet);
Console.WriteLine("Initial");
Test(network, testDataSet);
Console.WriteLine("StartLearning");
var cost = Double.PositiveInfinity;
var timer = new Stopwatch();
timer.Start();
var optimizer = new GradientDescent<Matrix, Vector>(3, 0.0007, 1,
x => {
timer.Stop();
Console.Write("Ignored {0}% of samples ", 100 * NeuralNetwork<Matrix>.counter / (double)trainDataSet.Inputs.Count());
Console.WriteLine("and gradient descent step time: {0} ms", timer.ElapsedMilliseconds);
NeuralNetwork<Matrix>.counter = 0;
Console.Write("trainSet: ");
cost = Test(x, trainDataSet, cost);
Console.Write(" || ");
Console.Write("testSet: ");
Test(x, testDataSet);
Console.WriteLine();
timer.Reset();
timer.Start();
});
var trainer = new Trainer<Matrix, Vector>(optimizer);
for (var i = 0; i < 10; ++i)
{
Console.WriteLine("Generation {0}", i);
trainer.Train(network, trainDataSet.Set);
optimizer.IterationCount += 1;
optimizer.InitialStepSize *= 2;
}
Console.WriteLine("EndLearning");
Test(network, testDataSet);
}
public void GetResult_With_Success_Outside_Anchor_Bounds_Four_Anchors()
{
var gradientDescent = new GradientDescent();
gradientDescent.SetAnchors(_anchors);
IDistance[] node =
{
new DistanceBase(1, 101.11),
new DistanceBase(3, 89.14),
new DistanceBase(4, 48.25),
new DistanceBase(2, 7.07),
};
ICoordinate result = gradientDescent.GetResult(0, node);
Assert.That(result.X, Is.EqualTo(44).Within(4));
Assert.That(result.Y, Is.EqualTo(0).Within(3));
Assert.That(result.Z, Is.EqualTo(85).Within(4));
}
private static void ExponentalGrowth()
{
IActivationFunction activationFunction = new SimpleActivationFunction();
decimal alpha = 0.01m;
GradientDescent gradientDescent = new GradientDescent(activationFunction, alpha);
decimal[][] rooms = new decimal[][] { new decimal[] { 0 }, new decimal[] { 1 },
new decimal[] { 2 }, new decimal[] { 3 }, new decimal[] { 4 }, new decimal[] { 5 },
new decimal[] { 6 }, new decimal[] { 7 }, new decimal[] { 8 }, new decimal[] { 9 } };
decimal[] prices = new decimal[] { 0, 100, 200, 300, 400, 500, 600, 700, 800, 900 };
var thehas = gradientDescent.Train(rooms, prices);
var sixRoomPrice = activationFunction.Calculate(new decimal[] { 6 }, thehas);
Console.WriteLine(sixRoomPrice);
}
/// <summary>
/// This procedure contains the user code. Input parameters are provided as regular arguments,
/// Output parameters as ref arguments. You don't have to assign output parameters,
/// they will have a default value.
