public void FitsToBestLineThroughOrigin()
{
// Mathematica: Fit[{{1,4.986},{2,2.347},{3,2.061},{4,-2.995},{5,-2.352},{6,-5.782}}, {x}, x]
// -> -0.467791 x
var x = Enumerable.Range(1, 6).Select(Convert.ToDouble).ToArray();
var y = new[] { 4.986, 2.347, 2.061, -2.995, -2.352, -5.782 };
var resp = Fit.LineThroughOrigin(x, y);
Assert.AreEqual(-0.467791, resp, 1e-4);
var resf = Fit.LineThroughOriginFunc(x, y);
foreach (var z in Enumerable.Range(-3, 10))
{
Assert.AreEqual(-0.467791 * z, resf(z), 1e-4);
}
var respSeq = SimpleRegression.FitThroughOrigin(Generate.Map2(x, y, Tuple.Create));
Assert.AreEqual(-0.467791, respSeq, 1e-4);
}
private List <AccordResult> CalculateLinearRegression(List <BalancePointPair> allBalancePointPairs, WthNormalParams normalParamsKey)
{
var allBalancePointGroups = allBalancePointPairs.GroupBy(s => new { s.CoolingBalancePoint, s.HeatingBalancePoint });
List <AccordResult> accordResults = new List <AccordResult>();
List <AccordResult> rejectedAccords = new List <AccordResult>();
foreach (var group in allBalancePointGroups)
{
try
{
List <BalancePointPair> IdenticalBalancePointPairsForAllReadings = group.ToList();
BalancePointPair _pointPair = IdenticalBalancePointPairsForAllReadings.First();
int readingsCount = IdenticalBalancePointPairsForAllReadings.Count;
double[] fullYData = new double[readingsCount];
double[] fullYDataDailyAvg = new double[readingsCount];
double[][] hcddMatrix = new double[readingsCount][];
double[][] hcddMatrixNonDaily = new double[readingsCount][];
foreach (BalancePointPair balancePointPair in IdenticalBalancePointPairsForAllReadings)
{
fullYData[IdenticalBalancePointPairsForAllReadings.IndexOf(balancePointPair)] = (balancePointPair.ActualUsage);
fullYDataDailyAvg[IdenticalBalancePointPairsForAllReadings.IndexOf(balancePointPair)]
= (balancePointPair.ActualUsage / balancePointPair.DaysInReading);
hcddMatrix[IdenticalBalancePointPairsForAllReadings.IndexOf(balancePointPair)] = new double[]
{
(balancePointPair.HeatingDegreeDays / balancePointPair.DaysInReading),
(balancePointPair.CoolingDegreeDays / balancePointPair.DaysInReading)
};
}
double[] avgHddsForEachReadingInYear = new double[readingsCount];
double[] avgCddsForEachReadingInYear = new double[readingsCount];
for (int i = 0; i < readingsCount; i++)
{
avgHddsForEachReadingInYear[i] = hcddMatrix[i][0];
avgCddsForEachReadingInYear[i] = hcddMatrix[i][1];
}
double[] modelParams = new double[3];
modelParams[0] = 0;
modelParams[1] = 0;
modelParams[2] = 0;
if (fullYData.Sum() == 0)
{
AccordResult empty = new AccordResult
{
bpPair = _pointPair
};
accordResults.Add(empty);
}
else if (_pointPair.HeatingBalancePoint == 0 && _pointPair.CoolingBalancePoint == 0)
{
double[] onesVector = new double[readingsCount];
for (int i = 0; i < readingsCount; i++)
{
onesVector[i] = 1;
}
modelParams[0] = Fit.LineThroughOrigin(onesVector, fullYDataDailyAvg);
OrdinaryLeastSquares ols = new OrdinaryLeastSquares()
{
UseIntercept = false
};
SimpleLinearRegression regressionAccord = ols.Learn(onesVector, fullYDataDailyAvg);
//double[] predictedAccord = regressionAccord.Transform(onesVector);
double r2 = MathNet.Numerics.GoodnessOfFit.CoefficientOfDetermination(onesVector.Select(x => x * modelParams[0]), fullYDataDailyAvg);
//double mean = fullYDataDailyAvg.Mean();
//if (mean != modelParams[0] || mean != regressionAccord.Slope)
//{
// Console.WriteLine("Hey!");
//}
//double r2Accord = regressionAccord.CoefficientOfDetermination(onesVector, fullYDataDailyAvg);
//double sxx = onesVector.Subtract(onesVector.Mean()).Pow(2).Sum();
//double hypothesizedValue = 0;
//try
//{
// TTest test = new TTest(
// estimatedValue: regressionAccord.Slope, standardError: sxx, degreesOfFreedom: _pointPair.ReadingsInNormalYear - 2,
// hypothesizedValue: hypothesizedValue, alternate: OneSampleHypothesis.ValueIsDifferentFromHypothesis
// );
// if (test.Significant)
// {
AccordResult accordResult = new AccordResult()
{
SimpleLinearRegression = regressionAccord,
R2Accord = r2,
IsSimpleSingleRegression = true,
HeatingBP = _pointPair.HeatingBalancePoint,
CoolingBP = _pointPair.CoolingBalancePoint,
Intercept = regressionAccord.Slope,
bpPair = _pointPair
};
accordResults.Add(accordResult);
// }
//}
//catch (Exception e)
//{
// Console.WriteLine(e.Message + e.StackTrace);
//}
}
else if (_pointPair.CoolingBalancePoint != 0 && _pointPair.HeatingBalancePoint != 0)
{
//modelParams = MultipleRegression.QR(hcddMatrix, fullYDataDailyAvg, intercept: true);
//Accord
//var ols = new OrdinaryLeastSquares()
//{
// UseIntercept = true
//};
try
{
MultipleLinearRegressionAnalysis mlra = new MultipleLinearRegressionAnalysis(intercept: true);
