/// <summary>Get a rainfall forecast for the specified station number.</summary>
/// <param name="stationNumber">The SILO station number.</param>
/// <returns>Stream of data.</returns>
public Stream Get(int stationNumber, DateTime nowDate, bool rainOnly)
{
// Get SILO data and write to a temporary file.
DateTime startDate = new DateTime(1981, 1, 1);
MemoryStream dataStream = WeatherFile.ExtractMetStreamFromSILO(stationNumber, startDate, DateTime.Now);
string siloFileName = GetTemporaryFileName();
string forecastFileName = null;
try
{
File.WriteAllBytes(siloFileName, dataStream.ToArray());
ChangeToWorkingDirectory();
// Run the MatLab script over the temporary SILO file.
POAMAforecast.Class1 forecast = new POAMAforecast.Class1();
MWArray metFile = new MWCharArray(siloFileName);
MWArray rainfallOnly = new MWNumericArray((double)1);
if (!rainOnly)
rainfallOnly = new MWNumericArray((double)0);
MWArray writeFiles = new MWNumericArray((double)1.0);
MWArray startDay = new MWNumericArray(Convert.ToDouble(nowDate.Day));
MWArray startMonth = new MWNumericArray(Convert.ToDouble(nowDate.Month));
MWArray startYear = new MWNumericArray(Convert.ToDouble(nowDate.Year));
forecast.calsite(metFile, rainfallOnly, writeFiles, startDay, startMonth, startYear);
forecastFileName = siloFileName.Replace(".sim", "") + "_" + nowDate.Year + ".sim";
// Get rid of temporary file.
File.Delete(siloFileName);
if (WebOperationContext.Current != null && WebOperationContext.Current.OutgoingRequest != null)
WebOperationContext.Current.OutgoingResponse.ContentType = "text/plain";
if (File.Exists(forecastFileName))
{
// Read in the forecast data.
byte[] bytes = File.ReadAllBytes(forecastFileName);
return new MemoryStream(bytes);
}
else
return null;
}
finally
{
if (File.Exists(siloFileName))
File.Delete(siloFileName);
if (forecastFileName != null && File.Exists(forecastFileName))
File.Delete(forecastFileName);
}
}
private MWNumericArray transpose(MWNumericArray array)
{
double[] tmp = (double[])array.ToVector(MWArrayComponent.Real);
return new MWNumericArray(tmp.Length, 1, tmp);
}
public void TrainModel(string featureFilePath, string completeFeatureFilePath, List<double[]> featureVector)
{
int dataLength = featureVector[0].Length;
double[] featureData = MatrixUtil.FlattenMatrix(featureVector);
MWCharArray rgbdFile = new MWCharArray(featureFilePath);
MWNumericArray newFeature = new MWNumericArray(dataLength, featureVector.Count, featureData);
rgbdfea.rgbdfea rgbdfea = new rgbdfea.rgbdfea();
rgbdfea.appendFeature(rgbdFile, newFeature);
TrainModel(completeFeatureFilePath);
}
public double[,] MakeSquare_vectorInput(double[] dimArray)
{
var input = new MWNumericArray(dimArray);
var result = this.Matlab.makesquare_in_vector(input);
return(Utility.MWArrayToMatrix(result));
}
public override MWNumericArray Process(MWNumericArray m)
{
MWArray[] r = Tools.FilterHandler.atscale(3, m);
this._m = r[1] as MWNumericArray;
this._s = r[2] as MWNumericArray;
return(r[0] as MWNumericArray);
}
public static MWNumericArray GetCipherKey(string key)
{
MWNumericArray ret = null;
if (Regex.IsMatch(key, @"^[a-zA-Z]+$"))
{
ret = (MWNumericArray)Program.aesMatObj.loadmatfile((MWArray)key);;
}
else
{
double[,] doubles = new double[4, 4];
string[] cells = key.Split(' ');
int count = -1;
for (int i = 0; i < 4; i++)
{
for (int j = 0; j < 4; j++)
{
doubles[i, j] = double.Parse(cells[++count]);
}
}
ret = (MWNumericArray)doubles;
}
return(ret);
}
public Image RunFilter(Image src)
{
filterDataContext db = new filterDataContext();
//Bilateral Bilateral = new Bilateral();
double[] sigma = new double[] { 3, 0.1 };
double w = 5;
MWNumericArray mw_sigma = new MWNumericArray(sigma);
//MWArray Result = Bilateral.bfilter2(image, w, mw_sigma);
//MWArray image = new MWNumericArray(src.Array.Array);
/*
int H = src.Height;
int W = src.Width;
int sq = W * H;
*/
MWArray[] Result = bilateral(2, src.image, w, mw_sigma);
//MWArray[] Result = bfilter2(2, src.image, w, mw_sigma);
MWNumericArray descriptor = null;
descriptor = (MWNumericArray)Result[0];
double[,] result = null;
result = (double[,])descriptor.ToArray(MWArrayComponent.Real);
Image res = new Image(result);
MWNumericArray e_descriptor = null;
e_descriptor = (MWNumericArray)Result[1];
WorkTime = (double)e_descriptor.ToScalarDouble();
