/// <summary>
/// Multiplying two matrices
/// </summary>
/// <param name="one">MatrixMB - first matrix</param>
/// <param name="two">MatrixMB - second matrix</param>
/// <returns>MatrixMB - new Matrix as a result of multiplication</returns>
/// <remarks>tested according to https://www.youtube.com/watch?v=kuixY2bCc_0 </remarks>
public static MatrixMB operator *(MatrixMB one, MatrixMB two)
{
var mat = new MatrixMB(one.Rows, two.Cols);
if (one.Cols != two.Rows)
{
throw new MatrixException(
"Cannot multiply two matrices. Matrix one cols number doesn't match matrix two rows size. " +
System.String.Format("Matrix one: {0}x{1}, Matrix two: {2}x{3}", one.Cols, one.Rows, two.Cols, two.Rows));
}
else
{
for (int i = 0; i < one.Rows; i++)
{
for (int j = 0; j < two.Cols; j++)
{
for (int k = 0; k < one.Cols; k++)
{
mat.Data[i][j] += one.Data[i][k] * two.Data[k][j];
}
}
}
return(mat);
}
}
/// <summary>
/// Creates matrix filled with zeros
/// </summary>
/// <param name="rows">int - matrix rows number</param>
/// <param name="cols">int - matrix cols number</param>
/// <returns>MatrixMB</returns>
public static MatrixMB Zeros(int rows, int cols)
{
MatrixMB mat = new MatrixMB(rows, cols);
mat.FillWithNumber(0);
return(mat);
}
/// <summary>
/// Create matrix filled with ones
/// </summary>
/// <param name="rows">int - matrix rows number</param>
/// <param name="cols">int - matrix cols number</param>
/// <returns>MatrixMB</returns>
public static MatrixMB Ones(int rows, int cols)
{
MatrixMB m = new MatrixMB(rows, cols);
m.FillWithNumber(1);
return(m);
}
/// <summary>
/// Gets specified row
/// </summary>
/// <param name="row">int - row index</param>
/// <returns>MatrixMB</returns>
public MatrixMB Row(int row)
{
MatrixMB mat = new MatrixMB(1, this.Cols);
mat.Data[0] = Data[row];
return(mat);
}
/// <summary>
/// Set column
/// </summary>
/// <param name="v">MatrixMB - column vector</param>
/// <param name="column">int - column index</param>
public void SetCol(MatrixMB v, int column)
{
for (int i = 0; i < Rows; i++)
{
Data[i][column] = v.Data[i][0];
}
}
public void CalculatingLowerUpperMatrices()
{
MatrixMB mat = new MatrixMB(2, 2);
mat.Data[0][0] = 4;
mat.Data[0][1] = 7;
mat.Data[1][0] = 2;
mat.Data[1][1] = 6;
mat.MakeLU();
Assert.AreEqual(4, mat.Data[0][0]);
Assert.AreEqual(7, mat.Data[0][1]);
Assert.AreEqual(2, mat.Data[1][0]);
Assert.AreEqual(6, mat.Data[1][1]);
var L = mat.L;
Assert.AreEqual(1, L.Data[0][0]);
Assert.AreEqual(0, L.Data[0][1]);
Assert.AreEqual(0.5, L.Data[1][0]);
Assert.AreEqual(1, L.Data[1][1]);
var U = mat.U;
Assert.AreEqual(4, U.Data[0][0]);
Assert.AreEqual(7, U.Data[0][1]);
Assert.AreEqual(0, U.Data[1][0]);
Assert.AreEqual(2.5, U.Data[1][1]);
}
/// <summary>
/// Substraction of two matrices
/// </summary>
/// <param name="one">MatrixMB - first matrix</param>
/// <param name="two">MatrixMB - second matrix</param>
/// <returns>MatrixMB - new MatrixMB as a result of substraction</returns>
public static MatrixMB operator -(MatrixMB one, MatrixMB two)
{
try
{
if (one.Cols == two.Cols && one.Rows == two.Rows)
{
var matrix = new MatrixMB(one.Rows, one.Cols);
int row, col;
for (row = 0; row < one.Rows; row++)
{
for (col = 0; col < one.Cols; col++)
{
matrix.Data[row][col] = one.Data[row][col] - two.Data[row][col];
}
}
return(matrix);
}
else
{
throw new System.Exception(System.String.Format("Cannot substract two matrices. Matrix size doesn't match. Matrix one: {0}x{1}, Matrix two: {2}x{3}", one.Cols, one.Rows, two.Cols, two.Rows));
}
}
catch (System.Exception ex)
{
throw new MatrixException("Matrix addition error", ex);
}
}
/// <summary>
/// Function resolves Ax = v in confirmity with solution vector
/// </summary>
/// <param name="vector">Matrix - solution vector</param>
/// <returns>Matrix</returns>
public MatrixMB SolveWith(MatrixMB vector)
{
if (Rows != Cols)
{
throw new MatrixException("Solve: Matrix is not square.");
}
if (Rows != vector.Rows)
{
throw new MatrixException("Solve: wrong number of results in solution vector.");
}
if (L == null)
{
MakeLU();
}
MatrixMB b = new MatrixMB(Rows, 1);
for (int i = 0; i < Rows; i++)
{
b[i, 0] = vector[PermutationVector[i], 0]; // switch two items in "v" due to permutation matrix
}
MatrixMB z = SubsForth(L, b);
MatrixMB x = SubsBack(U, z);
return(x);
}
/// <summary>
/// Default constructor
/// </summary>
/// <param name="result">LearnResult - learning result</param>
/// <param name="chartFilename">string - char filename</param>
/// <param name="includeDataTable">bool - true id learning data should be included</param>
public HistoryPDF(LearnByErrorLibrary.LearnResult result, String chartFilename, bool includeDataTable = false)
{
this.result = result;
this.chartFilename = chartFilename;
document = new Document();
document.Info.Title = "NBN C# - wynik uczenia sieci neuronowej";
document.Info.Subject = "Uczenie sieci neuronowej za pomocą algorytmu NBN C#";
document.Info.Author = "Marek Bar 33808";
document.Info.Comment = "...ponieważ tylko dobry kod się liczy.";
document.Info.Keywords = "C#, NBN, neuron, uczenie, sieć, nbn c#";
DefineStyles(document);
DefineCover(document);
DefineInfo(document, result);
DefineChartArea(document, chartFilename, Path.GetFileNameWithoutExtension(result.Filename));
DefineWeightsSection(document, result);
if (includeDataTable)
