public void TestFitIntercept()
{
var x2 = DenseMatrix.OfArray(new[, ]
{
{ 0.38349978, 0.61650022 },
{ 0.58853682, 0.41146318 }
});
var x3 = DenseMatrix.OfArray(new[, ]
{
{ 0.27677969, 0.70693172, 0.01628859 },
{ 0.08385139, 0.20692515, 0.70922346 }
});
var y = DenseVector.OfEnumerable(new double[] { 1, 1 });
var lr2WithoutIntercept = new LinearRegression(fitIntercept: false);
lr2WithoutIntercept.Fit(x2, y);
var lr2WithIntercept = new LinearRegression(fitIntercept: true);
lr2WithIntercept.Fit(x2, y);
var lr3WithoutIntercept = new LinearRegression(fitIntercept: false);
lr3WithoutIntercept.Fit(x3, y);
var lr3WithIntercept = new LinearRegression(fitIntercept: true);
lr3WithIntercept.Fit(x3, y);
Assert.AreEqual(lr2WithIntercept.Coef.Column(0).Count,
lr2WithoutIntercept.Coef.Column(0).Count);
Assert.AreEqual(lr3WithIntercept.Coef.Column(0).Count,
lr3WithoutIntercept.Coef.Column(0).Count);
}
public void TestLinearRegression()
{
// Control.LinearAlgebraProvider = new MklLinearAlgebraProvider();
// a simple dataset
var x = DenseMatrix.OfArray(new double[, ] {
{ 1 }, { 2 }
});
var y = DenseVector.OfEnumerable(new double[] { 1, 2 });
var clf = new LinearRegression();
clf.Fit(x, y);
Assert.AreEqual(1.0, clf.Coef.Column(0)[0], 1E-5);
//Assert.AreEqual(0.0, clf.Intercept[0]);
Assert.IsTrue(DenseVector.OfEnumerable(new double[] { 1, 2 }).AlmostEquals(clf.Predict(x).Column(0)));
// test it also for degenerate input
x = DenseMatrix.OfArray(new double[, ] {
{ 1 }
});
y = DenseVector.OfEnumerable(new double[] { 0 });
clf = new LinearRegression(fitIntercept: false);
clf.Fit(x, y);
Assert.AreEqual(0.0, clf.Coef.Column(0)[0]);
//assert_array_almost_equal(clf.intercept_, [0])
Assert.AreEqual(0.0, clf.Predict(x).Column(0)[0]);
}
private static void TestLinearRegression(FeatureVector training, FeatureVector test)
{
LinearRegression lr = new LinearRegression();
LinearRegressionModel lrModel = (LinearRegressionModel)lr.Fit(training);
FeatureVector predictions = lrModel.transform(test);
PrintPredictionsAndEvaluate(predictions);
}
public void TestLinearRegressionSparse()
{
const int n = 100;
Matrix x = SparseMatrix.Identity(n);
var beta = DenseVector.CreateRandom(n, new Normal());
var y = x * beta;
var ols = new LinearRegression(fitIntercept: true);
ols.Fit(x, y);
Assert.IsTrue((ols.Coef.Row(0) + ols.Intercept[0]).AlmostEquals(beta));
}
public void TestLinearRegressionSparseMultipleOutcome()
{
var random = new Random(0);
var r = SampleGenerator.MakeSparseUncorrelated(random: random);
Matrix x = SparseMatrix.OfMatrix(r.X);
Vector <double> y = r.Y.Column(0);
Matrix y1 = DenseMatrix.OfColumns(y.Count, 2, new[] { y, y });
int nFeatures = x.ColumnCount;
var ols = new LinearRegression();
ols.Fit(x, y1);
Assert.AreEqual(Tuple.Create(2, nFeatures), ols.Coef.Shape());
Assert.AreEqual(Tuple.Create(2, nFeatures), ols.Coef.Shape());
Matrix <double> yPred = ols.Predict(x);
ols.Fit(x, y);
Matrix <double> yPred1 = ols.Predict(x);
Assert.IsTrue(yPred1.Column(0).AlmostEquals(yPred.Column(0)));
Assert.IsTrue(yPred1.Column(0).AlmostEquals(yPred.Column(1)));
}
public void TestLinearRegressionMultipleOutcome()
{
var result = SampleGenerator.MakeRegression(shuffle: false, random: new Random(0));
Matrix y = DenseMatrix.OfColumns(
result.Y.RowCount,
2,
new[] { result.Y.Column(0), result.Y.Column(0) });
var numFeatures = result.X.RowCount;
var clf = new LinearRegression(fitIntercept: true);
clf.Fit(result.X, y);
Assert.AreEqual(Tuple.Create(2, numFeatures), clf.Coef.Shape());
