/// <summary>
/// Converts an array that contains multiple Matlab return parameters back into a list of .NET doubles.
/// </summary>
/// <param name="toList">MWArray variable, returned from Matlab code.</param>
/// <returns>List of .NET doubles.</returns>
public static List <double> MyToDoubleList(this MWArray toList)
{
var matNumericArray = toList;
var netArray = (MWNumericArray)matNumericArray.ToArray();
var result = new List <double>();
// Console.Write("{0}", netArray[1]);
for (int i = 1; i <= netArray.NumberOfElements; i++) // Matlab arrays are 1-based, thus the odd indexing.
{
result.Add(netArray[i].ToScalarDouble());
}
return(result);
}
public static double DTC_SRC_Mgain2(double Td, double fs, double Q)
{
double value = -1;
MWArray output = solve.solve_DTCSRC_Mgain2(Td, fs, Q);
MWNumericArray result = (MWNumericArray)output;
value = result.ToScalarDouble();
if (value < 0)
{
Console.WriteLine("Wrong DTC_SRC_Mgain1!");
System.Environment.Exit(-1);
}
return(value);
}
private void ButtonNeuralOpenPanel_Click(object sender, RoutedEventArgs e)
{
WindowNeuralControlPanel wnd = new WindowNeuralControlPanel();
wnd.Show();
if (wnd.DialogResult == true)
{
SARNeural neural = new SARNeural();
MWArray output = neural.Predict(wnd.X1, wnd.X2, wnd.X3, wnd.X4, wnd.X5, wnd.X6, wnd.X7, wnd.X8, wnd.X9, wnd.X10, wnd.X11, wnd.X12);
double[,] values = (double[, ])((MWNumericArray)output).ToArray(MWArrayComponent.Real);
RecorderCommand command = new RecorderCommand
{
Duration = TimeSpan.FromSeconds(2.0),
Joints = new List <CostumeJoint>
{
new CostumeJoint()
{
Name = "R.ShoulderF", Value = (float)values[0, 0]
},
new CostumeJoint()
{
Name = "R.ShoulderS", Value = (float)values[0, 1]
},
new CostumeJoint()
{
Name = "R.ElbowR", Value = (float)values[0, 2]
},
new CostumeJoint()
{
Name = "R.Elbow", Value = (float)values[0, 3]
},
new CostumeJoint()
{
Name = "R.WristR", Value = (float)values[0, 4]
},
new CostumeJoint()
{
Name = "R.WritsS", Value = (float)values[0, 5]
}
}
};
RobotAnswer answer = Robot.ExecuteCommand(command.Joints, command.Duration);
AppLog.Write(answer.ToString());
}
}
public void FromMatlabArray(MWArray data)
{
var k = data.ToArray();
for (int i = 0; i < k.GetLength(0); i++)
{
for (int j = 0; j < k.GetLength(1); j++)
{
object a = k.GetValue(i, j);
byte b = (byte)a;
Pixel p = new Pixel((int)b, (int)b, (int)b, 255);
SetPixel(j, i, p);
}
}
}
// MATLAB Functions
/// <summary>
/// Performs upsampling and downsampling on an array of values
/// </summary>
/// <param name="values"> The input array to resample </param>
/// <param name="ratio"> The ratio between upsampling and downsampling to perform </param>
/// <returns> The resampled array </returns>
public static List <float> MATLAB_Resample(float[] values, float ratio)
{
// Prepare Input for MATLAB function
Processing proc = new Processing();
MWArray[] input = new MWArray[2];
input[0] = new MWNumericArray(values);
input[1] = ratio;
// Call MATLAB function
return((
(double[])(
(MWNumericArray)proc.m_resample(1, input[0], input[1])[0]
).ToVector(MWArrayComponent.Real)
).ToList().Select(temp => (float)temp).ToList());
}
public static double[] resultsForReranking; //stores the image number when images are categories covered by similarity matrix
public static string matchClass()
{
//MessageBox.Show("match initialize");
MatchingClass matchtest = null;
MWCharArray output = null;
MWArray MWresult = null;
matchtest = new MatchingClass();
//MessageBox.Show("start match");
MWresult = matchtest.build_matching_test_correct_csharp2();
output = (MWCharArray)MWresult[1, 1];
matchedClass = output.ToString();
rerankingImageName = -1;
return(matchedClass);
}
/// <summary>
/// Converts MWArray input, which is a MATLAB structure, to an array of MWArray
/// </summary>
/// <param name="input"></param>
/// <returns></returns>
public static MWArray[] GetMWStructureFields(MWArray input)
{
var castInput = (MWStructArray)input;
var fields = castInput.FieldNames;
MWArray[] result = new MWArray[fields.Length];
for (int i = 0; i < fields.Length; i++)
{
result[i] = castInput.GetField(fields[i]);
}
return(result);
}
private void startDehaze_Click(object sender, EventArgs e)
{
//string Atext, convCoretext;
//convCoretext = textBox3.Text;
//Atext = textBox4.Text;
double Anum, convCorenum;
Anum = 0;
convCorenum = 0;
DehazeFunctionV2.Class1 v2c1 = new DehazeFunctionV2.Class1();
MWArray[] resultlist = new MWArray[2];
resultlist = v2c1.DehazeFunctionV2(2, pathname, Anum, convCorenum);
MWNumericArray Atmospherelight = (MWNumericArray)resultlist[0];
MWNumericArray SSIM = (MWNumericArray)resultlist[1];
textBox2.Text = SSIM.ToString();
textBox4.Text = Atmospherelight.ToString();
textBox3.Text = "15";
string pathname2;
pathname2 = "D:\\op\\cache\\temp.jpg"; //获得文件的绝对路径
//this.pictureBox2.Load(pathname2);//load貌似过时了？
// this.pictureBox2.Image = Image.FromFile(pathname2);
// pictureBox2.Image.Dispose();
FileStream pFileStream = new FileStream(pathname2, FileMode.Open, FileAccess.Read);
outputImg.Image = Image.FromStream(pFileStream);
pFileStream.Close();
pFileStream.Dispose();
//DheazeFunction.Class1 v1c1 = new DheazeFunction.Class1();
//MWArray[] resultlist = new MWArray[2];
//resultlist = v1c1.DheazeFunction(1, pathname);
//MWNumericArray Atmospherelight = (MWNumericArray)resultlist[0];
//textBox4.Text = Atmospherelight.ToString();
//textBox3.Text = "15";
//string pathname2;
//pathname2 = "D:\\op\\cache\\temp.jpg"; //获得文件的绝对路径
// //this.pictureBox2.Load(pathname2);//load貌似过时了？
// // this.pictureBox2.Image = Image.FromFile(pathname2);
// // pictureBox2.Image.Dispose();