{
MatrixMB mat = MatrixMB.Load(result.Filename);
AddMatrixTable(document, mat);
}
Section section = document.LastSection;
section.AddPageBreak();//index of pages
Paragraph paragraph = section.AddParagraph("Spis treści");
paragraph.Format.Font.Size = 14;
paragraph.Format.Font.Bold = true;
paragraph.Format.SpaceAfter = 24;
paragraph.Format.OutlineLevel = OutlineLevel.Level1;
paragraph = section.AddParagraph();
paragraph.Style = "TOC";
//first link
Hyperlink hyperlink = paragraph.AddHyperlink("Informacje na temat sieci neuronowej");
hyperlink.AddText("\r\n" + "Informacje na temat sieci neuronowej" + "\t");
hyperlink.AddPageRefField("Informacje na temat sieci neuronowej");
hyperlink = paragraph.AddHyperlink("Wykres przebiegu uczenia");
hyperlink.AddText("\r\n" + "Wykres przebiegu uczenia" + "\t");
hyperlink.AddPageRefField("Wykres przebiegu uczenia");
hyperlink = paragraph.AddHyperlink("Wagi otrzymane w procesie uczenia");
hyperlink.AddText("\r\n" + "Wagi otrzymane w procesie uczenia" + "\t");
hyperlink.AddPageRefField("Wagi otrzymane w procesie uczenia");
if (includeDataTable)
{
hyperlink = paragraph.AddHyperlink("Dane wejściowe");
hyperlink.AddText("\r\n" + "Dane wejściowe" + "\t");
hyperlink.AddPageRefField("Dane wejściowe");
}
}
/// <summary>
/// Gets specified column
/// </summary>
/// <param name="column">int - column index</param>
/// <returns>MatrixMB</returns>
public MatrixMB Col(int column)
{
MatrixMB matrix = new MatrixMB(Rows, 1);
for (int i = 0; i < Rows; i++)
{
matrix.Data[i][0] = Data[i][column];
}
return(matrix);
}
/// <summary>
/// Convert MatrixMB to VectorVertical
/// </summary>
/// <param name="mat">MatrixMB</param>
/// <returns>VectorVertical</returns>
public static VectorVertical ToVectorVertical(this MatrixMB mat)
{
VectorVertical vv = new VectorVertical(mat.Rows);
for (int row = 0; row < mat.Cols; row++)
{
vv.Data[row][0] = mat.Data[row][0];
}
return(vv);
}
/// <summary>
/// Creates identity matrix
/// </summary>
/// <param name="rows">int - matrix rows number</param>
/// <param name="cols">int - matrix cols number</param>
/// <returns>MatrixMB</returns>
public static MatrixMB Identity(int rows, int cols)
{
MatrixMB m = new MatrixMB(rows, cols);
for (int i = 0; i < System.Math.Min(rows, cols); i++)
{
m.Data[i][i] = 1;
}
return(m);
}
/// <summary>
/// Convert MatrixMB to VectorHorizontal
/// </summary>
/// <param name="mat">MatrixMB</param>
/// <returns>VectorHorizontal</returns>
public static VectorHorizontal ToVectorHorizontal(this MatrixMB mat)
{
VectorHorizontal vh = new VectorHorizontal(mat.Cols);
for (int col = 0; col < mat.Cols; col++)
{
vh.Data[0][col] = mat.Data[0][col];
}
return(vh);
}
/// <summary>
/// Create new Matrix from specified set of rows
/// </summary>
/// <param name="start">int - position of first row in original Matrix</param>
/// <param name="stop">int - position of last row in original Matrix</param>
/// <returns>MatrixMB - new matrix from selected rows</returns>
public MatrixMB CopyRows(int start, int stop)
{
MatrixMB m = new MatrixMB(stop - start + 1, this.Cols);
for (int i = start, c = 0; i <= stop; i++, c++)
{
m.Data[c] = this.Data[i];
}
return(m);
}
/// <summary>
/// Convert MatrixMB to Weights
/// </summary>
/// <param name="mat">MatrixMB</param>
/// <returns>Weights</returns>
public static Weights ToWeights(this MatrixMB mat)
{
Weights weights = new Weights(mat.Cols);
for (int col = 0; col < mat.Cols; col++)
{
weights.Data[0][col] = mat.Data[0][col];
}
return(weights);
}
public MatrixMB SolveEquatation(MatrixMB other)
{
return(this.Inverted * other);
/*Accord version - the most accurate*/
var m = Data.ToMultidimensionalArray(Rows, Cols).Solve(other.Data.ToMultidimensionalArray(other.Rows, other.Cols));
//var m = Data.ToMultidimensionalArray().Inverse().Multiply(other.Data.ToMultidimensionalArray());
MatrixMB tmp = new MatrixMB(m.GetLength(0), m.GetLength(1));
tmp.Data = m.ToJaggedArray(tmp.Rows, tmp.Cols);
return(tmp);
}
/// <summary>
/// Matrix eye - with ones on diagonal
/// </summary>
/// <param name="size">int - matrix size</param>
/// <returns>MatrixMB</returns>
public static MatrixMB Eye(int size)
{
MatrixMB m = new MatrixMB(size, size);
for (int i = 0; i < size; i++)
{
for (int j = 0; j < size; j++)
{
m.Data[i][j] = i == j ? 1 : 0;
}
}
return(m);
}
/// <summary>
/// Convert MatrixMB to Output
/// </summary>
/// <param name="mat">MatrixMB</param>
/// <returns>Output</returns>
public static Output ToOutput(this MatrixMB mat)
{
Output output = new Output(mat.Rows, mat.Cols);
for (int col = 0; col < mat.Cols; col++)
{
for (int row = 0; row < mat.Rows; row++)
{
output.Data[row][col] = mat.Data[row][col];
}
}
return(output);
}
/// <summary>
/// Convert MatrixMB to Input
/// </summary>
/// <param name="mat">MatrixMB</param>
/// <returns>Input</returns>
public static Input ToInput(this MatrixMB mat)
{
Input input = new Input(mat.Rows, mat.Cols);
for (int col = 0; col < mat.Cols; col++)
{
for (int row = 0; row < mat.Rows; row++)
{
input.Data[row][col] = mat.Data[row][col];
}
}
return(input);
}
/// <summary>
/// Create new matrix from specified set of columns
/// </summary>
/// <param name="start">int - position of first column</param>
/// <param name="stop">int - position of last column</param>
/// <returns>MatrixMB</returns>