db.add_b_res(src.Height, src.Height / src.Width, WorkTime, w, sigma[0], sigma[1]);
Result = null;
e_descriptor = null;
result = null;
mw_sigma = null;
GC.Collect();
return res;
}
public ActionResult GetEmployer(string[] sendforcalc)
{
var pop = 5;
var gem = 10;
var Pa = 0.2;
CalcTsp.Class1 calcTsp = new CalcTsp.Class1();
double[] list = new double[20 * 3];
int i = 0;
foreach (var item in sendforcalc)
{
string[] arr = item.Split(' ');
list[i] = i / 3;
list[i + 1] = double.Parse(arr[0]);
list[i + 2] = double.Parse(arr[1]);
i = i + 3;
}
MWNumericArray array = new MWNumericArray(20, 3, list);
MWArray resultObj = calcTsp.CalcTsp(array, pop, Pa, gem);
Array a = resultObj.ToArray();
var numa = a.GetValue(0, 0);
var aaaaa = numa.ToString();
int num = int.Parse(aaaaa);
return(Json(num, JsonRequestBehavior.AllowGet));
}
public static void Kands(MWNumericArray x, double pp, out double[] groupA, out double[] groupB)
{
var rlst = ToolHandler.Kands(2, x, pp);
groupA = (double[])((MWNumericArray)rlst[0]).ToVector(MWArrayComponent.Real);
groupB = (double[])((MWNumericArray)rlst[1]).ToVector(MWArrayComponent.Real);
}
private void PhaseRetrivalIteration(DataTable Dt, string SavePath)
{
if (Dt.NumUse() < 4)
{
Output("Please select at least four images, "
+ "including a focal image, a reference image and at least two median images.");
return;
}
if (Dt.NumType("Focal") != 1)
{
Output("Please select one and only one focal image.");
return;
}
if (Dt.NumType("Reference") != 1)
{
Output("Please select one and only one reference image.");
return;
}
string FocalName = null;
int FocalDist = 0;
List <String> MedianName = new List <string>();
List <int> MedianDist = new List <int>();
string ExportName = null;
foreach (DataRow Dr in Dt.Rows)
{
if (Dr["Type"].ToString() == "Focal")
{
FocalName = Dr.GetName();
FocalDist = int.Parse(Dr["Distance"].ToString());
}
if (Dr["Type"].ToString() == "Median")
{
MedianName.Add(Dr.GetName());
MedianDist.Add(int.Parse(Dr["Distance"].ToString()));
ExportName = "RetrievedPhase";
}
if (Dr["Type"].ToString() == "Reference")
{
continue;
}
}
MWCharArray MWMedianName = new MWCharArray(MedianName.ToArray());
MWNumericArray MWMedianDist = new MWNumericArray(1, MedianDist.Count, MedianDist.ToArray());
StreamWriter CancelWriter = new StreamWriter(SavePath + "\\Cancel.log");
CancelWriter.BaseStream.SetLength(0);
CancelWriter.WriteLine("Continue");
CancelWriter.Dispose();
Mf.RetrieveReferencePhase(SavePath, ExportName, MWParameter, 10000, FocalName, FocalDist, MWMedianName, MWMedianDist);
Token.ThrowIfCancellationRequested();
}
public override MWNumericArray Process(MWNumericArray m)
{
//throw new NotImplementedException();
var d = Tools.FilterHandler.detrend(m) as MWNumericArray;
return(d);
}
public bool Comp_Add(Component item)
{
if (item == null)
{
throw new System.ArgumentNullException("can't add a null Spectrum");
}
if (this.Comp_Contains(item))
{
return(false);
}
this._components.Add(item);
if (this._specs.Count > 0)
{
double[] d = new double[this._specs.Count];
for (int i = 0; i < this._specs.Count; i++)
{
d[i] = double.NaN;
}
if (this._y == null)
{
this._y = new MWNumericArray(this._specs.Count, 1, d);
}
else
{
this._y = Data.Tools.InsertColumn(this._y, d, this._y.Dimensions[0] + 1);
}
}
return(true);
}
/// <summary>
/// 过渡性质有NaN的光谱数据
/// </summary>
public void FilterNaN()
{
if (this._y == null)
{
return;
}
var idx = new List <int>();
for (int i = 1; i <= this._y.Dimensions[0]; i++)
{
bool tag = true;
for (int j = 1; j <= this._y.Dimensions[1]; j++)
{
if (double.IsNaN(this._y[i, j].ToScalarDouble()))
{
tag = false;
break;
}
}
if (tag)
{
idx.Add(i - 1);
}
}
if (idx.Count < this._specs.Count)
{
var lib = this.SubLib(idx.ToArray());
this._x = lib.X;
this._y = lib.Y;
this._specs = lib.Specs;
}
}
public SpecBase(string path = null)
{
if (String.IsNullOrWhiteSpace(path))
{
return;
}
var db = Serialize.Read <SpecBase>(path);
if (db == null)
{
return;
}
this._axis = db._axis;
this._components = db._components;
this._date = db._date;
this._description = db._description;
this._editor = db._editor;
this._fullPath = path;
this._specs = db._specs;
this._title = db._title;
this._x = db._x;
this._y = db._y;
// db.Dispose();
db = null;
}
public Spectrum this[int i]
{
get
{
var item = this._specs[i];
item = this.getAxisData(item, i);
