public static Double[,] Pow(this Double[,] a, Double[,] b)
{
if (a.Fits(b))
{
var rows = a.GetRows();
var cols = a.GetColumns();
var result = new Double[rows, cols];
for (var i = 0; i < rows; i++)
{
for (var j = 0; j < cols; j++)
{
result[i, j] = Math.Pow(a[i, j], b[i, j]);
}
}
return(result);
}
return(null);
}
}//end ApplyCovarianceFunction
private Double GetKrigedGageCorrelations(Double[,] I, Double[,] D, List<double> correlations)
{
double[,] weights = null;
double val = 0;
try
{
weights = GetVarianceWeights(I, D);
//weights have an n+1 due to the added LagrangeParameter
if (weights.GetLength(0) - 1 != correlations.Count) throw new Exception("weights-1 != indexGages count");
for (int i = 0; i < correlations.Count; i++)
{
val = val + weights[i, 0] * correlations[i];
}//next i
return val;
}
catch (Exception)
{
return double.NaN;
}//end try
}//end getKrigedGageValue
public static Double[,] And(this Double[,] a, Double[,] b)
{
if (a.Fits(b))
{
var rows = a.GetRows();
var cols = a.GetColumns();
var result = new Double[rows, cols];
for (var i = 0; i < rows; i++)
{
for (var j = 0; j < cols; j++)
{
result[i, j] = (a[i, j].ToBoolean() && b[i, j].ToBoolean()).ToNumber();
}
}
return(result);
}
return(null);
}
public static Double[,] IsGreaterOrEqual(this Double[,] a, Double[,] b)
{
if (a.Fits(b))
{
var rows = a.GetRows();
var cols = a.GetColumns();
var result = new Double[rows, cols];
for (var i = 0; i < rows; i++)
{
for (var j = 0; j < cols; j++)
{
result[i, j] = (a[i, j] >= b[i, j]).ToNumber();
}
}
return(result);
}
return(null);
}
//把数据从UI读取到中间文件
private void readUItoArray()
{
//清空字典
plate_info = new Dictionary <string, string>();
//重新初始化中间数组
tpl = new Info[8, 12];
od = new double[8, 12];
//string[,] odStr = new string[8, 12];//使用字符数组更好？
//读取文件到中间数组
//合法性检测
if (this.textName.Text.Trim() == null)
{
MessageBox.Show("请输入项目名称！", "系统提示");
return;
}
if (this.txtUnit.Text.Trim() == null)
{
MessageBox.Show("请输入单位名称！", "系统提示");
return;
}
//基本信息-》中间数据
plate_info["SaveTime"] = DateTime.Now.ToString();//保存文件时的时间
plate_info["Name"] = this.textName.Text.Trim();
plate_info["Lot"] = this.textLot.Text.Trim();
plate_info["LabDate"] = this.dateTimePicker1.Text.Trim();
plate_info["Unit"] = this.txtUnit.Text.Trim();
plate_info["Notice"] = "不要随意更改文件内容，否则再次读取时将发生错误。";
plate_info["Curve"] = this.getRadioIndex().ToString();//拟合的模型编号
//tpl-》中间数据
DataReadWrite drw = new DataReadWrite();
//从UI读取到中间数组
tpl = drw.readFromUI(this.dataGridView0, true);
od = drw.readFromUI(this.dataGridView1);
}
/// <summary>
/// Returns the solution matrix if the matrix is square or the least squares solution otherwise.
/// </summary>
///
/// <param name="matrix">The matrix for the linear problem.</param>
/// <param name="rightSide">The right side <c>b</c>.</param>
/// <param name="leastSquares">True to produce a solution even if the
/// <paramref name="matrix"/> is singular; false otherwise. Default is false.</param>
///
/// <remarks>
/// Please note that this does not check if the matrix is non-singular
/// before attempting to solve. If a least squares solution is desired
/// in case the matrix is singular, pass true to the <paramref name="leastSquares"/>
/// parameter when calling this function.
/// </remarks>
///
/// <example>
/// <code>
/// // Create a matrix. Please note that this matrix
/// // is singular (i.e. not invertible), so only a
/// // least squares solution would be feasible here.
///
/// Double[,] matrix =
/// {
/// { 1, 2, 3 },
/// { 4, 5, 6 },
/// { 7, 8, 9 },
/// };
///
/// // Define a right side matrix b:
/// Double[,] rightSide = { {1}, {2}, {3} };
///
/// // Solve the linear system Ax = b by finding x:
/// Double[,] x = Matrix.Solve(matrix, rightSide, leastSquares: true);
///
/// // The answer should be { {-1/18}, {2/18}, {5/18} }.
/// </code>
/// </example>
///
public static Double[,] Solve(this Double[,] matrix, Double[,] rightSide, bool leastSquares = false)
{
int rows = matrix.GetLength(0);
int cols = matrix.GetLength(1);
if (rows != rightSide.GetLength(0))
{
throw new DimensionMismatchException("rightSide",
"The number of rows in the right hand side matrix must be "
+ "equal to the number of rows in the problem matrix.");
}
if (leastSquares)
{
return(new SingularValueDecomposition(matrix,
computeLeftSingularVectors: true,
computeRightSingularVectors: true,
autoTranspose: true).Solve(rightSide));
}
if (rows == cols)
{
// Solve by LU Decomposition if matrix is square.
return(new LuDecomposition(matrix).Solve(rightSide));
}
else
{
if (cols < rows)
{
// Solve by QR Decomposition if not.
return(new QrDecomposition(matrix).Solve(rightSide));
}
else
{
return(new SingularValueDecomposition(matrix,
computeLeftSingularVectors: true,
computeRightSingularVectors: true,
autoTranspose: true).Solve(rightSide));
}
}
}
public static string perceptiveHash(Double[,] dm)
{
string temp = "";
int sum = 0, tempNum = 0;
const int Avg = 8;
int ddd;
foreach (Double d in dm)
{
if (d <= 0)
{
ddd = 0;
}
else
{
ddd = 1;
}
sum += ddd * (int)Math.Pow(2, tempNum);
tempNum++;
if (tempNum == Avg)
{
tempNum = 0;
if (sum != 0)
{
temp += (char)sum;
}
sum = 0;
}
}
if (tempNum > 1)
{
tempNum = 0;
if (sum != 0)
{
temp += (char)sum;
}
sum = 0;
}
return(temp);
}
/// <summary>
/// Creates a new Givens decomposition.
/// </summary>
/// <param name="matrix">The matrix to decompose.</param>
public GivensDecomposition(Double[,] matrix)
: base(matrix)
{
var Q = Helpers.One(_rows);
var R = (Double[, ])matrix.Clone();
// Main loop.
for (var j = 0; j < _columns - 1; j++)
{
for (var i = _rows - 1; i > j; i--)
{
var a = R[i - 1, j];
var b = R[i, j];
var G = Helpers.One(_rows);
var beta = Math.Sqrt(a * a + b * b);
var s = -b / beta;
var c = a / beta;
G[i - 1, i - 1] = c;
G[i - 1, i] = -s;
G[i, i - 1] = s;
G[i, i] = c;
R = Helpers.Multiply(G, R);
Q = Helpers.Multiply(Q, Helpers.Transpose(G));
}
}
for (var j = 0; j < _columns; j++)
{
if (R[j, j] == 0.0)
{
HasFullRank = false;
}
}
r = R;
q = Q;
}
public Double[] MethodI(Double[,] C, Double[] d, int n, double epsilon)
{
Double[] X0 = new Double[n];
double delta;
Double[] E = new Double[n];
Double[] X = new Double[n];
for (int i = 0; i < n; i++)
{
X0[i] = d[i];
}
do
{
for (int i = 0; i < n; i++)
{
X[i] = 0;
for (int j = 0; j < n; j++)
{
X[i] += C[i, j] * X0[j];
}
X[i] += d[i];
E[i] = Math.Abs(X[i] - X0[i]);
}
delta = E[0];
for (int i = 1; i < n; i++)
{
if (delta < E[i])
{
delta = E[i];
}
}
for (int i = 0; i < n; i++)
{
X0[i] = X[i];
}
} while (delta > epsilon);
return(X);
}
public static void EnYakınKomşuBaldanTatlıdır(Double[,] graph, List <int> path)
{
Dictionary <int, Double> shortestOnes = new Dictionary <int, Double>();
for (int i = 0; i < numberOfNodes; i++)
{
shortestOnes.Add(i, 100000);
}
int p = 0;
do
{
int position = path.Last();
int i = position;
for (int j = 0; j < numberOfNodes; j++)
{
if (graph[i, j] != 0 && shortestOnes[i] > graph[i, j] && !path.Contains(j))
{
shortestOnes[i] = graph[i, j];
position = j;
//graph[i, j] = 0;
}
//else
//{
// graph[i, j] = 0;
//}
}
path.Add(position);
cost += shortestOnes[i];
//for (int k = 0; k < numberOfNodes; k++)
// graph[k, i] = 0;
p++;
}while (path.Last() != 0 && p < numberOfNodes - 1);
cost += graph[path.Last(), 0];
path.Add(0);
}
/// <summary>
/// Guassian the specified matrix, upperMatrix and lowerMatrix.