Matrix <double> yPred = clf.Predict(result.X);
clf.Fit(result.X, result.Y);
Matrix <double> yPred1 = clf.Predict(result.X);
Assert.IsTrue(yPred1.Column(0).AlmostEquals(yPred.Column(0)));
Assert.IsTrue(yPred1.Column(0).AlmostEquals(yPred.Column(1)));
}
public void TestRidgeVsLstsq()
{
var random = new Random(0);
// we need more samples than features
const int nSamples = 5;
const int nFeatures = 4;
var y = DenseVector.CreateRandom(nSamples, new Normal {
RandomSource = random
});
var x = DenseMatrix.CreateRandom(nSamples, nFeatures, new Normal {
RandomSource = random
});
var ridge = new RidgeRegression(alpha: 0.0, fitIntercept: false);
var ols = new LinearRegression(fitIntercept: false);
ridge.Fit(x, y);
ols.Fit(x, y);
Assert.IsTrue(ridge.Coef.AlmostEquals(ols.Coef));
ridge.Fit(x, y);
ols.Fit(x, y);
Assert.IsTrue(ridge.Coef.AlmostEquals(ols.Coef));
}
public override double HandleNextTick(Tick t)
{
double value = double.NaN;
MA.HandleNextTick(t);
indicator.HandleNextTick(t);
if (!MA[0].Equals(double.NaN))
{
double[] MAArray = MA.ToArray();
double[] indArray = indicator.ToArray();
SortedList <double, double> tickHighs = new SortedList <double, double>();
SortedList <double, double> tickLows = new SortedList <double, double>();
SortedList <double, double> indHighs = new SortedList <double, double>();
SortedList <double, double> indLows = new SortedList <double, double>();
for (int i = 2; i < MAArray.Length; i++)
{
//high
if (MAArray[i] < MAArray[i - 1] && MAArray[i - 1] > MAArray[i - 2])
{
tickHighs.Add(i - 1, MAArray[i - 1]);
}
if (indArray[i] < indArray[i - 1] && indArray[i - 1] > indArray[i - 2])
{
indHighs.Add(i - 1, indArray[i - 1]);
}
//low
if (MAArray[i] > MAArray[i - 1] && MAArray[i - 1] < MAArray[i - 2])
{
tickLows.Add(i - 1, MAArray[i - 1]);
}
if (indArray[i] > indArray[i - 1] && indArray[i - 1] < indArray[i - 2])
{
indLows.Add(i - 1, indArray[i - 1]);
}
}
if (tickHighs.Count > 0 && indHighs.Count > 0 &&
tickLows.Count > 0 && indLows.Count > 0)
{
LinearRegression lrTickHighs = new LinearRegression();
LinearRegression lrTickLows = new LinearRegression();
LinearRegression lrindkHighs = new LinearRegression();
LinearRegression lrindLows = new LinearRegression();
lrTickHighs.Model(tickHighs.Keys.ToArray(), tickHighs.Values.ToArray());
lrTickLows.Model(tickLows.Keys.ToArray(), tickLows.Values.ToArray());
lrindkHighs.Model(indHighs.Keys.ToArray(), indHighs.Values.ToArray());
lrindLows.Model(indLows.Keys.ToArray(), indLows.Values.ToArray());
if (lrTickHighs.X2 > 0 && lrindkHighs.X2 < 0)
{
value = 1;
}
else if (lrTickLows.X2 < 0 && lrindLows.X2 > 0)
{
value = -1;
}
else
{
value = 0;
}
value = lrindkHighs.Fit(indArray[0]);
}
}
indicatorData.Enqueue(value);
return(value);
}
private void buttonForDataSplitNext_Click(object sender, EventArgs e)
{
trainingSetPercentage = (double)numericUpDownForTrainingSetPercent.Value / 100.0;
numFolds = (int)numericUpDownForNumFolds.Value;
double[] smaOut = null;
double[] wmaOut = null;
double[] emaOut = null;
double[] macdOut = null;
double[] stochasticsOut = null;
double[] williamsROut = null;
double[] rsiOut = null;
double[] closesOut = null;
var data = IndicatorService.GetData(code, targetDate, new string[] { "Tarih", "Kapanis" }, numberOfData + 1);
if (isSMAChecked)
{
smaOut = IndicatorDataPreprocessor.GetSMAOut(MovingAverage.Simple(code, targetDate, smaPeriod, numberOfData));
}
if (isWMAChecked)
{
wmaOut = IndicatorDataPreprocessor.GetWMAOut(MovingAverage.Weighted(code, targetDate, wmaPeriod, numberOfData));
