internal List <double> GetOutputDistribution(ISampleableDistribution <double> inputDistribution, SimpleLinearRegression regression)
{
var returnList = new List <double>();
for (int i = 0; i < 1000; i++)
{
returnList.Add(regression.Transform(inputDistribution.Generate()));
}
return(returnList);
}
public IEnumerable <XtoY> Predict(IEnumerable <double> xvalues)
{
EnsureAlreadyTrained();
double [] xvaluesArray = xvalues.ToArray();
double [] yvaluesArray = _simpleLinearRegression.Transform(xvaluesArray);
for (int i = 0; i < xvaluesArray.Length; ++i)
{
yield return(new XtoY()
{
X = xvaluesArray[i], Y = yvaluesArray[i]
});
}
}
public IEnumerable <XtoY> Predict(IEnumerable <double> xvalues)
{
EnsureAlreadyTrained();
double[] date = new double[mylist.Count];
for (int i = 0; i < mylist.Count; i++)
{
date[i] = i + 1;
}
double[] xvaluesArray = date;
double[] yvaluesArray = _simpleLinearRegression.Transform(xvaluesArray);
for (int i = 0; i < xvaluesArray.Length; ++i)
{
yield return(new XtoY()
{
X = xvaluesArray[i], Y = yvaluesArray[i]
});
}
}
private static void linearRegression()
{
// Declare some sample test data.
double[] inputs = { 80, 60, 10, 20, 30 };
double[] outputs = { 20, 40, 30, 50, 60 };
// Use Ordinary Least Squares to learn the regression
OrdinaryLeastSquares ols = new OrdinaryLeastSquares();
// Use OLS to learn the simple linear regression
SimpleLinearRegression regression = ols.Learn(inputs, outputs);
// Compute the output for a given input:
double y = regression.Transform(85); // The answer will be 28.088
// We can also extract the slope and the intercept term
// for the line. Those will be -0.26 and 50.5, respectively.
double s = regression.Slope; // -0.264706
double c = regression.Intercept; // 50.588235
}
public static void test3()
{
var poly2 = CreateFunc(1, 1);
Random rnd = new Random();
var pos = Enumerable.Range(0, 20).Select(x => new double [] { x, poly2(x) + rnd.NextDouble() }).ToArray();
double[] inputs = pos.Select(x => x[0]).ToArray();
double[] outputs = pos.Select(x => x[1]).ToArray();
var ls = new PolynomialLeastSquares()
{
Degree = 2
};
PolynomialRegression poly = ls.Learn(inputs, outputs);
double a = poly.Weights[0]; // a = 0
double b = poly.Weights[1]; // b = 0
double c = poly.Intercept; // c = 1
double[] predicted = poly.Transform(inputs);
double error = new SquareLoss(outputs).Loss(predicted);
var ols = new OrdinaryLeastSquares();
SimpleLinearRegression mul = ols.Learn(inputs, outputs);
double a1 = mul.Slope; // a = 0
double b1 = mul.Intercept; // b = 0
double[] simplepredict = mul.Transform(inputs);
double erroe2 = new SquaredHingeLoss(outputs).Loss(simplepredict);
Console.WriteLine("Done");
}
public void learn_test()
{
#region doc_learn
// Let's say we have some univariate, continuous sets of input data,
// and a corresponding univariate, continuous set of output data, such
// as a set of points in R². A simple linear regression is able to fit
// a line relating the input variables to the output variables in which
// the minimum-squared-error of the line and the actual output points
// is minimum.