/// </summary>
/// <returns>The guassian.</returns>
/// <param name="matrix">Matrix.</param>
/// <param name="upperMatrix">Upper matrix.</param>
/// <param name="lowerMatrix">Lower matrix.</param>
private static void Guassian(Double[,] matrix, ref Double[,] upperMatrix, ref Double[,] lowerMatrix)
{
Console.WriteLine(@"Matrix: ");
DisplayMatrix(matrix);
int degrees = matrix.Rank;
int rows = matrix.GetLength(0);
int cols = matrix.GetLength(1);
Double[] firstRow = new Double[cols];
Double[] secondRow = new Double[cols];
for (int i = 0; i < rows - 1; i++)
{
for (int j = i; j < rows - 1; j++)
{
for (int k = 0; k < cols; k++)
{
firstRow[k] = matrix[i, k];
if (j + 1 < rows)
{
secondRow[k] = matrix[j + 1, k];
lowerMatrix[j + 1, i] = Multiplier(firstRow, secondRow, i);
}
}
if (i + 1 < rows)
{
Console.WriteLine(@"Multiplier: {0} ", Multiplier(firstRow, secondRow, i));
Console.WriteLine();
Console.WriteLine(@"LowerMatrix: ");
DisplayMatrix(lowerMatrix);
Console.WriteLine();
ReplaceMatrix(ref upperMatrix, Elimination(firstRow, secondRow, Multiplier(firstRow, secondRow, i)), j + 1);
Console.WriteLine(@"UpperMatrix: ");
DisplayMatrix(upperMatrix);
Console.WriteLine();
}
}
}
}
public static Object Reduce(this Double[,] matrix, Function f, Object start)
{
var args = new Object[5];
var result = start;
var rows = matrix.GetRows();
var columns = matrix.GetColumns();
for (int i = 0, k = 0; i < rows; i++)
{
for (var j = 0; j < columns; j++, k++)
{
args[0] = result;
args[1] = matrix[i, j];
args[2] = (Double)k;
args[3] = (Double)i;
args[4] = (Double)j;
result = f(args);
}
}
return(result);
}
} //end getVarianceWeights
private Double[,] BuildCovarianceMatrix(Double[,] matrix, Double rangeParameterA, Double partialSillSigma)
{
double[,] covarianceMatrix = new double[matrix.GetLength(0), matrix.GetLength(1)];
try
{
for (int r = 0; r < matrix.GetLength(0); r++)
{
for (int c = 0; c < matrix.GetLength(1); c++)
{
covarianceMatrix[r, c] = ApplyCovarianceFunction(matrix[r, c], rangeParameterA, partialSillSigma);
} //nex c
} //next r
SetLagrangeParameter(ref covarianceMatrix);
return(covarianceMatrix);
}
catch (Exception e)
{
throw;
}
}//end BuildCovarianceMatrix
static void Main(string[] args)
{
Console.Title = "Tercer Desafio Practico";
String[] datosadmin = Init.Inicio();
int bloq = State.Bloqueo(datosadmin);
datosadmin[2] = bloq.ToString();
Escribir.Write(datosadmin, "admindata", "datadmin.dll");
if (bloq < 1)
{
Console.WriteLine("Usuario bloqueado, presione cualquier tecla para salir.");
}
else
{
String[,] datosempl = Empleados.Wordemploy();
Double[,] salarios = Sueldos.Calculos(datosempl);
String[] mayormenor = Sueldos.Mymn300(salarios, datosempl);
Salida.Escriturafinal(datosempl, salarios, mayormenor);
}
Console.WriteLine("Gracias por utilizar nuestros servicios, presione cualquier tecla para salir.");
Console.ReadKey();
}
} //end getKrigedGageValue
private Double[,] GetVarianceWeights(Double[,] I, Double[,] D)
{
//the set of wieghts that provide unbiased estimates with a minimum estimation variance is calculated by
//multiplying the inverted covariance matrix(I or C^-1) by the ungaged covarinace matrix (D matrix)
//Isaaks, E. H., and R. M. Srivastava (1989), An Introduction to Applied
// Geostatistics, 1st ed., Oxford Univ. Press, New York. pg295
int matrixLength = 0;
Double wSum = 0;
Double[,] w = null;
try
{
if (I.GetLength(0) != D.GetLength(0) ||
I.GetLength(1) != D.GetLength(0))
{
throw new Exception("rows != columns");
}
matrixLength = I.GetLength(0);
w = new Double[matrixLength, 1];
for (int i = 0; i < matrixLength; i++)
{
wSum = 0;
for (int j = 0; j < matrixLength; j++)
{
// matrix DOT product
wSum = wSum + I[i, j] * D[j, 0];
} //next r
w[i, 0] = wSum;
} //next i
return(w);
}
catch (Exception e)
{
throw;
} //end try
} //end getVarianceWeights
private Boolean ConstruMInv() //construimos una matriz identidad con las dimesiones de la matriz original
{
int i, j;
try {
mInv = new double[fc, cc];
for (i = 0; i < this.fc; i++)
{
for (j = 0; j < this.cc; j++)
{
this.mInv[i, j] = 0;
if (i == j)
{
this.mInv[i, j] = 1;
}
}
}
return(true);
} catch {
return(false);
}
}
public static Double[,] Inverse(Double[,] matrix)
{
var rows = matrix.GetLength(0);
var cols = matrix.GetLength(1);
var target = Helpers.One(cols);
if (cols < 24)
{
var lu = new LUDecomposition(matrix);
return(lu.Solve(target));
}
else if (Helpers.IsSymmetric(matrix))
{
var cho = new CholeskyDecomposition(matrix);
return(cho.Solve(target));
}
else
{
var qr = QRDecomposition.Create(matrix);
return(qr.Solve(target));
}
}
private Double[,] RowPivot(Double[,] matrix, int k)
{
//int row = 0;
for (int i = k + 1; i < matrix.GetLength(0); i++)
{
if (matrix[i, i] != 0)
{
Double[] x = new Double[matrix.GetLength(1)];
for (int j = 0; j < matrix.GetLength(1); j++)
{
x[j] = matrix[k, j];
matrix[k, j] = matrix[i, j];
matrix[i, j] = x[j];
}
break;
}
}
return(matrix);
}
//返回od数据
public double[,] readFromUI(DataGridView dgv)
{
//string[,] odStr=new string[8,12];
od = new double[8, 12];
for (int i = 0; i < 8; i++)
{
for (int j = 0; j < 12; j++)
{
if ((dgv.Rows[i].Cells[j].Value != null) && (dgv.Rows[i].Cells[j].Value.ToString() != ""))
{
//获取单元格内容字符串
od[i, j] = double.Parse(dgv.Rows[i].Cells[j].Value.ToString());
}
else
{
od[i, j] = -10000;
}
}
}
return od;
}
static Double Det(Double[,] A, Int32 n)
{
if (n == 0)
{
return(0);
}
if (n == 1)
{
return(A[0, 0]);
}
if (n == 2)
{
return(A[0, 0] * A[1, 1] - A[0, 1] * A[1, 0]);
}
Double AA = 0;
for (int i = 0; i < n; i++)
{
AA = AA + A[0, i] * System.Math.Pow(-1, i) * Det(AStar(A, n, 0, i), n - 1);
}
return(AA);
}
/*量化解碼*/
public void D_quantized(int[,] tem_y, int[,] tem_u, int[,] tem_v, Double[,] ten_y, Double[,] ten_u, Double[,] ten_v)
{
//Dz_y zigzag解碼後的y
//Dz_u zigzag解碼後的u
//Dz_v zigzag解碼後的v
int[,] uv_table = { { 16, 11, 10, 16, 24, 40, 51, 61 },
{ 12, 12, 14, 19, 26, 58, 60, 55 },
{ 14, 13, 16, 24, 40, 57, 69, 56 },
{ 14, 17, 22, 29, 51, 87, 80, 62 },
{ 18, 22, 37, 56, 68, 109, 103, 77 },
{ 24, 35, 55, 64, 81, 104, 113, 92 },
{ 49, 64, 78, 87, 103, 121, 120, 101 },
{ 72, 92, 95, 98, 112, 100, 103, 99 } };
int[,] y_table = { { 17, 18, 24, 47, 99, 99, 99, 99 },
{ 18, 21, 26, 66, 99, 99, 99, 99 },
{ 24, 26, 56, 99, 99, 99, 99, 99 },
{ 47, 66, 99, 99, 99, 99, 99, 99 },