return(item);
}
set
{
if (value == null)
{
return;
}
this._x = Data.Tools.SetColumn(this._x, value.Data.Y, i + 1);
if (this._y != null && value.Components != null && value.Components.Count > 0)
{
var dlst = (double[])Data.Tools.SelectRow(this._y, i + 1).ToVector(MWArrayComponent.Real);
for (int r = 0; r < this._components.Count; r++)
{
var c = value.Components.Where(d => d.Name == this._components[r].Name).FirstOrDefault();
if (c != null)
{
dlst[r] = c.ActualValue;
}
}
this._y = Data.Tools.SetRow(this._y, dlst, i + 1);
}
value.Components = null;
value.Data = null;
this._specs[i] = value;
}
}
public static MWNumericArray Bitmap2MWArray(Bitmap bmp)
{
MWNumericArray matrix = null;
byte[, ,] rgbImage = new byte[3, bmp.Height, bmp.Width];
for (int i = 0; i < bmp.Width; i++)
{
for (int j = 0; j < bmp.Height; j++)
{
if (bmp.GetPixel(i, j).R != 0)
{
}
rgbImage[0, j, i] = bmp.GetPixel(i, j).R;
rgbImage[1, j, i] = bmp.GetPixel(i, j).G;
rgbImage[2, j, i] = bmp.GetPixel(i, j).B;
}
}
try
{
matrix = new MWNumericArray();
matrix = rgbImage;
}
catch (Exception ex)
{
string s = ex.InnerException.Message;
}
return(matrix);
}
public static double[] InsertColumn(double[] a, double[] b, int idx)
{
var a1 = new MWNumericArray(1, a.Length, a);
var b1 = new MWNumericArray(1, b.Length, b);
return((double[])InsertColumn(a1, b1, idx).ToVector(MWArrayComponent.Real));
}
private void button3_Click(object sender, EventArgs e)
{
MWNumericArray theta = new MWNumericArray(1, sample_counts, sample_position);
MWNumericArray U_1 = new MWNumericArray(1, sample_counts, u1);
MWNumericArray U_2 = new MWNumericArray(1, sample_counts, u2);
Plot_User plot = new Plot_User();
plot.Plot_Circle(U_1, U_2);
MWArray[] result_1 = new Fit_Parameter.Class1().Fit_1(4, theta, U_1);
MWArray[] result_2 = new Fit_Parameter.Class1().Fit_2(4, theta, U_2);
// up = (MWNumericArray)new Sensor.Sensor().envelope(theta, U_1);
Array a = result_1.ToArray();
Array b = result_2.ToArray();
double envelope = (double.Parse(a.GetValue(2).ToString()) + double.Parse(b.GetValue(2).ToString())) / 2;
double alpha = (double.Parse(a.GetValue(3).ToString()) + double.Parse(b.GetValue(3).ToString())) / 2;
textBox13.Text = a.GetValue(0).ToString(); //A_1
textBox9.Text = a.GetValue(1).ToString(); //d_1
textBox14.Text = b.GetValue(0).ToString(); //A_2
textBox10.Text = b.GetValue(1).ToString(); //d_2
textBox18.Text = envelope.ToString(); //a_0
textBox23.Text = alpha.ToString(); //alpha
theta.Dispose();
U_1.Dispose();
U_2.Dispose();
}
public SparseMatrix(MWNumericArray matlabArray)
{
double[,] data = (double[,])matlabArray.ToArray(MWArrayComponent.Real);
int n = data.GetLength(0);
values = new Dictionary<long, double>();
rowElements = new HashSet<int>[n];
columnElements = new HashSet<int>[n];
size = n;
for (int i = 0; i < n; ++i)
{
rowElements[i] = new HashSet<int>();
columnElements[i] = new HashSet<int>();
}
for (int row = 0; row < n; ++row)
{
for (int col = 0; col < n; ++col)
{
if (data[row, col] != 0) this[row, col] = (double)data[row, col];
}
}
}
public static double[] corecurve(double[] x, double[] y)
{
var mx = new MWNumericArray(1, x.Length, x);
var my = new MWNumericArray(1, y.Length, y);
return((double[])((MWNumericArray)ToolHandler.corecurve(mx, my)).ToVector(MWArrayComponent.Real));
}
public void Train(SpecBase lib, bool needFilter = true)
{
//过滤掉性质有NaN的数据
lib.FilterNaN();
int[] idxs = lib.Specs.Select((d, idx) => new { s = d, idx = idx }).Where(d => d.s.Usage != UsageTypeEnum.Ignore).Select(d => d.idx).ToArray();
this._lib = Serialize.DeepClone <SpecBase>(lib.SubLib(idxs));
if (needFilter && this._filters != null)
{
this._lib.SetX(Preprocesser.Process(this._filters, this._lib), true);
}
//PCA分解
var handler = Tools.ModelHandler;
if (this._maxRank < 1)
{
this._maxRank = 10;
}
this._maxRank = Math.Min(this._maxRank, this._lib.X.Dimensions[1]);
var r = handler.IdentifyTrain(3, this._lib.X, this._maxRank);
this._p = (MWNumericArray)r[0];
this._w = (MWNumericArray)r[1];
this._t = (MWNumericArray)r[2];
this._trained = true;
}
private void getCV_btn_Click(object sender, EventArgs e)
{
/*if (mbSession == null)
* {
* System.Windows.Forms.MessageBox.Show("当前没有连接到设备","提示信息",MessageBoxButtons.OK,MessageBoxIcon.Warning);
* }
* else
* {
* ShowVCData form = new ShowVCData();
* form.SetmbSession(mbSession);