//FileStream pFileStream = new FileStream(pathname2, FileMode.Open, FileAccess.Read);
//outputImg.Image = Image.FromStream(pFileStream);
//pFileStream.Close();
//pFileStream.Dispose();
}
private void data()
{
try
{
readData.readdata output = new readData.readdata();
MWArray a = a_box.Text;
MWArray X = Convert.ToInt32(b_box.Text);
MWArray n = Convert.ToInt32(c_box.Text);
MWArray dim = Convert.ToInt32(d_box.Text);
output.readData(a, X, n, dim);
}
catch
{
MessageBox.Show("数据有误", "警告");
}
}
public double[] NextNormalRandomMatrix(double mu, double sigma, int samples)
{
var norm = new Normrand.MATLAB_NormalRandom();
MWArray M_mu = new MWNumericArray(mu);
MWArray M_sigma = new MWNumericArray(sigma);
MWArray M_n = new MWNumericArray(1);
MWArray M_m = new MWNumericArray(samples);
MWArray R = norm.Normrand(M_mu, M_sigma, M_n, M_m);
var result = new double[samples];
for (int i = 0; i < samples; i++)
{
result[i] = ((MWNumericArray)R[i + 1]).ToScalarDouble();
}
return(result);
}
public void calculate(out string out1, out string out2)
{
out1 = "";
out2 = "";
double[] a = { 1, 2, 3, 4, 5, 6 }; //定义两个输入参数
double[] b = { 1, 1, 1, 1, 1, 1 }; //它们是两个一维静态数组
double[,] c = new double[3, 2]; //定义C#中接收输出参数的类型
double[,] d = new double[3, 2]; //是两个二维数组
//把两个输入参数都转换成中间类型，中间类型也是矩阵所以要指明维数
//这里将两个输入参数转换为两个三行两列的矩阵
MWNumericArray matlab_a = new MWNumericArray(3, 2, a);
MWNumericArray matlab_b = new MWNumericArray(3, 2, b);
//输入参数成功转化为两个MWArray元素类型
MWArray[] agrsIn = new MWArray[] { matlab_a, matlab_b };
//声明输出参数是两个MWArray元素类型，一定要写数量
MWArray[] agrsOut = new MWArray[2];
//调用matlab函数，2表示输出参数的个数，输出参数前需要加 ref 关键字
//此例实现了两个三行两列的矩阵相加减
m2CClass.MatrixOpera(2, ref agrsOut, agrsIn);
//把两个输出参数转换成中间类型
MWNumericArray net_c = agrsOut[0] as MWNumericArray; //matlab函数第一个输出参数
MWNumericArray net_d = agrsOut[1] as MWNumericArray; //第二个输出参数
//转换成C#中的接收参数
c = (double[, ])net_c.ToArray();//转化为二维数组
d = (double[, ])net_d.ToArray();
//一定要注意最后接收参数的转化，不同类型的接收参数用的转换函数不同
//二维数组用ToArray()函数转换
//一维数组用ToVector(MWArrayComponent.Real)函数转换
//单个double值用ToScalarDouble()函数转换
//单个int值用ToScalarInteger()函数转换
for (int i = 0; i <= 2; i++)//输出结果验证
{
for (int j = 0; j <= 1; j++)
{
out1 += c[i, j].ToString() + " ";
out2 += d[i, j].ToString() + " ";
}
}
}
/// <summary>
/// 自动对齐
/// </summary>
/// <param name="ww">输入参考波形1的数据</param>
///WW(1,:)：左高低
///WW(2,:)：右高低
///WW(3,:)：左轨向
///WW(4,:)：右轨向
///WW(5,:)：轨距------(总共5列数据)
/// <param name="ww1">参考波形2的数据</param>
/// <returns>移动的点数</returns>
/// 备注：波形1不动，波形2减去点数
/// 波形2不动，波形1加上点数
public int AutoTranslation(float[][] ww, float[][] ww1)
{
int retVal = 0;
int rowLen_ww = ww.GetLength(0);
int colLen_ww = ww[0].Length;
MWNumericArray d_ww = new MWNumericArray(MWArrayComplexity.Real, rowLen_ww, colLen_ww);
//matlab中矩阵的下标是从1开始的，而C#是从0开始的
for (int i = 0; i < rowLen_ww; i++)
{
for (int j = 0; j < colLen_ww; j++)
{
d_ww[i + 1, j + 1] = ww[i][j];
}
}
int rowLen_ww1 = ww1.GetLength(0);
int colLen_ww1 = ww1[0].Length;
MWNumericArray d_ww1 = new MWNumericArray(MWArrayComplexity.Real, rowLen_ww1, colLen_ww1);
//matlab中矩阵的下标是从1开始的，而C#是从0开始的
for (int i = 0; i < rowLen_ww1; i++)
{
for (int j = 0; j < colLen_ww1; j++)
{
d_ww1[i + 1, j + 1] = ww1[i][j];
}
}
try
{
//调用算法
MWArray resultArray = ppmc.sub_preprocessing_alignment_data(d_ww, d_ww1);
//MWArray resultArray = null;
double[,] tmpArray = (double[, ])(resultArray.ToArray());
retVal = (int)tmpArray[0, 0];
}
catch (System.Exception ex)
{
MessageBox.Show(ex.Message);
}
return(retVal);
}
private float MatlabPeek(List <float> zposList, List <byte[, ]> bytelist, int max_cnt)
{
MWCellArray mwcell = new MWCellArray(max_cnt, 1);
for (int i = 0; i < max_cnt; i++)
{
MWNumericArray mwarr = bytelist[i];
mwcell[i + 1, 1] = mwarr;
}
MWNumericArray mwz = zposList.ToArray();
MWArray mwret = this.myDll2.GlobalFocusOfSinglePointROIForDll(mwz, mwcell);
float[,] ret = (float[, ])((MWNumericArray)mwret).ToArray(MWArrayComponent.Real);
float new_z_pos_m = ret[0, 0];
return(new_z_pos_m);
}
public void getGasCalculate(string database, string tableName, double l, double r, double t, out double dist, out double tick)
{
MWCharArray dataPath = new MWCharArray(database);
MWCharArray table = new MWCharArray(tableName);
MWNumericArray length = new MWNumericArray(l);
MWNumericArray radius = new MWNumericArray(r);
MWNumericArray thickness = new MWNumericArray(t);
MWArray[] argsOut = new MWArray[2];
MWArray[] argsIn = new MWArray[] { dataPath, table, length, radius, thickness };
m2CClass.GasLeak(2, ref argsOut, argsIn);
MWNumericArray distance = argsOut[0] as MWNumericArray;
MWNumericArray timeTick = argsOut[1] as MWNumericArray;
dist = (double)distance.ToScalarDouble();
tick = (double)timeTick.ToScalarDouble();
}
void graph(string fileName)
{
//chart1.Series["Data"].Points.AddXY(;
//chart1.Series["Data"].ChartType =
// SeriesChartType.FastLine;
//chart1.Series["Data"].Color = Color.Red;
label5.Visible = true;
try
{
plotSound = new soundPlotter.PlotSound();
}
catch (Exception e)
{
Console.WriteLine(e.InnerException);
}
try
{
MWCharArray input = new MWCharArray(fileName);
fileSelected = fileName;
MWArray[] result = new MWArray[2];
result = plotSound.plotSound(2, input);
textBox3.Text = result[0].ToString();
//double tempo = Convert.ToDouble(result[1].ToString());
textBox4.Text = Convert.ToDouble(result[1].ToString()).ToString();
}
catch (Exception e)
{
Console.WriteLine(e.InnerException);
}
string[] temp = fileName.Split('\\');
label6.Text = temp[temp.Length - 1];
label5.Visible = false;
IntPtr foundwindow = FindWindow("sunawtframe", "figure 2");
SetParent(foundwindow, panel1.Handle);
