static void Main(string[] args)
{
//
ScalarFunction x = "x";
ScalarFunction y = "y";
ScalarFunction a = "a";
ScalarFunction b = "b";
ScalarFunction c = "c";
ScalarFunction d = "d";
ScalarFunction i = "i";
ScalarFunction j = "j";
MatrixFunction m = NewMatrix(new Matrix <ScalarFunction>(
new[] {
new ScalarFunction[] { a, b },
new ScalarFunction[] { c, d }
}));
VectorFunction v = NewColumnVector(new ScalarFunction[] { x, y });
VectorFunction r = NewColumnVector(new ScalarFunction[] { i, j });
BinaryFunction k = MatrixMethods.RotationMethod(m, v, r);
k = k.Calculate();
//Console.WriteLine(((a * i + c * j) / (a * a + c * c)).Calculate());
//var sqrt = op_Exponentiation(op_Exponentiation(a, 2) + op_Exponentiation(c, 2), 0.5);
//var d_y = ((a * j - c * i) / (a * d - c * b)) * (a * b + c * d);
//var gg = ((x * (a * a + c * c)) + y) / sqrt == (a * i + c * j) / sqrt;
//var gg2 = NewScalarFind(x, gg).Calculate();
//var gg3 = gg2.Calculate(NewScalarFind(y, y == d_y));
Console.ReadKey();
}
/// <summary>
/// Apply smooth filter to bitmap
/// </summary>
public void ApplyFilter(Bitmap b, int nWeight)
{
ConvMatrix m = new ConvMatrix();
m.SetAll(1);
m.Pixel = nWeight;
m.Factor = nWeight + 8;
b = MatrixFunction.Conv3X3(b, m);
}
/// <summary>
/// Applying Emboss filter to the bitmap
/// </summary>
public void ApplyFilter(Bitmap b)
{
ConvMatrix m = new ConvMatrix();
m.SetAll(-1);
m.TopMid = m.MidLeft = m.MidRight = m.BottomMid = 0;
m.Pixel = 4;
m.Offset = 127;
b = MatrixFunction.Conv3X3(b, m);
}
public void RotateMethod()
{
MatrixFunction m = NewMatrix(new Matrix <ScalarFunction>(
new[] {
new ScalarFunction[] { "a", "b" },
new ScalarFunction[] { "c", "d" }
}));
VectorFunction v = NewColumnVector(new ScalarFunction[] { "x", "y" });
VectorFunction r = NewColumnVector(new ScalarFunction[] { "i", "j" });
BinaryFunction b = MatrixMethods.RotationMethod(m, v, r);
}
/// <summary>
/// Apply sphere effect to bitmap
/// </summary>
public void ApplyFilter(Bitmap b)
{
int nWidth = b.Width;
int nHeight = b.Height;
Point[,] fp = new Point[nWidth, nHeight];
Point mid = new Point();
mid.X = nWidth / 2;
mid.Y = nHeight / 2;
double theta, radius;
double newX, newY;
for (int x = 0; x < nWidth; ++x)
{
for (int y = 0; y < nHeight; ++y)
{
int trueX = x - mid.X;
int trueY = y - mid.Y;
theta = Math.Atan2((trueY), (trueX));
radius = Math.Sqrt(trueX * trueX + trueY * trueY);
double newRadius = radius * radius / (Math.Max(mid.X, mid.Y));
newX = mid.X + (newRadius * Math.Cos(theta));
if (newX > 0 && newX < nWidth)
{
fp[x, y].X = (int)newX;
}
else
{
fp[x, y].X = fp[x, y].Y = (int)0.0;
}
newY = mid.Y + (newRadius * Math.Sin(theta));
if (newY > 0 && newY < nHeight && newX > 0 && newX < nWidth)
{
fp[x, y].Y = (int)newY;
}
else
{
fp[x, y].X = fp[x, y].Y = (int)0.0;
}
}
}
MatrixFunction.OffsetFilterAbs(b, fp);
}
/// <summary>
/// Set transperenty to bitmap
/// </summary>
static public Bitmap ApplyFilter(Bitmap sourceImage, byte alphaComponent = 100)
{
ColorMatrix colorMatrix = new ColorMatrix(new float[][]
{
new float[] { 1, 0, 0, 0, 0 },
new float[] { 0, 1, 0, 0, 0 },
new float[] { 0, 0, 1, 0, 0 },
new float[] { 0, 0, 0, 0.3f, 0 },
new float[] { 0, 0, 0, 0, 1 }
});
return(MatrixFunction.ApplyColorMatrix(sourceImage, colorMatrix));
}
public DemoStats(double[][] dataMatrix)
{