* form.setKind(true);
* form.Show();
*
* }*/
double[] s = new double[500000];
for (int i = 0; i < 500000; i++)
{
s[i] = new Random().NextDouble();
}
Class1 class1 = new Class1();
MWNumericArray aaa = (MWNumericArray)s;
MWNumericArray res = (MWNumericArray)class1.FFT(aaa);
Console.WriteLine("OKKIKKKK");
}
public override MWNumericArray Process(MWNumericArray m)
{
//throw new NotImplementedException();
var d = Tools.FilterHandler.deriate2(m, this._winSize) as MWNumericArray;
return(d);
}
private void button1_Click(object sender, EventArgs e)
{
string Atext, convCoretext;
convCoretext = textBox3.Text;
Atext = textBox4.Text;
double Anum, convCorenum;
Anum = Convert.ToDouble(Atext);
convCorenum = Convert.ToDouble(convCoretext);
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();
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();
}
public void GrayScaleImageTest(ImageData imageData)
{
byte[] a = Convert.FromBase64String(imageData.Base64);
//MemoryStream memoryStream = new MemoryStream(a);
//Image image = System.Drawing.Image.FromStream(memoryStream);
Analyse z = new Analyse();
MWNumericArray mWNumericArray = a;
MWArray I = z.analyseImage(mWNumericArray, imageData.CoordinateXY[0], imageData.CoordinateXY[1]);
MWNumericArray I_num = I.ToArray();
Byte[] I_bytes = (Byte[])I_num.ToVector(MWArrayComponent.Real);
int w = I.Dimensions[0];
int h = I.Dimensions[1];
Bitmap bmp = new Bitmap(w, h, PixelFormat.Format8bppIndexed);
ColorPalette cp = bmp.Palette;
for (Int32 i = 0; i < 256; ++i)
{
cp.Entries[i] = Color.FromArgb(255, i, i, i);
}
bmp.Palette = cp;
BitmapData data = bmp.LockBits((new Rectangle(0, 0, bmp.Width, bmp.Height)), ImageLockMode.WriteOnly, bmp.PixelFormat);
Marshal.Copy(I_bytes, 0, data.Scan0, I_bytes.Length);
bmp.UnlockBits(data);
bmp.Save("image.png", ImageFormat.Png);
}
public Bitmap Hog(Bitmap inputBitmap)
{
var inputBitmapHeight = inputBitmap.Height;
var inputBitmapWidth = inputBitmap.Width;
var resultBitmap = new Bitmap(inputBitmapWidth, inputBitmapHeight);
var rgbArrayOfInputBitmap = new byte[3, inputBitmapHeight, inputBitmapWidth];
for (var j = 0; j < inputBitmapHeight; j++)
{
for (var i = 0; i < inputBitmapWidth; i++)
{
rgbArrayOfInputBitmap[0, j, i] = inputBitmap.GetPixel(i, j).R;
rgbArrayOfInputBitmap[1, j, i] = inputBitmap.GetPixel(i, j).G;
rgbArrayOfInputBitmap[2, j, i] = inputBitmap.GetPixel(i, j).B;
}
}
MWNumericArray imageMatrix = rgbArrayOfInputBitmap;
MWArray resultBitmapMatrix = 0;
resultBitmapMatrix = searchObjectByMatlabOperations.HOG_SVM(imageMatrix);
for (var i = 0; i < inputBitmapHeight; i++)
{
for (var j = 0; j < inputBitmapWidth; j++)
{
resultBitmap.SetPixel(j, i, Color.FromArgb(Convert.ToInt32(resultBitmapMatrix[i + 1, j + 1, 1].ToString()), Convert.ToInt32(resultBitmapMatrix[i + 1, j + 1, 2].ToString()), Convert.ToInt32(resultBitmapMatrix[i + 1, j + 1, 3].ToString())));
}
}
return(resultBitmap);
}
private bool PopulateValues(ref MWNumericArray valuesArray)
{
string input = Console.ReadLine();
string[] tokens = input.Split(',');
int tokensLength = tokens.Length;
if (tokensLength == 0)
{
return(false);
}
int value = 0, index = 0;
int[] values = new int[tokensLength];
foreach (string token in tokens)
{
if (int.TryParse(token, out value))
{
values.SetValue(value, index++);
}
else
{
return(false);
}
}
valuesArray = new MWNumericArray(1, tokensLength, values);
return(true);
}
draw drawtest = new draw();//创建类 draw 的实例
private void 打开OToolStripMenuItem_Click(object sender, EventArgs e)
{
OpenFileDialog d = new OpenFileDialog();
d.Filter = "(*.mat)|*.mat|所有文件(*.*)|*.*"; if (d.ShowDialog() == DialogResult.OK)
{
FileStream fs = File.OpenRead(d.FileName); StreamReader sr = new StreamReader(fs); string s;
string filename = d.FileName;
d1 = pcl.loaddata(filename);
// MessageBox.Show(filename);
MWArray[] agrsIn = new MWArray[] { d1 };
pcl.pces(4, ref agrsOut, agrsIn);
MWNumericArray x1 = agrsOut[0] as MWNumericArray;
MWNumericArray x2 = agrsOut[1] as MWNumericArray;
MWNumericArray x3 = agrsOut[2] as MWNumericArray;
MWNumericArray x4 = agrsOut[3] as MWNumericArray;
rbegin = (double[, ])x1.ToArray();
pend = (double[, ])x2.ToArray();
rpk = (double[, ])x3.ToArray();
f1 = (double[, ])d1.ToArray();
qt_mean = x4.ToScalarDouble();
textBox4.Text = qt_mean.ToString();