MoveWindow(foundwindow, -5, -80, panel1.Width + 10, panel1.Height + 90);
foundwindow = FindWindow("sunawtframe", "figure 1");
SetParent(foundwindow, panel2.Handle);
MoveWindow(foundwindow, -5, -80, panel2.Width + 10, panel2.Height + 90);
//SetWindowPos(foundwindow, -2, 0, 0, 800, 600, 0x0040);
//SetWindowLong(foundwindow, GWL_STYLE, WS_SYSMENU);
}
public float MatlabQPeek(List <float> zposList, List <byte[, ]> bytelist, int max_cnt, ref float z_pos1, ref float z_pos2, ref float z_pos3, ref float new_z_pos_m, bool bDirty, ref int bFineFlag)
{
DateTime starT = DateTime.Now;
int loop_cnt = 0;
float rect_cnt = 0;
float rect = 0;
try
{
MWCellArray mwcell = new MWCellArray(max_cnt, 1);
for (int i = 0; i < max_cnt; i++)
{
MWNumericArray mwarr = bytelist[i];
mwcell[i + 1, 1] = mwarr;
}
MWNumericArray mwz = zposList.ToArray();
loop_cnt = bDirty == false ? 0 : 1;
MWNumericArray mwCnt = loop_cnt;
//MWArray mwret = this.qFocus.QuickFocusOfSinglePointROIForDll(mwz, mwcell);
MWArray mwret = this.qfprep.QuickFocusOfSinglePointROIForDll(mwz, mwcell, mwCnt);
float[,] ret = (float[, ])((MWNumericArray)mwret).ToArray(MWArrayComponent.Real);
z_pos1 = ret[0, 0];
z_pos2 = ret[0, 1];
z_pos3 = ret[0, 2];
new_z_pos_m = ret[0, 3];
bFineFlag = (int)ret[0, 4];
z_pos1 = z_pos1 > 0.4 ? 0 : z_pos1;
z_pos2 = z_pos2 > 0.4 ? 0 : z_pos2;
z_pos3 = z_pos3 > 0.4 ? 0 : z_pos3;
if (rect_cnt == 1)
{
loop_cnt++;
}
else
{
loop_cnt = 0;
}
rect = new_z_pos_m;
}
catch (Exception ex)
{ LogHelper.AppLoger.Error(ex); }
finally
{
}
return(rect);
}
public double[] passaAlta(double[] sinal, int polos, int fc, int fs)
{
double[] res = new double[sinal.Length];
MWNumericArray aux_res2 = new MWNumericArray(new double[sinal.Length]);
MWNumericArray xdt = new MWNumericArray(new double[sinal.Length]);
Array aux_res3 = null;
Fltros obj = new Fltros();
xdt = sinal;
MWArray aux_res = obj.HighPassFilt(xdt, polos, fc, fs);
aux_res2 = (MWNumericArray)aux_res;
aux_res3 = aux_res2.ToVector(MWArrayComponent.Real);
res = (double[])aux_res3;
return(res);
}
public void MatlabFitting(Dictionary <Point, float> dicMap)
{
try
{
int max = dicMap.Count;
double[,] dbx = new double[max, 3];
MWCellArray mwary = new MWCellArray(max, 3);
List <Point> lst = new List <Point>(dicMap.Keys);
StringBuilder strDat = new StringBuilder();
for (int i = 0; i < lst.Count; i++)
{
Point p = lst[i];
float v = dicMap[p];
//float[] fv = new float[3] { (float)p.X, (float)p.Y, v };
dbx[i, 0] = (double)p.X;
dbx[i, 1] = (double)p.Y;
dbx[i, 2] = (double)v;
strDat.AppendFormat("{0},{1},{2}", p.X, p.Y, v);
strDat.AppendFormat(System.Environment.NewLine);
}
//save result to file
string path = @"c:\map\";
if (!System.IO.Directory.Exists(path))
{
System.IO.Directory.CreateDirectory(path);
}
var utf8WithoutBom = new System.Text.UTF8Encoding(true);
FileStream fs = new FileStream(path + "final_data.csv", FileMode.Create);
StreamWriter sw = new StreamWriter(fs, utf8WithoutBom);
sw.Write(strDat.ToString());
//清空缓冲区
sw.Flush();
//关闭流
sw.Close();
fs.Close();
MWNumericArray x = new MWNumericArray(dbx);
MWArray mwret = this.gfPrep.PreMapFunction(x);
//float[,] ret = (float[,])((MWNumericArray)mwret).ToArray(MWArrayComponent.Real);
}
catch (Exception ex)
{
LogHelper.AppLoger.Error(ex);
}
}
public ProblemOutput Solve(ProblemInput problem)
{
ProblemOutput solution = new ProblemOutput
{
D = problem.D,
HasSolution = false,
Value = double.NaN,
Solution = Enumerable.Repeat(double.NaN, problem.N).ToArray(),
SkipedIndexes = problem.SkipedIndexes
};
if (problem.N > 0)
{
MWArray[] input = new MWArray[4];
MWNumericArray _h = problem.Quadratic;
MWNumericArray _f = problem.Linear;
MWNumericArray _A = problem.ConstraintMatrix;
MWNumericArray _B = problem.ConstraintValues;
input[0] = _h;
input[1] = _f;
input[2] = _A;
input[3] = _B;
try
{
SolveQPClass r = new SolveQPClass();
MWArray[] result = new MWArray[3];
r.SolveQP(result.Length, ref result, input);
bool success = ((MWNumericArray)result[2]).ToScalarDouble() > 0;
if (success)
{
double[] x = (double[])(((MWNumericArray)result[0]).ToVector(MWArrayComponent.Real));
double val = ((MWNumericArray)result[1]).ToScalarDouble();
solution.HasSolution = success;
solution.Value = -val;
solution.Solution = x;
}
r.Dispose();
}
catch (Exception e)
{
return(solution);
}
}
return(solution);
}
/// <summary>
/// 调用matlab算法
/// </summary>
/// <param name="CSinput"></param>
/// <param name="PortName"></param>
public void Anaylase(double[] CSinput, string PortName)
{
//int retSize = 256; 256, i, retSize, mwin
double[,] CSoutput = new double[2, 256];
MWArray i = 2;
MWNumericArray mwin = new MWNumericArray(CSinput);
try
{
CSoutput = (double[, ])((MWNumericArray)myClass.xhcl(mwin)).ToArray(MWArrayComponent.Real);
InsertData(CSoutput, PortName, "ret");
}
catch (Exception ex)
{
MessageBox.Show(ex.ToString(), "ERROR");
}
}
public bool GetSignature(string SignaturePath, int UserId)
{
//int fakeId = 4;
#region MATLAB
var signaturePath = HttpContext.Current.Server.MapPath(SignaturePath);
Matlab.SignatureVerification.Signature sig = new Matlab.SignatureVerification.Signature();
MWArray mw = sig.main(signaturePath, UserId);
double value = (double)((MWNumericArray)mw).ToVector(MWArrayComponent.Real).GetValue(0);
if (value > 0.5)
{
return(true);
}
else
{
return(false);
}
#endregion
}
public static void GetPath(int [] jobsequence, int [] emergency)
{
try
{
sequence1 = null;
sequence2 = null;
MWArray[] trans = new MWArray[] { swarmsize, (MWNumericArray)jobsequence, (MWNumericArray)emergency };
Dyna.PSOUpdate(3, ref ending, trans);
MWNumericArray x1 = ending[1] as MWNumericArray;
MWNumericArray x2 = ending[2] as MWNumericArray;