{ 99, 99, 99, 99, 99, 99, 99, 99 },
{ 99, 99, 99, 99, 99, 99, 99, 99 },
{ 99, 99, 99, 99, 99, 99, 99, 99 },
{ 99, 99, 99, 99, 99, 99, 99, 99 } };
for (int m = 0; m < new_h; m += dim)
{
for (int n = 0; n < new_w; n += dim)
{
for (int i = 0; i < dim; i++)
{
for (int j = 0; j < dim; j++)
{
ten_y[i + m, j + n] = tem_y[i + m, j + n] * y_table[i, j];
ten_u[i + m, j + n] = tem_u[i + m, j + n] * uv_table[i, j];
ten_v[i + m, j + n] = tem_v[i + m, j + n] * uv_table[i, j];
}
}
}
}
}
string AlreadyMatchingTemplate(Double[,] rs)
{
try
{
SqlConnection cn = new SqlConnection(st.ConStr);
cn.Open();
SqlDataReader rd = new SqlCommand("SELECT * FROM employees", cn).ExecuteReader();
var rlist = new SortedDictionary <string, double>();
while (rd.Read())
{
byte[] data = (byte[])rd["iristemp"];
Double[,] dbv = (Double[, ])st.ByteArrayToObject(data);
/*if (ByteArrayCompare(data, rs))
* return rd["eid"] + " : " + rd["imgkey"];*/
rlist.Add(rd["eid"] + "-" + rd["ename"], st.TemplateEquals(dbv, rs));
}
cn.Close();
IEnumerable <KeyValuePair <string, double> > vals = rlist.OrderByDescending(k => k.Value);
if (vals.Count() > 0)
{
var val = vals.ElementAt(0);
if (val.Value > 60.0)
{
return(val.Key);
}
}
return(null);
}
catch (Exception ex)
{
st.WMsgBox(ex.ToString());
return(null);
}
}
private void algoritmoGauss(Double[,] matriz)
{
int tamMatriz = matriz.GetUpperBound(1);
int pivote = 0;
for (int i = 0; i <= tamMatriz - 1; i++)
{
if (matriz[i, i] == 0)
{
//Deberia lanzar un error
//rchTeOperaciones.Text += "Error: " + "no es posible resolver ecuacion";
}
else
{
if (matriz[i, i] != 1)
{
Double divisorPiv = matriz[i, i];
for (int col = 0; col <= tamMatriz; col++)
{
matriz[i, col] = matriz[i, col] / divisorPiv;
}
}
for (int ren = 0; ren < tamMatriz; ren++)
{
if (ren != pivote)
{
Double val0 = -Convert.ToDouble(matriz[ren, i]);
for (int col = 0; col <= tamMatriz; col++)
{
matriz[ren, col] = matriz[i, col] * val0 + matriz[ren, col];
}
}
}
}
pivote++;
}
}
/*以下為色彩轉換副程式*/
public void rgb_to_yuv(int[,,] rgbData, Double[,] arr_y, Double[,] arr_u, Double[,] arr_v)
{
//設定像點資料
for (int y = 0; y < Height; y++)
{
for (int x = 0; x < Width; x++)
{
arr_y[y, x] = 0.299 * rgbData[y, x, 0] + 0.587 * rgbData[y, x, 1] + 0.114 * rgbData[y, x, 2]; //亮度轉換
arr_u[y, x] = 0.5 * rgbData[y, x, 2] - 0.169 * rgbData[y, x, 0] - 0.331 * rgbData[y, x, 1] + 128; //藍色色度差轉換
arr_v[y, x] = 0.5 * rgbData[y, x, 0] - 0.419 * rgbData[y, x, 1] - 0.081 * rgbData[y, x, 2] + 128; //紅色色度差轉換
if (arr_y[y, x] < 0)
{
arr_y[y, x] = 0;
}
if (arr_y[y, x] > 255)
{
arr_y[y, x] = 255;
}
if (arr_u[y, x] < 0)
{
arr_u[y, x] = 0;
}
if (arr_u[y, x] > 255)
{
arr_u[y, x] = 255;
}
if (arr_v[y, x] < 0)
{
arr_v[y, x] = 0;
}
if (arr_v[y, x] > 255)
{
arr_v[y, x] = 255;
}
}
}
}
/*以下為取樣*/
public void sampling(Double[,] arr_u, Double[,] arr_v)
{
new_h = Height - Height % dim;
new_w = Width - Width % dim;
for (int i = 0; i < Height; i++)
{
for (int j = 0; j < Width; j++)
{
if (j % 2 == 0)
{
if (i % 2 == 0)
{
if (i + 1 < Height)
{
arr_v[i + 1, j] = arr_v[i, j];
}
if (i + 1 < Height && j + 1 < Width)
{
arr_v[i + 1, j + 1] = arr_v[i, j];
}
if (j + 1 < Width)
{
arr_v[i, j + 1] = arr_v[i, j];
}
}
else
{
arr_u[i - 1, j] = arr_u[i, j];
if (j + 1 < Width)
{
arr_u[i - 1, j + 1] = arr_u[i, j];
arr_u[i, j + 1] = arr_u[i, j];
}
}
}
}
}
}
public static Double[] GetAllDetail(Double[,] input, Int32 currentLevel)
{
Int32 Len = input.GetLength(0) >> currentLevel;
Double[] output = new Double[input.GetLength(0) * input.GetLength(0) - (Len * Len)];
Int32 OutIndex = 0;
for (int lev = currentLevel; lev > 0; lev--)
{
Int32 Bound = input.GetLength(0) >> lev - 1;
Len = input.GetLength(0) >> lev;
for (int i = 0; i < Len; i++)
{
for (int j = Len; j < Bound; j++)
{
output[OutIndex] = input[i, j];
OutIndex++;
}
}
for (int i = Len; i < Bound; i++)
{
for (int j = 0; j < Len; j++)
{
output[OutIndex] = input[i, j];
OutIndex++;
}
}
for (int i = Len; i < Bound; i++)
{
for (int j = Len; j < Bound; j++)
{
output[OutIndex] = input[i, j];
OutIndex++;
}
}
}
return(output);
}
public Double[,] IterForm(Double[,] A, Double[] b, int n)
{
C1 = new Double[n, n];
C2 = new Double[n];
//получение итерационной формы системы
for (int i = 0; i < n; i++)
{
for (int j = 0; j < n; j++)
{
if (i == j)
{
C1[i, j] = 0;
}
else
{
C1[i, j] = -A[i, j] / A[i, i];
}
}
C2[i] = b[i] / A[i, i];
}
return(C1);
}
public static object[,] _PFE(object[,] products,
object[,] valueDate,
object[,] forwardValueDates,
object[,] requiredPecentiles,
object[,] model,
object[,] nSims)
{
try
{
Product[] _products = XU.GetObject1D <Product>(products, "products");
Date _valueDate = XU.GetDate0D(valueDate, "valueDate");
Date[] _forwardValueDates = XU.GetDate1D(forwardValueDates, "forwardValueDates");
Double[] _requiredPecentiles = XU.GetDouble1D(requiredPecentiles, "requiredPecentiles");
NumeraireSimulator _model = XU.GetObject0D <NumeraireSimulator>(model, "model");
Int32 _nSims = XU.GetInt320D(nSims, "nSims");
Double[,] _result = XLValuation.PFE(_products, _valueDate, _forwardValueDates, _requiredPecentiles, _model, _nSims);
return(XU.ConvertToObjects(_result));
}
catch (Exception e)
{
return(XU.Error2D(e));
}
}
public static Boolean IsSymmetric(Double[,] matrix)
{
var rows = matrix.GetLength(0);
var cols = matrix.GetLength(1);
if (rows == cols)
{
for (var i = 0; i < rows; i++)
{
for (var j = 0; j < i; j++)
{
if (matrix[i, j] != matrix[j, i])
{
return(false);
}
}
}
return(true);
}
return(false);
}
public static void MultiplyMatricesTasks(Double[,] matrA, Double[,] matrB,
Double[,] result, Int32 n)
{
// Масив задач
Task[] tasks = new Task[ProcessorCount - 1];
// Кількість ітерацій на потік
Int32 iterationsPerTask = n / ProcessorCount;
Int32 i, currentStartNumber;
for (i = 0, currentStartNumber = 0; i < ProcessorCount - 1; i++, currentStartNumber += iterationsPerTask)
{
Int32 number = currentStartNumber;
// Створюємо та одразу запускаємо нову задачу
tasks[i] = Task.Run(() =>
MultiplyWithBounds(matrA, matrB, result, n, number, number + iterationsPerTask - 1));
}
// Для головного потоку
MultiplyWithBounds(matrA, matrB, result, n, currentStartNumber, currentStartNumber + iterationsPerTask - 1);
// Чекаємо, коли усі задачі завершаться
Task.WhenAll(tasks).Wait();
}
/// <summary>
/// Konstruktor.