public MatrixMB CopyColumns(int start, int stop)
{
MatrixMB m = new MatrixMB(this.Rows, stop - start + 1);
for (int i = 0; i < m.Rows; i++)
{
for (int j = start, c = 0; j <= stop; j++, c++)
{
m.Data[i][c] = this.Data[i][j];
}
}
return(m);
}
/// <summary>
/// Get matrix data nice format
/// </summary>
/// <param name="mat">MatrixMB</param>
/// <returns>string - matrix text representation</returns>
public static String NiceMatrixFormat(this MatrixMB mat)
{
string s = "";
for (int i = 0; i < mat.Rows; i++)
{
for (int j = 0; j < mat.Cols; j++)
{
s += String.Format("{0,5:0.00}", mat.Data[i][j]) + " ";
}
s += "\r\n";
}
return(s);
}
/// <summary>
/// Creates random filled matrix
/// </summary>
/// <param name="rows">int - matrix rows numer</param>
/// <param name="cols">int - matrix cols number</param>
/// <param name="dispersion">int - randomize range</param>
/// <returns>MatrixMB</returns>
public static MatrixMB Random(int rows, int cols, int dispersion)
{
System.Random random = new System.Random();
MatrixMB matrix = new MatrixMB(rows, cols);
int max = dispersion;
int min = -dispersion;
for (int i = 0; i < rows; i++)
{
for (int j = 0; j < cols; j++)
{
matrix.Data[i][j] = random.NextDouble() * (max - min) + min;
}
}
return(matrix);
}
/// <summary>
/// Convert Matrix to string representation
/// </summary>
/// <param name="mat">MatrixMB</param>
/// <returns>String</returns>
public static String MatrixToString(this MatrixMB mat)
{
System.Text.StringBuilder sb = new System.Text.StringBuilder();
for (var row = 0; row < mat.Rows; row++)
{
for (var col = 0; col < mat.Cols; col++)
{
sb.Append(mat.Data[row][col]);
sb.Append(", ");
}
sb.AppendLine();
}
return(sb.ToString());
}
/// <summary>
/// Check if matrix is filled with zeros only
/// </summary>
/// <param name="mat">MatrixMB</param>
/// <returns>bool</returns>
public static bool IsZeroed(this MatrixMB mat)
{
int counter = mat.Rows * mat.Cols;
for (int r = 0; r < mat.Rows; r++)
{
for (int c = 0; c < mat.Cols; c++)
{
if (mat.Data[r][c] == 0)
{
counter--;
}
}
}
return(counter == 0);
}
public void DivideMatrixByMatrix()
{
MatrixMB mat = new MatrixMB(2, 2);
mat.Data[0][0] = 4;
mat.Data[0][1] = 7;
mat.Data[1][0] = 2;
mat.Data[1][1] = 6;
MatrixMB b = new MatrixMB(2, 1);
b[0, 0] = 1;
b[1, 0] = 2;
var divided = mat.Inverted * b;
Assert.AreEqual(-0.8, Math.Round(divided[0, 0], 4));
Assert.AreEqual(0.6, Math.Round(divided[1, 0], 4));
}
/// <summary>
/// Execute operation on each value in matrix
/// </summary>
/// <param name="operation">MatrixValueOperation - operation type</param>
/// <param name="number">double - value</param>
/// <returns>MatrixMB</returns>
public MatrixMB OperationNew(MatrixAction operation, double number)
{
MatrixMB mat = this.Copy();
switch (operation)
{
case MatrixAction.Add:
{
mat.AddNumber(number);
} break;
case MatrixAction.Substract:
{
mat.SubNumber(number);
} break;
case MatrixAction.Multiply:
{
mat.MulByNumber(number);
} break;
case MatrixAction.Divide:
{
mat.DivByNumber(number);
} break;
case MatrixAction.Power:
{
mat.PowerByNumber(number);
} break;
case MatrixAction.Sqrt:
{
mat.SqrtFromNumber();
} break;
case MatrixAction.Abs:
{
mat.AbsoluteNumber();
} break;
}
return(mat);
}
/// <summary>
/// Check if object is Matrix - equal are Matrix and its derivates
/// </summary>
/// <param name="obj">object - some object</param>
/// <returns>bool - true - same, false - sth. else</returns>
public override bool Equals(object obj)
{
if (obj is MatrixMB)
{
try
{
MatrixMB m = (MatrixMB)obj;
return(this.Cols == m.Cols && this.Rows == m.Rows);
}
catch
{
return(false);
}
}
else
{
return(false);
}
}
/// <summary>
/// Function solves Ax = b for A as an upper triangular matrix
/// </summary>
/// <param name="A">MatrixMB - upper triangular matrix</param>
/// <param name="b">MatrixMB</param>
/// <returns>MatrixMB</returns>
public static MatrixMB SubsBack(MatrixMB A, MatrixMB b)
{
if (A.L == null)
{
A.MakeLU();
}
int n = A.Rows;
MatrixMB x = new MatrixMB(n, 1);
for (int i = n - 1; i > -1; i--)
{
x[i, 0] = b[i, 0];
for (int j = n - 1; j > i; j--)
{
x[i, 0] -= A[i, j] * x[j, 0];
}
x[i, 0] = x[i, 0] / A[i, i];
}
return(x);
}
/// <summary>
/// Function solves Ax = b for A as a lower triangular matrix
/// </summary>
/// <param name="A">MatrixMB - lower triangular matrix</param>
/// <param name="b">MatrixMB</param>
/// <returns>MatrixMB</returns>
public static MatrixMB SubsForth(MatrixMB A, MatrixMB b)
{
if (A.L == null)
{
A.MakeLU();
}
int n = A.Rows;
MatrixMB x = new MatrixMB(n, 1);
for (int i = 0; i < n; i++)
{
x[i, 0] = b[i, 0];
for (int j = 0; j < i; j++)
{
x[i, 0] -= A[i, j] * x[j, 0];
}
x[i, 0] = x[i, 0] / A[i, i];
}
return(x);
}
/// <summary>
/// Function resolves Ax = v in confirmity with solution vector
/// </summary>
/// <param name="vector">Matrix - solution vector</param>
/// <returns>Matrix</returns>
public MatrixMB SolveWith(MatrixMB vector)
{
if (Rows != Cols) throw new MatrixException("Solve: Matrix is not square.");
if (Rows != vector.Rows) throw new MatrixException("Solve: wrong number of results in solution vector.");
if (L == null) MakeLU();
MatrixMB b = new MatrixMB(Rows, 1);
for (int i = 0; i < Rows; i++) b[i, 0] = vector[PermutationVector[i], 0]; // switch two items in "v" due to permutation matrix
MatrixMB z = SubsForth(L, b);