sequence1 = (double[, ])x1.ToArray();
sequence2 = (double[, ])x2.ToArray();
}
catch (Exception str)
{
MessageBox.Show("Error!!" + str.ToString(), "Error", MessageBoxButtons.OK, MessageBoxIcon.Error);
}
}
public Image Apply(Image img)
{
MWArray arr = m_assets.ConnectedComponent(img.ToSingleMatlabArray());
var d = arr.ToArray();
Elements = new CCElement[d.GetLength(0)];
for (int i = 0; i < d.GetLength(0); i++)
{
var ooo = d.GetValue(i, 0);
Elements[i].Center_X = (double)ooo;
ooo = d.GetValue(i, 1);
Elements[i].Center_Y = (double)ooo;
ooo = d.GetValue(i, 2);
Elements[i].Area = (double)ooo;
}
return(img);
}
/// <summary>
/// Gets the rectangle where the brain is located
/// </summary>
public int[] CropBrain()
{
int[] brainRect = new int[4];
try
{
MWArray[] res = new MWArray[4];
res = FEC.CropBrain(4, this.imagePath);
brainRect[0] = int.Parse(res[0].ToString());
brainRect[1] = int.Parse(res[1].ToString());
brainRect[2] = int.Parse(res[2].ToString());
brainRect[3] = int.Parse(res[3].ToString());
return(brainRect);
}
catch (Exception ex)
{
return(brainRect);
}
}
/// <summary>
/// 设计主电路元件参数
/// </summary>
private void DesignCircuitParam()
{
double P = math_Pfull;
double Vin = math_Vin;
double Vo = math_Vo;
double No = math_No;
double fs = math_fs;
double Td = math_Tdead;
double Q = math_Q;
double RL = No * Math.Pow(Vo, 2) / P; //负载等效电阻
double n = Vin / Vo; //变比
double ws = 2 * Math.PI * fs;
double Re = 8 * n * n * RL / (No * Math.PI * Math.PI);
double Zr = Re * Q; //谐振阻抗
//求解Vo_act和wr
MWArray output = Formula.solve.solveSRC_wr(P, Vin, n, fs, Zr, Td);
MWNumericArray result = (MWNumericArray)output;
double Vo_act = result[1].ToScalarDouble(); //实际输出电压
double wr = result[2].ToScalarDouble(); //谐振角速度
if (Vo_act > Vo || Vo_act < Vo / 2)
{
Console.WriteLine("Wrong Vo!");
Environment.Exit(-1);
}
if (wr > ws || wr < ws / 2)
{
Console.WriteLine("Wrong wr!");
Environment.Exit(-1);
}
math_n = n;
math_fr = wr / 2 / Math.PI;
math_Lr = Zr / wr;
math_Cr = 1 / Zr / wr;
math_ψ = 0.5 * Vo * n / fs;
math_VSpmax = Vin;
math_VSsmax = Vo;
math_VCfmax = Vo;
}
private void IndoorWindow_Simulation_Apply_Click(object sender, RoutedEventArgs e)
{
double[] Location_Tx = { 1, 14, 1 };
double[] Location_Rx = { 2, 12, 2 };
MWNumericArray Location_Tx_m = new MWNumericArray(1, 3, Location_Tx);
MWNumericArray Location_Rx_m = new MWNumericArray(1, 3, Location_Rx);
Console.Write(Location_Tx_m.ToString());
MWArray[] agrsOut = new MWArray[14];
raytracingClass raytracing_m = new raytracingClass();
agrsOut = raytracing_m.raytracing(14, 0, rayparameter.Power_Tx, rayparameter.Frequency_Tx, 6.4, 0.0004, 1, 1, Location_Tx_m, Location_Rx_m, 0);
OutputWindow OutputWin = new OutputWindow();
OutputWin.Show();
}
public FrequencyInfo offlineAnalyze(string fileName)
{
Console.WriteLine("Start analyzing offline file：" + fileName);
MWCharArray filename = fileName;
MWArray[] argsIn = new MWArray[] { filename };
MWArray[] result = new MWArray[3];
vitalSignsExtract.offlineVitalSignsExtract(3, ref result, argsIn);
double[,] temp = (double[, ])result[0].ToArray();
int num = temp.GetLength(1);
double[] breath = new double[num];
double[] heartbeat = new double[num];
double[] t = new double[num];
for (int i = 0; i < num; i++)
{
breath[i] = temp[1, i];
heartbeat[i] = temp[2, i];
t[i] = temp[0, i];
}
double meanBreath = (double)(MWNumericArray)result[1];
double meanHeartbeat = (double)(MWNumericArray)result[2];
FrequencyInfo frequency = new FrequencyInfo(breath, heartbeat, t, meanBreath, meanHeartbeat, 0);
/*
* double[,] time = (double[,])result[1].ToArray();
* int num = frequency.GetLength(1);
* int[] breath = new int[num];
* heartbeat = new int[num];
* t = new double[num];
* for(int i = 0; i < num; i++)
* {
* breath[i] = (int)frequency[0, i];
* heartbeat[i] = (int)frequency[1, i];
* t[i] = time[0, i];
* }
* return breath;*/
Console.WriteLine("Analysis ends");
return(frequency);
//throw new NotImplementedException();
}
public double[][] NextNormalRandomMatrix(double mu, double sigma, int n, int m)
{
var norm = new Normrand.MATLAB_NormalRandom();
MWArray M_mu = new MWNumericArray(mu);
MWArray M_sigma = new MWNumericArray(sigma);
MWArray M_n = new MWNumericArray(n);
MWArray M_m = new MWNumericArray(m);
MWArray R = norm.Normrand(M_mu, M_sigma, M_n, M_m);
var result = new double[n][];
for (int i = 0; i < n; i++)
{
result[i] = new double[m];
for (int j = 0; j < m; j++)
{
result[i][j] = ((MWNumericArray)R[i + 1, j + 1]).ToScalarDouble();
}
}
return(result);
}
/// <summary>
/// Kaiser Window Filter
/// </summary>
/// <param name="inputData">Input waform data</param>
/// <param name="f">Band edges</param>
/// <param name="a"> Desired amplitude on the bands define by f</param>
/// <param name="dev">Passband ripple and stopband attenuation</param>
/// <param name="fs">Samplerate</param>
/// <returns>Output waveform data</returns>
public static double[] Kaiser(double[] inputData, double[] f, double[] a, double[] dev, double fs)
{
MWNumericArray inputDataMatlab = inputData;
MWNumericArray fMatlab = f;
MWNumericArray aMatlab = a;
MWNumericArray devMatlab = dev;
MWArray fsMatlab = fs;
MWArray[] resultMatlab;
double[,] result2D;
double[] result;
DSPClass dspTask = new DSPClass();
resultMatlab = dspTask.Kaiser(1, inputDataMatlab, fMatlab, aMatlab, devMatlab, fsMatlab);
result2D = (double[, ])resultMatlab[0].ToArray();
result = new double[result2D.Length];
Buffer.BlockCopy(result2D, 0, result, 0, result2D.Length * sizeof(double));
return(result);
}
public Image(string path)
{
MWNumericArray descriptor = null;
MWCharArray mw_path;
try
{
