protected override void EndProcessing()
{
var model = new MultipleLinearRegressionAnalysis();
double[][] inputs;
double[] outputs;
if (ParameterSetName == "XY")
{
inputs = Converter.ToDoubleJaggedArray(X);
outputs = Converter.ToDoubleArray(Y);
}
else
{
outputs = _data.GetColumn(OutputName).ToDoubleArray();
_data.RemoveColumn(OutputName);
inputs = _data.ToDoubleJaggedArray();
model.Inputs = _data.ColumnNames.ToArray <string>();
model.Output = OutputName;
}
double[] w = null;
if (Weights != null)
{
w = Converter.ToDoubleArray(Weights);
}
model.Learn(inputs, outputs, w);
WriteObject(model);
}
public MultipleLinearRegressionAnalysis Regress()
{
var analyzer = new MultipleLinearRegressionAnalysis(true)
{
Inputs = GetInputNames()
};
analyzer.Learn(InputData(), Filter.FilteredResult().Select(resultsFunc).ToArray());
return(analyzer);
}
public void TestLinearRegressionTest()
{
var inputs = new double[][] {
// age, smokes?
new double[] { 55, 0 },
new double[] { 28, 0 },
new double[] { 65, 1 },
new double[] { 46, 0 },
new double[] { 86, 1 },
new double[] { 56, 1 },
new double[] { 85, 0 },
new double[] { 33, 0 },
new double[] { 21, 1 },
new double[] { 42, 1 },
};
var output = new double[] {
// Whether each patient had lung cancer or not
0, 0, 0, 1, 1, 1, 0, 0, 0, 1
};
// Accord.NET
var analysis = new MultipleLinearRegressionAnalysis();
analysis.OrdinaryLeastSquares.UseIntercept = true;
analysis.OrdinaryLeastSquares.IsRobust = false;
analysis.Learn(inputs, output);
// Estimator
var x = DataMap.FromJagged(inputs, new string[] { "age", "smokes" });
var y = new DataMap();
y.Add("lung_cancer", output);
var est = new LinearRegressionEstimator();
est.Fit(x, y);
var model = est.Model;
Assert.Equal(analysis.Coefficients.Count, model.Coefficients.Count);
Assert.Equal(analysis.Coefficients[0].Value, model.Coefficients[0].Value);
Assert.Equal(analysis.Coefficients[1].Value, model.Coefficients[1].Value);
Assert.Equal(analysis.Coefficients[2].Value, model.Coefficients[2].Value);
}
private void Procesar()
{
try {
if (gvArchivo.Rows.Count == 0)
{
MessageBox.Show("Primero se debe realizar la carga de datos históricos", "Mensaje", MessageBoxButtons.OK, MessageBoxIcon.Warning);
return;
}
tabControl1.SelectedIndex = 1;
string[] independentNames = Helper.UtilFunction.getVariablesIndependientesDefecto();
string dependentName = Helper.UtilFunction.getVariableDependienteDefecto();
DataTable independent = sourceTable.DefaultView.ToTable(false, independentNames);
DataTable dependent = sourceTable.DefaultView.ToTable(false, dependentName);
inputs = independent.ToJagged();
outputs = dependent.Columns[dependentName].ToArray();
MultipleLinearRegressionAnalysis mlr = new MultipleLinearRegressionAnalysis(intercept: true)
{
Inputs = independentNames,
Output = dependentName
};
// Compute the Linear Regression Analysis
MultipleLinearRegression reg = mlr.Learn(inputs, outputs);
gvEstadistica.DataSource = mlr.Coefficients;
// var listaCoeficientes = mlr.Coefficients.ToList();
listaCoeficientes = mlr.Coefficients.ToList();
var coeficientes = mlr.CoefficientValues;
proceso = true;
MessageBox.Show("Se ejecuto el algoritmo correctamente", "Message", MessageBoxButtons.OK, MessageBoxIcon.Information);
} catch (Exception ex) {
MessageBox.Show(ex.Message, "Mensaje", MessageBoxButtons.OK, MessageBoxIcon.Error);
}
}
private void btnSampleRunAnalysis_Click(object sender, EventArgs e)
{
// Check requirements
if (sourceTable == null)
{
MessageBox.Show("A sample spreadsheet can be found in the " +
"Resources folder in the same directory as this application.",
"Please load some data before attempting an analysis");
return;
}
if (checkedListBox1.CheckedItems.Count == 0)
{
MessageBox.Show("Please select the dependent input variables to be used in the regression model.",