var transpMatrix = MatrixFunction.TransposeMatrix(dataMatrix);
Correlations = new CorrelationsAnalysis(transpMatrix, dataMatrix[0].Length - 1);
Regressions = new RegressionAnalysis(dataMatrix, dataMatrix[0].Length - 1);
Descriptive = new DescriptiveStatistics[transpMatrix.Length];
for (int i = 0; i < Descriptive.Length; ++i)
{
Descriptive[i] = new DescriptiveStatistics(transpMatrix[i]);
}
Forecast = new Forecast(dataMatrix, transpMatrix.Length - 1);
}
/// <summary>
/// Apply water effect to bitmap
/// </summary>
public void ApplyFilter(Bitmap b, short nWave)
{
int nWidth = b.Width;
int nHeight = b.Height;
Point[,] pt = new Point[nWidth, nHeight];
Point mid = new Point();
mid.X = nWidth / 2;
mid.Y = nHeight / 2;
double newX, newY;
double xo, yo;
for (int x = 0; x < nWidth; ++x)
{
for (int y = 0; y < nHeight; ++y)
{
xo = ((double)nWave * Math.Sin(2.0 * 3.1415 * (float)y / 128.0));
yo = ((double)nWave * Math.Cos(2.0 * 3.1415 * (float)x / 128.0));
newX = (x + xo);
newY = (y + yo);
if (newX > 0 && newX < nWidth)
{
pt[x, y].X = (int)newX;
}
else
{
pt[x, y].X = 0;
}
if (newY > 0 && newY < nHeight)
{
pt[x, y].Y = (int)newY;
}
else
{
pt[x, y].Y = 0;
}
}
}
MatrixFunction.OffsetFilterAbs(b, pt);
}
/// <summary>
/// Apply swiirl effect to bitmap
/// </summary>
public void ApplyFilter(Bitmap b, double fDegree)
{
int nWidth = b.Width;
int nHeight = b.Height;
Point[,] pt = new Point[nWidth, nHeight];
Point mid = new Point();
mid.X = nWidth / 2;
mid.Y = nHeight / 2;
double theta, radius;
double newX, newY;
for (int x = 0; x < nWidth; ++x)
{
for (int y = 0; y < nHeight; ++y)
{
int trueX = x - mid.X;
int trueY = y - mid.Y;
theta = Math.Atan2((trueY), (trueX));
radius = Math.Sqrt(trueX * trueX + trueY * trueY);
newX = mid.X + (radius * Math.Cos(theta + fDegree * radius));
if (newX > 0 && newX < nWidth)
{
pt[x, y].X = (int)newX;
}
newY = mid.Y + (radius * Math.Sin(theta + fDegree * radius));
if (newY > 0 && newY < nHeight)
{
pt[x, y].Y = (int)newY;
}
}
}
MatrixFunction.OffsetFilterAbs(b, pt);
}
private static void Main(string[] args)
{
int n = 629, m = 10;
var matrix = ReadDataMatrix(@"D:\Универ\3 курс\2 семестр\Тер вер и мат стат\movie_metadata_1.csv");
var transpMatrix = MatrixFunction.TransposeMatrix(matrix);
Dictionary <double, double> laplasMatrix = ReadLaplasMatrix("2.txt");
Console.WriteLine("Описательная статистика");
for (int k = 0; k < m; ++k)
{
PearsonConsentCriterion pcc = new PearsonConsentCriterion(transpMatrix[k].Take(n).ToArray(), laplasMatrix);
KolmogorovConsentCriterion kcc = new KolmogorovConsentCriterion(transpMatrix[k]);
DescriptiveStatistics ds = new DescriptiveStatistics(transpMatrix[k]);
Console.WriteLine();
Console.WriteLine("Среднее арифметическое = {0}", ds.ArithmeticalMean);
Console.WriteLine("Мода = {0}", ds.Mode);
Console.WriteLine("Медиана = {0}", ds.Median);
Console.WriteLine("Дисперсия = {0}", ds.Dispersion);
Console.WriteLine("Асимметрия = {0}", ds.Assimmetry);
Console.WriteLine("Эксцесс = {0}", ds.Excess);
Console.WriteLine("Стандартное отклонение = {0}", ds.StandardDeviation);
Console.WriteLine("Коэффициент вариации = {0}", ds.VariationCoefficient);
Console.WriteLine("Размах вариации = {0}", ds.VariationRange);
Console.WriteLine("Среднее значение x: " + pcc.AverageValueX);
Console.WriteLine("Среднее квадратичное отклонение: " + pcc.MeanSquareDeviation);