}
if (isEMAChecked)
{
emaOut = IndicatorDataPreprocessor.GetEMAOut(MovingAverage.Exponential(code, targetDate, emaPeriod, numberOfData));
}
if (isMACDChecked)
{
macdOut = IndicatorDataPreprocessor.GetMACDOut(new MovingAverageConvergenceDivergence(code, targetDate, firstPeriod, secondPeriod, triggerPeriod, numberOfData));
}
if (isStochasticsChecked)
{
stochasticsOut = IndicatorDataPreprocessor.GetStochasticsOut(new Stochastics(code, targetDate, fastKPeriod, fastDPeriod, slowDPeriod, numberOfData));
}
if (isWilliamsRChecked)
{
williamsROut = IndicatorDataPreprocessor.GetWilliamsROut(WilliamsR.Wsr(code, targetDate, williamsRPeriod, numberOfData));
}
if (isRSIChecked)
{
rsiOut = IndicatorDataPreprocessor.GetRSIOut(RelativeStrengthIndex.Rsi(code, targetDate, rsiPeriod, numberOfData));
}
closesOut = IndicatorDataPreprocessor.GetClosesOut(numberOfData, data);
int minRowCount = 1000000;
if (smaOut != null)
{
minRowCount = smaOut.Length;
}
if (wmaOut != null)
{
minRowCount = minRowCount < wmaOut.Length ? minRowCount : wmaOut.Length;
}
if (emaOut != null)
{
minRowCount = minRowCount < emaOut.Length ? minRowCount : emaOut.Length;
}
if (macdOut != null)
{
minRowCount = minRowCount < macdOut.Length ? minRowCount : macdOut.Length;
}
if (rsiOut != null)
{
minRowCount = minRowCount < rsiOut.Length ? minRowCount : rsiOut.Length;
}
if (williamsROut != null)
{
minRowCount = minRowCount < williamsROut.Length ? minRowCount : williamsROut.Length;
}
if (stochasticsOut != null)
{
minRowCount = minRowCount < stochasticsOut.Length ? minRowCount : stochasticsOut.Length;
}
if (closesOut != null)
{
minRowCount = minRowCount < closesOut.Length ? minRowCount : closesOut.Length;
}
var fv = new FeatureVector();
if (isSMAChecked)
{
fv.AddColumn("SMA", smaOut.Select(p => (object)p.ToString(CultureInfo.InvariantCulture)).Take(minRowCount).ToArray());
}
if (isWMAChecked)
{
fv.AddColumn("WMA", wmaOut.Select(p => (object)p.ToString(CultureInfo.InvariantCulture)).Take(minRowCount).ToArray());
}
if (isEMAChecked)
{
fv.AddColumn("EMA", emaOut.Select(p => (object)p.ToString(CultureInfo.InvariantCulture)).Take(minRowCount).ToArray());
}
if (isMACDChecked)
{
fv.AddColumn("MACD", macdOut.Select(p => (object)p.ToString(CultureInfo.InvariantCulture)).Take(minRowCount).ToArray());
}
if (isRSIChecked)
{
fv.AddColumn("RSI", rsiOut.Select(p => (object)p.ToString(CultureInfo.InvariantCulture)).Take(minRowCount).ToArray());
}
if (isWilliamsRChecked)
{
fv.AddColumn("WilliamsR", williamsROut.Select(p => (object)p.ToString(CultureInfo.InvariantCulture)).Take(minRowCount).ToArray());
}
if (isStochasticsChecked)
{
fv.AddColumn("Stochastics", stochasticsOut.Select(p => (object)p.ToString(CultureInfo.InvariantCulture)).Take(minRowCount).ToArray());
}
fv.AddColumn("label", closesOut.Select(p => (object)string.Format("{0:0.0}", p).ToString(CultureInfo.InvariantCulture)).Take(minRowCount).ToArray());
var training = new FeatureVector();
var test = new FeatureVector();
int count = fv.Values[0].Length;
for (int i = 0; i < fv.ColumnName.Count; i++)
{
training.AddColumn(fv.ColumnName[i], fv.Values[i].Take((int)(count * trainingSetPercentage)).ToArray());
}
for (int i = 0; i < fv.ColumnName.Count; i++)
{
test.AddColumn(fv.ColumnName[i], fv.Values[i].Skip((int)(count * trainingSetPercentage)).Take(count).ToArray()); // Take(count) means take the rest of all elements, number of the rest of the elements is smaller than count.