mlra.Learn(hcddMatrix, fullYDataDailyAvg);
//
//MultipleLinearRegression regressionAccord = ols.Learn(hcddMatrix, fullYDataDailyAvg);
var regressionAccord = mlra.Regression;
double[] predicted = regressionAccord.Transform(hcddMatrix);
double r2Accord = new RSquaredLoss(numberOfInputs: 2, expected: fullYDataDailyAvg)
{
Adjust = false
}.Loss(predicted);
double r2Coeff = regressionAccord.CoefficientOfDetermination(hcddMatrix, fullYDataDailyAvg, adjust: false);
//double r2Math = MathNet.Numerics.GoodnessOfFit.CoefficientOfDetermination(hcddMatrix.Select(
// x => (x[0] * regressionAccord.Weights[0]) + (x[1] * regressionAccord.Weights[1]) + regressionAccord.Intercept
//), fullYDataDailyAvg);
//double r2MathPred = MathNet.Numerics.GoodnessOfFit.CoefficientOfDetermination(predicted, fullYDataDailyAvg);
AccordResult accordResult = new AccordResult()
{
//MultipleRegression = regressionAccord,
R2Accord = r2Accord,
R2Coeff = r2Coeff,
HeatingBP = _pointPair.HeatingBalancePoint,
CoolingBP = _pointPair.CoolingBalancePoint,
IsSimpleSingleRegression = false,
MLRA = mlra,
Intercept = regressionAccord.Intercept,
bpPair = _pointPair,
IsMultipleLinearRegression = true
};
if (mlra.Coefficients.All(x => x.TTest.Significant))
{
accordResults.Add(accordResult);
}
else
{
rejectedAccords.Add(accordResult);
}
}
catch (Exception e)
{
Console.WriteLine(e.Message + " " + e.StackTrace);
}
}
else if (_pointPair.HeatingBalancePoint > 0)
{
// Tuple<double, double> heatingTuple = Fit.Line(avgHddsForEachReadingInYear, fullYDataDailyAvg);
// modelParams[0] = heatingTuple.Item1;
// modelParams[1] = heatingTuple.Item2;
// double r = MathNet.Numerics.GoodnessOfFit.CoefficientOfDetermination(
// avgHddsForEachReadingInYear.Select(x => heatingTuple.Item1 + heatingTuple.Item2 * x), fullYDataDailyAvg);
OrdinaryLeastSquares ols = new OrdinaryLeastSquares()
{
UseIntercept = true
};
SimpleLinearRegression regressionAccord = ols.Learn(avgHddsForEachReadingInYear, fullYDataDailyAvg);
double[] predictedAccord = regressionAccord.Transform(avgHddsForEachReadingInYear);
double rAccord = new RSquaredLoss(1, fullYDataDailyAvg).Loss(predictedAccord);
//double rAccord2 = regressionAccord.CoefficientOfDetermination(avgHddsForEachReadingInYear, fullYDataDailyAvg, adjust: false);
//double r2Math = MathNet.Numerics.GoodnessOfFit.CoefficientOfDetermination(avgHddsForEachReadingInYear.Select(
// x => (x * regressionAccord.Slope) + regressionAccord.Intercept
// ), fullYDataDailyAvg);
//double r2 = MathNet.Numerics.GoodnessOfFit.CoefficientOfDetermination(predictedAccord, fullYDataDailyAvg);
int degreesOfFreedom = _pointPair.ReadingsInNormalYear - 2;
double ssx = Math.Sqrt((avgHddsForEachReadingInYear.Subtract(avgHddsForEachReadingInYear.Mean())).Pow(2).Sum());
double s = Math.Sqrt(((fullYDataDailyAvg.Subtract(predictedAccord).Pow(2)).Sum()) / degreesOfFreedom);
double error = regressionAccord.GetStandardError(avgHddsForEachReadingInYear, fullYDataDailyAvg);
double seSubB = s / ssx;
double hypothesizedValue = 0;
TTest tTest = new TTest(
estimatedValue: regressionAccord.Slope, standardError: seSubB, degreesOfFreedom: degreesOfFreedom,
hypothesizedValue: hypothesizedValue, alternate: OneSampleHypothesis.ValueIsDifferentFromHypothesis
);
AccordResult accordResult = new AccordResult()
{
SimpleLinearRegression = regressionAccord,
R2Accord = rAccord,
IsSimpleSingleRegression = true,
HeatingBP = _pointPair.HeatingBalancePoint,
CoolingBP = _pointPair.CoolingBalancePoint,
TTest = tTest,
Intercept = regressionAccord.Intercept,
bpPair = _pointPair
};
if (tTest.Significant)
{
accordResults.Add(accordResult);
}
else
{
rejectedAccords.Add(accordResult);
}
}
else if (_pointPair.CoolingBalancePoint > 0)
{
//Tuple<double, double> coolingTuple = Fit.Line(avgCddsForEachReadingInYear, fullYDataDailyAvg);
//modelParams[0] = coolingTuple.Item1;
//modelParams[2] = coolingTuple.Item2;
OrdinaryLeastSquares ols = new OrdinaryLeastSquares()
{
UseIntercept = true
};
SimpleLinearRegression regressionAccord = ols.Learn(avgCddsForEachReadingInYear, fullYDataDailyAvg);
double[] predictedAccord = regressionAccord.Transform(avgCddsForEachReadingInYear);
double rAccord = new RSquaredLoss(1, fullYDataDailyAvg).Loss(predictedAccord);
//double r2Math = MathNet.Numerics.GoodnessOfFit.CoefficientOfDetermination(avgCddsForEachReadingInYear.Select(
// x => (x * regressionAccord.Slope) + regressionAccord.Intercept
// ), fullYDataDailyAvg);
//double r2 = MathNet.Numerics.GoodnessOfFit.CoefficientOfDetermination(predictedAccord, fullYDataDailyAvg);