MatrixMB x = SubsBack(U, z);
return x;
}
/// <summary>
/// Multiplying two matrices
/// </summary>
/// <param name="one">MatrixMB - first matrix</param>
/// <param name="two">MatrixMB - second matrix</param>
/// <returns>MatrixMB - new Matrix as a result of multiplication</returns>
/// <remarks>tested according to https://www.youtube.com/watch?v=kuixY2bCc_0 </remarks>
public static MatrixMB operator *(MatrixMB one, MatrixMB two)
{
var mat = new MatrixMB(one.Rows, two.Cols);
if (one.Cols != two.Rows)
{
throw new MatrixException(
"Cannot multiply two matrices. Matrix one cols number doesn't match matrix two rows size. " +
System.String.Format("Matrix one: {0}x{1}, Matrix two: {2}x{3}", one.Cols, one.Rows, two.Cols, two.Rows));
}
else
{
for (int i = 0; i < one.Rows; i++)
{
for (int j = 0; j < two.Cols; j++)
{
for (int k = 0; k < one.Cols; k++)
{
mat.Data[i][j] += one.Data[i][k] * two.Data[k][j];
}
}
}
return mat;
}
}
/// <summary>
/// Set column
/// </summary>
/// <param name="v">MatrixMB - column vector</param>
/// <param name="column">int - column index</param>
public void SetCol(MatrixMB v, int column)
{
for (int i = 0; i < Rows; i++)
{
Data[i][column] = v.Data[i][0];
}
}
public MatrixMB SolveEquatation(MatrixMB other)
{
return this.Inverted * other;
/*Accord version - the most accurate*/
var m = Data.ToMultidimensionalArray(Rows,Cols).Solve(other.Data.ToMultidimensionalArray(other.Rows,other.Cols));
//var m = Data.ToMultidimensionalArray().Inverse().Multiply(other.Data.ToMultidimensionalArray());
MatrixMB tmp = new MatrixMB(m.GetLength(0), m.GetLength(1));
tmp.Data = m.ToJaggedArray(tmp.Rows, tmp.Cols);
return tmp;
}
/// <summary>
/// Creates random filled matrix
/// </summary>
/// <param name="rows">int - matrix rows numer</param>
/// <param name="cols">int - matrix cols number</param>
/// <param name="dispersion">int - randomize range</param>
/// <returns>MatrixMB</returns>
public static MatrixMB Random(int rows, int cols, int dispersion)
{
System.Random random = new System.Random();
MatrixMB matrix = new MatrixMB(rows, cols);
int max = dispersion;
int min = -dispersion;
for (int i = 0; i < rows; i++)
{
for (int j = 0; j < cols; j++)
{
matrix.Data[i][j] = random.NextDouble() * (max - min) + min;
}
}
return matrix;
}
/// <summary>
/// Create new Matrix from specified set of rows
/// </summary>
/// <param name="start">int - position of first row in original Matrix</param>
/// <param name="stop">int - position of last row in original Matrix</param>
/// <returns>MatrixMB - new matrix from selected rows</returns>
public MatrixMB CopyRows(int start, int stop)
{
MatrixMB m = new MatrixMB(stop - start + 1, this.Cols);
for (int i = start, c = 0; i <= stop; i++, c++)
{
m.Data[c] = this.Data[i];
}
return m;
}
/// <summary>
/// Creates identity matrix
/// </summary>
/// <param name="rows">int - matrix rows number</param>
/// <param name="cols">int - matrix cols number</param>
/// <returns>MatrixMB</returns>
public static MatrixMB Identity(int rows, int cols)
{
MatrixMB m = new MatrixMB(rows, cols);
for (int i = 0; i < System.Math.Min(rows, cols); i++)
{
m.Data[i][i] = 1;
}
return m;
}
/// <summary>
/// Loads matrix from file
/// </summary>
/// <param name="filename">String - filename</param>
/// <returns>MatrixMB</returns>
public static MatrixMB Load(System.String filename)
{
char splitter = ',';
try
{
if (!System.IO.File.Exists(filename) || filename == "")
{
throw new System.Exception("Cannot find file: " + filename);
}
string text;
using (System.IO.StreamReader streamReader = new System.IO.StreamReader(filename, System.Text.Encoding.UTF8))
{
text = streamReader.ReadToEnd();
}
var lines = text.Split(new char[] { '\r', '\n' });
var rows = lines.Length;
foreach (var line in lines)
{
if (line == "") rows--;
}
string[] splitted = lines[0].Split(new char[] { splitter });
if (splitted.Length == 1)
{
splitter = ' ';
splitted = lines[0].Split(new char[] { splitter });
}
if (splitted.Length == 1)
{
splitter = ';';
splitted = lines[0].Split(new char[] { splitter });
}
if (splitted.Length == 1)
{
splitter = '\t';
splitted = lines[0].Split(new char[] { splitter });
}
var cols = splitted.Length;
for (int i = 0; i < splitted.Length; i++) { if (splitted[i] == "") cols--; }
MatrixMB m = new MatrixMB(rows, cols);
var row = 0;
foreach (var line in lines)
{
if (line != "")
{
var data = line.TrimEnd(new char[] { ',', ' ' }).Split(new char[] { splitter });
var col = 0;
foreach (var number in data)
{
if (number != "")
{
m.Data[row][col] = double.Parse(number.Replace('.', ',').Trim());
col++;
}
}
row++;
}
}
m.Name = System.IO.Path.GetFileNameWithoutExtension(filename);
return m;
}
catch (System.Exception ex)
{
throw new MatrixException("Loading matrix from file error (splitter: " + splitter.ToString() + "). File: " + filename, ex);
}
}
public void TransposeMatrix()
{
MatrixMB mat = new MatrixMB(3, 3);
mat[0, 0] = 1;
mat[0, 1] = 2;
mat[0, 2] = 3;
mat[1, 0] = 4;
mat[1, 1] = 5;
mat[1, 2] = 6;
mat[2, 0] = 7;
mat[2, 1] = 8;
mat[2, 2] = 9;
var t = mat.Transposed;
Assert.AreEqual(1, t[0, 0]);
Assert.AreEqual(2, t[1, 0]);
Assert.AreEqual(3, t[2, 0]);
Assert.AreEqual(4, t[0, 1]);
Assert.AreEqual(5, t[1, 1]);
Assert.AreEqual(6, t[2, 1]);
Assert.AreEqual(7, t[0, 2]);
Assert.AreEqual(8, t[1, 2]);