/// </summary>
/// <param name="costMatrix">Macierz kosztu przejścia.</param>
public TSPFitness(Double[,] costMatrix)
{
this.costMatrix = (Double[,])costMatrix.Clone();
}
/// <summary>
/// Construct an eigenvalue decomposition.</summary>
/// <param name="value">
/// The matrix to be decomposed.</param>
/// <param name="assumeSymmetric">
/// Defines if the matrix should be assumed as being symmetric
/// regardless if it is or not. Default is <see langword="false"/>.</param>
/// <param name="inPlace">
/// Pass <see langword="true"/> to perform the decomposition in place. The matrix
/// <paramref name="value"/> will be destroyed in the process, resulting in less
/// memory comsumption.</param>
public EigenvalueDecomposition(Double[,] value, bool assumeSymmetric, bool inPlace)
{
if (value == null)
{
throw new ArgumentNullException("value", "Matrix cannot be null.");
}
if (value.GetLength(0) != value.GetLength(1))
{
throw new ArgumentException("Matrix is not a square matrix.", "value");
}
n = value.GetLength(1);
V = new Double[n, n];
d = new Double[n];
e = new Double[n];
this.symmetric = assumeSymmetric;
if (this.symmetric)
{
V = inPlace ? value : (Double[,])value.Clone();
// Tridiagonalize.
this.tred2();
// Diagonalize.
this.tql2();
}
else
{
H = inPlace ? value : (Double[,])value.Clone();
ort = new Double[n];
// Reduce to Hessenberg form.
this.orthes();
// Reduce Hessenberg to real Schur form.
this.hqr2();
}
}
public void SetAccel(Double[,] accelValues)
{
AccelValues = accelValues;
}
private CoordinateDataBuffer()
{
_ellipseCoordinates = new Point();
PointerCoordinates = new Point[2];
AccelValues = new Double[2, 3];
}
/// <summary>
/// Instructs the algorithm to prepare it's data.
/// </summary>
public void PrepareToStart()
{
Random random = new Random();
/* Build up those matrixes */
m_Matrix1 = new Double[m_Matrix1X, m_Matrix1Y];
m_Matrix2 = new Double[m_Matrix2X, m_Matrix2Y];
m_ResultMatrix = new Double[m_Matrix2X, m_Matrix1Y];
/* Populate matrix 1 */
for (Int32 x = 0; x < m_Matrix1X; x++)
{
for (Int32 y = 0; y < m_Matrix1Y; y++ )
m_Matrix1[x, y] = Math.Round(random.NextDouble() * 100, 2);
}
/* Populate Matrix 2*/
for (Int32 x = 0; x < m_Matrix2X; x++)
{
for (Int32 y = 0; y < m_Matrix2Y; y++)
m_Matrix2[x, y] = Math.Round(random.NextDouble() * 100, 2);
}
}
internal void unpopulate()
{
digitalData = null;
analogPulse = null;
}
//Fuzzy C-Means process thread\\
private void fuzzycmeansprocessThread()
{
timer2.Start();
BackgroundWorker b = new BackgroundWorker();
int period = 0;
int periodTrackbarValue = periodTrackBar.Value;
metroPanel1.Visible = false;
metroPanel2.Visible = false;
metroPanel3.Visible = true;
this.Text = "Process";
fuzzyrfmTimerLabel.Visible = false;
viewFuzzyBtn.Visible = false;
viewResultBtn.Visible = false;
winChartViewer1.Image = null;
b.DoWork += (object sender, DoWorkEventArgs e) =>
{
query = "SELECT TOP 1 * FROM [CSS].[dbo].[transaction] order by purchaseDate DESC";
sqlConnection();
sqlCon.Open();
command = new SqlCommand();
command.CommandText = query;
command.CommandType = CommandType.Text;
command.Connection = sqlCon;
sa = new SqlDataAdapter(command);
dt = new DataTable();
sa.Fill(dt);
dateCutOff = Convert.ToDateTime(dt.Rows[0][3]);
query = "SELECT * FROM [CSS].[dbo].[transaction] order by [CID]";
command.CommandText = query;
command.CommandType = CommandType.Text;
command.Connection = sqlCon;
sa = new SqlDataAdapter(command);
dt = new DataTable();
sa.Fill(dt);
//Customer table emptied\\
delQuery = "delete [CSS].[DBO].customer";
command = new SqlCommand(delQuery, sqlCon);
command.CommandText = delQuery;
sa = new SqlDataAdapter(command);
command.Connection = sqlCon;
command.ExecuteNonQuery();
custName = new String[1];
custID = new String[1];
DateTime date = Convert.ToDateTime(dt.Rows[0][3]);
Double mntry = Convert.ToInt64(dt.Rows[0][2]);
X = new Double[dt.Rows.Count, 3];
int frq = 1;
int idx = 0;
DataRow dr;
dr = dt.Rows[0];
//Customer data preprocessing\\
for (int i = 0; i < periodTrackbarValue; i++)
{
period += DateTime.DaysInMonth(dateCutOff.Year, dateCutOff.Month - i);
}
for (int i = 0; i < dt.Rows.Count - 1; i++)
{
if (Convert.ToInt32((dateCutOff - Convert.ToDateTime(dr.Table.Rows[i][3])).TotalDays) <= period)
{
custName[idx] = dr.Table.Rows[i][1].ToString();
custID[idx] = dr.Table.Rows[i][4].ToString();
if (custID[idx] == dt.Rows[i + 1][4].ToString())
{
if (Convert.ToInt32((dateCutOff - Convert.ToDateTime(dt.Rows[i + 1][3])).TotalDays) <= period)
{
if (Convert.ToDateTime(dt.Rows[i + 1][3]) > Convert.ToDateTime(dr.Table.Rows[i][3]))
{
date = Convert.ToDateTime(dt.Rows[i + 1][3]);
}
frq += 1;
mntry += Convert.ToInt64(dt.Rows[i + 1][2]);
}
}
else if (custID[idx] != dt.Rows[i + 1][4].ToString())
{
X[idx, 0] = Convert.ToInt32((dateCutOff - date).TotalDays);
X[idx, 1] = frq;
X[idx, 2] = mntry;
idx += 1;
Array.Resize(ref custName, idx + 1);
Array.Resize(ref custID, idx + 1);
frq = 1;
mntry = Convert.ToInt64(dt.Rows[i + 1][2]);
date = Convert.ToDateTime(dt.Rows[i + 1][3]);
}
}
}
num = Enumerable.Range(0, X.GetLength(0)).Count(i => X[i, 0] != 0);
//Customer calculated data insertion to customer table in SQL\\
for (int i = 0; i < num; i++)
{
String customerQuery = "INSERT customer (CID, customerName, Frequency, totalPurchase, lastPurchase, clusterIndex) VALUES ('" + custID[i] + "', '" + custName[i].Replace("'", " ") + "', '" + X[i, 1] + "', '" + X[i, 2] + "', '" + dateCutOff.AddDays(-X[i, 0]) + "', '" + 0 + "')";
command.CommandText = customerQuery;
command.CommandType = CommandType.Text;
command.Connection = sqlCon;