int degreesOfFreedom = _pointPair.ReadingsInNormalYear - 2;
double ssx = Math.Sqrt(avgCddsForEachReadingInYear.Subtract(avgCddsForEachReadingInYear.Mean()).Pow(2).Sum());
double s = Math.Sqrt(((fullYDataDailyAvg.Subtract(predictedAccord).Pow(2)).Sum()) / degreesOfFreedom);
double seSubB = s / ssx;
double hypothesizedValue = 0;
double myT = seSubB / regressionAccord.Slope;
TTest tTest = new TTest(
estimatedValue: regressionAccord.Slope, standardError: seSubB, degreesOfFreedom: degreesOfFreedom,
hypothesizedValue: hypothesizedValue, alternate: OneSampleHypothesis.ValueIsDifferentFromHypothesis
);
AccordResult accordResult = new AccordResult()
{
SimpleLinearRegression = regressionAccord,
R2Accord = rAccord,
IsSimpleSingleRegression = true,
HeatingBP = _pointPair.HeatingBalancePoint,
CoolingBP = _pointPair.CoolingBalancePoint,
TTest = tTest,
Intercept = regressionAccord.Intercept,
bpPair = _pointPair
};
if (tTest.Significant)
{
accordResults.Add(accordResult);
}
else
{
rejectedAccords.Add(accordResult);
}
}
;
}
catch (Exception e)
{
Console.WriteLine(normalParamsKey.AccID + " " + normalParamsKey.UtilID + " " + normalParamsKey.UnitID + " " + e.Message + e.StackTrace);
}
}
//rejectedAccords = rejectedAccords.OrderByDescending(s => s.R2Accord).ToList();
//accordResults = accordResults.OrderByDescending(s => s.R2Accord).ToList();
return(accordResults);
}
private AccordResult CalculateLinearRegression(List <BalancePointPair> allBalancePointPairs, WthNormalParams normalParamsKey)
{
var allBalancePointGroups = allBalancePointPairs.GroupBy(s => new { s.CoolingBalancePoint, s.HeatingBalancePoint });
List <AccordResult> accordResults = new List <AccordResult>();
foreach (var group in allBalancePointGroups)
{
try
{
List <BalancePointPair> IdenticalBalancePointPairsFromAllReadings = group.ToList();
BalancePointPair _pointPair = IdenticalBalancePointPairsFromAllReadings.First();
int readingsCount = IdenticalBalancePointPairsFromAllReadings.Count;
double[] fullYData = new double[readingsCount];
double[] fullYDataDailyAvg = new double[readingsCount];
double[][] hcddMatrix = new double[readingsCount][];
double[][] hcddMatrixNonDaily = new double[readingsCount][];
foreach (BalancePointPair balancePointPair in IdenticalBalancePointPairsFromAllReadings)
{
fullYData[IdenticalBalancePointPairsFromAllReadings.IndexOf(balancePointPair)] = (balancePointPair.ActualUsage);
fullYDataDailyAvg[IdenticalBalancePointPairsFromAllReadings.IndexOf(balancePointPair)]
= (balancePointPair.ActualUsage / balancePointPair.DaysInReading);
hcddMatrix[IdenticalBalancePointPairsFromAllReadings.IndexOf(balancePointPair)] = new double[]
{
(balancePointPair.HeatingDegreeDays / balancePointPair.DaysInReading),
(balancePointPair.CoolingDegreeDays / balancePointPair.DaysInReading)
};
}
if (!(fullYData.Sum() > 0))
{
return(new AccordResult());
}
double[] avgHddsForEachReadingInYear = new double[readingsCount];
double[] avgCddsForEachReadingInYear = new double[readingsCount];
for (int i = 0; i < readingsCount; i++)
{
avgHddsForEachReadingInYear[i] = hcddMatrix[i][0];
avgCddsForEachReadingInYear[i] = hcddMatrix[i][1];
}
double[] modelParams = new double[3];
modelParams[0] = 0;
modelParams[1] = 0;
modelParams[2] = 0;
if (_pointPair.HeatingBalancePoint == 0 && _pointPair.CoolingBalancePoint == 0)
{
double[] onesVector = new double[readingsCount];
for (int i = 0; i < readingsCount; i++)
{
onesVector[i] = 1;
}
modelParams[0] = Fit.LineThroughOrigin(onesVector, fullYDataDailyAvg);
OrdinaryLeastSquares ols = new OrdinaryLeastSquares()
{
UseIntercept = false
};
double r2 = MathNet.Numerics.GoodnessOfFit.CoefficientOfDetermination(
onesVector.Select(x => x * modelParams[0]), fullYDataDailyAvg);
AccordResult accordResult = new AccordResult()
{
IsSimpleSingleRegression = true,
HeatingBP = _pointPair.HeatingBalancePoint,
CoolingBP = _pointPair.CoolingBalancePoint,
Intercept = modelParams[0],
R2Accord = r2,
//R2Accord = 0
};
accordResults.Add(accordResult);
}
else if (_pointPair.CoolingBalancePoint != 0 && _pointPair.HeatingBalancePoint != 0)
{
try
{
MultipleLinearRegressionAnalysis mlra = new MultipleLinearRegressionAnalysis(intercept: true);
mlra.Learn(hcddMatrix, fullYDataDailyAvg);
var regressionAccord = mlra.Regression;
double[] predictedAccord = regressionAccord.Transform(hcddMatrix);
double r2Accord = new RSquaredLoss(numberOfInputs: 2, expected: fullYDataDailyAvg)
{
Adjust = false
}.Loss(predictedAccord);
double r2Coeff = regressionAccord.CoefficientOfDetermination(hcddMatrix, fullYDataDailyAvg, adjust: false);
bool FTestFailed = !mlra.FTest.Significant;
AccordResult accordResult = new AccordResult()