Assert.AreEqual(9, t[2, 2]);
}
/// <summary>
/// Substraction of two matrices
/// </summary>
/// <param name="one">MatrixMB - first matrix</param>
/// <param name="two">MatrixMB - second matrix</param>
/// <returns>MatrixMB - new MatrixMB as a result of substraction</returns>
public static MatrixMB operator -(MatrixMB one, MatrixMB two)
{
try
{
if (one.Cols == two.Cols && one.Rows == two.Rows)
{
var matrix = new MatrixMB(one.Rows, one.Cols);
int row, col;
for (row = 0; row < one.Rows; row++)
{
for (col = 0; col < one.Cols; col++)
{
matrix.Data[row][col] = one.Data[row][col] - two.Data[row][col];
}
}
return matrix;
}
else
{
throw new System.Exception(System.String.Format("Cannot substract two matrices. Matrix size doesn't match. Matrix one: {0}x{1}, Matrix two: {2}x{3}", one.Cols, one.Rows, two.Cols, two.Rows));
}
}
catch (System.Exception ex)
{
throw new MatrixException("Matrix addition error", ex);
}
}
public void SumTwoMatrices()
{
MatrixMB mat1 = new MatrixMB(5, 5);
MatrixMB mat2 = new MatrixMB(5, 5);
mat1.FillWithNumber(1);
mat2.FillWithNumber(2);
var mat = mat1 + mat2;
Assert.AreEqual(75, mat.SumAllValues());
}
public void SubstractTwoMatrices()
{
MatrixMB mat1 = new MatrixMB(5, 5);
MatrixMB mat2 = new MatrixMB(5, 5);
mat1.FillWithNumber(4);
mat2.FillWithNumber(2);
var mat = mat1 - mat2;
Assert.AreEqual(50, mat.SumAllValues());
}
/// <summary>
/// Calculate lower and upper traingular matrix - this is LU decomposition of matrix
/// </summary>
public void MakeLU()
{
//try
//{
// if (!IsSquare) throw new MatrixException("The matrix is not square!");
// var lu = new Accord.Math.Decompositions.LuDecomposition(this.Data.ToMultidimensionalArray(Rows, Cols));
// var l = lu.LowerTriangularFactor;
// var u = lu.UpperTriangularFactor;
// L = new MatrixMB(l.GetLength(0), l.GetLength(1), l.ToJaggedArray(l.GetLength(0), l.GetLength(1)));
// U = new MatrixMB(u.GetLength(0), u.GetLength(1), u.ToJaggedArray(u.GetLength(0), u.GetLength(1)));
// PermutationVector = lu.PivotPermutationVector;
//}
//catch (System.Exception ex)
//{
// throw new System.Exception(ex.Message);
//}
//return;
//--------------------------------------------------------------------
if (!IsSquare)
{
throw new MatrixException("The matrix is not square!");
}
L = MatrixMB.Identity(Rows, Cols);
U = this.Copy();
PermutationVector = new int[Rows];
for (int i = 0; i < Rows; i++)
{
PermutationVector[i] = i;
}
double p = 0;
double pom2;
int k0 = 0;
int pom1 = 0;
for (int k = 0; k < Rows - 1; k++)
{
p = 0;
for (int i = k; i < Rows; i++) // find the row with the biggest pivot
{
if (System.Math.Abs(U.Data[i][k]) > p)
{
p = System.Math.Abs(U.Data[i][k]);
k0 = i;
}
}
if (p == 0)
{
throw new MatrixException("Making LU: matrix is singular!");
}
pom1 = PermutationVector[k];
PermutationVector[k] = PermutationVector[k0];
PermutationVector[k0] = pom1;
for (int i = 0; i < k; i++)
{
pom2 = L.Data[k][i];
L.Data[k][i] = L.Data[k0][i];
L.Data[k0][i] = pom2;
}
if (k != k0)
{
detOfP *= -1;
}
for (int i = 0; i < Rows; i++)
{
pom2 = U.Data[k][i];
U.Data[k][i] = U.Data[k0][i];
U.Data[k0][i] = pom2;
}
for (int i = k + 1; i < Rows; i++)
{
L.Data[i][k] = U.Data[i][k] / U.Data[k][k];
for (int j = k; j < Rows; j++)
{
U.Data[i][j] -= L.Data[i][k] * U.Data[k][j];
}
}
}
}
/// <summary>
/// Function solves Ax = b for A as a lower triangular matrix
/// </summary>
/// <param name="A">MatrixMB - lower triangular matrix</param>
/// <param name="b">MatrixMB</param>
/// <returns>MatrixMB</returns>
public static MatrixMB SubsForth(MatrixMB A, MatrixMB b)
{
if (A.L == null) A.MakeLU();
int n = A.Rows;
MatrixMB x = new MatrixMB(n, 1);
for (int i = 0; i < n; i++)
{
x[i, 0] = b[i, 0];
for (int j = 0; j < i; j++) x[i, 0] -= A[i, j] * x[j, 0];
x[i, 0] = x[i, 0] / A[i, i];
}
return x;
}
/// <summary>
/// Initialize NBN with data from specified file
/// </summary>
/// <param name="filename">String - filename</param>
/// <returns>bool - success flag</returns>
private bool loadInputData(String filename)
{
try
{
if (!File.Exists(filename))
{
throw new NeuralNetworkError(String.Format(Properties.Settings.Default.NBN1, filename));
}
try
{
var data = MatrixMB.Load(filename);
if (MatLabCompareDataFolder.Length > 0)
{
//data from matlab are normalized
string name = Path.GetFileNameWithoutExtension(Directory.GetFiles(MatLabCompareDataFolder, "*.dat")[0]);
inputLearn = MatrixMB.Load(string.Format("{0}\\uczenie_wejscie_{1}.txt", MatLabCompareDataFolder, name)).ToInput();
inputTest = MatrixMB.Load(string.Format("{0}\\testowanie_wejscie_{1}.txt", MatLabCompareDataFolder, name)).ToInput();
outputLearn = MatrixMB.Load(string.Format("{0}\\uczenie_wyjscie_{1}.txt", MatLabCompareDataFolder, name)).ToOutput();
outputTest = MatrixMB.Load(string.Format("{0}\\testowanie_wyjscie_{1}.txt", MatLabCompareDataFolder, name)).ToOutput();
}
else
{
input = data.CopyColumnsWithoutLast().ToInput();
input.Normalize();
output = data.LastColumn.ToOutput();
output.Normalize();
if (IsClassification)
{
int Tnp = data.Rows;
inputLearn = input.CopyRows(Tnp - 1).ToInput();
inputTest = input.CopyRows(Tnp - 1).ToInput();
outputLearn = output.CopyRows(Tnp - 1).ToOutput();
outputTest = output.CopyRows(Tnp - 1).ToOutput();
}
else
{
int[] ind = data.Rows.RandomPermutation();
int Tnp = Math.Round(data.Rows * 0.7).ToInt();