"Please choose at least one input variable");
}
// Finishes and save any pending changes to the given data
dgvAnalysisSource.EndEdit();
sourceTable.AcceptChanges();
// Gets the column of the dependent variable
String dependentName = (string)comboBox1.SelectedItem;
DataTable dependent = sourceTable.DefaultView.ToTable(false, dependentName);
// Gets the columns of the independent variables
List <string> names = new List <string>();
foreach (string name in checkedListBox1.CheckedItems)
{
names.Add(name);
}
String[] independentNames = names.ToArray();
DataTable independent = sourceTable.DefaultView.ToTable(false, independentNames);
// Creates the input and output matrices from the source data table
this.inputs = independent.ToJagged();
this.outputs = dependent.Columns[dependentName].ToArray();
// Creates the Simple Descriptive Analysis of the given source
var sda = new DescriptiveAnalysis()
{
ColumnNames = independentNames
}.Learn(inputs);
// TODO: Standardize the InputNames/OutputNames properties
// Populates statistics overview tab with analysis data
dgvDistributionMeasures.DataSource = sda.Measures;
// Creates the Logistic Regression Analysis of the given source
this.lra = new LogisticRegressionAnalysis()
{
Inputs = independentNames,
Output = dependentName
};
// Compute the Logistic Regression Analysis
LogisticRegression lr = lra.Learn(inputs, outputs);
// Populates coefficient overview with analysis data
dgvLogisticCoefficients.DataSource = lra.Coefficients;
// Populate details about the fitted model
tbChiSquare.Text = lra.ChiSquare.Statistic.ToString("N5");
tbPValue.Text = lra.ChiSquare.PValue.ToString("N5");
checkBox1.Checked = lra.ChiSquare.Significant;
tbDeviance.Text = lra.Deviance.ToString("N5");
tbLogLikelihood.Text = lra.LogLikelihood.ToString("N5");
// Create the Multiple Linear Regression Analysis of the given source
this.mlr = new MultipleLinearRegressionAnalysis(intercept: true)
{
Inputs = independentNames,
Output = dependentName
};
// Compute the Linear Regression Analysis
MultipleLinearRegression reg = mlr.Learn(inputs, outputs);
dgvLinearCoefficients.DataSource = mlr.Coefficients;
dgvRegressionAnova.DataSource = mlr.Table;
tbRSquared.Text = mlr.RSquared.ToString("N5");
tbRSquaredAdj.Text = mlr.RSquareAdjusted.ToString("N5");
tbChiPValue.Text = mlr.ChiSquareTest.PValue.ToString("N5");
tbFPValue.Text = mlr.FTest.PValue.ToString("N5");
tbZPValue.Text = mlr.ZTest.PValue.ToString("N5");
tbChiStatistic.Text = mlr.ChiSquareTest.Statistic.ToString("N5");
tbFStatistic.Text = mlr.FTest.Statistic.ToString("N5");
tbZStatistic.Text = mlr.ZTest.Statistic.ToString("N5");
cbChiSignificant.Checked = mlr.ChiSquareTest.Significant;
cbFSignificant.Checked = mlr.FTest.Significant;
cbZSignificant.Checked = mlr.ZTest.Significant;
// Populate projection source table
string[] cols = independentNames;
if (!independentNames.Contains(dependentName))
{
cols = independentNames.Concatenate(dependentName);
}
DataTable projSource = sourceTable.DefaultView.ToTable(false, cols);
dgvProjectionSource.DataSource = projSource;
}
public string ProcessLinearRegression(DataTable sourceTable, string _dependentName, string _independentName)
{
try
{
double[][] inputs;
double[] outputs;
LinearRegressionOutput output = new LinearRegressionOutput();
// Gets the column of the dependent variable
String dependentName = _dependentName;
DataTable dependent = sourceTable.DefaultView.ToTable(false, dependentName);
String[] independentNames = _independentName.Split(',');
//String[] independentNames = names.ToArray();
DataTable independent = sourceTable.DefaultView.ToTable(false, independentNames);
// Creates the input and output matrices from the source data table
inputs = independent.ToJagged();
outputs = dependent.Columns[dependentName].ToArray();