command = new SqlCommand(customerQuery, sqlCon);
command.ExecuteNonQuery();
}
fuzzyCMeans();
};
b.RunWorkerCompleted += (object sender, RunWorkerCompletedEventArgs e) =>
{
this.TopMost = true;
this.BringToFront();
this.Focus();
sqlCon.Open();
timer2.Stop();
clusterProgressBar.Visible = false;
clusterProgressBar.Enabled = false;
rfmprocessBtn.Visible = true;
rfmprocessBtn.Focus();
this.TopMost = false;
viewFuzzyBtn.Visible = true;
fuzzycmeansTimerLabel.Location = new Point(163, 9);
query = "SELECT TOP 1 * FROM [CSS].[dbo].[historyIndex] order by historyID DESC";
command.CommandText = query;
command.CommandType = CommandType.Text;
command.Connection = sqlCon;
sa = new SqlDataAdapter(command);
dt = new DataTable();
sa.Fill(dt);
query = "UPDATE [CSS].[dbo].historyIndex set fuzzyProcessTime ='" + timeSpan + "', MPCScore='" + MPCScore + "', fuzzyRFMTime=null where historyID ='" + dt.Rows[0][0].ToString() + "'";
command.CommandText = query;
command.CommandType = CommandType.Text;
command.Connection = sqlCon;
command.ExecuteNonQuery();
sqlCon.Close();
};
clusterProgressBar.Enabled = true;
b.RunWorkerAsync();
}
public Mat4(Double[,] data)
{
_data = data;
}
private Double[,] v; // right singular vectors
#endregion Fields
#region Constructors
/// <summary>Constructs a new singular value decomposition.</summary>
/// <param name="value">
/// The matrix to be decomposed.</param>
public SingularValueDecomposition(Double[,] value)
{
if (value == null)
{
throw new ArgumentNullException("value", "Matrix cannot be null.");
}
//m should not less than n
Double[,] a;
m = value.GetLength(0); // rows
n = value.GetLength(1); // cols
if (m < n)
{
throw new System.Exception("rows should not less than cols in value matrix");
}
// Proceed anyway
a = (Double[,])value.Clone();
int nu = System.Math.Min(m, n);
int ni = System.Math.Min(m + 1, n);
s = new Double[ni];
u = new Double[m, nu];
v = new Double[n, n];
Double[] e = new Double[n];
Double[] work = new Double[m];
// Will store ordered sequence of indices after sorting.
si = new int[ni]; for (int i = 0; i < ni; i++) si[i] = i;
// Reduce A to bidiagonal form, storing the diagonal elements in s and the super-diagonal elements in e.
int nct = System.Math.Min(m - 1, n);
int nrt = System.Math.Max(0, System.Math.Min(n - 2, m));
for (int k = 0; k < System.Math.Max(nct, nrt); k++)
{
if (k < nct)
{
// Compute the transformation for the k-th column and place the k-th diagonal in s[k].
// Compute 2-norm of k-th column without under/overflow.
s[k] = 0;
for (int i = k; i < m; i++)
{
s[k] = Hypotenuse(s[k], a[i, k]);
}
if (s[k] != 0)
{
if (a[k, k] < 0)
s[k] = -s[k];
for (int i = k; i < m; i++)
a[i, k] /= s[k];
a[k, k] += 1;
}
s[k] = -s[k];
}
for (int j = k + 1; j < n; j++)
{
if ((k < nct) & (s[k] != 0))
{
// Apply the transformation.
Double t = 0;
for (int i = k; i < m; i++)
{
t += a[i, k] * a[i, j];
}
t = -t / a[k, k];
for (int i = k; i < m; i++)
{
a[i, j] += t * a[i, k];
}
}
// Place the k-th row of A into e for the subsequent calculation of the row transformation.
e[j] = a[k, j];
}
if (k < nct)
{
// Place the transformation in U for subsequent back
// multiplication.
for (int i = k; i < m; i++)
u[i, k] = a[i, k];
}
if (k < nrt)
{
// Compute the k-th row transformation and place the k-th super-diagonal in e[k].
// Compute 2-norm without under/overflow.
e[k] = 0;
for (int i = k + 1; i < n; i++)
e[k] = Hypotenuse(e[k], e[i]);
if (e[k] != 0)
{
if (e[k + 1] < 0)
e[k] = -e[k];
for (int i = k + 1; i < n; i++)
e[i] /= e[k];
e[k + 1] += 1;
}
e[k] = -e[k];
if ((k + 1 < m) & (e[k] != 0))
{
// Apply the transformation.
for (int i = k + 1; i < m; i++)
work[i] = 0;
int k1 = k + 1;
for (int i = k1; i < m; i++)
{
for (int j = k1; j < n; j++)
{
work[i] += e[j] * a[i, j];
}
}
for (int j = k1; j < n; j++)
{
Double t = -e[j] / e[k1];
for (int i = k1; i < m; i++)
{
a[i, j] += t * work[i];
}
}
}
// Place the transformation in V for subsequent back multiplication.
for (int i = k + 1; i < n; i++)
v[i, k] = e[i];
}
}
// Set up the final bidiagonal matrix or order p.
int p = System.Math.Min(n, m + 1);
if (nct < n) s[nct] = a[nct, nct];
if (m < p) s[p - 1] = 0;
if (nrt + 1 < p) e[nrt] = a[nrt, p - 1];
e[p - 1] = 0;
//generate U.
for (int j = nct; j < nu; j++)
{
for (int i = 0; i < m; i++)
u[i, j] = 0;
u[j, j] = 1;
}
for (int k = nct - 1; k >= 0; k--)
{
if (s[k] != 0)
{
for (int j = k + 1; j < nu; j++)
{
Double t = 0;
for (int i = k; i < m; i++)
{
t += u[i, k] * u[i, j];
}
t = -t / u[k, k];
for (int i = k; i < m; i++)
{
u[i, j] += t * u[i, k];
}
}
for (int i = k; i < m; i++)
{
u[i, k] = -1.0 * u[i, k];
}
u[k, k] = 1 + u[k, k];
for (int i = 0; i < k - 1; i++)
u[i, k] = 0;
}
else
{
for (int i = 0; i < m; i++)
u[i, k] = 0;
u[k, k] = 1;
}
}
//generate V.
for (int k = n - 1; k >= 0; k--)
{
if ((k < nrt) & (e[k] != 0))
{
// TODO: The following is a pseudo correction to make SVD
// work on matrices with n > m (less rows than columns).
// For the proper correction, compute the decomposition of the
// transpose of A and swap the left and right eigenvectors
// Original line:
// for (int j = k + 1; j < nu; j++)
// Pseudo correction:
// for (int j = k + 1; j < n; j++)
for (int j = k + 1; j < n; j++) // pseudo-correction
{
Double t = 0;
for (int i = k + 1; i < n; i++)
{
t += v[i, k] * v[i, j];
}
t = -t / v[k + 1, k];
for (int i = k + 1; i < n; i++)
{
v[i, j] += t * v[i, k];
}
}
}
for (int i = 0; i < n; i++)
{
v[i, k] = 0;
}
v[k, k] = 1;
}
// Main iteration loop for the singular values.
int pp = p - 1;
int iter = 0;
while (p > 0)
{
int k, kase;
// Here is where a test for too many iterations would go.