Console.WriteLine("Критерий Пирсона: " + pcc.PearsonCriterionValue);
Console.WriteLine("Табличный критерий Пирсона = " + 63.6567);
Console.WriteLine("Критерий Колмогорова = " + kcc.KolmogorovCriterionValue);
Console.WriteLine("Табличный критерий Колмогорова = 1,950\n\n");
Console.WriteLine();
}
Console.WriteLine("Нажмите, чтобы увидеть далее...");
Console.ReadLine();
Console.WriteLine("Корреляционный анализ");
CorrelationsAnalysis correlationsAnalyses = new CorrelationsAnalysis(transpMatrix, m - 1);
Console.WriteLine("Матрица корреляции");
foreach (double[] correlationCoef in correlationsAnalyses.PairCorrelationsMatrix)
{
for (int j = 0; j < correlationCoef.Length; j++)
{
Console.Write("{0} ", Math.Round(correlationCoef[j], 5));
}
Console.WriteLine();
}
Console.WriteLine(); Console.WriteLine("Коэффициенты значимости для матрицы парных корреляций: ");
foreach (double[] t in correlationsAnalyses.MatrixSignificanceFactors)
{
for (int j = 0; j < t.Length; j++)
{
Console.Write("{0} ", Math.Round(t[j], 5));
}
Console.WriteLine();
}
Console.WriteLine();
Console.WriteLine("Коэффициенты значимости:");
foreach (double t in correlationsAnalyses.ParametersSignificanceFactors)
{
Console.WriteLine(t);
}
Console.WriteLine();
var multipleCoefficientY = correlationsAnalyses.SelectiveMultipleCoefficient;
var determinationCoefficientY = correlationsAnalyses.DeterminationCoefficient;
Console.WriteLine("Выборочный множественный коэффициент Y: " + multipleCoefficientY);
Console.WriteLine("Коэффициент детерминации: " + determinationCoefficientY);
if (determinationCoefficientY > 0.8)
{
Console.WriteLine("Модель адекватна");
}
Console.WriteLine();
Console.WriteLine("Матрица частной корреляции");
foreach (double[] t in correlationsAnalyses.PartialCorrelationsMatrix)
{
for (int j = 0; j < correlationsAnalyses.PartialCorrelationsMatrix.Length; j++)
{
Console.Write("{0,6} ", Math.Round(t[j], 4));
}
Console.WriteLine();
}
Console.WriteLine();
Console.WriteLine("Нажмите, чтобы увидеть далее...");
Console.ReadLine();
Console.WriteLine("Регрессионный анализ");
RegressionAnalysis ra = new RegressionAnalysis(matrix, m - 1);
Console.WriteLine("Коэффициенты регрессии:");
for (int i = 0; i < ra.RegressionCoefficients.Length; i++)
{
if (i == 0)
{
Console.WriteLine("a = {0}", ra.RegressionCoefficients[i]);
}
else
{
Console.WriteLine("b{0} = {1}", i, ra.RegressionCoefficients[i]);
}
}
Console.WriteLine("Значимость коэффициентов регрессии:");
for (int i = 0; i < m; i++)
{
Console.WriteLine("b{0} = {1}", (i + 1), ra.RegressionCoefficientsSignificance[i]);
}
Console.WriteLine("Доверительные интервалы коэффициентов регрессии:");
for (int i = 0; i < m; i++)
{
Console.WriteLine("{0} <= b{1} <= {2}", ra.ConfidenceIntervalsOfCoefficients[i].Item1, (i + 1), ra.ConfidenceIntervalsOfCoefficients[i].Item2);
}
Console.WriteLine("Коэффициент значимости уравнения регрессии:");
Console.WriteLine(ra.RegressionEquationSignificance);
Console.WriteLine();
Console.WriteLine("Коэффициенты эластичности");
double[] elast = ra.ElasticityCoefficients;
for (int u = 0; u < m; u++)
{
Console.WriteLine("x{0} = {1}", (u + 1), elast[u]);
}
Forecast f = new Forecast(matrix, m - 1);
Console.WriteLine("Прогнозирование");
Console.WriteLine(f.Value[0] + " < y < " + f.Value[1]);
Console.WriteLine();
}