for (int i = 0; i < 12000; i++)
{
data[i].X = (int)i;//强制类型转换，将double转为int，可能会丢失数据
data[i].Y = (int)((1000 - f1[0, i *5]) * 250 / 4500 + 100);
}
this.timer1.Enabled = true; //可以使用
this.timer1.Interval = 100; //定时时间为100毫秒
this.timer1.Tick += new System.EventHandler(this.timer1_Tick);
this.timer1.Start();//启动定时器
}
}
/// <summary>
/// Generalised eigenvalues of A and B (analog to MATLAB M = eig(A,B))
/// </summary>
/// <param name="B">Matrix B</param>
/// <returns>Generalised eigenvalues</returns>
public Complex[] eige(Matrix B)
{
//calling Matlab API
Eig testob = new Eig();
//Matlab Array which gets result of Matlab function eig(A,B)
res = testob.Eigenvalues(2, (MWNumericArray)this.matrix, (MWNumericArray)B.matrix, this.Rows());
//arrays for real and imaginary parts
real = (MWNumericArray)res[0];
imag = (MWNumericArray)res[1];
//copying parts into CSharp arrays
double[,] resCR = (double[, ])real.ToArray(MWArrayComponent.Real);
double[,] resCI = (double[, ])imag.ToArray(MWArrayComponent.Real);
this.eigenvalues = new Complex[21];
Complex[] eigenbuf = new Complex[this.rows];
for (int i = 0; i < this.rows; i++)
{
eigenbuf[i] = new Complex(resCR[i, 0], resCI[i, 0]);
eigenbuf[i] = eigenbuf[i].Pow(0.5);
}
Complex buf = new Complex();
buf.quickSort(ref eigenbuf, 0, this.rows - 1);
for (int i = 0; i < 21; i++)
{
this.eigenvalues[i] = eigenbuf[200 + i - 1];
}
return(this.eigenvalues);
}
public Form1()
{
InitializeComponent();
//create Object from your dll
mymatrix.Class1 MyObject = new mymatrix.Class1();
//run the method which gets the data and save in a MWArray object
MWArray MatlabData = MyObject.mymatrix();
//cast the data to MWNumericArray
MWNumericArray TableValuesMat = (MWNumericArray)MatlabData;
// now cast to a double array
double[,] TableValues = (double[, ])TableValuesMat.ToArray();
// now convert 2d array to a table in gridview:
int height = TableValues.GetLength(0);
int width = TableValues.GetLength(1);
this.dataGridView1.ColumnCount = width;
for (int r = 0; r < height; r++)
{
DataGridViewRow row = new DataGridViewRow();
row.CreateCells(this.dataGridView1);
for (int c = 0; c < width; c++)
{
row.Cells[c].Value = TableValues[r, c];
}
this.dataGridView1.Rows.Add(row);
}
}
public static double CalcWeist(double[] x, double[] y)
{
Logger log = new Logger();
log.Debug("Matlab prepare");
Weist w = new Weist();
MWNumericArray matlabX = new MWNumericArray(MWArrayComplexity.Real, x.Length, 1);
MWNumericArray matlabY = new MWNumericArray(MWArrayComplexity.Real, y.Length, 1);
MWArray[] agrsOut = new MWArray[3];
MWArray[] agrsIn = new MWArray[] { (MWNumericArray)matlabX, (MWNumericArray)matlabY };
for (int i = 0; i < x.Length; i++)
{
matlabX[i + 1] = x[i];
matlabY[i + 1] = y[i];
}
log.Debug("Matlab calculate");
w.weist(3, ref agrsOut, agrsIn);
double o = double.Parse(agrsOut[0].ToString());
double z = double.Parse(agrsOut[1].ToString());
string info = agrsOut[2].ToString();
log.Debug("Matlab done");
return(o);
}
public double[,] doFFT(double[] sinal, double freqAm)
{
double[] resY = new double[sinal.Length];
double[] resX = new double[sinal.Length];
double[,] res = new double[sinal.Length, 2];
MWNumericArray aux_res2 = new MWNumericArray(new double[sinal.Length]);
MWNumericArray aux_res2X = new MWNumericArray(new double[sinal.Length]);
MWNumericArray xdt = new MWNumericArray(new double[sinal.Length]);
Array aux_res3 = null;
Array aux_res3X = null;
fftMatlab2 obj = new fftMatlab2();
xdt = sinal;
MWArray aux_res = obj.IB_fft(xdt, freqAm);
MWArray aux_resX = obj.IB_fftx(xdt, freqAm);
aux_res2X = (MWNumericArray)aux_resX;
aux_res2 = (MWNumericArray)aux_res;
aux_res3X = aux_res2X.ToVector(MWArrayComponent.Real);
aux_res3 = aux_res2.ToVector(MWArrayComponent.Real);
resY = (double[])aux_res3;
resX = (double[])aux_res3X;
for (int i = 0; i < resX.Length; i++)
{
res[i, 0] = resY[i]; //linha 0 valores de Y
res[i, 1] = resX[i]; //linha 1 valores de X
}
return(res);
}
// Generate the feature vector of the given image.
public double[] GenerateFeature(short[,] pixelData)
{
// Initialize the parameters of feature (data).
MWStructArray fea_first = new MWStructArray(1, 1, new string[] { "pixels", "maxsize" });
fea_first.SetField("pixels", new MWNumericArray(pixelData));
fea_first.SetField("maxsize", 75);
// Initialize the parameters of dictionary.