// Creates the Simple Descriptive Analysis of the given source
var sda = new DescriptiveAnalysis()
{
ColumnNames = independentNames
}.Learn(inputs);
// TODO: Standardize the InputNames/OutputNames properties
// Populates statistics overview tab with analysis data
DescriptiveMeasureCollection DMC = sda.Measures;
DistributionMesure dm = new DistributionMesure()
{
//Column = sda.Measures.GetEnumerator(x => x),
};
//DMC.s
//output.DistributionMeasuresDataSource = dm;
// Creates the Logistic Regression Analysis of the given source
this.lra = new LogisticRegressionAnalysis()
{
Inputs = independentNames,
Output = dependentName
};
// Create the Multiple Linear Regression Analysis of the given source
this.mlr = new MultipleLinearRegressionAnalysis(intercept: true)
{
Inputs = independentNames,
Output = dependentName
};
// Compute the Linear Regression Analysis
MultipleLinearRegression reg = mlr.Learn(inputs, outputs);
LinearRegressionCoefficientCollection LCC = mlr.Coefficients;
List <LinearCoefficients> lcs = new List <LinearCoefficients>();
foreach (var rc in LCC)
{
LinearCoefficients lc = new LinearCoefficients()
{
Name = rc.Name,
Value = rc.Value,
StandardError = rc.StandardError,
TStatistic = rc.TTest.Statistic,
P_ValueofT = rc.TTest.PValue,
FStatistic = rc.FTest.Statistic,
P_ValueofF = rc.FTest.PValue,
ConfidenceUpper = rc.ConfidenceUpper,
ConfidenceLower = rc.ConfidenceLower
};
lcs.Add(lc);
}
output.LinearCoefficientsDataSource = lcs;
//AnovaSourceCollection RDS = mlr.Table;
List <RegressionAnova> acs = new List <RegressionAnova>();
foreach (var RDS in mlr.Table)
{
RegressionAnova ra = new RegressionAnova()
{
Source = RDS.Source,
DegreesOfFreedom = RDS.DegreesOfFreedom,
SumOfSquares = RDS.SumOfSquares,
MeanSquares = RDS.MeanSquares,
FStatistic = RDS.Statistic,
PValueSignificance = (RDS.Significance == null) ? 0 : RDS.Significance.PValue
};
acs.Add(ra);
}
output.RegressionAnovaDataSource = acs;
output.RSquared = mlr.RSquared.ToString("N5");
output.RSquaredAdj = mlr.RSquareAdjusted.ToString("N5");
output.ChiPValue = mlr.ChiSquareTest.PValue.ToString("N5");
output.FPValue = mlr.FTest.PValue.ToString("N5");
output.ZPValue = mlr.ZTest.PValue.ToString("N5");
output.ChiStatistic = mlr.ChiSquareTest.Statistic.ToString("N5");
output.FStatistic = mlr.FTest.Statistic.ToString("N5");
output.ZStatistic = mlr.ZTest.Statistic.ToString("N5");
output.ChiSignificantChecked = mlr.ChiSquareTest.Significant;
output.FSignificantChecked = mlr.FTest.Significant;
output.ZSignificantChecked = mlr.ZTest.Significant;
// Populate projection source table
string[] cols = independentNames;
if (!independentNames.Contains(dependentName))
{
cols = independentNames.Concatenate(dependentName);
}
DataTable projSource = sourceTable.DefaultView.ToTable(false, cols);
//output.ProjectionSourceDataSource = projSource;
DataTable independentProj = projSource.DefaultView.ToTable(false, lra.Inputs);
DataTable dependentProj = projSource.DefaultView.ToTable(false, lra.Output);
double[][] input = independentProj.ToJagged();
double[] output1;
output1 = mlr.Regression.Transform(input);
DataTable result = projSource.Clone();
for (int i = 0; i < input.Length; i++)
{
DataRow row = result.NewRow();
for (int j = 0; j < lra.Inputs.Length; j++)
{
row[lra.Inputs[j]] = input[i][j];
}
row[lra.Output] = output1[i];
result.Rows.Add(row);
}
output.ProjectionResultDataSource = result;
var jsonResult = JsonConvert.SerializeObject(output, Formatting.Indented);
return(jsonResult);
}
catch (Exception ex)
{
throw new Exception("Error:" + ex);
}
}
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);
}
public void learn_Test()
{
#region doc_learn_part1
// Consider the following data. An experimenter would
// like to infer a relationship between two variables
// A and B and a corresponding outcome variable R.