{
IsMultipleLinearRegression = true,
HeatingBP = _pointPair.HeatingBalancePoint,
CoolingBP = _pointPair.CoolingBalancePoint,
Intercept = regressionAccord.Intercept,
B2 = regressionAccord.Weights[0],
B4 = regressionAccord.Weights[1],
R2Accord = r2Accord,
FTestFailed = FTestFailed
};
//int degreesOfFreedom = normalParamsKey.MoCt - 3;
double degreesOfFreedomAsDouble = mlra.Regression.GetDegreesOfFreedom(readingsCount);
int degreesOfFreedom = Convert.ToInt32(degreesOfFreedomAsDouble);
//if (degreesOfFreedom != 9)
//{
// Log.Warning($"Multivariable regression. DOF expected to be 9. is: {degreesOfFreedom}");
//}
//if (degreesOfFreedom != dof)
//{
// Console.WriteLine($"dof different. mlra.dof = {dof} expected = {degreesOfFreedom}");
//}
double s = Math.Sqrt(fullYDataDailyAvg.Subtract(predictedAccord).Pow(2).Sum() / degreesOfFreedom);
double ssxHdd = Math.Sqrt((avgHddsForEachReadingInYear.Subtract(avgHddsForEachReadingInYear.Mean())).Pow(2).Sum());
double ssxCdd = Math.Sqrt((avgCddsForEachReadingInYear.Subtract(avgCddsForEachReadingInYear.Mean())).Pow(2).Sum());
double seSubHdd = s / ssxHdd;
double seSubCdd = s / ssxCdd;
double tStatisticHdd = regressionAccord.Weights[0] / seSubHdd;
double tStatisticCdd = regressionAccord.Weights[1] / seSubCdd;
double tCriticalFivePercent = 2.262156;
double tCriticalTenPercent = 1.833113;
bool myTestHdd = Math.Abs(tStatisticHdd) >= tCriticalTenPercent;
bool myTestCdd = Math.Abs(tStatisticCdd) >= tCriticalTenPercent;
//if (myTestHdd != mlra.Coefficients[0].TTest.Significant && degreesOfFreedom != 9)
//{
// Console.WriteLine($"nope. mystat - {tStatisticHdd} accordstat - {mlra.Coefficients[0].TTest.Statistic} " +
// $"accordCritical - {mlra.Coefficients[0].TTest.CriticalValue}");
//}
//if (myTestCdd != mlra.Coefficients[1].TTest.Significant && degreesOfFreedom != 9)
//{
// Console.WriteLine($"nope. mystat - {tStatisticCdd} accordstat - {mlra.Coefficients[1].TTest.Statistic} " +
// $"accordCritical - {mlra.Coefficients[1].TTest.CriticalValue}");
//}
//if (mlra.Coefficients.All(x => x.TTest.Significant) &&
// mlra.Coefficients.All(x => x.Value > 0) &&
// mlra.Regression.Intercept > 0 &&
// r2Accord >= 0.7500)
//{
// accordResults.Add(accordResult);
//}
if (
myTestHdd &&
myTestCdd &&
mlra.Coefficients.All(x => x.Value > 0) &&
mlra.Regression.Intercept > 0
//&& accordResult.R2Accord >= 0.75
)
{
accordResults.Add(accordResult);
}
}
catch (Exception e)
{
Log.Debug($"AccID/UtilID/UnitID: {normalParamsKey.AccID}/{normalParamsKey.UtilID}/{normalParamsKey.UnitID} >> " +
$"MultipleLinearRegressionAnalysis Exception: {e.Message}");
}
}
else if (_pointPair.HeatingBalancePoint > 0)
{
OrdinaryLeastSquares ols = new OrdinaryLeastSquares()
{
UseIntercept = true
};
SimpleLinearRegression regressionAccord = ols.Learn(avgHddsForEachReadingInYear, fullYDataDailyAvg);
double[] predictedAccord = regressionAccord.Transform(avgHddsForEachReadingInYear);
double r2Accord = new RSquaredLoss(1, fullYDataDailyAvg).Loss(predictedAccord);
//int degreesOfFreedom = normalParamsKey.MoCt - 2;
double degreesOfFreedomAsDouble = regressionAccord.GetDegreesOfFreedom(readingsCount);
int degreesOfFreedom = Convert.ToInt32(degreesOfFreedomAsDouble);
//if (degreesOfFreedom != 10)
//{
// Log.Warning($"Single variable regression. DOF expected to be 10. is: {degreesOfFreedom}");
//}
double ssx = Math.Sqrt((avgHddsForEachReadingInYear.Subtract(avgHddsForEachReadingInYear.Mean())).Pow(2).Sum());
double s = Math.Sqrt(fullYDataDailyAvg.Subtract(predictedAccord).Pow(2).Sum() / degreesOfFreedom);
double error = regressionAccord.GetStandardError(avgHddsForEachReadingInYear, fullYDataDailyAvg);
double seSubB = s / ssx;
double hypothesizedValue = 0;
double tStatistic = regressionAccord.Slope / seSubB;
double tCriticalFivePercent = 2.228138;
double tCriticalTenPercent = 1.812461;
bool myTest = Math.Abs(tStatistic) >= tCriticalTenPercent;
//TTest tTest = new TTest(
// estimatedValue: regressionAccord.Slope, standardError: seSubB, degreesOfFreedom: degreesOfFreedom,
// hypothesizedValue: hypothesizedValue, alternate: OneSampleHypothesis.ValueIsDifferentFromHypothesis
// );
//if (myTest != tTest.Significant)
//{
// Console.WriteLine($"nope. mystat - {tStatistic} accordstat - {tTest.Statistic} accordCritical - {tTest.CriticalValue}");
//}
AccordResult accordResult = new AccordResult()
{
IsSimpleSingleRegression = true,
HeatingBP = _pointPair.HeatingBalancePoint,
Intercept = regressionAccord.Intercept,
B2 = regressionAccord.Slope,
R2Accord = r2Accord
};
//if (tTest.Significant && accordResult.B2 > 0 && r2Accord >= 0.7500)
//{
// accordResults.Add(accordResult);