// Declare some sample test data.
double[] inputs = { 80, 60, 10, 20, 30 };
double[] outputs = { 20, 40, 30, 50, 60 };
// Use Ordinary Least Squares to learn the regression
OrdinaryLeastSquares ols = new OrdinaryLeastSquares();
// Use OLS to learn the simple linear regression
SimpleLinearRegression regression = ols.Learn(inputs, outputs);
// Compute the output for a given input:
double y = regression.Transform(85); // The answer will be 28.088
// We can also extract the slope and the intercept term
// for the line. Those will be -0.26 and 50.5, respectively.
double s = regression.Slope; // -0.264706
double c = regression.Intercept; // 50.588235
#endregion
// Expected slope and intercept
double eSlope = -0.264706;
double eIntercept = 50.588235;
Assert.AreEqual(28.088235294117649, y, 1e-10);
Assert.AreEqual(eSlope, s, 1e-5);
Assert.AreEqual(eIntercept, c, 1e-5);
Assert.IsFalse(double.IsNaN(y));
}
public void logarithm_learn()
{
#region doc_learn
// This is the same data from the example available at
// http://mathbits.com/MathBits/TISection/Statistics2/logarithmic.htm
// Declare your inputs and output data
double[] inputs = { 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11 };
double[] outputs = { 6, 9.5, 13, 15, 16.5, 17.5, 18.5, 19, 19.5, 19.7, 19.8 };
// Transform inputs to logarithms
double[] logx = Matrix.Log(inputs);
// Use Ordinary Least Squares to learn the regression
OrdinaryLeastSquares ols = new OrdinaryLeastSquares();
// Use OLS to learn the simple linear regression
SimpleLinearRegression lr = ols.Learn(logx, outputs);
// Compute predicted values for inputs
double[] predicted = lr.Transform(logx);
// Get an expression representing the learned regression model
// We just have to remember that 'x' will actually mean 'log(x)'
string result = lr.ToString("N4", CultureInfo.InvariantCulture);
// Result will be "y(x) = 6.1082x + 6.0993"
// The mean squared error between the expected and the predicted is
double error = new SquareLoss(outputs).Loss(predicted); // 0.261454
#endregion
Assert.AreEqual(0.26145460024250794, error, 1e-8);
Assert.AreEqual(6.1081800414945704, lr.Slope, 1e-8);
Assert.AreEqual(6.0993411396126653, lr.Intercept, 1e-8);
Assert.AreEqual("y(x) = 6.1082x + 6.0993", result);
}
public void new_api_test()
{
#region doc_learn
// Fix the random number generator
Accord.Math.Random.Generator.Seed = 0;
double[,] data = // This is the same data used in the RANSAC sample app
{
{ 1.0, 0.79 }, { 3, 2.18 }, { 5, 5.99 }, { 7.0, 7.65 },
{ 9.0, 9.55 }, { 11, 11.89 }, { 13, 13.73 }, { 15.0, 14.77 },
{ 17.0, 18.00 }, { 1.2, 1.45 }, { 1.5, 1.18 }, { 1.8, 1.92 },
{ 2.1, 1.47 }, { 2.4, 2.41 }, { 2.7, 2.35 }, { 3.0, 3.41 },
{ 3.3, 3.78 }, { 3.6, 3.21 }, { 3.9, 4.76 }, { 4.2, 5.03 },
{ 4.5, 4.19 }, { 4.8, 3.81 }, { 5.1, 6.07 }, { 5.4, 5.74 },
{ 5.7, 6.39 }, { 6, 6.11 }, { 6.3, 6.86 }, { 6.6, 6.35 },
{ 6.9, 7.9 }, { 7.2, 8.04 }, { 7.5, 8.48 }, { 7.8, 8.07 },
{ 8.1, 8.22 }, { 8.4, 8.41 }, { 8.7, 9.4 }, { 9, 8.8 },
{ 9.3, 8.44 }, { 9.6, 9.32 }, { 9.9, 9.18 }, { 10.2, 9.86 },
{ 10.5, 10.16 }, { 10.8, 10.28 }, { 11.1, 11.07 }, { 11.4, 11.66 },
{ 11.7, 11.13 }, { 12, 11.55 }, { 12.3, 12.62 }, { 12.6, 12.27 },
{ 12.9, 12.33 }, { 13.2, 12.37 }, { 13.5, 12.75 }, { 13.8, 14.44 },
{ 14.1, 14.71 }, { 14.4, 13.72 }, { 14.7, 14.54 }, { 15, 14.67 },
{ 15.3, 16.04 }, { 15.6, 15.21 }, { 1, 3.9 }, { 2, 11.5 },
{ 3.0, 13.0 }, { 4, 0.9 }, { 5, 5.5 }, { 6, 16.2 },
{ 7.0, 0.8 }, { 8, 9.4 }, { 9, 9.5 }, { 10, 17.5 },
{ 11.0, 6.3 }, { 12, 12.6 }, { 13, 1.5 }, { 14, 1.5 },
{ 2.0, 10 }, { 3, 9 }, { 15, 2 }, { 15.5, 1.2 },
};
// First, fit simple linear regression directly for comparison reasons.