inputTest = input.CopyRows(Tnp, input.Rows - 1).ToInput();
inputLearn = input.CopyRows(Tnp - 1).ToInput();
outputTest = output.CopyRows(Tnp, input.Rows - 1).ToOutput();
outputLearn = output.CopyRows(Tnp - 1).ToOutput();
}
}
try
{
if (IsResearchMode)//export for matlab - out of use
{
string name = Path.GetFileNameWithoutExtension(filename);
DateTime d = DateTime.Now;
string dir = String.Format("{0}\\Executed research\\{1}\\{2}_{3}_{4}_{5}_{6}_{7}",
Path.GetDirectoryName(filename), name,
d.Year, d.Month, d.Day, d.Hour, d.Minute, d.Second);
if (!Directory.Exists(dir))
{
Directory.CreateDirectory(dir);
}
extractionFolder = dir;
_reasearch_folder = dir;
string learn_input = String.Format("{0}\\{1}_learn_input.dat", dir, name);
string test_input = String.Format("{0}\\{1}_test_input.dat", dir, name);
string learn_output = String.Format("{0}\\{1}_learn_output.dat", dir, name);
string test_output = String.Format("{0}\\{1}_test_output.dat", dir, name);
string initialWweights = String.Format("{0}\\{1}_initial_weights.dat", dir, name);
inputTest.Store(test_input);
inputLearn.Store(learn_input);
outputTest.Store(test_output);
outputLearn.Store(learn_output);
}
}
catch (Exception)
{ }
}
catch (Exception iex)
{
throw new NeuralNetworkError(String.Format(Properties.Settings.Default.NBN2, filename), iex);
}
return(true);
}
catch (Exception ex)
{
updateErrorNBN(ex);
return(false);
}
}
/// <summary>
/// Matrix eye - with ones on diagonal
/// </summary>
/// <param name="size">int - matrix size</param>
/// <returns>MatrixMB</returns>
public static MatrixMB Eye(int size)
{
MatrixMB m = new MatrixMB(size, size);
for (int i = 0; i < size; i++)
{
for (int j = 0; j < size; j++)
{
m.Data[i][j] = i == j ? 1 : 0;
}
}
return m;
}
public void Creation()
{
MatrixMB mat = new MatrixMB(5, 5);
mat.FillWithNumber(1);
Assert.AreEqual(25, mat.SumAllValues());
}
/// <summary>
/// Left division
/// </summary>
/// <param name="one">MatrixMB - first matrix</param>
/// <param name="two">MatrixMB - second matrix</param>
/// <returns>MatrixMB - divided</returns>
public static MatrixMB Left(ref MatrixMB one, ref MatrixMB two)
{
return one.Inverted * two;
}
public void Test__WeightCorrectionCalculation()
{
//this is just check of calculation: ((hessian+mu*I)\gradient)' if is correct
/*
>> %((hessian+mu*I)\gradient)'
>> hessian = [1 2; 3 4]
hessian =
1 2
3 4
>> mu = 2
mu =
2
>> I = eye(2)
I =
1 0
0 1
>> gradient=[1;2]
gradient =
1
2
>> res=((hessian+mu*I)\gradient)'
res =
0.1667 0.2500
*/
var hessian = new MatrixMB(2, 2);
//no matter if its hessian result or not
hessian[0, 0] = 1;
hessian[0, 1] = 2;
hessian[1, 0] = 3;
hessian[1, 1] = 4;
double mu = 2;
var I = MatrixMB.Eye(2);
var gradient = new MatrixMB(2, 1);
gradient[0, 0] = 1;
gradient[1, 0] = 2;
// var diff = ((hessian.HessianMat + (I * setting.MU)).Inverted * hessian.GradientMat).Transposed;
var add = hessian + I * mu;
var inv = add.Inverted;
var div = inv * gradient;
var res = div.Transposed;
Assert.AreEqual(1, res.Rows);
Assert.AreEqual(2, res.Cols);
Assert.AreEqual(0.1667, Math.Round(res[0, 0],4));
Assert.AreEqual(0.25, Math.Round(res[0, 1],4));
}
/// <summary>
/// Create matrix filled with ones
/// </summary>
/// <param name="rows">int - matrix rows number</param>
/// <param name="cols">int - matrix cols number</param>
/// <returns>MatrixMB</returns>
public static MatrixMB Ones(int rows, int cols)
{
MatrixMB m = new MatrixMB(rows, cols);
m.FillWithNumber(1);
return m;
}
/// <summary>
/// Gets specified row
/// </summary>
/// <param name="row">int - row index</param>
/// <returns>MatrixMB</returns>
public MatrixMB Row(int row)
{
MatrixMB mat = new MatrixMB(1, this.Cols);
mat.Data[0] = Data[row];
return mat;
}
/// <summary>
/// Function solves Ax = b for A as an upper triangular matrix
/// </summary>
/// <param name="A">MatrixMB - upper triangular matrix</param>
/// <param name="b">MatrixMB</param>
/// <returns>MatrixMB</returns>
public static MatrixMB SubsBack(MatrixMB A, MatrixMB b)
{
if (A.L == null) A.MakeLU();
int n = A.Rows;
MatrixMB x = new MatrixMB(n, 1);
for (int i = n - 1; i > -1; i--)
{
x[i, 0] = b[i, 0];
for (int j = n - 1; j > i; j--) x[i, 0] -= A[i, j] * x[j, 0];
x[i, 0] = x[i, 0] / A[i, i];
}
return x;
}
/// <summary>
/// Gets specified column
/// </summary>
/// <param name="column">int - column index</param>
/// <returns>MatrixMB</returns>
public MatrixMB Col(int column)
{
MatrixMB matrix = new MatrixMB(Rows, 1);
for (int i = 0; i < Rows; i++)
{
matrix.Data[i][0] = Data[i][column];
}
return matrix;
}
/// <summary>
/// Creates matrix filled with zeros
/// </summary>
/// <param name="rows">int - matrix rows number</param>
/// <param name="cols">int - matrix cols number</param>
/// <returns>MatrixMB</returns>
public static MatrixMB Zeros(int rows, int cols)
{
MatrixMB mat = new MatrixMB(rows, cols);
mat.FillWithNumber(0);
return mat;
}
public void MultiplyTwoMatrices()
{
MatrixMB mat1 = new MatrixMB(2, 3);
mat1.Data[0][0] = 1;
mat1.Data[0][1] = 2;
mat1.Data[0][2] = 3;
mat1.Data[1][0] = 4;
mat1.Data[1][1] = 5;
mat1.Data[1][2] = 6;
MatrixMB mat2 = new MatrixMB(3, 2);
mat2.Data[0][0] = 7;
mat2.Data[0][1] = 8;
mat2.Data[1][0] = 9;
mat2.Data[1][1] = 10;
mat2.Data[2][0] = 11;
mat2.Data[2][1] = 12;
var mat = mat1 * mat2;
Assert.AreEqual(58, mat[0,0]);
Assert.AreEqual(64, mat[0, 1]);