mw_path = new MWCharArray(path);
}
catch (System.Exception ex)
{
System.Windows.Forms.MessageBox.Show(ex.ToString());
return;
}
Loader Loader = new Loader();
image = Loader.loadimage(mw_path);
descriptor = (MWNumericArray)image;
try
{
Array = new ImageArray((double[,])descriptor.ToArray(MWArrayComponent.Real));
}
catch (InvalidCastException exc)
{
descriptor = null;
mw_path = null;
Loader = null;
GC.Collect();
return;
}
Height = Array.Array.GetLength(0);
Width = Array.Array.GetLength(1);
GetBitmap();
descriptor = null;
mw_path = null;
Loader = null;
GC.Collect();
}
static void Main(string[] args)
{
MWArray In = null;
MWArray Out = null;
try {
DiffTpye testMethod = new DiffTpye(); //创建一个实例
In = 4; //此处发生隐式转换
//double数组
Out = testMethod.magicDouble(In);
Console.WriteLine("The doubleArray result is :\n{0}\n", (MWNumericArray)Out);
//char数组
Out = testMethod.magicChar(In);
Console.WriteLine("The CharArray result is :\n{0}\n", Out);
//cell数组
Out = testMethod.magicCell(In);
//Return Out.ToArray()
// {object[1, 1]}
// [0, 0]: {double[4, 4]}
Console.WriteLine("The CellArray result is :\n{0}\n", Out);
//Return ((MWCellArray)Out).ToArray().GetValue(0,0)
//{double[4, 4]}
//struct数组
Out = testMethod.magicStruct(In);
Console.WriteLine("The StructArray result is :\n{0}", ((MWStructArray)Out).GetField("value"));
//Return ((MWStructArray)Out).GetField("value").ToArray()
// {double[4, 4]}
} catch (Exception e) {
Console.WriteLine(e);
} finally {
Console.Read();
}
}
//@param s_id subject id
//@param ecg_in a single sample value of the subject's ECG
public void enrollment(double[] ecg_in, int s_id)
{
for( int i = 0; i < ecg_in.Length; i++)
{
ecg[i] = ecg_in[i];
}
//Create a MWArray to pass value into converted matlab function
MWNumericArray subj_id = new MWNumericArray(s_id);
MWNumericArray ecg_raw = new MWNumericArray(1280, 1, ecg);
MWArray temp = AClass.process(ecg_raw, M); //process ecg data(filter and AC)
//autocorrelated signal is added to both gallery
MWArray gal = new MWNumericArray();
gal = gallery;
gallery = AClass.addtogallery(gal, temp);
MWArray gal1 = new MWNumericArray();
gal1 = enrollee_gallery;
enrollee_gallery = AClass.addtogallery(gal1, temp);
//subject id is added to the id vector
MWArray id_temp = new MWNumericArray();
id_temp = id;
id = AClass.addtoid(id_temp, subj_id);
MWArray id_temp1 = new MWNumericArray();
id_temp1 = enrollee_id;
enrollee_id = AClass.addtoid(id_temp1, subj_id);
//the weight matric is calculated
weights = AClass.dlda(gallery, id);
//enrollee gallery is projected
projected_gallery = AClass.projection(gallery, weights); //use enrollee gallery if want to project enrollee gallery
}
/// <summary>
/// Provides a single output, 2-input MWArray interface to the addtogallery
/// M-function.
/// </summary>
/// <remarks>
/// </remarks>
/// <param name="gal">Input argument #1</param>
/// <param name="addition">Input argument #2</param>
/// <returns>An MWArray containing the first output argument.</returns>
///
public MWArray addtogallery(MWArray gal, MWArray addition)
{
return mcr.EvaluateFunction("addtogallery", gal, addition);
}
/// <summary>
/// Provides an interface for the pdchsim function in which the input and output
/// arguments are specified as an array of MWArrays.
/// </summary>
/// <remarks>
/// This method will allocate and return by reference the output argument
/// array.<newpara></newpara>
/// </remarks>
/// <param name="numArgsOut">The number of output arguments to return</param>
/// <param name= "argsOut">Array of MWArray output arguments</param>
/// <param name= "argsIn">Array of MWArray input arguments</param>
///
public void pdchsim(int numArgsOut, ref MWArray[] argsOut, MWArray[] argsIn)
{
mcr.EvaluateFunction("pdchsim", numArgsOut, ref argsOut, argsIn);
}
/// <summary>
/// Provides a single output, 2-input interface to the pdchsim M-function.
/// </summary>
/// <remarks>
/// </remarks>
/// <param name="traffic">Input argument #1</param>
/// <param name="pdchuse">Input argument #2</param>
/// <returns>An MWArray containing the first output argument.</returns>
///
public MWArray pdchsim(MWArray traffic, MWArray pdchuse)
{
return mcr.EvaluateFunction("pdchsim", traffic, pdchuse);
}
/// <summary>
/// Provides the standard 4-input interface to the newton M-function.
/// </summary>
/// <remarks>
/// M-Documentation:
/// [x, y] = newton(A,b,x0,niter);
/// solves the linear least squares problem with nonnegative variables using the
/// newton's algorithm in [1].
/// Input:
/// A: [MxN] matrix
/// b: [Mx1] vector
/// x0: [Nx1] vector of initial values. x0 > 0. Default value: ones(n,1)
/// niter: Number of iterations. Default value: 10
/// Output
/// x: solution
/// y: complementary solution
/// [1] Portugal, Judice and Vicente, A comparison of block pivoting and
/// interior point algorithms for linear least squares problems with
/// nonnegative variables, Mathematics of Computation, 63(1994), pp. 625-643
/// Uriel Roque
/// 02.05.2006
/// </remarks>
/// <param name="numArgsOut">The number of output arguments to return.</param>
/// <param name="A">Input argument #1</param>
/// <param name="b">Input argument #2</param>
/// <param name="x0">Input argument #3</param>
/// <param name="niter">Input argument #4</param>
/// <returns>An Array of length "numArgsOut" containing the output
/// arguments.</returns>
///
public MWArray[] newton(int numArgsOut, MWArray A, MWArray b,
MWArray x0, MWArray niter)
{
return mcr.EvaluateFunction(numArgsOut, "newton", A, b, x0, niter);
}
/// <summary>
/// Provides a single output, 4-input interface to the newton M-function.