MWStructArray dic_first = new MWStructArray(1, 1, new string[] { "dicsize", "patchsize", "samplenum", "dic" });
dic_first.SetField("dicsize", 200);
dic_first.SetField("patchsize", 16);
dic_first.SetField("samplenum", 100);
MWArray array = new MWNumericArray(this.dict_dimension[0], this.dict_dimension[1], MatrixUtil.FlattenMatrix(this.dic, this.dict_dimension[0], this.dict_dimension[1]));
dic_first.SetField("dic", array);
// Orthogonal matching pursuit encoder
Stopwatch ompTimer = Stopwatch.StartNew();
MWArray rgbdfea = ompNormal.extract_feature_normal(fea_first, dic_first);
ompTimer.Stop();
Array feature = rgbdfea.ToArray();
Console.WriteLine("Feature Extraction Time: {0} ms", ompTimer.ElapsedMilliseconds);
double[] feature_vector = new double[feature.Length];
for (int i = 0; i < feature.Length; i++)
feature_vector[i] = System.Convert.ToDouble(feature.GetValue(i, 0));
return feature_vector;
}
public override bool ProcessInput()
{
Console.WriteLine("Please input the values for x separated by commas");
string input = Console.ReadLine();
string[] tokens = input.Split(',');
int tokensLength = tokens.Length;
if (tokensLength == 0)
{
return(false);
}
int value = 0, index = 0;
int[] values = new int[tokensLength];
foreach (string token in tokens)
{
if (int.TryParse(token, out value))
{
values.SetValue(value, index++);
}
else
{
return(false);
}
}
xValues = new MWNumericArray(1, tokensLength, values);
return(true);
}
private void bgwMatlabGen_DoWork(object sender, DoWorkEventArgs e)
{
ArrayList lit = e.Argument as ArrayList;
e.Result = true;
try
{
MWNumericArray fc_Hz = (double)lit[0],
chanUpdate_ms = (double)lit[1],
polarMod = (double)lit[2],
mediaPara = (double[])lit[3],
angSpread_deg = (double)lit[4],
chanMod = (double)lit[5];
MWCharArray traFilePath = lit[6] as string,
launAnteFilePath = lit[7] as string,
recvAnteFilePath = lit[8] as string,
chanParaSavePath = lit[9] as string,
chanSavePath = lit[10] as string;
MWArray[] matlabRlt = aeroChan.AeroChanGenerate(2, fc_Hz, chanUpdate_ms, polarMod, mediaPara, angSpread_deg, chanMod, traFilePath, launAnteFilePath, recvAnteFilePath, chanParaSavePath, chanSavePath);
if ((matlabRlt[0] as MWNumericArray).ToScalarInteger() != 0)
{
e.Result = false;
lastError = matlabRlt[1].ToString();
return;
}
}
catch (Exception ex)
{
e.Result = false;
lastError = "调用Matlab失败！";
}
}
public void CalDistrubtionProcess_Prev(double[] dwx, double[] dwvelo, string folderPath, string fileName)
{
MWNumericArray wx = new MWNumericArray(dwx);
Array.Clear(dwx, 0, dwx.Length);
MWNumericArray wvelo = new MWNumericArray(dwvelo);
Array.Clear(dwvelo, 0, dwvelo.Length);
MWArray resultMW = anc.sub_calculate_distrubtion_with_one_output(wx, wvelo);
Array resultArray = resultMW.ToArray();
int count = resultArray.Length / 3;
double[] d1 = new double[count];
double[] d2 = new double[count];
double[] d3 = new double[count];
double[,] resultArray_A = (double[, ])resultArray;
for (int i = 0; i < count; i++)
{
d1[i] = resultArray_A[i, 0];
d2[i] = resultArray_A[i, 1];
d3[i] = resultArray_A[i, 2];
}
CreateExcel(d1, d2, d3, folderPath, fileName);
}
static void Main(string[] args)
{
Directory.SetCurrentDirectory(@"G:\");
POAMAforecast.Class1 d = new POAMAforecast.Class1();
MWArray metFile = new MWCharArray(@"Burnie.sim");
MWArray startMonth = new MWNumericArray((double) 6.0);
MWArray startDay = new MWNumericArray((double) 1.0);
MWArray rainOnly = new MWNumericArray((double)1.0);
MWArray writeFiles = new MWNumericArray((double)1.0);
MWArray lastYearOnly = new MWNumericArray((double)1.0);
d.calsite(metFile, startMonth, startDay, rainOnly, writeFiles, lastYearOnly);
}
public Image addNoise(string type, Image src, double a, double b)
{
MWLogicalArray mw_print = new MWLogicalArray(Form1.print);
MWArray Result = noise(type, new MWNumericArray(src.Array.Array), a, b, mw_print);
Image res = new Image((double[,])((MWNumericArray)Result).ToArray(MWArrayComponent.Real));
MWNumericArray tmp = new MWNumericArray(res.Array.Array);
res.image = (MWArray)tmp;
Result = null;
tmp = null;
GC.Collect();
return res;
}
/// <summary>
/// 生成信号序列
/// </summary>
/// <param name="signal_type">信号类型</param>
/// <param name="ray_angles">波束方向集合</param>
/// <param name="ray_strengths">波束强度集合</param>
/// <param name="ray_taus">波束时延集合</param>
/// <param name="N">序列点数</param>
/// <param name="args">args参数集合，与信号类型相关</param>
/// <returns>信号序列</returns>
public MWNumericArray GenerateSignal_MW(Signal_Type signal_type, double[] ray_angles, double[] ray_strengths, double[] ray_taus, int N, double[] args)
{
if (ray_angles.Length != ray_strengths.Length || ray_strengths.Length != ray_taus.Length)
throw new Exception("波束角度、强度和延时数组必须具有相同长度！");
if (ray_angles.Length == 0)
return null;
MWNumericArray len = new MWNumericArray(N);