double[] x = data.GetColumn(0); // Extract the independent variable
double[] y = data.GetColumn(1); // Extract the dependent variable
// Use Ordinary Least Squares to learn the regression
OrdinaryLeastSquares ols = new OrdinaryLeastSquares();
// Estimate a line passing through the (x, y) points
SimpleLinearRegression regression = ols.Learn(x, y);
// Now, compute the values predicted by the
// regression for the original input points
double[] commonOutput = regression.Transform(x);
// Now, fit simple linear regression using RANSAC
int maxTrials = 1000;
int minSamples = 20;
double probability = 0.950;
double errorThreshold = 1000;
// Create a RANSAC algorithm to fit a simple linear regression
var ransac = new RANSAC <SimpleLinearRegression>(minSamples)
{
Probability = probability,
Threshold = errorThreshold,
MaxEvaluations = maxTrials,
// Define a fitting function
Fitting = (int[] sample) =>
{
// Build a Simple Linear Regression model
return(new OrdinaryLeastSquares()
.Learn(x.Get(sample), y.Get(sample)));
},
// Define a inlier detector function
Distances = (SimpleLinearRegression r, double threshold) =>
{
var inliers = new List <int>();
for (int i = 0; i < x.Length; i++)
{
// Compute error for each point
double error = r.Transform(x[i]) - y[i];
// If the square error is low enough,
if (error * error < threshold)
{
inliers.Add(i); // the point is considered an inlier.
}
}
return(inliers.ToArray());
}
};
// Now that the RANSAC hyperparameters have been specified, we can
// compute another regression model using the RANSAC algorithm:
int[] inlierIndices;
SimpleLinearRegression robustRegression = ransac.Compute(data.Rows(), out inlierIndices);
// Compute the output of the model fitted by RANSAC
double[] ransacOutput = robustRegression.Transform(x);
#endregion
Assert.AreEqual(ransac.TrialsNeeded, 0);
Assert.AreEqual(ransac.TrialsPerformed, 1);
string a = inlierIndices.ToCSharp();
string b = ransacOutput.ToCSharp();
int[] expectedInliers = new int[] { 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75 };
double[] expectedOutput = new double[] { 1.96331236445045, 3.42042856976283, 4.87754477507521, 6.33466098038758, 7.79177718569996, 9.24889339101234, 10.7060095963247, 12.1631258016371, 13.6202420069495, 2.10902398498169, 2.32759141577855, 2.5461588465754, 2.76472627737226, 2.98329370816912, 3.20186113896597, 3.42042856976283, 3.63899600055969, 3.85756343135654, 4.0761308621534, 4.29469829295026, 4.51326572374711, 4.73183315454397, 4.95040058534082, 5.16896801613768, 5.38753544693454, 5.6061028777314, 5.82467030852825, 6.04323773932511, 6.26180517012196, 6.48037260091882, 6.69894003171568, 6.91750746251253, 7.13607489330939, 7.35464232410625, 7.5732097549031, 7.79177718569996, 8.01034461649682, 8.22891204729367, 8.44747947809053, 8.66604690888738, 8.88461433968424, 9.1031817704811, 9.32174920127795, 9.54031663207481, 9.75888406287167, 9.97745149366852, 10.1960189244654, 10.4145863552622, 10.6331537860591, 10.8517212168559, 11.0702886476528, 11.2888560784497, 11.5074235092465, 11.7259909400434, 11.9445583708402, 12.1631258016371, 12.3816932324339, 12.6002606632308, 1.96331236445045, 2.69187046710664, 3.42042856976283, 4.14898667241902, 4.87754477507521, 5.6061028777314, 6.33466098038758, 7.06321908304377, 7.79177718569996, 8.52033528835615, 9.24889339101234, 9.97745149366852, 10.7060095963247, 11.4345676989809, 2.69187046710664, 3.42042856976283, 12.1631258016371, 12.5274048529652 };