double[][] example =
{
// A B R
new double[] { 6.41, 10.11, 26.1 },
new double[] { 6.61, 22.61, 33.8 },
new double[] { 8.45, 11.11, 52.7 },
new double[] { 1.22, 18.11, 16.2 },
new double[] { 7.42, 12.81, 87.3 },
new double[] { 4.42, 10.21, 12.5 },
new double[] { 8.61, 11.94, 77.5 },
new double[] { 1.73, 13.13, 12.1 },
new double[] { 7.47, 17.11, 86.5 },
new double[] { 6.11, 15.13, 62.8 },
new double[] { 1.42, 16.11, 17.5 },
};
// For this, we first extract the input and output
// pairs. The first two columns have values for the
// input variables, and the last for the output:
double[][] inputs = example.GetColumns(new[] { 0, 1 });
double[] output = example.GetColumn(2);
// We can create a new multiple linear analysis for the variables
var mlra = new MultipleLinearRegressionAnalysis(intercept: true);
// Compute the analysis and obtain the estimated regression
MultipleLinearRegression regression = mlra.Learn(inputs, output);
#endregion
// We can also show a summary ANOVA
// Accord.Controls.DataGridBox.Show(regression.Table);
#region doc_learn_part2
// And also extract other useful information, such
// as the linear coefficients' values and std errors:
double[] coef = mlra.CoefficientValues;
double[] stde = mlra.StandardErrors;
// Coefficients of performance, such as r²
double rsquared = mlra.RSquared; // 0.62879
// Hypothesis tests for the whole model
ZTest ztest = mlra.ZTest; // 0.99999
FTest ftest = mlra.FTest; // 0.01898
// and for individual coefficients
TTest ttest0 = mlra.Coefficients[0].TTest; // 0.00622
TTest ttest1 = mlra.Coefficients[1].TTest; // 0.53484
// and also extract confidence intervals
DoubleRange ci = mlra.Coefficients[0].Confidence; // [3.2616, 14.2193]
// We can use the analysis to predict an output for a sample
double y = mlra.Regression.Transform(new double[] { 10, 15 });
// We can also obtain confidence intervals for the prediction:
DoubleRange pci = mlra.GetConfidenceInterval(new double[] { 10, 15 });
// and also prediction intervals for the same prediction:
DoubleRange ppi = mlra.GetPredictionInterval(new double[] { 10, 15 });
#endregion
Assert.AreEqual(3, coef.Length);
Assert.AreEqual(8.7405051051757816, coef[0]);
Assert.AreEqual(1.1198079243314365, coef[1], 1e-10);
Assert.AreEqual(-19.604474518407862, coef[2], 1e-10);
Assert.IsFalse(coef.HasNaN());
Assert.AreEqual(2.375916659234715, stde[0], 1e-10);
Assert.AreEqual(1.7268508921418664, stde[1], 1e-10);
Assert.AreEqual(30.989640986710953, stde[2], 1e-10);
Assert.IsFalse(coef.HasNaN());
Assert.AreEqual(0.62879941171298936, rsquared, 1e-10);
Assert.AreEqual(0.99999999999999822, ztest.PValue, 1e-10);
Assert.AreEqual(0.018986050133298293, ftest.PValue, 1e-10);
Assert.AreEqual(0.0062299844256985537, ttest0.PValue, 1e-10);
Assert.AreEqual(0.53484850318449118, ttest1.PValue, 1e-14);
Assert.IsFalse(Double.IsNaN(ttest1.PValue));
Assert.AreEqual(3.2616314640800566, ci.Min, 1e-10);
Assert.AreEqual(14.219378746271506, ci.Max, 1e-10);
double[][] im = mlra.InformationMatrix;
double mse = regression.GetStandardError(inputs, output);
DoubleRange epci = regression.GetConfidenceInterval(new double[] { 10, 15 }, mse, inputs.Length, im);