Assert.AreEqual(139,mat[1, 0]);
Assert.AreEqual(154, mat[1, 1]);
}
/// <summary>
/// Create new matrix from specified set of columns
/// </summary>
/// <param name="start">int - position of first column</param>
/// <param name="stop">int - position of last column</param>
/// <returns>MatrixMB</returns>
public MatrixMB CopyColumns(int start, int stop)
{
MatrixMB m = new MatrixMB(this.Rows, stop - start + 1);
for (int i = 0; i < m.Rows; i++)
{
for (int j = start, c = 0; j <= stop; j++, c++)
{
m.Data[i][c] = this.Data[i][j];
}
}
return m;
}
public void FillMatrix()
{
MatrixMB mat = new MatrixMB(3, 3);
mat.Data[0][0] = 1;
mat.Data[0][1] = 2;
mat.Data[0][2] = 3;
mat.Data[1][0] = 4;
mat.Data[1][1] = 5;
mat.Data[1][2] = 6;
mat.Data[2][0] = 7;
mat.Data[2][1] = 8;
mat.Data[2][2] = 9;
Assert.AreEqual(1, mat[0, 0]);
Assert.AreEqual(2, mat[0, 1]);
Assert.AreEqual(3, mat[0, 2]);
Assert.AreEqual(4, mat[1, 0]);
Assert.AreEqual(5, mat[1, 1]);
Assert.AreEqual(6, mat[1, 2]);
Assert.AreEqual(7, mat[2, 0]);
Assert.AreEqual(8, mat[2, 1]);
Assert.AreEqual(9, mat[2, 2]);
}
/// <summary>
/// Calculate lower and upper traingular matrix - this is LU decomposition of matrix
/// </summary>
public void MakeLU()
{
//try
//{
// if (!IsSquare) throw new MatrixException("The matrix is not square!");
// var lu = new Accord.Math.Decompositions.LuDecomposition(this.Data.ToMultidimensionalArray(Rows, Cols));
// var l = lu.LowerTriangularFactor;
// var u = lu.UpperTriangularFactor;
// L = new MatrixMB(l.GetLength(0), l.GetLength(1), l.ToJaggedArray(l.GetLength(0), l.GetLength(1)));
// U = new MatrixMB(u.GetLength(0), u.GetLength(1), u.ToJaggedArray(u.GetLength(0), u.GetLength(1)));
// PermutationVector = lu.PivotPermutationVector;
//}
//catch (System.Exception ex)
//{
// throw new System.Exception(ex.Message);
//}
//return;
//--------------------------------------------------------------------
if (!IsSquare) throw new MatrixException("The matrix is not square!");
L = MatrixMB.Identity(Rows, Cols);
U = this.Copy();
PermutationVector = new int[Rows];
for (int i = 0; i < Rows; i++) PermutationVector[i] = i;
double p = 0;
double pom2;
int k0 = 0;
int pom1 = 0;
for (int k = 0; k < Rows - 1; k++)
{
p = 0;
for (int i = k; i < Rows; i++) // find the row with the biggest pivot
{
if (System.Math.Abs(U.Data[i][k]) > p)
{
p = System.Math.Abs(U.Data[i][k]);
k0 = i;
}
}
if (p == 0)
{
throw new MatrixException("Making LU: matrix is singular!");
}
pom1 = PermutationVector[k];
PermutationVector[k] = PermutationVector[k0];
PermutationVector[k0] = pom1;
for (int i = 0; i < k; i++)
{
pom2 = L.Data[k][i];
L.Data[k][i] = L.Data[k0][i];
L.Data[k0][i] = pom2;
}
if (k != k0) detOfP *= -1;
for (int i = 0; i < Rows; i++)
{
pom2 = U.Data[k][i];
U.Data[k][i] = U.Data[k0][i];
U.Data[k0][i] = pom2;
}
for (int i = k + 1; i < Rows; i++)
{
L.Data[i][k] = U.Data[i][k] / U.Data[k][k];
for (int j = k; j < Rows; j++)
{
U.Data[i][j] -= L.Data[i][k] * U.Data[k][j];
}
}
}
}
public void InvertMatrix()
{
Console.WriteLine("Testowanie odwracania macierzy");
//mat.Inverted * mat = mat.Identity
//Jeśli A ma odwrotną A to odwrotna A jest równa A
MatrixMB mat = new MatrixMB(2,2);
mat.Data[0][0] = 4;
mat.Data[0][1] = 7;
mat.Data[1][0] = 2;
mat.Data[1][1] = 6;
Console.WriteLine("Macierz odwracana");
Console.WriteLine(mat.MatrixToString());
double det = mat.Determinant;
Assert.AreNotEqual(0, det);
Assert.AreEqual(10, det);
var inv = mat.Inverted;
Console.WriteLine("Macierz odwrócona");
Console.WriteLine(inv.MatrixToString());
Assert.AreEqual(0.6000, Math.Round(inv.Data[0][0],4));
Assert.AreEqual(-0.7000, Math.Round(inv.Data[0][1],4));
Assert.AreEqual(-0.2000, Math.Round(inv.Data[1][0],4));
Assert.AreEqual(0.4000, Math.Round(inv.Data[1][1],4));
var A = inv.Inverted;
Console.WriteLine("Odwrócenie macierzy odwróconej");
Console.WriteLine(inv.MatrixToString());
int accuracy = 15;
for (int i = 0; i < A.Rows; i++)
{
for (int j = 0; j < A.Cols; j++)
{
decimal o = Math.Round((decimal)mat[i, j], accuracy);
decimal a = Math.Round((decimal)A[i, j], accuracy);
Console.WriteLine(string.Format("Orginał: {0}\tPorównywana: {1}", o, a));
}
}
for (int i = 0; i < A.Rows; i++)
{
for (int j = 0; j < A.Cols; j++)
{
decimal o = Math.Round((decimal)mat[i, j], accuracy);
decimal a = Math.Round((decimal)A[i, j], accuracy);
Assert.AreEqual(o, a);
}
}
var identity = inv * mat;
Assert.AreEqual(1, Math.Round(identity.Data[0][0], accuracy));
Assert.AreEqual(0, Math.Round(identity.Data[0][1], accuracy));
Assert.AreEqual(0, Math.Round(identity.Data[1][0], accuracy));
Assert.AreEqual(1, Math.Round(identity.Data[1][1], accuracy));
}
/// <summary>
/// Run NBN training and its test - this is the first version
/// </summary>
/// <param name="trials">int - number of learning trials</param>
/// <returns>LearnResult</returns>R
private LearnResult RunFirstVersion(int trials)
{
backupSettings = new NeuralNetworkSettings();
backupSettings.MaxError = settings.MaxError;
backupSettings.MaxIterations = settings.MaxIterations;
backupSettings.MU = settings.MU;
backupSettings.MUH = settings.MUH;
backupSettings.MUL = settings.MUL;
backupSettings.Scale = settings.Scale;
Trials = trials;
LearnResult result = new LearnResult();
result.Filename = Filename;
if (MatLabCompareDataFolder.Length > 0)
{