/// </summary>
private void RunScript(List <Point3d> P, Mesh M, double step, bool reset, bool go, ref object Pos, ref object status, ref object Trails)
{
if (reset || GD == null || P.Count != GD.parts.Count)
{
GD = new GradientDescent(M, P);
}
if (go)
{
// update live variables
GD.MaxSpeed = step;
// update System
GD.Update();
// expiring solution forces the component to update
Component.ExpireSolution(true);
}
// extracts positions and trails
pts = new Point3d[P.Count];
trs = new Polyline[P.Count];
pStat = new bool[P.Count];
for (int i = 0; i < GD.parts.Count; i++)
{
pts[i] = GD.parts[i].pos;
if (GD.parts[i].trail.IsValid)
{
trs[i] = GD.parts[i].trail;
}
else
{
trs[i] = null;
}
pStat[i] = GD.parts[i].alive;
}
Pos = pts;
status = pStat;
Trails = trs;
}
public static IServiceCollection AddLocationCalculatorRouter(this IServiceCollection services)
{
var locationBuilder = new LocationCalculatorBuilder <GradientDescent, Anchor>();
var anchors = new IAnchor[]
{
new Anchor(9, 60, 27, 512, 0),
new Anchor(9, 7, 27, 513, 0),
new Anchor(35, 96, 27, 514, 0),
new Anchor(99, 89, 27, 515, 0),
new Anchor(145, 66, 27, 516, 0),
new Anchor(160, 9, 27, 517, 0),
new Anchor(97, 8, 27, 518, 0),
new Anchor(56, 9, 27, 519, 0),
};
// GradientDescent gradientDescent = locationBuilder.WithAnchors(
// new Anchor(53.8, 73.4, 92, 208),
// new Anchor(905.8, 0, 1.5, 211),
// new Anchor(0, 458.5, 76.4, 204),
// new Anchor(925.3, 398.3, 92, 206))
// .Build();
GradientDescent gradientDescent = locationBuilder.WithAnchors(anchors).Build();
var particleFilterBuilder = new LocationCalculatorBuilder <Location.ParticleFilter.ParticleFilter, Anchor>(new Location.ParticleFilter.ParticleFilter(3500, 30));
var particleFilter = particleFilterBuilder.WithAnchors(anchors).Build();
var particleFilter3DBuilder = new LocationCalculatorBuilder <ParticleFilter3D, Anchor>(new ParticleFilter3D(3500, 30));
var particleFilter3D = particleFilter3DBuilder.WithAnchors(anchors).Build();
var particleAreaFilterBuilder = new LocationCalculatorBuilder <ParticleAreaFilter, Anchor>(new ParticleAreaFilter(3500, 30));
var particleAreaFilter = particleAreaFilterBuilder.WithAnchors(anchors).Build();
var comexBuilder = new LocationCalculatorBuilder <ComexCalculator, Anchor>();
var comex = comexBuilder.WithAnchors(anchors).Build();
services.AddLocationCalculatorRouter(builder =>
{
builder.AddLocationCalculator <MessageType4>(particleFilter);
});
return(services);
}
public static void Test(string trainImagesPath, string trainLabelsPath, string testImagesPath, string testLabelsPath)
{
var dataSet = LoadDataSet(trainImagesPath, trainLabelsPath, testImagesPath, testLabelsPath);
GC.Collect();
var network = NetworkBuilder.Create(dataSet
, new Tanh()
, new List<int> { 50 }
);
var cost = Double.PositiveInfinity;
var optimizer = new GradientDescent<Vector, Vector>(5, 0.02, 2,
x => {
Console.Write("trainSet: ");
cost = Test(x, dataSet, cost);
Console.Write(" || ");
Console.Write("testSet: ");
Test(x, testDataSet);
Console.WriteLine();
});
var trainer = new Trainer<Vector, Vector>(optimizer);
Console.WriteLine("Initial");
Test(network, dataSet);
Console.WriteLine("StartLearning");
for (var i = 0; i < 3; ++i)