switch (signal_type)
{
case Signal_Type.PSEUDO_DOPPLER:
{
MWNumericArray fl = new MWNumericArray(args[0]);
MWNumericArray fc = new MWNumericArray(args[1]);
MWNumericArray fs = new MWNumericArray(args[2]);
MWNumericArray fswitch = new MWNumericArray(args[3]);
MWNumericArray R = new MWNumericArray(args[4]);
MWNumericArray NA = new MWNumericArray(args[5]);
MWNumericArray sum = (MWNumericArray)sptb.GenerateSignal_PseudoDoppler(new MWNumericArray(ray_strengths[0]),
fl,
fc,
fs,
fswitch,
R,
NA,
new MWNumericArray(ray_angles[0]),
new MWNumericArray(ray_taus[0]),
len);
for (int i = 1; i < ray_angles.Length; i++)
{
MWNumericArray sub = (MWNumericArray)sptb.GenerateSignal_PseudoDoppler(new MWNumericArray(ray_strengths[i]),
fl,
fc,
fs,
fswitch,
R,
NA,
new MWNumericArray(ray_angles[i]),
new MWNumericArray(ray_taus[i]),
len);
sum = (MWNumericArray)sptb.Add(sum, sub);
}
return sum;
}
}
return null;
}
public static void Analyze(System.ComponentModel.BackgroundWorker worker)
{
int i = 0;
foreach (string file in Directory.GetFiles("C:\\Users\\Forpatril\\Documents\\Visual Studio 2010\\Projects\\Diploma_cs\\Images", "*.jpg"))
{
Image img = new Image(file);
for (int w = 2; w < 35; w++)
for (int sig = 1; sig < w / 2; sig++)
{
filterclass fc = new filterclass();
Noise n = new Noise();
Image img_n = n.addNoise("gaussian", img, 0, 0.055);
double[] p = new double[] {w, sig, 0.2};
MWNumericArray parameters = new MWNumericArray(p);
MWLogicalArray mw_print = new MWLogicalArray(Form1.print);
MWArray[] Result = fc.filter2(2, img_n.image, "bilateral", parameters, mw_print);
MWNumericArray descriptor = null;
descriptor = (MWNumericArray)Result[0];
double[,] result = null;
result = (double[,])descriptor.ToArray(MWArrayComponent.Real);
Image ret = new Image(result);
MWNumericArray e_descriptor = null;
e_descriptor = (MWNumericArray)Result[1];
ret.Time = (double)e_descriptor.ToScalarDouble();
filterDataContext db = new filterDataContext();
psnrClass ps = new psnrClass();
double psnr = (double)((MWNumericArray)ps.psnr(ret.image, img.image)).ToScalarDouble();
db.add_line("bilateral", "gaussian", img.Height, img.Width, ret.Time, psnr, p[0], p[1], p[2]);
Result = null;
e_descriptor = null;
result = null;
parameters = null;
img_n = null;
n = null;
descriptor = null;
ret = null;
ps = null;
fc = null;
GC.Collect();
}
worker.ReportProgress(i++);
img = null;
GC.Collect();
}
}
//@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
}
public Image RunFilter(Image src, int id)
{
filterDataContext db = new filterDataContext();
double[] p = new double[Form1.pars.par[id].Length];
p = Form1.pars.par[id];
MWNumericArray parameters = new MWNumericArray(p);
MWLogicalArray mw_print = new MWLogicalArray(Form1.print);
MWArray[] Result = filter2(2, src.image, type[id], parameters, mw_print);
MWNumericArray descriptor = null;
descriptor = (MWNumericArray)Result[0];
double[,] result = null;
result = (double[,])descriptor.ToArray(MWArrayComponent.Real);
Image res = new Image(result);
MWNumericArray e_descriptor = null;
e_descriptor = (MWNumericArray)Result[1];
res.Time = WorkTime = (double)e_descriptor.ToScalarDouble();
//db.add_b_res(src.Height, src.Height / src.Width, WorkTime, w, sigma[0], sigma[1]);
Result = null;
e_descriptor = null;
result = null;
parameters = null;
GC.Collect();
return res;
}
public static double[,] cont_wavelet_transform(double[] input_signal, double[] scale, string wavelet)
{
CWT.CWT_Class c = new CWT.CWT_Class();
int numSamples = input_signal.Length;// SPM.Count;
MWNumericArray data = new MWNumericArray(MWArrayComplexity.Real, MWNumericType.Double, numSamples);
MWNumericArray scale_m = new MWNumericArray(MWArrayComplexity.Real, MWNumericType.Double, scale.Length);
MWCharArray name = new MWCharArray(wavelet);
for (int idx = 1; idx <= scale.Length; idx++)
{
scale_m[idx] = scale[idx - 1];
}
// Initialize data
for (int idx = 1; idx <= numSamples; idx++)
{
data[idx] = input_signal[idx - 1];
}
MWNumericArray res = (MWNumericArray)c.PerformCWT(data, scale_m, name);
double[,] coeffs = (double[,])(res.ToArray(MWArrayComponent.Real));
return coeffs;
}
private SparseMatrix GenerateInnerWeights(int size)
{
ESNclass esn = new ESNclass();
MWNumericArray _size = new MWNumericArray(size);
MWNumericArray _ratio = new MWNumericArray(0.05f);
MWArray ret = esn.ESNInnerWeights(_size, _ratio);
return new SparseMatrix((MWNumericArray)ret);
}
/// <summary>
/// 生成指纹
/// </summary>
/// <param name="signal_type">信号类型</param>
/// <param name="fingerprint_name">指纹类型</param>
/// <param name="signals">各接收站的信号集合</param>
/// <param name="args">args参数集合，与信号类型有关</param>
/// <returns>指纹参数集合，与指纹类型有关</returns>
public Fingerprint GenerateFingerprint(Signal_Type signal_type, Fingerprint_Name fingerprint_name, MWNumericArray[] signals, double[] args)
{