//}
if (myTest &&
accordResult.B2 > 0 &&
accordResult.Intercept > 0
//&& r2Accord >= 0.7500
)
{
accordResults.Add(accordResult);
}
}
else if (_pointPair.CoolingBalancePoint > 0)
{
OrdinaryLeastSquares ols = new OrdinaryLeastSquares()
{
UseIntercept = true
};
SimpleLinearRegression regressionAccord = ols.Learn(avgCddsForEachReadingInYear, fullYDataDailyAvg);
double[] predictedAccord = regressionAccord.Transform(avgCddsForEachReadingInYear);
double r2Accord = new RSquaredLoss(1, fullYDataDailyAvg).Loss(predictedAccord);
//int degreesOfFreedom = normalParamsKey.MoCt - 2;
double degreesOfFreedomAsDouble = regressionAccord.GetDegreesOfFreedom(readingsCount);
int degreesOfFreedom = Convert.ToInt32(degreesOfFreedomAsDouble);
//if (degreesOfFreedom != 10)
//{
// Log.Warning($"Single variable regression. DOF expected to be 10. is: {degreesOfFreedom}");
//}
double ssx = Math.Sqrt(avgCddsForEachReadingInYear.Subtract(avgCddsForEachReadingInYear.Mean()).Pow(2).Sum());
double s = Math.Sqrt(fullYDataDailyAvg.Subtract(predictedAccord).Pow(2).Sum() / degreesOfFreedom);
double seSubB = s / ssx;
double hypothesizedValue = 0;
double tStatistic = regressionAccord.Slope / seSubB;
double tCriticalFivePercent = 2.22813885198627;
double tCriticalTenPercent = 1.812461;
bool myTest = Math.Abs(tStatistic) >= tCriticalTenPercent;
//TTest tTest = new TTest(
// estimatedValue: regressionAccord.Slope, standardError: seSubB, degreesOfFreedom: degreesOfFreedom,
// hypothesizedValue: hypothesizedValue, alternate: OneSampleHypothesis.ValueIsDifferentFromHypothesis
// );
//if (myTest != tTest.Significant)
//{
// Console.WriteLine($"nope. mystat - {tStatistic} accordstat - {tTest.Statistic} accordCritical - {tTest.CriticalValue}");
//}
AccordResult accordResult = new AccordResult()
{
IsSimpleSingleRegression = true,
CoolingBP = _pointPair.CoolingBalancePoint,
Intercept = regressionAccord.Intercept,
B4 = regressionAccord.Slope,
R2Accord = r2Accord
};
//if (tTest.Significant && accordResult.B4 > 0 && r2Accord >= 0.7500)
//{
// accordResults.Add(accordResult);
//}
if (
myTest &&
accordResult.B4 > 0
//&& r2Accord >= 0.7500
)
{
accordResults.Add(accordResult);
}
}
}
catch (Exception e)
{
Log.Debug($"AccID/UtilID/UnitID: {normalParamsKey.AccID}/{normalParamsKey.UtilID}/{normalParamsKey.UnitID} >> {e.Message} {e.StackTrace}");
}
}
AccordResult accordWinner = accordResults
.Where(s => s.Intercept >= 0)
.OrderByDescending(s => s.R2Accord).ToList().FirstOrDefault();
return(accordWinner);
}
private AccordResult CalculateLinearRegression(List <BalancePointPair> allBalancePointPairs, WthNormalParams normalParamsKey)
{
var allBalancePointGroups = allBalancePointPairs.GroupBy(s => new { s.CoolingBalancePoint, s.HeatingBalancePoint });
List <AccordResult> accordResults = new List <AccordResult>();
foreach (var group in allBalancePointGroups)
{
try
{
List <BalancePointPair> IdenticalBalancePointPairsFromAllReadings = group.ToList();
BalancePointPair _pointPair = IdenticalBalancePointPairsFromAllReadings.First();
int readingsCount = IdenticalBalancePointPairsFromAllReadings.Count;
double[] fullYData = new double[readingsCount];
double[] fullYDataDailyAvg = new double[readingsCount];
double[][] hcddMatrix = new double[readingsCount][];
double[][] hcddMatrixNonDaily = new double[readingsCount][];
foreach (BalancePointPair balancePointPair in IdenticalBalancePointPairsFromAllReadings)
{
fullYData[IdenticalBalancePointPairsFromAllReadings.IndexOf(balancePointPair)] = (balancePointPair.ActualUsage);
fullYDataDailyAvg[IdenticalBalancePointPairsFromAllReadings.IndexOf(balancePointPair)]
= (balancePointPair.ActualUsage / balancePointPair.DaysInReading);
hcddMatrix[IdenticalBalancePointPairsFromAllReadings.IndexOf(balancePointPair)] = new double[]
{
(balancePointPair.HeatingDegreeDays / balancePointPair.DaysInReading),
(balancePointPair.CoolingDegreeDays / balancePointPair.DaysInReading)
};
}
double[] avgHddsForEachReadingInYear = new double[readingsCount];
double[] avgCddsForEachReadingInYear = new double[readingsCount];
for (int i = 0; i < readingsCount; i++)
{
avgHddsForEachReadingInYear[i] = hcddMatrix[i][0];
avgCddsForEachReadingInYear[i] = hcddMatrix[i][1];
}
double[] modelParams = new double[3];
modelParams[0] = 0;
modelParams[1] = 0;
modelParams[2] = 0;
if (_pointPair.HeatingBalancePoint == 0 && _pointPair.CoolingBalancePoint == 0)
{
double[] onesVector = new double[readingsCount];
for (int i = 0; i < readingsCount; i++)
{
onesVector[i] = 1;
}
modelParams[0] = Fit.LineThroughOrigin(onesVector, fullYDataDailyAvg);
OrdinaryLeastSquares ols = new OrdinaryLeastSquares()