{
Console.WriteLine("next generation");
trainer.Train(network, dataSet.Set);
optimizer.IterationCount += 1;
optimizer.InitialStepSize *= 2;
}
Console.WriteLine("EndLearning");
Test(network, testDataSet);
}
public void GradientDescent_Line()
{
var line = new Line(Point3d.WorldOrigin, new Point3d(1, 1, 0));
var gd = new GradientDescent(GradientDescentOptions.Default)
{
Options = { MaxIterations = 1 }
};
var input = 1;
gd.Options.MaxIterations = 100;
gd.Options.LearningRate = 100;
gd.Minimize(
values => line.PointAt(values[0]).Y,
new List <double> {
input
}
);
var err = gd.Result.Error <= gd.Options.ErrorThreshold;
var value = gd.Result.Values[0] <= 0.01;
var gLength = gd.Result.GradientLength <= gd.Options.Limit;
Assert.True(err || value || gLength);
}
public void CanUpdateBatch()
{
AdaptationStrategy strategy = new GradientDescent(1.0, 1);
VectorBatch result = wc_2.Run(input_batch);
wc_2.BackPropagate(gradient_batch);
VectorBatch inputGradient = wc_2.InputGradient(gradient_batch);
wc_2.Update(strategy);
NetworkVector biasesCheck = new NetworkVector(new double[] { 8, 7 });
WeightsMatrix weightsCheck = new WeightsMatrix(new double[, ] {
{ -4, -6, -8 }, { -6, -10, -14 }
});
Assert.AreEqual(biasesCheck, wc_2.Biases);
Assert.AreEqual(weightsCheck, wc_2.Weights);
for (int i = 0; i < inputGradient.Count; i++)
{
Assert.AreEqual(inputgradient_check[i], inputGradient[i]);
}
}
public void MainFunctionToRun()
{
var values2 = FileToArray.Converter(@"C:\Users\emiol\Downloads\TestPir.txt");
var values = FileToArray.Converter(@"C:\Users\emiol\Downloads\TestPir2.txt");
var resultPath = @"C:\Users\emiol\Desktop\2nd year Assignment\Neuro Computing\Neuro Computing\TestNeuroComputing\Result.txt";
RelateTwoArrays relateTwoArrays = new RelateTwoArrays();
relateTwoArrays.RelateTwoTuples(values.ToList(), values2.ToList());
GradientDescent gradientDescent = new GradientDescent(relateTwoArrays.firstVector.ToArray(), relateTwoArrays.secondVector.ToArray());
gradientDescent.calculateGradientDescent(0.0000001f, 300, 100000);
var result = gradientDescent.result;
using (StreamWriter writer = new StreamWriter(resultPath))
{
for (int i = 0; i < result.Count; i++)
{
string value = (i + 1) + ". " + result.ElementAt(i).Item1 + " (" + result.ElementAt(i).Item2.alpha + "; " + result.ElementAt(i).Item2.beta + "; " +
result.ElementAt(i).Item2.gamma + "; " + result.ElementAt(i).Item2.deltaX + "; "
+ result.ElementAt(i).Item2.deltaY + "; " + result.ElementAt(i).Item2.deltaZ + ")";
writer.WriteLine(value);
}
}
}
// fields
// constructor (public if we use it from RunScript)
public Particle(GradientDescent GraDesc, Point3d pos)
{
}
public MainWindowModel()
{
model = new PlotModel()
{
Title = "Ejemplo"
};
ScatterSeries series = new ScatterSeries();
double maxX = 0;
StreamReader sr = new StreamReader("data.txt");
string[] initParams = sr.ReadLine().Split(' ');
int m = int.Parse(initParams[0]);
int n = int.Parse(initParams[1]);
float[][] input = new float[m][];
for (int i = 0; i < m; i++)
{
input[i] = new float[n];
}
float[] output = new float[m];
for (int i = 0; i < m; i++)
{
string[] xy = sr.ReadLine().Split(' ');
double x = double.Parse(xy[0]);
double y = double.Parse(xy[1]);