/// </summary>
/// <remarks>
/// M-Documentation:
/// [x, y] = newton(A,b,x0,niter);
/// solves the linear least squares problem with nonnegative variables using the
/// newton's algorithm in [1].
/// Input:
/// A: [MxN] matrix
/// b: [Mx1] vector
/// x0: [Nx1] vector of initial values. x0 > 0. Default value: ones(n,1)
/// niter: Number of iterations. Default value: 10
/// Output
/// x: solution
/// y: complementary solution
/// [1] Portugal, Judice and Vicente, A comparison of block pivoting and
/// interior point algorithms for linear least squares problems with
/// nonnegative variables, Mathematics of Computation, 63(1994), pp. 625-643
/// Uriel Roque
/// 02.05.2006
/// </remarks>
/// <param name="A">Input argument #1</param>
/// <param name="b">Input argument #2</param>
/// <param name="x0">Input argument #3</param>
/// <param name="niter">Input argument #4</param>
/// <returns>An MWArray containing the first output argument.</returns>
///
public MWArray newton(MWArray A, MWArray b, MWArray x0, MWArray niter)
{
return mcr.EvaluateFunction("newton", A, b, x0, niter);
}
/// <summary>
/// Provides a single output, 1-input interface to the newton M-function.
/// </summary>
/// <remarks>
/// M-Documentation:
/// [x, y] = newton(A,b,x0,niter);
/// solves the linear least squares problem with nonnegative variables using the
/// newton's algorithm in [1].
/// Input:
/// A: [MxN] matrix
/// b: [Mx1] vector
/// x0: [Nx1] vector of initial values. x0 > 0. Default value: ones(n,1)
/// niter: Number of iterations. Default value: 10
/// Output
/// x: solution
/// y: complementary solution
/// [1] Portugal, Judice and Vicente, A comparison of block pivoting and
/// interior point algorithms for linear least squares problems with
/// nonnegative variables, Mathematics of Computation, 63(1994), pp. 625-643
/// Uriel Roque
/// 02.05.2006
/// </remarks>
/// <param name="A">Input argument #1</param>
/// <returns>An MWArray containing the first output argument.</returns>
///
public MWArray newton(MWArray A)
{
return mcr.EvaluateFunction("newton", A);
}
/// <summary>
/// Provides the standard 1-input MWArray interface to the loadimage M-function.
/// </summary>
/// <remarks>
/// </remarks>
/// <param name="numArgsOut">The number of output arguments to return.</param>
/// <param name="path">Input argument #1</param>
/// <returns>An Array of length "numArgsOut" containing the output
/// arguments.</returns>
///
public MWArray[] loadimage(int numArgsOut, MWArray path)
{
return mcr.EvaluateFunction(numArgsOut, "loadimage", path);
}
/// <summary>
/// Provides an interface for the addtoid function in which the input and output
/// arguments are specified as an array of MWArrays.
/// </summary>
/// <remarks>
/// This method will allocate and return by reference the output argument
/// array.<newpara></newpara>
/// </remarks>
/// <param name="numArgsOut">The number of output arguments to return</param>
/// <param name= "argsOut">Array of MWArray output arguments</param>
/// <param name= "argsIn">Array of MWArray input arguments</param>
///
public void addtoid(int numArgsOut, ref MWArray[] argsOut, MWArray[] argsIn)
{
mcr.EvaluateFunction("addtoid", numArgsOut, ref argsOut, argsIn);
}
/// <summary>
/// Provides a single output, 2-input MWArray interface to the projection
/// M-function.
/// </summary>
/// <remarks>
/// </remarks>
/// <param name="input">Input argument #1</param>
/// <param name="weight">Input argument #2</param>
/// <returns>An MWArray containing the first output argument.</returns>
///
public MWArray projection(MWArray input, MWArray weight)
{
return mcr.EvaluateFunction("projection", input, weight);
}
/// <summary>
/// Provides the standard 2-input MWArray interface to the addtoid M-function.
/// </summary>
/// <remarks>
/// </remarks>
/// <param name="numArgsOut">The number of output arguments to return.</param>
/// <param name="id">Input argument #1</param>
/// <param name="subj_id">Input argument #2</param>
/// <returns>An Array of length "numArgsOut" containing the output
/// arguments.</returns>
///
public MWArray[] addtoid(int numArgsOut, MWArray id, MWArray subj_id)
{
return mcr.EvaluateFunction(numArgsOut, "addtoid", id, subj_id);
}
/// <summary>
/// Provides a single output, 2-input MWArray interface to the addtoid M-function.
/// </summary>
/// <remarks>
/// </remarks>
/// <param name="id">Input argument #1</param>
/// <param name="subj_id">Input argument #2</param>
/// <returns>An MWArray containing the first output argument.</returns>
///
public MWArray addtoid(MWArray id, MWArray subj_id)
{
return mcr.EvaluateFunction("addtoid", id, subj_id);
}
/// <summary>
/// Provides the standard 2-input MWArray interface to the addtogallery M-function.
/// </summary>
/// <remarks>
/// </remarks>
/// <param name="numArgsOut">The number of output arguments to return.</param>
/// <param name="gal">Input argument #1</param>
/// <param name="addition">Input argument #2</param>
/// <returns>An Array of length "numArgsOut" containing the output
/// arguments.</returns>
///
public MWArray[] addtogallery(int numArgsOut, MWArray gal,
MWArray addition)
{
return mcr.EvaluateFunction(numArgsOut, "addtogallery",
gal, addition);
}
/// <summary>
/// Provides an interface for the SchwartzAndSmith function in which the input and
/// output
/// arguments are specified as an array of MWArrays.
/// </summary>
/// <remarks>
/// This method will allocate and return by reference the output argument
/// array.<newpara></newpara>
/// M-Documentation:
/// Script: MAIN - fonction principale du programme
/// Permet la vue globable sur tout le processing
/// </remarks>
/// <param name="numArgsOut">The number of output arguments to return</param>
/// <param name= "argsOut">Array of MWArray output arguments</param>
/// <param name= "argsIn">Array of MWArray input arguments</param>
///
public void SchwartzAndSmith(int numArgsOut, ref MWArray[] argsOut, MWArray[] argsIn)
{
mcr.EvaluateFunction("SchwartzAndSmith", numArgsOut, ref argsOut, argsIn);
}
/// <summary>
/// Provides a single output, 1-input MWArrayinterface to the SchwartzAndSmith
/// M-function.