Assert.AreEqual(epci.Min, pci.Min, 1e-10);
Assert.AreEqual(epci.Max, pci.Max, 1e-10);
Assert.AreEqual(55.27840511658215, pci.Min, 1e-10);
Assert.AreEqual(113.91698568006086, pci.Max, 1e-10);
Assert.AreEqual(28.783074454641557, ppi.Min, 1e-10);
Assert.AreEqual(140.41231634200145, ppi.Max, 1e-10);
}
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);
}
public void gh_937()
{
#region doc_learn_database
// Note: this example uses a System.Data.DataTable to represent input data,
// but note that this is not required. The data could have been represented
// as jagged double matrices (double[][]) directly.
// If you have to handle heterogeneus data in your application, such as user records
// in a database, this data is best represented within the framework using a .NET's
// DataTable object. In order to try to learn a classification or regression model
// using this datatable, first we will need to convert the table into a representation
// that the machine learning model can understand. Such representation is quite often,
// a matrix of doubles (double[][]).
var data = new DataTable("Customer Revenue Example");
data.Columns.Add("Day", "CustomerId", "Time (hour)", "Weather", "Revenue");
data.Rows.Add("D1", 0, 8, "Sunny", 101.2);
data.Rows.Add("D2", 1, 10, "Sunny", 24.1);
data.Rows.Add("D3", 2, 10, "Rain", 107);
data.Rows.Add("D4", 3, 16, "Rain", 223);
data.Rows.Add("D5", 4, 15, "Rain", 1);
data.Rows.Add("D6", 5, 20, "Rain", 42);
data.Rows.Add("D7", 6, 12, "Cloudy", 123);
data.Rows.Add("D8", 7, 12, "Sunny", 64);
// One way to perform this conversion is by using a Codification filter. The Codification
// filter can take care of converting variables that actually denote symbols (i.e. the
// weather in the example above) into representations that make more sense given the assumption
// of a real vector-based classifier.
// Create a codification codebook
var codebook = new Codification()
{
{ "Weather", CodificationVariable.Categorical },
{ "Time (hour)", CodificationVariable.Continuous },
{ "Revenue", CodificationVariable.Continuous },
};
// Learn from the data
codebook.Learn(data);
// Now, we will use the codebook to transform the DataTable into double[][] vectors. Due
// the way the conversion works, we can end up with more columns in your output vectors
// than the ones started with. If you would like more details about what those columns
// represent, you can pass then as 'out' parameters in the methods that follow below.
string[] inputNames; // (note: if you do not want to run this example yourself, you
string outputName; // can see below the new variable names that will be generated)
// Now, we can translate our training data into integer symbols using our codebook:
double[][] inputs = codebook.Apply(data, "Weather", "Time (hour)").ToJagged(out inputNames);
double[] outputs = codebook.Apply(data, "Revenue").ToVector(out outputName);
// (note: the Apply method transform a DataTable into another DataTable containing the codified
// variables. The ToJagged and ToVector methods are then used to transform those tables into
// double[][] matrices and double[] vectors, respectively.