{
UseIntercept = false
};
double r2 = MathNet.Numerics.GoodnessOfFit.CoefficientOfDetermination(
onesVector.Select(x => x * modelParams[0]), fullYDataDailyAvg);
AccordResult accordResult = new AccordResult()
{
IsSimpleSingleRegression = true,
HeatingBP = _pointPair.HeatingBalancePoint,
CoolingBP = _pointPair.CoolingBalancePoint,
Intercept = modelParams[0],
R2Accord = r2,
};
accordResults.Add(accordResult);
}
else if (_pointPair.CoolingBalancePoint != 0 && _pointPair.HeatingBalancePoint != 0)
{
try
{
MultipleLinearRegressionAnalysis mlra = new MultipleLinearRegressionAnalysis(intercept: true);
mlra.Learn(hcddMatrix, fullYDataDailyAvg);
var regressionAccord = mlra.Regression;
double[] predicted = regressionAccord.Transform(hcddMatrix);
double r2Accord = new RSquaredLoss(numberOfInputs: 2, expected: fullYDataDailyAvg)
{
Adjust = false
}.Loss(predicted);
double r2Coeff = regressionAccord.CoefficientOfDetermination(hcddMatrix, fullYDataDailyAvg, adjust: false);
bool FTestFailed = !mlra.FTest.Significant;
AccordResult accordResult = new AccordResult()
{
IsMultipleLinearRegression = true,
HeatingBP = _pointPair.HeatingBalancePoint,
CoolingBP = _pointPair.CoolingBalancePoint,
Intercept = regressionAccord.Intercept,
B2 = regressionAccord.Weights[0],
B4 = regressionAccord.Weights[1],
R2Accord = r2Accord,
FTestFailed = FTestFailed
};
if (mlra.Coefficients.All(x => x.TTest.Significant))
{
accordResults.Add(accordResult);
}
}
catch (Exception e)
{
Log.Debug(normalParamsKey.AccID + " " + normalParamsKey.UtilID + " " + normalParamsKey.UnitID + " " + e.Message + " " + e.StackTrace);
}
}
else if (_pointPair.HeatingBalancePoint > 0)
{
OrdinaryLeastSquares ols = new OrdinaryLeastSquares()
{
UseIntercept = true
};
SimpleLinearRegression regressionAccord = ols.Learn(avgHddsForEachReadingInYear, fullYDataDailyAvg);
double[] predictedAccord = regressionAccord.Transform(avgHddsForEachReadingInYear);
double r2Accord = new RSquaredLoss(1, fullYDataDailyAvg).Loss(predictedAccord);
int degreesOfFreedom = normalParamsKey.MoCt - 2;
double ssx = Math.Sqrt((avgHddsForEachReadingInYear.Subtract(avgHddsForEachReadingInYear.Mean())).Pow(2).Sum());
double s = Math.Sqrt(((fullYDataDailyAvg.Subtract(predictedAccord).Pow(2)).Sum()) / degreesOfFreedom);
double error = regressionAccord.GetStandardError(avgHddsForEachReadingInYear, fullYDataDailyAvg);
double seSubB = s / ssx;
double hypothesizedValue = 0;
TTest tTest = new TTest(
estimatedValue: regressionAccord.Slope, standardError: seSubB, degreesOfFreedom: degreesOfFreedom,
hypothesizedValue: hypothesizedValue, alternate: OneSampleHypothesis.ValueIsDifferentFromHypothesis
);
AccordResult accordResult = new AccordResult()
{
IsSimpleSingleRegression = true,
HeatingBP = _pointPair.HeatingBalancePoint,
Intercept = regressionAccord.Intercept,
B2 = regressionAccord.Slope,
R2Accord = r2Accord
};
if (tTest.Significant)
{
accordResults.Add(accordResult);
}
}
else if (_pointPair.CoolingBalancePoint > 0)
{
OrdinaryLeastSquares ols = new OrdinaryLeastSquares()
{
UseIntercept = true
};
SimpleLinearRegression regressionAccord = ols.Learn(avgCddsForEachReadingInYear, fullYDataDailyAvg);
double[] predictedAccord = regressionAccord.Transform(avgCddsForEachReadingInYear);
double rAccord = new RSquaredLoss(1, fullYDataDailyAvg).Loss(predictedAccord);
int degreesOfFreedom = normalParamsKey.MoCt - 2;
double ssx = Math.Sqrt(avgCddsForEachReadingInYear.Subtract(avgCddsForEachReadingInYear.Mean()).Pow(2).Sum());
double s = Math.Sqrt(((fullYDataDailyAvg.Subtract(predictedAccord).Pow(2)).Sum()) / degreesOfFreedom);
double seSubB = s / ssx;
double hypothesizedValue = 0;
double myT = seSubB / regressionAccord.Slope;
TTest tTest = new TTest(
estimatedValue: regressionAccord.Slope, standardError: seSubB, degreesOfFreedom: degreesOfFreedom,
hypothesizedValue: hypothesizedValue, alternate: OneSampleHypothesis.ValueIsDifferentFromHypothesis
);
AccordResult accordResult = new AccordResult()
{
IsSimpleSingleRegression = true,
CoolingBP = _pointPair.CoolingBalancePoint,
Intercept = regressionAccord.Intercept,
B4 = regressionAccord.Slope,
R2Accord = rAccord
};
if (tTest.Significant)
{
accordResults.Add(accordResult);
}
}
;
}
catch (Exception e)
{
Log.Debug(normalParamsKey.AccID + " " + normalParamsKey.UtilID + " " + normalParamsKey.UnitID + " " + e.Message + e.StackTrace);
}
}
AccordResult accordWinner = accordResults
.Where(s => s.Intercept >= 0)
.OrderByDescending(s => s.R2Accord).ToList().FirstOrDefault();
return(accordWinner);
}
private List <BalancePointPair> CalculateLinearRegression(List <BalancePointPair> allBalancePointPairs, WthNormalParams normalParamsKey)