Assert.IsTrue(inlierIndices.IsEqual(expectedInliers));
Assert.IsTrue(ransacOutput.IsEqual(expectedOutput, 1e-10));
}
public void prediction_test()
{
// example data from http://www.real-statistics.com/regression/confidence-and-prediction-intervals/
double[][] input =
{
new double[] { 5, 80 },
new double[] { 23, 78 },
new double[] { 25, 60 },
new double[] { 48, 53 },
new double[] { 17, 85 },
new double[] { 8, 84 },
new double[] { 4, 73 },
new double[] { 26, 79 },
new double[] { 11, 81 },
new double[] { 19, 75 },
new double[] { 14, 68 },
new double[] { 35, 72 },
new double[] { 29, 58 },
new double[] { 4, 92 },
new double[] { 23, 65 },
};
double[] cig = input.GetColumn(0);
double[] exp = input.GetColumn(1);
// Use Ordinary Least Squares to learn the regression
OrdinaryLeastSquares ols = new OrdinaryLeastSquares();
// Use OLS to learn the simple linear regression
SimpleLinearRegression regression = ols.Learn(cig, exp);
Assert.AreEqual(1, regression.NumberOfInputs);
Assert.AreEqual(1, regression.NumberOfOutputs);
double x0 = 20;
double y0 = regression.Transform(x0);
Assert.AreEqual(y0, 73.1564, 1e-4);
double syx = regression.GetStandardError(cig, exp);
Assert.AreEqual(7.974682, syx, 1e-5);
double ssx = cig.Subtract(cig.Mean()).Pow(2).Sum();
Assert.AreEqual(2171.6, ssx, 1e-5);
double n = exp.Length;
double x0c = x0 - cig.Mean();
double var = 1 / n + (x0c * x0c) / ssx;
Assert.AreEqual(0.066832443052741455, var, 1e-10);
double expected = syx * Math.Sqrt(var);
double actual = regression.GetStandardError(x0, cig, exp);
Assert.AreEqual(2.061612, expected, 1e-5);
Assert.AreEqual(expected, actual, 1e-10);
DoubleRange ci = regression.GetConfidenceInterval(x0, cig, exp);
Assert.AreEqual(ci.Min, 68.702569616457751, 1e-5);
Assert.AreEqual(ci.Max, 77.610256563931543, 1e-5);
actual = regression.GetPredictionStandardError(x0, cig, exp);
Assert.AreEqual(8.2368569010499666, actual, 1e-10);
DoubleRange pi = regression.GetPredictionInterval(x0, cig, exp);
Assert.AreEqual(pi.Min, 55.361765613397054, 1e-5);
Assert.AreEqual(pi.Max, 90.95106056699224, 1e-5);
}
public RecognitionState Recognition(double[] inputs, double[] outputs)
{
RecognitionState ret = new RecognitionState();
if (inputs.Length > 5)
{
/*一次线性回归*/
SimpleLinearRegression regression = this.ols.Learn(inputs, outputs, null);
double k = regression.Slope;
const double TAN45 = 1.0;
const double TAN15 = 0.2679492;
double k1 = Math.Abs(k);
if (k1 > TAN45)
{
ret.Slope = SlopeState.Steep;
}
else if (k1 > TAN15)
{
ret.Slope = SlopeState.moderate;
}
else
{
ret.Slope = SlopeState.gentle;
}
/*二次线性回归*/
PolynomialRegression poly = this.pls.Learn(inputs, outputs, null);
double a = poly.Weights[0];
double b = poly.Weights[1];
if (k > 0 && a > 0)
{
ret.Shape = ShapeState.Rise;
}
else if (k > 0 && a < 0)
{
ret.Shape = ShapeState.FallAfterRise;
}
else if (k < 0 && a < 0)
{
ret.Shape = ShapeState.Fall;
}
else if (k < 0 && a > 0)
{
ret.Shape = ShapeState.RiseAfterFall;
}
double last = inputs[inputs.Length - 1];