// If we would like to learn a linear regression model for this data, there are two possible
// ways depending on which aspect of the linear regression we are interested the most. If we
// are interested in interpreting the linear regression, performing hypothesis tests with the
// coefficients and performing an actual _linear regression analysis_, then we can use the
// MultipleLinearRegressionAnalysis class for this. If however we are only interested in using
// the learned model directly to predict new values for the dataset, then we could be using the
// MultipleLinearRegression and OrdinaryLeastSquares classes directly instead.
// This example deals with the former case. For the later, please see the documentation page
// for the MultipleLinearRegression class.
// We can create a new multiple linear analysis for the variables
var mlra = new MultipleLinearRegressionAnalysis(intercept: true)
{
// We can also inform the names of the new variables that have been created by the
// codification filter. Those can help in the visualizing the analysis once it is
// data-bound to a visual control such a Windows.Forms.DataGridView or WPF DataGrid:
Inputs = inputNames, // will be { "Weather: Sunny", "Weather: Rain, "Weather: Cloudy", "Time (hours)" }
Output = outputName // will be "Revenue"
};
// To overcome linear dependency errors
mlra.OrdinaryLeastSquares.IsRobust = true;
// Compute the analysis and obtain the estimated regression
MultipleLinearRegression regression = mlra.Learn(inputs, outputs);
// And then predict the label using
double predicted = mlra.Transform(inputs[0]); // result will be ~72.3
// Because we opted for doing a MultipleLinearRegressionAnalysis instead of a simple
// linear regression, we will have further information about the regression available:
int inputCount = mlra.NumberOfInputs; // should be 4
int outputCount = mlra.NumberOfOutputs; // should be 1
double r2 = mlra.RSquared; // should be 0.12801838425195311
AnovaSourceCollection a = mlra.Table; // ANOVA table (bind to a visual control for quick inspection)
double[][] h = mlra.InformationMatrix; // should contain Fisher's information matrix for the problem
ZTest z = mlra.ZTest; // should be 0 (p=0.999, non-significant)
#endregion
Assert.AreEqual(72.279574468085144d, predicted, 1e-8);
Assert.AreEqual(4, inputCount, 1e-8);
Assert.AreEqual(1, outputCount, 1e-8);
Assert.AreEqual(0.12801838425195311, r2, 1e-8);
Assert.AreEqual(0.11010987669344097, a[0].Statistic, 1e-8);
string str = h.ToCSharp();
double[][] expectedH = new double[][]
{
new double[] { 0.442293243337911, -0.069833718526197, -0.228692384542512, -0.0141758263063635, 0.143767140269202 },
new double[] { -0.0698337185261971, 0.717811616891116, -0.112258662892007, -0.0655549422852099, 0.535719235472913 },
new double[] { -0.228692384542512, -0.112258662892007, 0.717434922237013, -0.0232803210243207, 0.376483874802496 },
new double[] { -0.0141758263063635, -0.0655549422852099, -0.0232803210243207, 0.0370082984668314, -0.103011089615894 },
new double[] { 0.143767140269202, 0.535719235472913, 0.376483874802496, -0.103011089615894, 1.05597025054461 }
};
Assert.IsTrue(expectedH.IsEqual(h, 1e-8));
Assert.AreEqual(0, z.Statistic, 1e-8);
Assert.AreEqual(1, z.PValue, 1e-8);
}
public void learn_Test()
{
#region doc_learn_part1
// Consider the following data. An experimenter would
// like to infer a relationship between two variables
// A and B and a corresponding outcome variable R.