series.Points.Add(new ScatterPoint(x, y));
output[i] = (float)y;
for (int j = 0; j < n; j++)
{
input[i][j] = (float)Math.Pow(x, j);
}
if (x > maxX)
{
maxX = x;
}
}
sr.Close();
float[][] outputParam = new float[m][];
for (int i = 0; i < m; i++)
{
outputParam[i] = new float[] { output[i] };
}
model.Series.Add(series);
float[] theta = new float[n];
//TODO: Calcular hipotesis pambi
GradientDescent.Start(RegressionGradient, RegressionCost, input, outputParam, ref theta, .005f, 0, 5000);
//
int steps = 50;
LineSeries hypothesis = new LineSeries();
for (int i = 0; i <= steps; i++)
{
double[] x = new double[n];
for (int j = 0; j < n; j++)
{
x[j] = Math.Pow((i * maxX) / steps, j);
}
double y = 0;
for (int j = 0; j < n; j++)
{
y += x[j] * theta[j];
}
hypothesis.Points.Add(new DataPoint(x[1], y));
}
model.Series.Add(hypothesis);
}
public Form1()
{
InitializeComponent();
//Main objective function
Func <DV, D> fOBJ = delegate(DV abx)
{
// y = a*ln(x) + b
D a = abx[0];
D b = abx[1];
D x = abx[2];
//Calculate result
D r = a * AD.Log(x) + b;
//D r = a * x*x+ b;
return(r);
};
//Original Dataset Variables
const double aOrig = 3.0;
const double bOrig = 8.0;
double[] xOrig = new double[100];
double[] yOrig = new double[100];
#region Calculate Original data
//Calculate original dataset
for (int x = 0; x < 100; x++)
{
xOrig[x] = x + 1;
DV param = new DV(new D[] { aOrig, bOrig, xOrig[x] });
yOrig[x] = fOBJ(param);
}
//Create series
Series origSeries = new Series("Original")
{
ChartType = SeriesChartType.Line,
Color = Color.Black,
BorderDashStyle = ChartDashStyle.Solid,
BorderWidth = 2,
ChartArea = chartResults.ChartAreas[0].Name,
LegendText = "Original a=" + aOrig.ToString("F3") + " b=" + bOrig.ToString("F3")
};
for (int i = 0; i < xOrig.Length; i++)
{
origSeries.Points.AddXY(xOrig[i], yOrig[i]);
}
#endregion
#region Calculate random error version
double[] yNoisy = new double[100];
Random rand = new Random();
//Noisy version
for (int i = 0; i < xOrig.Length; i++)
{
double percentModifyA = 0.98 + (1.02 - 0.98) * rand.NextDouble();
double percentModifyB = 0.98 + (1.02 - 0.98) * rand.NextDouble();
double percentModifyY = 1.0; // 0.98 + (1.02 - 0.98) * rand.NextDouble();
DV param = new DV(new D[] { aOrig *percentModifyA, bOrig *percentModifyB, xOrig[i] });
yNoisy[i] = fOBJ(param) * percentModifyY;
}
//Create series
Series noisySeries = new Series("Noisy")
{
ChartType = SeriesChartType.Point,
Color = Color.DarkGray,
MarkerStyle = MarkerStyle.Star4,
MarkerSize = 7,
BorderWidth = 0,
ChartArea = chartResults.ChartAreas[0].Name
};
for (int i = 0; i < xOrig.Length; i++)
{
noisySeries.Points.AddXY(xOrig[i], yNoisy[i]);
}
#endregion
#region Calculate a and b using error function and gradient descent
//Error equation
Func <DV, D> fMSE = delegate(DV ab)
{
//Get parameters
D a = ab[0];
D b = ab[1];
//Compute squared error for each point
D errorSum = 0;
for (int i = 0; i < xOrig.Length; i++)
{
//Compute object function value
DV param = new DV(new D[] { a, b, xOrig[i] });
D yCalc = fOBJ(param);
//Check for error
if (double.IsNaN(yCalc))
{
continue;
}
//Compute error between noisy version and calculated version
D err = AD.Pow(yNoisy[i] - yCalc, 2);