// This section of the program inspects for
// negligible elements in the s and e arrays. On
// completion the variables kase and k are set as follows.
// kase = 1 if s(p) and e[k-1] are negligible and k<p
// kase = 2 if s(k) is negligible and k<p
// kase = 3 if e[k-1] is negligible, k<p, and
// s(k), ..., s(p) are not negligible (qr step).
// kase = 4 if e(p-1) is negligible (convergence).
for (k = p - 2; k >= -1; k--)
{
if (k == -1)
break;
if (System.Math.Abs(e[k]) <=
tiny + eps * (System.Math.Abs(s[k]) + System.Math.Abs(s[k + 1])))
{
e[k] = 0;
break;
}
}
if (k == p - 2)
{
kase = 4;
}
else
{
int ks;
for (ks = p - 1; ks >= k; ks--)
{
if (ks == k)
break;
Double t = (ks != p ? System.Math.Abs(e[ks]) : 0) +
(ks != k + 1 ? System.Math.Abs(e[ks - 1]) : 0);
if (System.Math.Abs(s[ks]) <= tiny + eps * t)
{
s[ks] = 0;
break;
}
}
if (ks == k)
kase = 3;
else if (ks == p - 1)
kase = 1;
else
{
kase = 2;
k = ks;
}
}
k++;
// Perform the task indicated by kase.
switch (kase)
{
// Deflate negligible s(p).
case 1:
{
Double f = e[p - 2];
e[p - 2] = 0;
for (int j = p - 2; j >= k; j--)
{
Double t = Hypotenuse(s[j], f);
Double cs = s[j] / t;
Double sn = f / t;
s[j] = t;
if (j != k)
{
f = -sn * e[j - 1];
e[j - 1] = cs * e[j - 1];
}
for (int i = 0; i < n; i++)
{
t = cs * v[i, j] + sn * v[i, p - 1];
v[i, p - 1] = -sn * v[i, j] + cs * v[i, p - 1];
v[i, j] = t;
}
}
}
break;
// Split at negligible s(k).
case 2:
{
Double f = e[k - 1];
e[k - 1] = 0;
for (int j = k; j < p; j++)
{
Double t = Hypotenuse(s[j], f);
Double cs = s[j] / t;
Double sn = f / t;
s[j] = t;
f = -sn * e[j];
e[j] = cs * e[j];
for (int i = 0; i < m; i++)
{
t = cs * u[i, j] + sn * u[i, k - 1];
u[i, k - 1] = -sn * u[i, j] + cs * u[i, k - 1];
u[i, j] = t;
}
}
}
break;
// Perform one qr step.
case 3:
{
// Calculate the shift.
Double scale = System.Math.Max(System.Math.Max(System.Math.Max(System.Math.Max(System.Math.Abs(s[p - 1]), System.Math.Abs(s[p - 2])), System.Math.Abs(e[p - 2])), System.Math.Abs(s[k])), System.Math.Abs(e[k]));
Double sp = s[p - 1] / scale;
Double spm1 = s[p - 2] / scale;
Double epm1 = e[p - 2] / scale;
Double sk = s[k] / scale;
Double ek = e[k] / scale;
Double b = ((spm1 + sp) * (spm1 - sp) + epm1 * epm1) / 2;
Double c = (sp * epm1) * (sp * epm1);
double shift = 0;
if ((b != 0) | (c != 0))
{
if (b < 0)
shift = -System.Math.Sqrt(b * b + c);
else
shift = System.Math.Sqrt(b * b + c);
shift = c / (b + shift);
}
Double f = (sk + sp) * (sk - sp) + (Double)shift;
Double g = sk * ek;
// Chase zeros.
for (int j = k; j < p - 1; j++)
{
Double t = Hypotenuse(f, g);
Double cs = f / t;
Double sn = g / t;
if (j != k) e[j - 1] = t;
f = cs * s[j] + sn * e[j];
e[j] = cs * e[j] - sn * s[j];
g = sn * s[j + 1];
s[j + 1] = cs * s[j + 1];
for (int i = 0; i < n; i++)
{
/*t = cs * v[i, j] + sn * v[i, j + 1];
v[i, j + 1] = -sn * v[i, j] + cs * v[i, j + 1];
v[i, j] = t;*/
Double vij = v[i, j]; // *vj;
Double vij1 = v[i, j + 1]; // *vj1;
t = cs * vij + sn * vij1;
v[i, j + 1] = -sn * vij + cs * vij1;
v[i, j] = t;
}
t = Hypotenuse(f, g);
cs = f / t;
sn = g / t;
s[j] = t;
f = cs * e[j] + sn * s[j + 1];
s[j + 1] = -sn * e[j] + cs * s[j + 1];
g = sn * e[j + 1];
e[j + 1] = cs * e[j + 1];
if (j < m - 1)
{
for (int i = 0; i < m; i++)
{
/* t = cs * u[i, j] + sn * u[i, j + 1];
u[i, j + 1] = -sn * u[i, j] + cs * u[i, j + 1];
u[i, j] = t;*/
Double uij = u[i, j]; // *uj;
Double uij1 = u[i, j + 1]; // *uj1;
t = cs * uij + sn * uij1;
u[i, j + 1] = -sn * uij + cs * uij1;
u[i, j] = t;
}
}
}
e[p - 2] = f;
iter = iter + 1;
}
break;
// Convergence.
case 4:
{
// Make the singular values positive.
if (s[k] <= 0)
{
s[k] = (s[k] < 0 ? -s[k] : 0);
for (int i = 0; i <= pp; i++)
v[i, k] = -v[i, k];
}
// Order the singular values.
while (k < pp)
{
if (s[k] >= s[k + 1])
break;
Double t = s[k];
s[k] = s[k + 1];
s[k + 1] = t;
int ti = si[k];
si[k] = si[k + 1];
si[k + 1] = ti;
if (k < n - 1)
{
for (int i = 0; i < n; i++)
{
t = v[i, k + 1];
v[i, k + 1] = v[i, k];
v[i, k] = t;
}
}
if (k < m - 1)
{
for (int i = 0; i < m; i++)
{
t = u[i, k + 1];
u[i, k + 1] = u[i, k];
u[i, k] = t;
}
}
k++;
}
iter = 0;
p--;
}
break;
}
}
}
/// <summary>
/// Tworzy okno dialogowe dla wczytania danych z pliku.
/// </summary>
private void loadDataButton_Click(object sender, EventArgs e)
{
OpenFileDialog openFileDialog = new OpenFileDialog();
openFileDialog.Filter = "txt files (*.txt)|*.txt|All files (*.*)|*.*";
DialogResult result = openFileDialog.ShowDialog();
if (result == DialogResult.OK) // Test result.