result.Filename = string.Format("{0}\\{1}.dat", MatLabCompareDataFolder, Path.GetFileNameWithoutExtension(Directory.GetFiles(MatLabCompareDataFolder, "*.dat")[0]));
}
if (!loadInputData(Filename))
{
updateErrorNBN("Dane nie zostały wczytane.");
return(result);
}
if (OnDebug != null)
{
debug("Data loaded from file: " + Filename);
}
var tmp = new System.Collections.Generic.List <int>();
tmp.Add(inputLearn.Cols);
//setting number of hidden neurons
for (int i = 0; i < Handle; i++)
{
tmp.Add(1);
}
tmp.Add(1);
var vh = new VectorHorizontal(tmp.Count);
for (int i = 0; i < vh.Length; i++)
{
vh[i] = tmp[i];
}
switch (NBN_Topography)
{
case 0:
{
topo = Topography.Generate(TopographyType.BMLP, vh);
} break;
case 1:
{
topo = Topography.Generate(TopographyType.MLP, vh);
} break;
}
result.Topo = topo.Data[0];
if (OnDebug != null)
{
debug(topo.ToString());
}
if (topo == null)
{
updateErrorNBN("Topologia sieci nie została utworzona.");
return(result);
}
info = this.checkInputs(ref inputLearn, ref outputLearn, ref topo, out indexes);//here are set indexes
result.TopoIndex = indexes.Data[0];
result.Info = info;
if (OnDebug != null)
{
debug(indexes.ToString());
debug(info.ToString());
}
Activation act = new Activation(info.nn);
act.FillWithNumber(NBN_Activation);
act.setValue(info.nn - 1, 0);
result.ActivationFunction = act.Data[0];
Gain gain = new Gain(info.nn);
gain.FillWithNumber(NBN_Gain);
result.GainValue = gain.Data[0];
result.Settings = this.settings;
for (trial = 0; trial < trials; trial++)
{
Weights initialWeights = new Weights(info.nw);
if (MatLabCompareDataFolder.Length > 0)
{
initialWeights = MatrixMB.Load(string.Format("{0}\\poczatkowe_wagi_proba_{1}.txt", MatLabCompareDataFolder, trial + 1)).ToWeights();
}
else
{
initialWeights = Weights.Generate(info.nw);
}
if (IsResearchMode)
{
string initialWeightsFile = String.Format("{0}\\{1}{2}_initial_weights.dat", _reasearch_folder, trial, Path.GetFileNameWithoutExtension(result.Filename));
initialWeights.Store(initialWeightsFile);
}
initialWeights.Name = "Initial";
if (OnDebug != null)
{
debug(String.Format("\r\nTrial {0} from {1}\r\n", trial + 1, trials));
debug(initialWeights.ToString());
}
settings = null;
settings = NeuralNetworkSettings.Default();
settings.MaxError = backupSettings.MaxError;
settings.MaxIterations = backupSettings.MaxIterations;
settings.MU = backupSettings.MU;
settings.MUH = backupSettings.MUH;
settings.MUL = backupSettings.MUL;
settings.Scale = backupSettings.Scale;
I = MatrixMB.Eye(info.nw);
tic();//learn time measure start
var tr = Train(ref this.settings, ref this.info, ref this.inputLearn, ref this.outputLearn,
ref this.topo, initialWeights, ref act, ref gain, ref indexes);
String LearnExecutionTime = toc(); //learn time measure stop
LearnTimeList = time.ElapsedTicks; //learn time measure save
result.Add(tr.weights.Data[0], SSE.ToDoubleArray(), RMSE.ToDoubleArray());
result.LearnRMSE = (double)RMSE[RMSE.Count];
LearnRmseList = LastRMSE;
if (OnDebug != null)
{
debug(tr.weights.ToString());
debug("\r\nLearn execution time: " + LearnExecutionTime + "(hours:minutes:seconds:miliseconds)\r\n");
debug("\r\nLearn SSE: " + tr.sse.ToString() + "\r\n");
debug("\r\nLearn RMSE: " + result.LearnRMSE.ToString() + "\r\n");
}
updateError(result.LearnRMSE);
NetworkInfo infoTest = info.Copy();
infoTest.np = inputTest.Rows;
tic();
error.CalculateError(ref infoTest, ref inputTest, ref outputTest, ref topo, tr.weights, ref act, ref gain, ref indexes);
var TestExecutionTime = toc();
TestTimeList = time.ElapsedTicks;
result.TestRMSE = Math.Sqrt(error.Error / infoTest.np);
TestRmseList = result.TestRMSE;
result.TestingRmseList.Add(result.TestRMSE);
if (OnDebug != null)
{
debug("\r\nTest execution time: " + TestExecutionTime + "(hours:minutes:seconds:miliseconds)\r\n");
debug("\r\nTest SSE: " + error.Error.ToString() + "\r\n");
debug("\r\nTest RMSE: " + result.TestRMSE.ToString() + "\r\n");
}
//if (result.LearnRMSE < Threshold) IsTrainOK++;
if (result.SSE[trial][result.SSE[trial].Length - 1] < Threshold)
{
IsTrainOK++;
}
}
result.LearnRMSE = AverageLearnRMSE;
result.TestRMSE = AverageTestRMSE;
result.setStatisticsData(LearnRMSE, TestRMSE, LearnTime, TestTime, Trials);
result.SuccessRate = (double)IsTrainOK / Trials;
if (IsResearchMode)//save research
{
try
{
string filename = extractionFolder + "\\research result.pdf";
PDFGenerate data = new PDFGenerate();
data.Filename = filename;
data.Result = result;
data.ChartFilename = GeneratePlot(result.RMSE, Path.GetFileNameWithoutExtension(result.Filename));
HistoryPDF pdf = new HistoryPDF(data.Result, data.ChartFilename, true);
pdf.Save(data.Filename);
}
catch { }
}
return(result);
}
public void MatrixDeterminant()
{
MatrixMB mat = new MatrixMB(3, 3);
mat.Data[0][0] = 1;
mat.Data[0][1] = 9;
mat.Data[0][2] = 1;
mat.Data[1][0] = 9;
mat.Data[1][1] = 1;
mat.Data[1][2] = 9;
mat.Data[2][0] = 1;
mat.Data[2][1] = 9;
mat.Data[2][2] = 1;
double det = mat.Determinant;
Assert.AreEqual(det, 0);
Assert.AreEqual(1, mat.Data[0][0]);
Assert.AreEqual(9, mat.Data[0][1]);
Assert.AreEqual(1, mat.Data[0][2]);
Assert.AreEqual(9, mat.Data[1][0]);
Assert.AreEqual(1, mat.Data[1][1]);
Assert.AreEqual(9, mat.Data[1][2]);
Assert.AreEqual(1, mat.Data[2][0]);
Assert.AreEqual(9, mat.Data[2][1]);
Assert.AreEqual(1, mat.Data[2][2]);
}