double s = 2 * a * last + b;
double s1 = Math.Abs(s);
if (s1 > TAN45)
{
ret.Speed = SpeedState.Rapid;
}
else if (s1 > TAN15)
{
ret.Speed = SpeedState.Steady;
}
else
{
ret.Speed = SpeedState.Slow;
}
/*显示图形*/
if (this.showForm != null)
{
double[] outputs2 = regression.Transform(inputs);
double[] outputs3 = poly.Transform(inputs);
this.showForm.ShowGraph(inputs, outputs, inputs, outputs2, inputs, outputs3);
}
Console.WriteLine("k={0},a={1},b={2}", k, a, b);
}
return(ret);
}
private void btnCompute_Click(object sender, EventArgs e)
{
DataTable dataTable = dgvAnalysisSource.DataSource as DataTable;
if (dataTable == null)
{
return;
}
// Gather the available data
double[][] data = dataTable.ToArray();
// First, fit simple linear regression directly for comparison reasons.
double[] x = data.GetColumn(0); // Extract the independent variable
double[] y = data.GetColumn(1); // Extract the dependent variable
// Use Ordinary Least Squares to learn the regression
OrdinaryLeastSquares ols = new OrdinaryLeastSquares();
// Estimate a line passing through the (x, y) points
SimpleLinearRegression regression = ols.Learn(x, y);
// Now, compute the values predicted by the
// regression for the original input points
double[] commonOutput = regression.Transform(x);
// Now, fit simple linear regression using RANSAC
int maxTrials = (int)numMaxTrials.Value;
int minSamples = (int)numSamples.Value;
double probability = (double)numProbability.Value;
double errorThreshold = (double)numThreshold.Value;
// Create a RANSAC algorithm to fit a simple linear regression
var ransac = new RANSAC <SimpleLinearRegression>(minSamples)
{
Probability = probability,
Threshold = errorThreshold,
MaxEvaluations = maxTrials,
// Define a fitting function
Fitting = delegate(int[] sample)
{
// Retrieve the training data
double[] inputs = x.Get(sample);
double[] outputs = y.Get(sample);
// Build a Simple Linear Regression model
return(new OrdinaryLeastSquares().Learn(inputs, outputs));
},
// Define a check for degenerate samples
Degenerate = delegate(int[] sample)
{
// In this case, we will not be performing such checks.
return(false);
},
// Define a inlier detector function
Distances = delegate(SimpleLinearRegression r, double threshold)
{
List <int> inliers = new List <int>();
for (int i = 0; i < x.Length; i++)
{
// Compute error for each point
double error = r.Transform(x[i]) - y[i];
// If the squared error is below the given threshold,
// the point is considered to be an inlier.
if (error * error < threshold)
{
inliers.Add(i);
}
}
return(inliers.ToArray());
}
};
// Now that the RANSAC hyperparameters have been specified, we can
// compute another regression model using the RANSAC algorithm:
int[] inlierIndices;
SimpleLinearRegression robustRegression = ransac.Compute(data.Length, out inlierIndices);
if (robustRegression == null)
{
lbStatus.Text = "RANSAC failed. Please try again after adjusting its parameters.";
return; // the RANSAC algorithm did not find any inliers and no model was created
}
// Compute the output of the model fitted by RANSAC
double[] ransacOutput = robustRegression.Transform(x);
// Create scatter plot comparing the outputs from the standard
// linear regression and the RANSAC-fitted linear regression.