{
try
{
costMatrix = LoadFile(openFileDialog.FileName);
InsertData(costMatrix);
}
catch (Exception ex)
{
MessageBox.Show("Nie można wczytać pliku z danymi: " + ex.Message);
}
}
}
//从前一个窗体获得数据
private void getData()
{
//获取第一窗体输入的数字
//int num = ((Form1)this.Owner).num;
plate_info = ((Form1)this.Owner).plate_info;
tpl = ((Form1)this.Owner).tpl;
od = ((Form1)this.Owner).od;
}
public MainForm()
{
InitializeComponent();
selecitonModifierComboBox.DisplayMember = "Key";
selecitonModifierComboBox.ValueMember = "Value";
selecitonModifierComboBox.DataSource = new BindingSource(ModifierCombinationDict, null);
selectionAlgorithmComboBox.DisplayMember = "Key";
selectionAlgorithmComboBox.ValueMember = "Value";
selectionAlgorithmComboBox.DataSource = new BindingSource(SelectionStrategiesDict, null);
selectionAlgorithmComboBox.SelectedIndex = 1;
{
crossoverStategies = new List<Object>();
var type = typeof(PermutationChromosome.ICrossOverStrategy);
var types = AppDomain.CurrentDomain.GetAssemblies().ToList()
.SelectMany(s => s.GetTypes())
.Where(p => type.IsAssignableFrom(p) && !type.IsEquivalentTo(p));
foreach (var t in types)
{
PermutationChromosome.ICrossOverStrategy crossoverStrategy
= (PermutationChromosome.ICrossOverStrategy)t.GetConstructor(Type.EmptyTypes).Invoke(null);
crossoverStategies.Add(crossoverStrategy);
}
this.crossoverAlgorithmComboBox.DataSource = crossoverStategies;
}
{
mutationStategies = new List<Object>();
var type = typeof(PermutationChromosome.IMutationStrategy);
var types = AppDomain.CurrentDomain.GetAssemblies().ToList()
.SelectMany(s => s.GetTypes())
.Where(p => type.IsAssignableFrom(p) && !type.IsEquivalentTo(p));
foreach (var t in types)
{
PermutationChromosome.IMutationStrategy mutationStrategy
= (PermutationChromosome.IMutationStrategy)t.GetConstructor(Type.EmptyTypes).Invoke(null);
mutationStategies.Add(mutationStrategy);
}
this.mutationAlgorithmComboBox.DataSource = mutationStategies;
}
this.Text = "Algorytmów genetycznych na przykładzie problemu komiwojażera";
Double INF = Double.PositiveInfinity;
// Miasta - odleglości miedzy miastami.
costMatrix = new Double[,]
{
{ INF, 304.0, 486.0, 584.0, 341.0}, //Gdansk
{ 304.0, INF, 280.0, 403.0, 304.0}, //poznan
{ 486.0, 280.0, INF, 304.0, 341.0}, //wrocla
{ 584.0, 403.0, 304.0, INF, 299}, //krakow
{ 341.0, 304, 341.0, 299, INF}, //warszawa
};
InsertData(costMatrix);
/*
* Utwórz background workera dla asynchronicznego przetwarzania.
*/
worker = new BackgroundWorker();
worker.WorkerSupportsCancellation = true;
worker.WorkerReportsProgress = true;
worker.DoWork += new DoWorkEventHandler((Object sender, DoWorkEventArgs args) =>
{
worker.ReportProgress(1);
while (population.NextGeneration() && !worker.CancellationPending)
{
if (population.Generation % 1000 == 0)
{
worker.ReportProgress((Int32)population.Generation);
}
}
worker.ReportProgress((Int32)population.Generation);
});
worker.ProgressChanged += new ProgressChangedEventHandler((Object sender, ProgressChangedEventArgs args) =>
{
progressLabel.Text = "Iteracja: " + args.ProgressPercentage;
if (showAllCheckbox.Checked && stopCondition.Leader != null)
{
textBox1.AppendText("Najlepsze dopasowanie: " + (1.0 / stopCondition.Leader.Evaluate()) + Environment.NewLine);
textBox1.AppendText(stopCondition.Leader.ToString() + Environment.NewLine);
}
});
worker.RunWorkerCompleted += new RunWorkerCompletedEventHandler((Object sender, RunWorkerCompletedEventArgs args) =>
{
textBox1.AppendText("Najlepsze dopasowanie: " + (1.0 / stopCondition.Leader.Evaluate()) + Environment.NewLine);
textBox1.AppendText(stopCondition.Leader.ToString() + Environment.NewLine);
startProcessingButton.Text = "Start";
TimeStop = DateTime.Now;
TimeSpan roznica = TimeStop - TimeStart;
textBox1.AppendText("Czas pracy: " + roznica.TotalMilliseconds + "ms" + Environment.NewLine);
textBox1.AppendText("====================" + Environment.NewLine);
});
}
public void fuzzyCMeans()
{
P = new Double[maxIter];
P[0] = 0;
cltr = Decimal.ToInt32(clusterSizeNUD.Value);
ctr = 1;
V = new Double[cltr, 3];
command = new SqlCommand();
query = "SELECT * FROM customer order by CID";
sqlConnection();
command.CommandText = query;
command.CommandType = CommandType.Text;
command.Connection = sqlCon;
sa = new SqlDataAdapter(command);
dt = new DataTable();
sa.Fill(dt);
custCltr = new int[dt.Rows.Count];
r = new Double[cltr, 2];
f = new Double[cltr, 2];
m = new Double[cltr, 2];
segmentD = new Dictionary<String, String>{{"Superstar","1"},{"Golden","2"},{"Typical","3"},{"Occasional","4"},
{"Everyday","5"},{"Dormant","6"}};
rfmd = new Dictionary<String, String>{
{"000","Dormant Customer 18"},{"010","Dormant Customer 12"},{"020","Dormant Customer 6"},{"030","Dormant Customer 3"},
{"001","Dormant Customer 17"},{"011","Dormant Customer 11"},{"021","Dormant Customer 5"},{"031","Dormant Customer 2"},
{"002","Dormant Customer 16"},{"012","Dormant Customer 10"},{"022","Dormant Customer 4"},{"032","Dormant Customer 1"},
{"003","Occasional Customer 14"},{"013","Occasional Customer 13"},{"023","Golden Customer 8"},{"033","Golden Customer 7"},
{"004","Occasional Customer 12"},{"014","Occasional Customer 11"},{"024","Superstar 8"},{"034","Superstar 7"},
{"100","Dormant Customer 15"},{"110","Dormant Customer 9"},{"120","Everyday Shopper 18"},{"130","Everyday Shopper 17"},
{"101","Dormant Customer 14"},{"111","Dormant Customer 8"},{"121","Everyday Shopper 16"},{"131","Everyday Shopper 15"},
{"102","Dormant Customer 13"},{"112","Dormant Customer 7"},{"122","Everyday Shopper 14"},{"132","Everyday Shopper 13"},
{"103","Occasional Customer 10"},{"113","Occasional Customer 9"},{"123","Golden Customer 6"},{"133","Golden Customer 5"},
{"104","Occasional Customer 8"},{"114","Occasional Customer 7"},{"124","Superstar 6"},{"134","Superstar 5"},
{"200","Typical Customer 14"},{"210","Typical Customer 13"},{"220","Everyday Shopper 12"},{"230","Everyday Shopper 11"},
{"201","Typical Customer 12"},{"211","Typical Customer 11"},{"221","Everyday Shopper 10"},{"231","Everyday Shopper 9"},
{"202","Occasional Customer 6"},{"212","Occasional Customer 5"},{"222","Everyday Shopper 8"},{"232","Everyday Shopper 7"},
{"203","Occasional Customer 4"},{"213","Occasional Customer 3"},{"223","Golden Customer 4"},{"233","Golden Customer 3"},
{"204","Occasional Customer 2"},{"214","Occasional Customer 1"},{"224","Superstar 4"},{"234","Superstar 3"},
{"300","Typical Customer 10"},{"310","Typical Customer 9"},{"320","Everyday Shopper 6"},{"330","Everyday Shopper 5"},