{
var updatedBalancePointPairs = new List <BalancePointPair>();
var allBalancePointGroups = allBalancePointPairs.GroupBy(s => new { s.CoolingBalancePoint, s.HeatingBalancePoint });
foreach (var group in allBalancePointGroups)
{
List <BalancePointPair> IdenticalBalancePointPairsForAllReadings = group.ToList();
int readingsCount = IdenticalBalancePointPairsForAllReadings.Count;
BalancePointPair _pointPair = IdenticalBalancePointPairsForAllReadings.First();
bool NonWeatherDependant = (normalParamsKey.B2_Original == 0 && normalParamsKey.B4_Original == 0);
//List<double> expUsageDaily = new List<double>();
//List<double> hddsDaily = new List<double>();
//List<double> cddsDaily = new List<double>();
//List<double> actualUsageDaily = new List<double>();
double[] fullXData = new double[readingsCount];
//double[] fullXData_Original = new double[12 * balancePointPairGroup.Count];
double[] xDays = new double[readingsCount];
double[] fullYData = new double[readingsCount];
double[] fullYDataAvg = new double[readingsCount];
//double?[] hddsDaily = new double?[readingsCount * _pointPair.DaysInNormalYear];
//double?[] cddsDaily = new double?[readingsCount * _pointPair.DaysInNormalYear];
//double[][] hcddMatrix = new double[2][];
//hcddMatrix[0] = new double[readingsCount];
//hcddMatrix[1] = new double[readingsCount];
double[][] hcddMatrix = new double[readingsCount][];
foreach (BalancePointPair balancePointPair in IdenticalBalancePointPairsForAllReadings)
{
//double[] hddsDailyAvgInReading = new double[readingsCount];
//double[] cddsDailyAvgInReading = new double[readingsCount];
//expUsageDaily.Add(Convert.ToDouble(balancePointPair.ExpUsage) / balancePointPair.Days);
//hddsDaily.Add(balancePointPair.HeatingDegreeDays / balancePointPair.Days);
//cddsDaily.Add(balancePointPair.CoolingDegreeDays / balancePointPair.Days);
//actualUsageDaily.Add(balancePointPair.ActualUsage / balancePointPair.Days);
fullXData[IdenticalBalancePointPairsForAllReadings.IndexOf(balancePointPair)] = Convert.ToDouble(balancePointPair.ExpUsage_New);
xDays[IdenticalBalancePointPairsForAllReadings.IndexOf(balancePointPair)] = balancePointPair.DaysInReading;
fullYData[IdenticalBalancePointPairsForAllReadings.IndexOf(balancePointPair)] = (balancePointPair.ActualUsage);
fullYDataAvg[IdenticalBalancePointPairsForAllReadings.IndexOf(balancePointPair)] = (balancePointPair.ActualUsage / balancePointPair.DaysInReading);
//hddsDaily[IdenticalBalancePointPairsForAllReadings.IndexOf(balancePointPair) * ]
// = balancePointPair.HddList[IdenticalBalancePointPairsForAllReadings.IndexOf(balancePointPair)];
//cddsDaily[IdenticalBalancePointPairsForAllReadings.IndexOf(balancePointPair)]
// = balancePointPair.CddList[IdenticalBalancePointPairsForAllReadings.IndexOf(balancePointPair)];
//hddsDailyAvgInReading[IdenticalBalancePointPairsForAllReadings.IndexOf(balancePointPair)]
// = (balancePointPair.HeatingDegreeDays / balancePointPair.DaysInReading);
//cddsDailyAvgInReading[IdenticalBalancePointPairsForAllReadings.IndexOf(balancePointPair)]
// = (balancePointPair.CoolingDegreeDays / balancePointPair.DaysInReading);
hcddMatrix[IdenticalBalancePointPairsForAllReadings.IndexOf(balancePointPair)] = new double[] {
(balancePointPair.HeatingDegreeDays / balancePointPair.DaysInReading),
(balancePointPair.CoolingDegreeDays / balancePointPair.DaysInReading)
};
//hcddMatrix[0][IdenticalBalancePointPairsForAllReadings.IndexOf(balancePointPair)] = (balancePointPair.HeatingDegreeDays / balancePointPair.DaysInReading);
//hcddMatrix[1][IdenticalBalancePointPairsForAllReadings.IndexOf(balancePointPair)] = (balancePointPair.CoolingDegreeDays / balancePointPair.DaysInReading);
}
double[] avgHddsForEachReadingInYear = new double[readingsCount];
double[] avgCddsForEachReadingInYear = new double[readingsCount];
for (int i = 0; i < readingsCount; i++)
{
avgHddsForEachReadingInYear[i] = hcddMatrix[i][0];
avgCddsForEachReadingInYear[i] = hcddMatrix[i][1];
}
//double[] expUsageByDay = new double[daysInYear];
//double[] fullYDataDaiy = new double[daysInYear];
//List<double> expUsageByDay = new List<double>();
//List<double> fullYDataByDay = new List<double>();
//foreach (BalancePointPair balancePointPair in IdenticalBalancePointPairsForAllReadings)
//{
// double dailyExpusage = 0;
// dailyExpusage += normalParamsKey.B3 * balancePointPair.HddList[IdenticalBalancePointPairsForAllReadings.IndexOf(balancePointPair)].Value;
// dailyExpusage += normalParamsKey.B5 * balancePointPair.CddList[IdenticalBalancePointPairsForAllReadings.IndexOf(balancePointPair)].Value;