errorSum += err;
}
//Compute least square
D mse = AD.Pow(errorSum, 0.5);
//return results
return(mse);
};
//Calculate optimization for a and b
DV startPoint = new DV(new D[] { 10, 5 });
int calcsF;
int calcsGradient;
DV result = GradientDescent.FirstOrder_OneDimensionalMethod(fMSE, startPoint, 0.01, out calcsF, out calcsGradient);//, out calcsHessian);
double aFit = result[0];
double bFit = result[1];
DV paramMseFit = new DV(new D[] { aFit, bFit });
double mseFit = fMSE(paramMseFit);
//Create series
Series fitSeries = new Series("Fit")
{
ChartType = SeriesChartType.Point,
Color = Color.Red,
MarkerSize = 3,
MarkerStyle = MarkerStyle.Circle,
ChartArea = chartResults.ChartAreas[0].Name,
LegendText = "Fit a=" + aFit.ToString("F3") + " b=" + bFit.ToString("F3") + " MSE=" + mseFit.ToString("F3")
};
for (int i = 0; i < xOrig.Length; i++)
{
DV param = new DV(new D[] { aFit, bFit, xOrig[i] });
fitSeries.Points.AddXY(xOrig[i], fOBJ(param));
}
#endregion
//Add series to chart
chartResults.Series.Clear();
chartResults.Series.Add(origSeries);
chartResults.Series.Add(noisySeries);
chartResults.Series.Add(fitSeries);
}
public void InitNetwork(ECostType costType, CostSettings costSettings, EOptimizerType optimizerType, OptimizerSettings optimizerSettings)
{
Utility.Dims InShape;
Utility.Dims OutShape;
Utility.Dims WShape;
for (int i = 1; i < Layers.Count; i++)
{
Data.Data["a" + i.ToString()] = new Matrix(Layers[i].NCount, 1);
InShape = new Utility.Dims(Layers[i].NCount, 1);
Data.Data["b" + i.ToString()] = Matrix.RandomMatrix(Layers[i].NCount, 1, 1, EDistrubution.Gaussian);
OutShape = new Utility.Dims(Layers[i].NCount, 1);
Data.Data["W" + i.ToString()] = Matrix.RandomMatrix(Layers[i - 1].NCount, Layers[i].NCount, 1, EDistrubution.Gaussian);
WShape = new Utility.Dims(Layers[i - 1].NCount, Layers[i].NCount);
Layers[i].SetSettings(new LayerSettings(InShape, OutShape, WShape));
}
Data.Data["a0"] = new Matrix(Layers[0].NCount, 1);
InShape = new Utility.Dims(Layers[0].NCount, 1);
Data.Data["b0"] = new Matrix(Layers[0].NCount, 1);
OutShape = new Utility.Dims(Layers[0].NCount, 1);
Data.Data["W0"] = new Matrix(Layers[0].NCount * Layers[1].NCount, Layers[1].NCount);
WShape = new Utility.Dims(Layers[0].NCount * Layers[1].NCount, Layers[1].NCount);
Layers[0].SetSettings(new LayerSettings(InShape, OutShape, WShape));
switch (costType)
{
case ECostType.Invalid:
throw new ArgumentException("Invalid Cost Function Selected!");
case ECostType.CrossEntropyCost:
CostFunction = new CrossEntropyCost((CrossEntropyCostSettings)costSettings);
break;
case ECostType.ExponentionalCost:
CostFunction = new ExponentionalCost((ExponentionalCostSettings)costSettings);
break;
case ECostType.GeneralizedKullbackLeiblerDivergence:
CostFunction = new GeneralizedKullbackLeiblerDivergence((GeneralizedKullbackLeiblerDivergenceSettings)costSettings);
break;
case ECostType.HellingerDistance:
CostFunction = new HellingerDistance((HellingerDistanceSettings)costSettings);
break;
case ECostType.ItakuraSaitoDistance:
CostFunction = new ItakuraSaitoDistance((ItakuraSaitoDistanceSettings)costSettings);
break;
case ECostType.KullbackLeiblerDivergence:
CostFunction = new KullbackLeiblerDivergence((KullbackLeiblerDivergenceSettings)costSettings);
break;
case ECostType.QuadraticCost:
CostFunction = new QuadraticCost((QuadraticCostSettings)costSettings);
break;
default:
throw new ArgumentException("Invalid Cost Function Selected!");
}
switch (optimizerType)
{
case EOptimizerType.Invalid:
throw new ArgumentException("Invalid Optimizer Function Selected!");
case EOptimizerType.AdaDelta:
OptimizerFunction = new AdaDelta((AdaDeltaSettings)optimizerSettings);
break;
case EOptimizerType.AdaGrad:
OptimizerFunction = new AdaGrad((AdaGradSettings)optimizerSettings);
break;
case EOptimizerType.Adam:
OptimizerFunction = new Adam((AdamSettings)optimizerSettings);
break;
case EOptimizerType.Adamax:
OptimizerFunction = new Adamax((AdamaxSettings)optimizerSettings);
break;
case EOptimizerType.GradientDescent:
OptimizerFunction = new GradientDescent((GradientDescentSettings)optimizerSettings);
break;
case EOptimizerType.Momentum:
OptimizerFunction = new Momentum((MomentumSettings)optimizerSettings);
break;
case EOptimizerType.Nadam:
OptimizerFunction = new Nadam((NadamSettings)optimizerSettings);
break;
case EOptimizerType.NesterovMomentum:
OptimizerFunction = new NesterovMomentum((NesterovMomentumSettings)optimizerSettings);
break;
case EOptimizerType.RMSProp:
OptimizerFunction = new RMSProp((RMSPropSettings)optimizerSettings);
break;
default:
throw new ArgumentException("Invalid Optimizer Function Selected!");
}
}
public static void Test_Compute()
{
List <string> movie_titles = LoadMovies();
int num_movies = movie_titles.Count;
// Step 1: create my ratings with missing entries
double[] my_ratings = new double[num_movies];
int[] my_ratings_r = new int[num_movies];
for (int i = 0; i < num_movies; ++i)
{
my_ratings[i] = 0;
}
my_ratings[1] = 4;
my_ratings[98] = 2;
my_ratings[7] = 3;
my_ratings[12] = 5;
my_ratings[54] = 4;
my_ratings[64] = 5;
my_ratings[66] = 3;
my_ratings[69] = 5;
my_ratings[183] = 4;
my_ratings[226] = 5;
my_ratings[355] = 5;
for (int i = 0; i < num_movies; ++i)
{
my_ratings_r[i] = my_ratings[i] > 0 ? 1 : 0;
}
// Step 2: load the current ratings of all users, i.e., Y and R
List <List <double> > Y = DblDataTableUtil.LoadDataSet("Y.txt");
List <List <int> > R = IntDataTableUtil.LoadDataSet("R.txt");
int num_users;
DblDataTableUtil.GetSize(Y, out num_movies, out num_users);
// Step 3: insert my ratings into the existing Y and R (as the first column)
num_users++;
List <RatedItem> records = new List <RatedItem>();
for (int i = 0; i < num_movies; ++i)
{
double[] rec_Y = new double[num_users];
bool[] rec_R = new bool[num_users];
for (int j = 0; j < num_users; ++j)
{
if (j == 0)
{
rec_Y[j] = my_ratings[i];
rec_R[j] = my_ratings_r[i] == 1;
}
else
{
rec_Y[j] = Y[i][j - 1];
rec_R[j] = R[i][j - 1] == 1;
}
}
RatedItem rec = new RatedItem(null, rec_Y, rec_R);
records.Add(rec);
}
int num_features = 10;
double lambda = 10;
CollaborativeFilteringRS <RatedItem> algorithm = new CollaborativeFilteringRS <RatedItem>();
algorithm.Stepped += (s, step) =>
{
Console.WriteLine("#{0}: {1}", step, s.Cost);
};
algorithm.RegularizationLambda = lambda;
algorithm.MaxLocalSearchIteration = 100;
GradientDescent local_search = algorithm.LocalSearch as GradientDescent;
