private void Prognoz()
{
var transpMatrix = MatrixFunction.TransposeMatrix(dataMatrix);
int parametersCount = dataMatrix[0].Length, measuresCount = dataMatrix.Length;
double[] resultParameter = transpMatrix[resultParameterNumber];
List <double>[] withoutResultParameter = new List <double> [measuresCount];
for (int i = 0; i < measuresCount; i++)
{
withoutResultParameter[i] = new List <double>();
withoutResultParameter[i].Add(1);
for (int j = 0; j < parametersCount; ++j)
{
if (j != resultParameterNumber)
{
withoutResultParameter[i].Add(dataMatrix[i][j]);
}
}
}
var xMatrix = withoutResultParameter.Select(o => o.ToArray()).ToArray();
var transpWithoutMatrix = MatrixFunction.TransposeMatrix(xMatrix);
RegressionAnalysis ra = new RegressionAnalysis(dataMatrix, resultParameterNumber);
var result = ra.RegressionCoefficients;
double s = ra.ResidualDispersion;
double t = 4.587;
double[] a = new double[2];
a[0] = MatrixFunction.ScalarProductOfVectors(x0, result) - t * s * Math.Sqrt(MatrixFunction.ScalarProductOfVectors(MatrixFunction.TransposeMatrixVectorProduct(x0, MatrixFunction.InverseMatrix(MatrixFunction.MultiplicateMatrix(transpWithoutMatrix, xMatrix))), x0));
a[1] = MatrixFunction.ScalarProductOfVectors(x0, result) + t * s * Math.Sqrt(MatrixFunction.ScalarProductOfVectors(MatrixFunction.TransposeMatrixVectorProduct(x0, MatrixFunction.InverseMatrix(MatrixFunction.MultiplicateMatrix(transpWithoutMatrix, xMatrix))), x0));
Value = a;
}
private void CalcRegressionCoefficients()
{
var transpMatrix = MatrixFunction.TransposeMatrix(dataMatrix);
int parametersCount = dataMatrix[0].Length, measuresCount = dataMatrix.Length;
double[] resultParameter = transpMatrix[resultParameterNumber];
List <double>[] withoutResultParameter = new List <double> [measuresCount];
for (int i = 0; i < measuresCount; i++)
{
withoutResultParameter[i] = new List <double>();
withoutResultParameter[i].Add(1);
for (int j = 0; j < parametersCount; ++j)
{
if (j != resultParameterNumber)
{
withoutResultParameter[i].Add(dataMatrix[i][j]);
}
}
}
xMatrix = withoutResultParameter.Select(o => o.ToArray()).ToArray();
var transpWithoutMatrix = MatrixFunction.TransposeMatrix(xMatrix);
RegressionCoefficients = MatrixFunction.MatrixVectorMultiplication(MatrixFunction.MultiplicateMatrix(MatrixFunction.InverseMatrix(MatrixFunction.MultiplicateMatrix(transpWithoutMatrix, xMatrix)), transpWithoutMatrix), resultParameter);
RegressionCoefficients = RegressionCoefficients.Take(parametersCount).ToArray();
}
private void CalcDispersion()
{
var yWithWave = MatrixFunction.MatrixVectorMultiplication(xMatrix, RegressionCoefficients);
var averageY = yWithWave.Average();
var devivationsYSum = yWithWave.Select(y => Math.Pow(y - averageY, 2)).Sum();
ExplicatedDispersion = devivationsYSum / parametersCount;
ResidualDispersion = devivationsYSum / (measuresCount - parametersCount - 1);
}
private void CalcElasticityCoefficients()
{
double[] result = new double[dataMatrix.Length];
var tm = MatrixFunction.TransposeMatrix(dataMatrix);
double y = tm[tm.Length - 1].Average();
result[tm.Length - 1] = RegressionCoefficients[tm.Length - 1];
for (int i = 0; i < tm.Length - 1; ++i)
{
result[i] = RegressionCoefficients[i] * tm[i].Average() / y;
}
ElasticityCoefficients = result;
}
private void CalcRegressionCoefficientsSignificance()
{
double[] sb = new double[parametersCount];
var temperory = MatrixFunction.InverseMatrix(MatrixFunction.MultiplicateMatrix(MatrixFunction.TransposeMatrix(xMatrix), xMatrix));
for (int j = 0; j < parametersCount; j++)
{
sb[j] = ResidualDispersion * temperory[j][j];
}
RegressionCoefficientsSignificance = new double[parametersCount];
for (int i = 0; i < parametersCount; ++i)
{
RegressionCoefficientsSignificance[i] = RegressionCoefficients[i] / sb[i];
}
}
private void CalcConfidenceIntervalsOfCoefficients()
{
double[] sb = new double[parametersCount];
var temperory = MatrixFunction.InverseMatrix(MatrixFunction.MultiplicateMatrix(MatrixFunction.TransposeMatrix(xMatrix), xMatrix));
for (int j = 0; j < parametersCount; j++)
{
sb[j] = ResidualDispersion * temperory[j][j];
}
double t = 4.587;
ConfidenceIntervalsOfCoefficients = new Tuple <double, double> [parametersCount];
for (int i = 0; i < parametersCount; ++i)
{
ConfidenceIntervalsOfCoefficients[i] = new Tuple <double, double>(RegressionCoefficients[i] - t * sb[i], RegressionCoefficients[i] + t * sb[i]);
}
}
private void CalcPartialCorrelationsMatrix()
{
int n = PairCorrelationsMatrix.Length;
PartialCorrelationsMatrix = new double[n][];
for (int i = 0; i < n; i++)
{
PartialCorrelationsMatrix[i] = new double[n];
}
for (int i = 0; i < n; i++)
{
for (int j = 0; j < n; j++)
{
PartialCorrelationsMatrix[i][j] = MatrixFunction.AlgebraicComplement(PairCorrelationsMatrix, i, j)
/ Math.Sqrt(MatrixFunction.AlgebraicComplement(PairCorrelationsMatrix, i, i)
* MatrixFunction.AlgebraicComplement(PairCorrelationsMatrix, j, j));
}
}
}
private void CalcPairCorrelationsMatrix()
{
var squaredDevivations = CalcSquaredDevivatiosForAllCofficients(dataMatrix);
var devivations = CalcDevivatiosForAllCofficients(dataMatrix);
int coefCount = dataMatrix.Length;
int measurementsCount = dataMatrix[0].Length;
PairCorrelationsMatrix = new double[coefCount][];
for (int k = 0; k < coefCount; k++)
{
PairCorrelationsMatrix[k] = new double[coefCount];
}
for (int i = 0; i < coefCount; ++i)
{
for (int j = i; j < coefCount; ++j)
{
if (i == j)
{
PairCorrelationsMatrix[i][j] = 1;
}
else
{
var xDevivations = devivations[i];
var yDevivations = devivations[j];
var xSquaredDevivation = squaredDevivations[i];
var ySquaredDevivation = squaredDevivations[j];
PairCorrelationsMatrix[i][j] = (MatrixFunction.ScalarProductOfVectors(xDevivations, yDevivations))
/ (xSquaredDevivation * ySquaredDevivation * measurementsCount);
PairCorrelationsMatrix[j][i] = PairCorrelationsMatrix[i][j];
}
}
}
}
private void CalcSelectiveMultipleCoefficient(int resultParameterNumber)
{
SelectiveMultipleCoefficient = Math.Sqrt(1 - MatrixFunction.MatrixDeterminant(PairCorrelationsMatrix)
/ MatrixFunction.AlgebraicComplement(PairCorrelationsMatrix, resultParameterNumber, resultParameterNumber));
}