/// </summary>
/// <remarks>
/// M-Documentation:
/// Script: MAIN - fonction principale du programme
/// Permet la vue globable sur tout le processing
/// </remarks>
/// <param name="maxiter">Input argument #1</param>
/// <returns>An MWArray containing the first output argument.</returns>
///
public MWArray SchwartzAndSmith(MWArray maxiter)
{
return mcr.EvaluateFunction("SchwartzAndSmith", maxiter);
}
/// <summary>
/// Provides a single output, 1-input MWArrayinterface to the loadimage M-function.
/// </summary>
/// <remarks>
/// </remarks>
/// <param name="path">Input argument #1</param>
/// <returns>An MWArray containing the first output argument.</returns>
///
public MWArray loadimage(MWArray path)
{
return mcr.EvaluateFunction("loadimage", path);
}
/// <summary>
/// Provides a single output, 3-input MWArrayinterface to the SchwartzAndSmith
/// M-function.
/// </summary>
/// <remarks>
/// M-Documentation:
/// Script: MAIN - fonction principale du programme
/// Permet la vue globable sur tout le processing
/// </remarks>
/// <param name="maxiter">Input argument #1</param>
/// <param name="x0">Input argument #2</param>
/// <param name="e0">Input argument #3</param>
/// <returns>An MWArray containing the first output argument.</returns>
///
public MWArray SchwartzAndSmith(MWArray maxiter, MWArray x0, MWArray e0)
{
return mcr.EvaluateFunction("SchwartzAndSmith", maxiter, x0, e0);
}
/// <summary>
/// Provides an interface for the loadimage function in which the input and output
/// arguments are specified as an array of MWArrays.
/// </summary>
/// <remarks>
/// This method will allocate and return by reference the output argument
/// array.<newpara></newpara>
/// </remarks>
/// <param name="numArgsOut">The number of output arguments to return</param>
/// <param name= "argsOut">Array of MWArray output arguments</param>
/// <param name= "argsIn">Array of MWArray input arguments</param>
///
public void loadimage(int numArgsOut, ref MWArray[] argsOut, MWArray[] argsIn)
{
mcr.EvaluateFunction("loadimage", numArgsOut, ref argsOut, argsIn);
}
/// <summary>
/// Provides a single output, 6-input MWArrayinterface to the SchwartzAndSmith
/// M-function.
/// </summary>
/// <remarks>
/// M-Documentation:
/// Script: MAIN - fonction principale du programme
/// Permet la vue globable sur tout le processing
/// </remarks>
/// <param name="maxiter">Input argument #1</param>
/// <param name="x0">Input argument #2</param>
/// <param name="e0">Input argument #3</param>
/// <param name="maturity">Input argument #4</param>
/// <param name="y">Input argument #5</param>
/// <param name="k_in1">Input argument #6</param>
/// <returns>An MWArray containing the first output argument.</returns>
///
public MWArray SchwartzAndSmith(MWArray maxiter, MWArray x0, MWArray e0, MWArray
maturity, MWArray y, MWArray k_in1)
{
return mcr.EvaluateFunction("SchwartzAndSmith", maxiter, x0, e0, maturity, y, k_in1);
}
/// <summary>
/// Provides a single output, 2-input interface to the newton M-function.
/// </summary>
/// <remarks>
/// M-Documentation:
/// [x, y] = newton(A,b,x0,niter);
/// solves the linear least squares problem with nonnegative variables using the
/// newton's algorithm in [1].
/// Input:
/// A: [MxN] matrix
/// b: [Mx1] vector
/// x0: [Nx1] vector of initial values. x0 > 0. Default value: ones(n,1)
/// niter: Number of iterations. Default value: 10
/// Output
/// x: solution
/// y: complementary solution
/// [1] Portugal, Judice and Vicente, A comparison of block pivoting and
/// interior point algorithms for linear least squares problems with
/// nonnegative variables, Mathematics of Computation, 63(1994), pp. 625-643
/// Uriel Roque
/// 02.05.2006
/// </remarks>
/// <param name="A">Input argument #1</param>
/// <param name="b">Input argument #2</param>
/// <returns>An MWArray containing the first output argument.</returns>
///
public MWArray newton(MWArray A, MWArray b)
{
return mcr.EvaluateFunction("newton", A, b);
}
/// <summary>
/// Provides a single output, 10-input MWArrayinterface to the SchwartzAndSmith
/// M-function.
/// </summary>
/// <remarks>
/// M-Documentation:
/// Script: MAIN - fonction principale du programme
/// Permet la vue globable sur tout le processing
/// </remarks>
/// <param name="maxiter">Input argument #1</param>
/// <param name="x0">Input argument #2</param>
/// <param name="e0">Input argument #3</param>
/// <param name="maturity">Input argument #4</param>
/// <param name="y">Input argument #5</param>
/// <param name="k_in1">Input argument #6</param>
/// <param name="mu_e_in1">Input argument #7</param>
/// <param name="sigma_x_in1">Input argument #8</param>
/// <param name="sigma_e_in1">Input argument #9</param>
/// <param name="p_xe_in1">Input argument #10</param>
/// <returns>An MWArray containing the first output argument.</returns>
///
public MWArray SchwartzAndSmith(MWArray maxiter, MWArray x0, MWArray e0, MWArray
maturity, MWArray y, MWArray k_in1, MWArray mu_e_in1,
MWArray sigma_x_in1, MWArray sigma_e_in1, MWArray p_xe_in1)
{
return mcr.EvaluateFunction("SchwartzAndSmith", maxiter, x0, e0, maturity, y, k_in1, mu_e_in1, sigma_x_in1, sigma_e_in1, p_xe_in1);
}
/// <summary>
/// Provides the standard 2-input interface to the newton M-function.
/// </summary>
/// <remarks>
/// M-Documentation:
/// [x, y] = newton(A,b,x0,niter);
/// solves the linear least squares problem with nonnegative variables using the
/// newton's algorithm in [1].
/// Input:
/// A: [MxN] matrix
/// b: [Mx1] vector
/// x0: [Nx1] vector of initial values. x0 > 0. Default value: ones(n,1)
/// niter: Number of iterations. Default value: 10
/// Output
/// x: solution
/// y: complementary solution
/// [1] Portugal, Judice and Vicente, A comparison of block pivoting and
/// interior point algorithms for linear least squares problems with
/// nonnegative variables, Mathematics of Computation, 63(1994), pp. 625-643
/// Uriel Roque
/// 02.05.2006
/// </remarks>
/// <param name="numArgsOut">The number of output arguments to return.</param>
/// <param name="A">Input argument #1</param>
/// <param name="b">Input argument #2</param>
/// <returns>An Array of length "numArgsOut" containing the output
/// arguments.</returns>
///
public MWArray[] newton(int numArgsOut, MWArray A, MWArray b)
{
return mcr.EvaluateFunction(numArgsOut, "newton", A, b);
}
/// <summary>
/// Provides the standard 3-input MWArray interface to the SchwartzAndSmith
/// M-function.