local_search.Alpha = 0.005;
double[] Ymean;
algorithm.DoMeanNormalization(records, out Ymean);
algorithm.Compute(records, num_features);
algorithm.UndoMeanNormalization(records, Ymean);
int userId = 0;
int topK = 10;
List <RatedItem> highest_ranks = algorithm.SelectHigestRanked(userId, records, topK);
for (int i = 0; i < highest_ranks.Count; ++i)
{
RatedItem rec = highest_ranks[i];
Console.WriteLine("#{0}: ({1}) {2}", i + 1, rec.UserRanks[0], movie_titles[rec.ItemIndex]);
}
}
//>>>>Here RUN Gradient Descent
private void GradientDescentOptimization()
{
IsThereDataAvailable = false;
double[] InitialParameters = new double[(VLoopModes.Count - 1) * 3];
InitialParameters = GettingInitialValues();
double[] LearningRate = new double[InitialParameters.Length];
for (int i = 0; i < InitialParameters.Length / 3; i++)
{
LearningRate[0 + i * 3] = TryToDouble(textBoxLRateFreq.Text);
LearningRate[1 + i * 3] = TryToDouble(textBoxLRateZeta.Text);
LearningRate[2 + i * 3] = TryToDouble(textBoxLRateMass.Text);
}
GD = new GradientDescent(
InitialParameters,
LearningRate,
(int)TryToDouble(textBoxIterations.Text)
);
GD.GD_Settings.Delta = TryToDouble(textBoxDelta.Text);
GD.GD_Settings.Error = new Range {
MaxValue = 0.0001, MinValue = 0
};
GD.Solve(CostFucntion);
#region Writting Results
textBoxOPFreq.Clear();
textBoxOPZeta.Clear();
textBoxOPMass.Clear();
for (int i = 0; i < GD.OPTResult.Parameters.Length / 3; i++)
{
textBoxOPFreq.Text = textBoxOPFreq.Text + " " + GD.OPTResult.Parameters[0 + i * 3].ToString("00.000");
textBoxOPZeta.Text = textBoxOPZeta.Text + " " + GD.OPTResult.Parameters[1 + i * 3].ToString("0.000");
textBoxOPMass.Text = textBoxOPMass.Text + " " + GD.OPTResult.Parameters[2 + i * 3].ToString("0.000");
}
textBoxOPError.Text = GD.OPTResult.target[0].ToString();
#endregion
IsThereDataAvailable = true;
PlottingGAData();
newVLoopModes = new List <Mode>();
newVLoopModes.Add(new Mode
{
Freq = VLoopModes[0].Freq,
Zeta = VLoopModes[0].Zeta,
Mass = VLoopModes[0].Mass
});
for (int i = 0; i < InitialParameters.Length / 3; i++)
{
Mode mode = new Mode();
mode.Freq = GD.OPTResult.Parameters[0 + i * 3];
mode.Zeta = GD.OPTResult.Parameters[1 + i * 3];
mode.Mass = GD.OPTResult.Parameters[2 + i * 3];
newVLoopModes.Add(mode);
}
FRF[] Struct_sim = new FRF[FormMain.Struct_ref.Length];
for (int i = 0; i < FormMain.Struct_ref.Length; i++)
{
Complex Res = Tool.GetStructureResponse(FormMain.Struct_ref[i].Freq, newVLoopModes, "Velocity");
Struct_sim[i] = new FRF(FormMain.Struct_ref[i].Freq, Res);
}
FormMain.DrawResult(Struct_sim);
}
public void TrainNetwork()
{
Console.WriteLine("Training Network");
SampleSet samples = GenerateSamples(category.Compositions);
ann = RNN();
ann.rebuffer(samples.maxSequenceLength());
ann.initializeWeights(rnd);
GradientDescent trainer = new GradientDescent();
trainer.setNet(ann);
trainer.setRnd(rnd);
trainer.setPermute(false);
trainer.setTrainingSet(samples);
trainer.setLearningRate(learningrate);
trainer.setMomentum(momentum);
trainer.setEpochs(epochs);
trainer.train();
Save();
}
public NeuralNetwork UseGradientDescent(double learningRate)
{
Optimizer = new GradientDescent(learningRate);
return(this);
}