double[][] example =
{
// A B R
new double[] { 6.41, 10.11, 26.1 },
new double[] { 6.61, 22.61, 33.8 },
new double[] { 8.45, 11.11, 52.7 },
new double[] { 1.22, 18.11, 16.2 },
new double[] { 7.42, 12.81, 87.3 },
new double[] { 4.42, 10.21, 12.5 },
new double[] { 8.61, 11.94, 77.5 },
new double[] { 1.73, 13.13, 12.1 },
new double[] { 7.47, 17.11, 86.5 },
new double[] { 6.11, 15.13, 62.8 },
new double[] { 1.42, 16.11, 17.5 },
};
// For this, we first extract the input and output
// pairs. The first two columns have values for the
// input variables, and the last for the output:
double[][] inputs = example.GetColumns(new[] { 0, 1 });
double[] output = example.GetColumn(2);
// We can create a new multiple linear analysis for the variables
var mlra = new MultipleLinearRegressionAnalysis(intercept: true);
// Compute the analysis and obtain the estimated regression
MultipleLinearRegression regression = mlra.Learn(inputs, output);
#endregion
// We can also show a summary ANOVA
// Accord.Controls.DataGridBox.Show(regression.Table);
#region doc_learn_part2
// And also extract other useful information, such
// as the linear coefficients' values and std errors:
double[] coef = mlra.CoefficientValues;
double[] stde = mlra.StandardErrors;
// Coefficients of performance, such as r²
double rsquared = mlra.RSquared; // 0.62879
// Hypothesis tests for the whole model
ZTest ztest = mlra.ZTest; // 0.99999
FTest ftest = mlra.FTest; // 0.01898
// and for individual coefficients
TTest ttest0 = mlra.Coefficients[0].TTest; // 0.00622
TTest ttest1 = mlra.Coefficients[1].TTest; // 0.53484
// and also extract confidence intervals
DoubleRange ci = mlra.Coefficients[0].Confidence; // [3.2616, 14.2193]
// We can use the analysis to predict an output for a sample
double y = mlra.Regression.Transform(new double[] { 10, 15 });
// We can also obtain confidence intervals for the prediction:
DoubleRange pci = mlra.GetConfidenceInterval(new double[] { 10, 15 });
// and also prediction intervals for the same prediction:
DoubleRange ppi = mlra.GetPredictionInterval(new double[] { 10, 15 });
#endregion
Assert.AreEqual(3, coef.Length);
Assert.AreEqual(8.7405051051757816, coef[0]);
Assert.AreEqual(1.1198079243314365, coef[1], 1e-10);
Assert.AreEqual(-19.604474518407862, coef[2], 1e-10);
Assert.IsFalse(coef.HasNaN());
Assert.AreEqual(2.375916659234715, stde[0], 1e-10);
Assert.AreEqual(1.7268508921418664, stde[1], 1e-10);
Assert.AreEqual(30.989640986710953, stde[2], 1e-10);
Assert.IsFalse(coef.HasNaN());
Assert.AreEqual(0.62879941171298936, rsquared, 1e-10);
Assert.AreEqual(0.99999999999999822, ztest.PValue, 1e-10);
Assert.AreEqual(0.018986050133298293, ftest.PValue, 1e-10);
Assert.AreEqual(0.0062299844256985537, ttest0.PValue, 1e-10);
Assert.AreEqual(0.53484850318449118, ttest1.PValue, 1e-14);
Assert.IsFalse(Double.IsNaN(ttest1.PValue));
Assert.AreEqual(3.2616314640800566, ci.Min, 1e-10);
Assert.AreEqual(14.219378746271506, ci.Max, 1e-10);
double[][] im = mlra.InformationMatrix;
double mse = regression.GetStandardError(inputs, output);
DoubleRange epci = regression.GetConfidenceInterval(new double[] { 10, 15 }, mse, inputs.Length, im);
Assert.AreEqual(epci.Min, pci.Min, 1e-10);
Assert.AreEqual(epci.Max, pci.Max, 1e-10);
Assert.AreEqual(55.27840511658215, pci.Min, 1e-10);
Assert.AreEqual(113.91698568006086, pci.Max, 1e-10);
Assert.AreEqual(28.783074454641557, ppi.Min, 1e-10);
Assert.AreEqual(140.41231634200145, ppi.Max, 1e-10);
}