// expUsageByDay.Add(dailyExpusage);
// fullYDataByDay.Add(balancePointPair.ActualUsage / balancePointPair.DaysInReading);
//}
//double[] hddsDailyArr = hddsDaily.ToArray();
//double[] cddsDailyArr = cddsDaily.ToArray();
//double[][] xy = new double[cddsDailyArr.Length][];
//for (int i = 0; i < cddsDailyArr.Length; i++)
//{
// double[] row = new double[2];
// xy[i] = row;
//}
//for (int i = 0; i < hddsDailyArr.Length; i++)
//{
// xy[i][0] = hddsDailyArr[i];
// xy[i][1] = cddsDailyArr[i];
//}
//double[] p = Fit.LinearMultiDim(hcddMatrix, fullYDataAvg,
// d => 1.0,
// d => d[0],
// d => d[1]);
//Matrix<double>.Build.DenseOfColumnArrays(hcddMatrix);
//Tuple<double, double> pSingular;
double[] p = new double[3];
p[0] = 0;
p[1] = 0;
p[2] = 0;
if (_pointPair.HeatingBalancePoint == 0 && _pointPair.CoolingBalancePoint == 0)
{
double[] onesVector = new double[readingsCount];
for (int i = 0; i < readingsCount; i++)
{
onesVector[i] = 1;
}
p[0] = Fit.LineThroughOrigin(onesVector, fullYDataAvg);
}
else if (_pointPair.CoolingBalancePoint != 0 && _pointPair.HeatingBalancePoint != 0)
{
p = MultipleRegression.QR(hcddMatrix, fullYDataAvg, intercept: true);
}
else if (_pointPair.CoolingBalancePoint == 0)
{
Tuple <double, double> heatingTuple = Fit.Line(avgHddsForEachReadingInYear, fullYDataAvg);
p[0] = heatingTuple.Item1;
p[1] = heatingTuple.Item2;
}
else if (_pointPair.HeatingBalancePoint == 0)
{
Tuple <double, double> coolingTuple = Fit.Line(avgCddsForEachReadingInYear, fullYDataAvg);
p[0] = coolingTuple.Item1;
p[2] = coolingTuple.Item2;
}
//double[] p = Fit.MultiDim(hcddMatrix, fullYDataAvg, intercept: true);
//double[] fullXData_NewFit = new double[fullYDataAvg.Length];
double[] fullXData_NewFit = new double[readingsCount];
foreach (BalancePointPair balancePointPair in IdenticalBalancePointPairsForAllReadings)
{
double t1 = 0;
if (IsDoubleNotNaNOrInfinity(p[0]))
{
t1 = p[0] * balancePointPair.DaysInReading;
}
double t2 = 0;
if (IsDoubleNotNaNOrInfinity(p[1]))
{
t2 = p[1] * balancePointPair.HeatingDegreeDays;
}
double t3 = 0;
if (IsDoubleNotNaNOrInfinity(p[2]))
{
t3 = p[2] * balancePointPair.CoolingDegreeDays;
}
fullXData_NewFit[IdenticalBalancePointPairsForAllReadings.IndexOf(balancePointPair)] = t1 + t2 + t3;
}
//double rBest = GoodnessOfFit.CoefficientOfDetermination(
// hcddMatrix.Select(x => p[0] + (p[1] * x[0]) + (p[2] * x[1])),
// fullYDataAvg);
double rBest = GoodnessOfFit.CoefficientOfDetermination(fullXData_NewFit, fullYData);
//double[,] comma = new double[hddsDailyArr.Length, hddsDailyArr.Length];
//for(int i = 0; i < hddsDailyArr.Length; i++)
//{
// double left = xy[i / 2][0];
// double right = xy[i / 2][1];
// comma[i, i] = [left, right];
//}
//double RSquared = GoodnessOfFit.CoefficientOfDetermination(xy.Select((x, y) => p[0], +(p[1] * xy[0]) + (p[2] * y)), ydata);
//double RSquared = GoodnessOfFit.CoefficientOfDetermination(comma => p[0], +(p[1] * xy[0]) + (p[2] * y))), ydata);
BalancePointPair groupLeader = _pointPair;
double rSquared = GoodnessOfFit.CoefficientOfDetermination(fullXData, fullYData);
//double rSquaredDaily = GoodnessOfFit.CoefficientOfDetermination(expUsageByDay, fullYDataByDay);
double standardError_New = MathNet.Numerics.GoodnessOfFit.StandardError(fullXData, fullYData, groupLeader.ReadingsInNormalYear - 2);
groupLeader.StandardError = standardError_New;
if (!Double.IsNaN(rBest) && !Double.IsInfinity(rBest))
{
groupLeader.RSquared_New = rBest;
}
else
{
groupLeader.RSquared_New = null;
}
groupLeader.B1_New = decimal.Round(Convert.ToDecimal(p[0]), 9, MidpointRounding.AwayFromZero);
groupLeader.B2_New = decimal.Round(Convert.ToDecimal(p[1]), 9, MidpointRounding.AwayFromZero);
groupLeader.B4_New = decimal.Round(Convert.ToDecimal(p[2]), 9, MidpointRounding.AwayFromZero);
//if (NonWeatherDependant)
//{
// groupLeader.RSquared = 0;
// groupLeader.NewRSquaredNonWeather = 0;
//double b = Fit.LineThroughOrigin(xDays, fullYData);
//double[] newXData = new double[xDays.Length];
//for (int i = 0; i < newXData.Length; i++)
//{
// newXData[i] = xDays[i] * b;
//}
//double NewRSquaredNonWeather = GoodnessOfFit.CoefficientOfDetermination(newXData, fullYData);
//if (!Double.IsNaN(NewRSquaredNonWeather) && !Double.IsInfinity(NewRSquaredNonWeather))
//{
// groupLeader.NewRSquaredNonWeather = GoodnessOfFit.CoefficientOfDetermination(newXData, fullYData);
// groupLeader.B1_New = decimal.Round(Convert.ToDecimal(b), 9, MidpointRounding.AwayFromZero);
//}
//else
//{
// groupLeader.NewRSquaredNonWeather = null;
// groupLeader.B1_New = null;
//}
//}
updatedBalancePointPairs.Add(groupLeader);
}
return(updatedBalancePointPairs);
}