{"301","Typical Customer 8"},{"311","Typical Customer 7"},{"321","Everyday Shopper 4"},{"331","Everyday Shopper 3"},
{"302","Typical Customer 6"},{"312","Typical Customer 5"},{"322","Everyday Shopper 2"},{"332","Everyday Shopper 1"},
{"303","Typical Customer 4"},{"313","Typical Customer 3"},{"323","Golden Customer 2"},{"333","Golden Customer 1"},
{"304","Typical Customer 2"},{"314","Typical Customer 1"},{"324","Superstar 2"},{"334","Superstar 1"},};
//RFM criteria value input to array\\
rfmc = new int[3, 8];
rfmc[0, 0] = Convert.ToInt32(recQLTlow.Text);
rfmc[0, 1] = Convert.ToInt32(recRCNThigh.Text);
rfmc[0, 2] = Convert.ToInt32(recLTlow.Text);
rfmc[0, 3] = Convert.ToInt32(recQLThigh.Text);
rfmc[0, 4] = Convert.ToInt32(recLTA.Text);
rfmc[0, 5] = Convert.ToInt32(recLThigh.Text);
rfmc[1, 0] = Convert.ToInt32(freRlow.Text);
rfmc[1, 1] = Convert.ToInt32(freVRhigh.Text);
rfmc[1, 2] = Convert.ToInt32(freOlow.Text);
rfmc[1, 3] = Convert.ToInt32(freRhigh.Text);
rfmc[1, 4] = Convert.ToInt32(freVO.Text);
rfmc[1, 5] = Convert.ToInt32(freOhigh.Text);
rfmc[2, 0] = Convert.ToInt32(monLlow.Text + "000000");
rfmc[2, 1] = Convert.ToInt32(monVLhigh.Text + "000000");
rfmc[2, 2] = Convert.ToInt32(monMlow.Text + "000000");
rfmc[2, 3] = Convert.ToInt32(monLhigh.Text + "000000");
rfmc[2, 4] = Convert.ToInt32(monHlow.Text + "000000");
rfmc[2, 5] = Convert.ToInt32(monMhigh.Text + "000000");
rfmc[2, 6] = Convert.ToInt32(monVH.Text + "000000");
rfmc[2, 7] = Convert.ToInt32(monHhigh.Text + "000000");
//Random number generation\\
u = new Double[dt.Rows.Count, cltr];
for (int k = 0; k < cltr; k++)
{
for (int i = 0; i < dt.Rows.Count; i++)
{
u[i, k] = rnd.NextDouble();
}
}
//Fuzzy C-Means Method\\
do
{
//Cluster Centroid Function\\
for (int k = 0; k < cltr; k++)
{
for (int j = 0; j < 3; j++)
{
for (int i = 0; i < dt.Rows.Count; i++)
{
temp1 += Math.Pow(u[i, k], w);
temp2 += Math.Pow(u[i, k], w) * X[i, j];
}
V[k, j] = temp2 / temp1;
temp1 = 0;
temp2 = 0;
}
}
//Objective Function\\
for (int i = 0; i < dt.Rows.Count; i++)
{
for (int k = 0; k < cltr; k++)
{
for (int j = 0; j < 3; j++)
{
temp3 += Math.Pow(X[i, j] - V[k, j], w);
}
temp4 += temp3 * Math.Pow(u[i, k], 2);
temp3 = 0;
}
}
P[ctr] += temp4;
temp4 = 0;
//Membership Degree Function\\
for (int i = 0; i < dt.Rows.Count; i++)
{
temp10 = 0;
for (int k = 0; k < cltr; k++)
{
for (int k2 = 0; k2 < cltr; k2++)
{
for (int j = 0; j < 3; j++)
{
temp5 += Math.Pow(X[i, j] - V[k, j], w);
temp6 += Math.Pow(X[i, j] - V[k2, j], w);
}
temp7 = Math.Pow(temp5, -1);
temp8 = Math.Pow(temp6, -1);
temp9 += temp8;
temp5 = 0;
temp6 = 0;
}
u[i, k] = temp7 / temp9;
if (temp10 < u[i, k])
{
temp10 = u[i, k];
custCltr[i] = k;
}
temp7 = 0;
temp8 = 0;
temp9 = 0;
}
}
if (ctr == 3)
{
createChart(winChartViewer1, 1);
}
if (ctr % 50 == 0)
{
createChart(winChartViewer1, 1);
}
ctr++;
} while (ctr < maxIter && Math.Abs((P[ctr - 1] - P[ctr - 2])) > tc);
createChart(winChartViewer1, 1);
MPC(PC());
for (int i = 0; i < dt.Rows.Count; i++)
{
temp1 = 0;
for (int k = 0; k < cltr; k++)
{
if (temp1 < u[i, k])
{
temp1 = u[i, k];
custCltr[i] = k;
}
}
}
}
/// <summary>
/// Prepares the data before training.
/// </summary>
public virtual void PrepareData()
{
_SequenceCount = ((_WorkingDataset[0].GetLength(0) - _PortionOfDatasetReserved) - _WindowWidth) - _DistanceToForcastHorrison;
Int32 inputCount = _InputNodes.Count;
Int32 outputCount = _OutputNodes.Count;
_InputSequences = new Double[_SequenceCount, _WindowWidth, inputCount];
_ExpectedOutputs = new Double[_SequenceCount, outputCount];
for (Int32 i = 0; i < _SequenceCount; i++)
{
for (Int32 j = 0; j < _WindowWidth; j++)
{
for (Int32 k = 0; k < inputCount; k++) _InputSequences[i, j, k] = _WorkingDataset[k][i + j];
for (Int32 l = 0; l < outputCount; l++) _ExpectedOutputs[i, l] = _WorkingDataset[l][i + j + _DistanceToForcastHorrison];
}
}
}
//把数据从UI读取到中间文件
private void readUItoArray()
{
//清空字典
plate_info = new Dictionary<string, string>();
//重新初始化中间数组
tpl = new Info[8, 12];
od = new double[8, 12];
//string[,] odStr = new string[8, 12];//使用字符数组更好？
//读取文件到中间数组
//合法性检测
if (this.textName.Text.Trim() == null)
{
MessageBox.Show("请输入项目名称！", "系统提示");
return;
}
if (this.txtUnit.Text.Trim() == null)
{
MessageBox.Show("请输入单位名称！", "系统提示");
return;
}
//基本信息-》中间数据
plate_info["SaveTime"] = DateTime.Now.ToString();//保存文件时的时间
plate_info["Name"] = this.textName.Text.Trim();
plate_info["Lot"] = this.textLot.Text.Trim();
plate_info["LabDate"] = this.dateTimePicker1.Text.Trim();
plate_info["Unit"] = this.txtUnit.Text.Trim();
plate_info["Notice"] = "不要随意更改文件内容，否则再次读取时将发生错误。";
plate_info["Curve"] = this.getRadioIndex().ToString();//拟合的模型编号
//tpl-》中间数据
DataReadWrite drw = new DataReadWrite();
//从UI读取到中间数组
tpl = drw.readFromUI(this.dataGridView0, true);
od = drw.readFromUI(this.dataGridView1);
}
/// <summary>
/// Construct an eigenvalue decomposition.</summary>
///
/// <param name="value">
/// The matrix to be decomposed.</param>
/// <param name="assumeSymmetric">
/// Defines if the matrix should be assumed as being symmetric
/// regardless if it is or not. Default is <see langword="false"/>.</param>
/// <param name="inPlace">
/// Pass <see langword="true"/> to perform the decomposition in place. The matrix
/// <paramref name="value"/> will be destroyed in the process, resulting in less
/// memory comsumption.</param>
/// <param name="sort">
/// Pass <see langword="true"/> to sort the eigenvalues and eigenvectors at the end
/// of the decomposition.</param>
///
public EigenvalueDecomposition(Double[,] value, bool assumeSymmetric,
bool inPlace = false, bool sort = false)
{
if (value == null)
throw new ArgumentNullException("value", "Matrix cannot be null.");
if (value.GetLength(0) != value.GetLength(1))
throw new ArgumentException("Matrix is not a square matrix.", "value");
n = value.GetLength(1);
V = new Double[n, n];
d = new Double[n];
e = new Double[n];
this.symmetric = assumeSymmetric;
if (this.symmetric)
{
V = inPlace ? value : (Double[,])value.Clone();
// Tridiagonalize.
this.tred2();
// Diagonalize.
this.tql2();
}
else
{
H = inPlace ? value : (Double[,])value.Clone();
ort = new Double[n];
// Reduce to Hessenberg form.
this.orthes();
// Reduce Hessenberg to real Schur form.
this.hqr2();
}
if (sort)
{
// Sort eigenvalues and vectors in descending order
var idx = Vector.Range(n);
Array.Sort(idx, (i, j) =>
{
if (Math.Abs(d[i]) == Math.Abs(d[j]))
return -Math.Abs(e[i]).CompareTo(Math.Abs(e[j]));
return -Math.Abs(d[i]).CompareTo(Math.Abs(d[j]));
});
this.d = this.d.Get(idx);
this.e = this.e.Get(idx);
this.V = this.V.Get(null, idx);
}
}