/// </summary>
/// <remarks>
/// M-Documentation:
/// Script: MAIN - fonction principale du programme
/// Permet la vue globable sur tout le processing
/// </remarks>
/// <param name="numArgsOut">The number of output arguments to return.</param>
/// <param name="maxiter">Input argument #1</param>
/// <param name="x0">Input argument #2</param>
/// <param name="e0">Input argument #3</param>
/// <returns>An Array of length "numArgsOut" containing the output
/// arguments.</returns>
///
public MWArray[] SchwartzAndSmith(int numArgsOut, MWArray maxiter, MWArray x0,
MWArray e0)
{
return mcr.EvaluateFunction(numArgsOut, "SchwartzAndSmith", maxiter, x0, e0);
}
/// <summary>
/// Provides an interface for the newton function in which the input and output
/// arguments are specified as an array of MWArrays.
/// </summary>
/// <remarks>
/// This method will allocate and return by reference the output argument
/// array.<newpara></newpara>
/// M-Documentation:
/// [x, y] = newton(A,b,x0,niter);
/// solves the linear least squares problem with nonnegative variables using the
/// newton's algorithm in [1].
/// Input:
/// A: [MxN] matrix
/// b: [Mx1] vector
/// x0: [Nx1] vector of initial values. x0 > 0. Default value: ones(n,1)
/// niter: Number of iterations. Default value: 10
/// Output
/// x: solution
/// y: complementary solution
/// [1] Portugal, Judice and Vicente, A comparison of block pivoting and
/// interior point algorithms for linear least squares problems with
/// nonnegative variables, Mathematics of Computation, 63(1994), pp. 625-643
/// Uriel Roque
/// 02.05.2006
/// </remarks>
/// <param name="numArgsOut">The number of output arguments to return</param>
/// <param name= "argsOut">Array of MWArray output arguments</param>
/// <param name= "argsIn">Array of MWArray input arguments</param>
///
public void newton(int numArgsOut, ref MWArray[] argsOut, MWArray[] argsIn)
{
mcr.EvaluateFunction("newton", numArgsOut, ref argsOut, argsIn);
}
/// <summary>
/// Provides the standard 6-input MWArray interface to the SchwartzAndSmith
/// M-function.
/// </summary>
/// <remarks>
/// M-Documentation:
/// Script: MAIN - fonction principale du programme
/// Permet la vue globable sur tout le processing
/// </remarks>
/// <param name="numArgsOut">The number of output arguments to return.</param>
/// <param name="maxiter">Input argument #1</param>
/// <param name="x0">Input argument #2</param>
/// <param name="e0">Input argument #3</param>
/// <param name="maturity">Input argument #4</param>
/// <param name="y">Input argument #5</param>
/// <param name="k_in1">Input argument #6</param>
/// <returns>An Array of length "numArgsOut" containing the output
/// arguments.</returns>
///
public MWArray[] SchwartzAndSmith(int numArgsOut, MWArray maxiter, MWArray x0,
MWArray e0, MWArray maturity, MWArray y, MWArray k_in1)
{
return mcr.EvaluateFunction(numArgsOut, "SchwartzAndSmith", maxiter, x0, e0, maturity, y, k_in1);
}
/// <summary>
/// Provides the standard 2-input interface to the pdchsim M-function.
/// </summary>
/// <remarks>
/// </remarks>
/// <param name="numArgsOut">The number of output arguments to return.</param>
/// <param name="traffic">Input argument #1</param>
/// <param name="pdchuse">Input argument #2</param>
/// <returns>An Array of length "numArgsOut" containing the output
/// arguments.</returns>
///
public MWArray[] pdchsim(int numArgsOut, MWArray traffic, MWArray pdchuse)
{
return mcr.EvaluateFunction(numArgsOut, "pdchsim", traffic, pdchuse);
}
/// <summary>
/// Provides the standard 12-input MWArray interface to the SchwartzAndSmith
/// M-function.
/// </summary>
/// <remarks>
/// M-Documentation:
/// Script: MAIN - fonction principale du programme
/// Permet la vue globable sur tout le processing
/// </remarks>
/// <param name="numArgsOut">The number of output arguments to return.</param>
/// <param name="maxiter">Input argument #1</param>
/// <param name="x0">Input argument #2</param>
/// <param name="e0">Input argument #3</param>
/// <param name="maturity">Input argument #4</param>
/// <param name="y">Input argument #5</param>
/// <param name="k_in1">Input argument #6</param>
/// <param name="mu_e_in1">Input argument #7</param>
/// <param name="sigma_x_in1">Input argument #8</param>
/// <param name="sigma_e_in1">Input argument #9</param>
/// <param name="p_xe_in1">Input argument #10</param>
/// <param name="lambda_e_in1">Input argument #11</param>
/// <param name="lambda_x_in1">Input argument #12</param>
/// <returns>An Array of length "numArgsOut" containing the output
/// arguments.</returns>
///
public MWArray[] SchwartzAndSmith(int numArgsOut, MWArray maxiter, MWArray x0,
MWArray e0, MWArray maturity, MWArray y, MWArray k_in1,
MWArray mu_e_in1, MWArray sigma_x_in1, MWArray
sigma_e_in1, MWArray p_xe_in1, MWArray lambda_e_in1,
MWArray lambda_x_in1)
{
return mcr.EvaluateFunction(numArgsOut, "SchwartzAndSmith", maxiter, x0, e0, maturity, y, k_in1, mu_e_in1, sigma_x_in1, sigma_e_in1, p_xe_in1, lambda_e_in1, lambda_x_in1);
}
/// <summary>
/// Provides the standard 1-input MWArray interface to the F_EigenSys M-function.
/// </summary>
/// <remarks>
/// M-Documentation:
/// Syntax: [eVec,eVal]=F_EigenSys(M);
/// - Eigen values and vectors of M, sorted by decreasing eigen values.
/// Author: Lu Juwei - U of Toronto
/// Created in 27 May 2001
/// Modified in 6 August 2003
/// </remarks>
/// <param name="numArgsOut">The number of output arguments to return.</param>
/// <param name="M">Input argument #1</param>
/// <returns>An Array of length "numArgsOut" containing the output
/// arguments.</returns>
///
public MWArray[] F_EigenSys(int numArgsOut, MWArray M)
{
return mcr.EvaluateFunction(numArgsOut, "F_EigenSys", M);
}
/// <summary>
/// Provides an interface for the F_EigenSys function in which the input and output
/// arguments are specified as an array of MWArrays.
/// </summary>
/// <remarks>
/// This method will allocate and return by reference the output argument
/// array.<newpara></newpara>
/// M-Documentation:
/// Syntax: [eVec,eVal]=F_EigenSys(M);
/// - Eigen values and vectors of M, sorted by decreasing eigen values.
/// Author: Lu Juwei - U of Toronto
/// Created in 27 May 2001
/// Modified in 6 August 2003
/// </remarks>
/// <param name="numArgsOut">The number of output arguments to return</param>
/// <param name= "argsOut">Array of MWArray output arguments</param>
/// <param name= "argsIn">Array of MWArray input arguments</param>
///
public void F_EigenSys(int numArgsOut, ref MWArray[] argsOut, MWArray[] argsIn)
{
mcr.EvaluateFunction("F_EigenSys", numArgsOut, ref argsOut, argsIn);
}