}
if (numFolds > 0)
{
BinaryClassificationEvaluator bce1 = new BinaryClassificationEvaluator();
LinearRegression linearRegression = new LinearRegression();
CrossValidator cvLinReg = new CrossValidator(linearRegression, bce1, numFolds);
CrossValidatorModel cvLinRegModel = (CrossValidatorModel)cvLinReg.Fit(training);
FeatureVector linRegPredictions = cvLinRegModel.transform(test);
bce1.evaluate(linRegPredictions);
linRegAcc = bce1.Accuracy;
BinaryClassificationEvaluator bce2 = new BinaryClassificationEvaluator();
LogisticRegression logisticRegression = new LogisticRegression();
CrossValidator cvLogReg = new CrossValidator(logisticRegression, bce2, numFolds);
CrossValidatorModel cvLogRegModel = (CrossValidatorModel)cvLogReg.Fit(training);
FeatureVector logRegPredictions = cvLogRegModel.transform(test);
bce2.evaluate(logRegPredictions);
logRegAcc = bce2.Accuracy;
BinaryClassificationEvaluator bce3 = new BinaryClassificationEvaluator();
NaiveBayes naiveBayes = new NaiveBayes();
CrossValidator cvNaiBay = new CrossValidator(naiveBayes, bce3, numFolds);
CrossValidatorModel cvNaiBayModel = (CrossValidatorModel)cvNaiBay.Fit(training);
FeatureVector naiBayPredictions = cvNaiBayModel.transform(test);
bce3.evaluate(naiBayPredictions);
naiBayAcc = bce3.Accuracy;
}
else
{
BinaryClassificationEvaluator bce1 = new BinaryClassificationEvaluator();
LinearRegression linearRegression = new LinearRegression();
LinearRegressionModel linearRegressionModel = (LinearRegressionModel)linearRegression.Fit(training);
FeatureVector linRegPredictions = linearRegressionModel.transform(test);
bce1.evaluate(linRegPredictions);
linRegAcc = bce1.Accuracy;
BinaryClassificationEvaluator bce2 = new BinaryClassificationEvaluator();
LogisticRegression logicticRegression = new LogisticRegression();
LogisticRegressionModel logisticRegressionModel = (LogisticRegressionModel)logicticRegression.Fit(training);
FeatureVector logRegPredictions = logisticRegressionModel.transform(test);
bce2.evaluate(logRegPredictions);
logRegAcc = bce2.Accuracy;
BinaryClassificationEvaluator bce3 = new BinaryClassificationEvaluator();
NaiveBayes naiveBayes = new NaiveBayes();
NaiveBayesModel naiveBayesModel = (NaiveBayesModel)naiveBayes.Fit(training);
FeatureVector naiBayPredictions = naiveBayesModel.transform(test);
bce3.evaluate(naiBayPredictions);
naiBayAcc = bce3.Accuracy;
}
labelForLinRegAcc.Text = linRegAcc.ToString();
labelForLogRegAcc.Text = logRegAcc.ToString();
labelForNaiBayAcc.Text = naiBayAcc.ToString();
panelForResults.BringToFront();
}
private static double CalculateAccuracy(List <int> indicators, int mlAlgorithm, bool isCrossValidationEnabled, int minRowCount, double trainingSetPercentage, double[] smaOut, double[] wmaOut, double[] emaOut, double[] macdOut, double[] rsiOut, double[] williamsROut, double[] stochasticsOut, double[] closesOut)
{
FeatureVector vector = new FeatureVector();
if (indicators.Contains(IndicatorService.SMA))
{
vector.AddColumn("SMA", smaOut.Select(p => (object)p.ToString(CultureInfo.InvariantCulture)).Take(minRowCount).ToArray());
}
if (indicators.Contains(IndicatorService.WMA))
{
vector.AddColumn("WMA", wmaOut.Select(p => (object)p.ToString(CultureInfo.InvariantCulture)).Take(minRowCount).ToArray());
}
if (indicators.Contains(IndicatorService.EMA))
{
vector.AddColumn("EMA", emaOut.Select(p => (object)p.ToString(CultureInfo.InvariantCulture)).Take(minRowCount).ToArray());
}
if (indicators.Contains(IndicatorService.MACD))
{
vector.AddColumn("MACD", macdOut.Select(p => (object)p.ToString(CultureInfo.InvariantCulture)).Take(minRowCount).ToArray());