CreateScatterplot(graphInput, x, y, commonOutput, ransacOutput,
x.Get(inlierIndices), y.Get(inlierIndices));
lbStatus.Text = "Regression created! Please compare the RANSAC "
+ "regression (blue) with the simple regression (in red).";
}
public double GetTransform(double xPred)
{
return(regression.Transform(xPred));
}
public HttpResponseMessage ScrapeSheet(SheetDocumentModel model)
{
try
{
var watch = System.Diagnostics.Stopwatch.StartNew();
var filePath = AppDomain.CurrentDomain.BaseDirectory + "//RawData//" + model.FileName;
File.WriteAllBytes(filePath, Convert.FromBase64String(model.FileData));
var connectionString =
$"Provider=Microsoft.ACE.OLEDB.12.0;Data Source={filePath};Extended Properties=Excel 12.0;";
var adapter = new OleDbDataAdapter("SELECT * FROM [Sheet1$]", connectionString);
var ds = new DataSet();
adapter.Fill(ds, "DataTable");
var data = ds.Tables["DataTable"].AsEnumerable();
var scrapedResult = data.Where(w => w.Field <DateTime?>("DateTime") != null).Select(x =>
new SheetResult()
{
DateTime = x.Field <DateTime?>("DateTime"),
Value = x.Field <double?>("Value"),
Unit = x.Field <string>("Unit"),
}).ToList();
var regressionInput =
scrapedResult.Where(w => w.Value != null).Select(s => Convert.ToDouble(s.DateTime?.Hour)).ToArray();
var regressionOutput =
scrapedResult.Where(w => w.Value != null).Select(s => Convert.ToDouble(s.Value.Value)).ToArray();
// Use Ordinary Least Squares to learn the regression
OrdinaryLeastSquares ols = new OrdinaryLeastSquares();
// Use OLS to learn the simple linear regression
SimpleLinearRegression regression = ols.Learn(regressionInput, regressionOutput);
// Compute the output for a given input:
foreach (var v in scrapedResult)
{
if (v.Value != null)
{
continue;
}
v.Value = regression.Transform(Convert.ToDouble(v.DateTime?.Hour));
v.PredictedValue = true;
regressionInput =
scrapedResult.Where(w => w.Value != null).Select(s => Convert.ToDouble(s.DateTime?.Hour)).ToArray();
regressionOutput =
scrapedResult.Where(w => w.Value != null).Select(s => Convert.ToDouble(s.Value.Value)).ToArray();
regression = ols.Learn(regressionInput, regressionOutput);
}
var list = new JavaScriptSerializer().Serialize(scrapedResult);
var dataFormatted = JToken.Parse(list).ToString(Formatting.Indented);
_db.Data.Add(new DataEntity()
{
CreatedOn = DateTime.Now,
IdCollectionType = (int)CollectionTypeEnum.Sheet,
JsonObject = dataFormatted
});
_db.SaveChanges();
watch.Stop();
var elapsedMs = watch.ElapsedMilliseconds;
System.Diagnostics.Debug.WriteLine("Timp sheet scraper: " + elapsedMs);
return(Request.CreateResponse(HttpStatusCode.OK, scrapedResult));
}
catch (Exception ex)
{
return(Request.CreateResponse(HttpStatusCode.InternalServerError));
}
}
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)
};
}
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 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);
}