}
if (indicators.Contains(IndicatorService.RSI))
{
vector.AddColumn("RSI", rsiOut.Select(p => (object)p.ToString(CultureInfo.InvariantCulture)).Take(minRowCount).ToArray());
}
if (indicators.Contains(IndicatorService.WilliamsR))
{
vector.AddColumn("WilliamsR", williamsROut.Select(p => (object)p.ToString(CultureInfo.InvariantCulture)).Take(minRowCount).ToArray());
}
if (indicators.Contains(IndicatorService.Stochastics))
{
vector.AddColumn("Stochastics", stochasticsOut.Select(p => (object)p.ToString(CultureInfo.InvariantCulture)).Take(minRowCount).ToArray());
}
vector.AddColumn("label", closesOut.Select(p => (object)string.Format("{0:0.0}", p).ToString(CultureInfo.InvariantCulture)).Take(minRowCount).ToArray());
new CSVExporter(vector).Export("c:\\users\\yasin\\indicatorOutput.csv");
int count = vector.Values[0].Length;
FeatureVector training = new FeatureVector();
for (int i = 0; i < vector.ColumnName.Count; i++)
{
training.AddColumn(vector.ColumnName[i], vector.Values[i].Take((int)(count * trainingSetPercentage)).ToArray());
}
FeatureVector test = new FeatureVector();
for (int i = 0; i < vector.ColumnName.Count; i++)
{
test.AddColumn(vector.ColumnName[i], vector.Values[i].Skip((int)(count * trainingSetPercentage)).Take(count).ToArray());
}
double accuracy = 0;
if (mlAlgorithm == MLAService.LIN_REG)
{
var linReg = new LinearRegression();
var bce = new BinaryClassificationEvaluator();
if (isCrossValidationEnabled)
{
var cv = new CrossValidator(linReg, bce, 10);
var cvModel = (CrossValidatorModel)cv.Fit(training);
var predictions = cvModel.transform(test);
bce.evaluate(predictions);
accuracy = bce.Accuracy;
}
else
{
var linRegModel = (LinearRegressionModel)linReg.Fit(training);
var predictions = linRegModel.transform(test);
bce.evaluate(predictions);
accuracy = bce.Accuracy;
}
}
else if (mlAlgorithm == MLAService.LOG_REG)
{
var logReg = new LogisticRegression();
var bce = new BinaryClassificationEvaluator();
if (isCrossValidationEnabled)
{
var cv = new CrossValidator(logReg, bce, 10);
var cvModel = (CrossValidatorModel)cv.Fit(training);
var predictions = cvModel.transform(test);
bce.evaluate(predictions);
accuracy = bce.Accuracy;
}
else
{
var logRegModel = (LogisticRegressionModel)logReg.Fit(training);
var predictions = logRegModel.transform(test);
bce.evaluate(predictions);
accuracy = bce.Accuracy;
}
}
else if (mlAlgorithm == MLAService.NAI_BAY)
{
var naiBay = new NaiveBayes();
var bce = new BinaryClassificationEvaluator();
if (isCrossValidationEnabled)
{
var cv = new CrossValidator(naiBay, bce, 10);
var cvModel = (CrossValidatorModel)cv.Fit(training);
var predictions = cvModel.transform(test);
bce.evaluate(predictions);
accuracy = bce.Accuracy;
}
else
{
var naiBayModel = (NaiveBayesModel)naiBay.Fit(training);
var predictions = naiBayModel.transform(test);
bce.evaluate(predictions);
accuracy = bce.Accuracy;
}
}
return(accuracy);
}
public void TestRidgeVsLstsq()
{
var random = new Random(0);
// we need more samples than features
const int nSamples = 5;
const int nFeatures = 4;
var y = DenseVector.CreateRandom(nSamples, new Normal { RandomSource = random });
var x = DenseMatrix.CreateRandom(nSamples, nFeatures, new Normal { RandomSource = random });
var ridge = new RidgeRegression(alpha: 0.0, fitIntercept: false);
var ols = new LinearRegression(fitIntercept: false);
ridge.Fit(x, y);
ols.Fit(x, y);
Assert.IsTrue(ridge.Coef.AlmostEquals(ols.Coef));
ridge.Fit(x, y);
ols.Fit(x, y);
Assert.IsTrue(ridge.Coef.AlmostEquals(ols.Coef));
}