switch(fingerprint_name)
{
case Fingerprint_Name.S:
{
Fingerprint_S.S[] ss = new Fingerprint_S.S[signals.Length];
for (int i = 0; i < signals.Length; i++)
{
if (signals[i] == null)
ss[i] = new Fingerprint_S.S();
else
{
MWNumericArray s = (MWNumericArray)sptb.CalcStrength(signals[i]);
ss[i] = new Fingerprint_S.S(s.ToScalarDouble());
}
}
Fingerprint_S fp = new Fingerprint_S(ss);
return fp;
}
case Fingerprint_Name.SA:
{
Fingerprint_SA.SA[] sas = new Fingerprint_SA.SA[signals.Length];
switch(signal_type)
{
case Signal_Type.PSEUDO_DOPPLER:
{
MWNumericArray fl = new MWNumericArray(args[0]);
MWNumericArray fc = new MWNumericArray(args[1]);
MWNumericArray fs = new MWNumericArray(args[2]);
MWNumericArray fswitch = new MWNumericArray(args[3]);
MWNumericArray R = new MWNumericArray(args[4]);
MWNumericArray NA = new MWNumericArray(args[5]);
for (int i = 0; i < signals.Length; i++)
{
if (signals[i] == null)
sas[i] = new Fingerprint_SA.SA();
else
{
MWNumericArray s = (MWNumericArray)sptb.CalcStrength(signals[i]);
MWNumericArray aoa = (MWNumericArray)sptb.CalcPseudoDopplerAOA(signals[i], fl, fc, fs, fswitch, R, NA);
sas[i] = new Fingerprint_SA.SA(s.ToScalarDouble(), aoa.ToScalarDouble());
}
}
Fingerprint_SA fp = new Fingerprint_SA(sas);
return fp;
}
}
return null;
}
}
return null;
}
public MWArray ToSingleMatlabArray()
{
MWNumericArray result = null;
byte[,] data = new byte[Height, Width];
for (int i = 0; i < Height; i++)
{
for (int j = 0; j < Width; j++)
{
Pixel p = GetPixel(j, i);
data[i, j] = p.R;
}
}
result = new MWNumericArray(MWArrayComplexity.Real, MWNumericType.UInt8, Height, Width);
result = data;
return result;
}
/// <summary>
/// 进行单次指纹生成仿真，包括信号生成、加噪和指纹生成
/// </summary>
/// <param name="signal_type">信号类型</param>
/// <param name="noise_type">噪声类型</param>
/// <param name="noise_params">噪声参数集合，与噪声类型有关</param>
/// <param name="fingerprint_name">指纹类型</param>
/// <param name="ray_angles">波束方向集合</param>
/// <param name="ray_strengths">波束强度集合</param>
/// <param name="ray_taus">波束时延集合</param>
/// <param name="N">信号点数</param>
/// <param name="args">args参数集合，与信号类型有关</param>
/// <returns></returns>
public Fingerprint SingleFingerprintSim(Signal_Type signal_type, Noise_Type noise_type, double[] noise_params,
Fingerprint_Name fingerprint_name, List<double>[] ray_angles, List<double>[] ray_strengths,
List<double>[] ray_taus, int N, double[] args)
{
MWNumericArray[] signals = new MWNumericArray[ray_angles.GetLength(0)];
for(int i = 0; i < signals.Length; i++)
{
signals[i] = this.GenerateSignal_MW(signal_type, ray_angles[i].ToArray(), ray_strengths[i].ToArray(), ray_taus[i].ToArray(), N, args);
signals[i] = this.AddNoise_MW(noise_type, signals[i], noise_params);
}
Fingerprint fp = this.GenerateFingerprint(signal_type, fingerprint_name, signals, args);
return fp;
}
public Vector SolveLinearEquation2(Vector y) // this * result = y
{
List<int> rows = new List<int>();
List<int> cols = new List<int>();
List<double> fvalues = new List<double>();
foreach (long i in values.Keys)
{
rows.Add(Row(i)+1);
cols.Add(Column(i)+1);
fvalues.Add(this[i]);
}
MWNumericArray _rows = new MWNumericArray(rows.Count, 1, rows.ToArray());
MWNumericArray _cols = new MWNumericArray(cols.Count, 1, cols.ToArray());
MWNumericArray _values = new MWNumericArray(fvalues.Count, 1, fvalues.ToArray());
MWNumericArray _y = new MWNumericArray(y.Elements.Length, 1, y.Elements);
solveclass solve = new solveclass();
MWNumericArray ret = (MWNumericArray)solve.solve(_rows, _cols, _values, _y);
double[,] fret = (double[,])ret.ToArray(MWArrayComponent.Real);
double[] fret2 = new double[fret.GetLength(0)];
for (int i = 0; i < fret2.Length; ++i)
{
fret2[i] = (double)fret[i, 0];
}
return new Vector(fret2);
}
/*
private void button1_Click_1(object sender, EventArgs e)
{
brisiPrikaz();
sistem = 1;
melodijaPt();
ton1(900, 5f);
visaj(900, 1.5f);
ton2(50, 2f,false);
ton2(150, 2f, true);
ton4(100, 3f,true);
ton4(200, 3f, false);
ton8(250, 3f, false);
ton8(300, 3f, true);
visaj(300, 3f);
podaljsaj(300, 3f);
ton16(350, 3f, true);
ton16(400, 3f, false);
ton32(450, 3f, true);
ton32(500, 3f, false);
pavza1(550);
pavza2(600);
pavza4(650);
pavza8(700);
ton1(750, 1.5f);
visaj(750, 1.5f);
pavza2(800);
pavza2(850);
ton1(900, 1.5f);
visaj(900, 1.5f);
sistem = 2;
melodijaPt();
ton1(900, 1.5f);
visaj(900, 1.5f);
pomCrta(200, -1f);
pomCrta(200, 7f);
ton1(200, 5.5f);
ton2(200, -0.5f, false);
ton2(50, -0.5f, false);
ton2(150, 2f, true);
ton4(100, 3f, true);
ton8(250, 3f, false);
ton8(300, 3f, true);
visaj(300, 3f);
ton16(350, 3f, true);
ton16(400, 3f, false);
pavza1(550);
pavza2(600);
pavza4(650);
pavza8(700);
sistem = 3;
melodijaPt();
visaj(550, 1f);
ton32(550, 1f, true);
ton1(900, 1.5f);
visaj(900, 1.5f);
ton2(50, 2f, false);
ton2(150, 2f, true);
ton4(100, 3f, true);
ton4(200, 3f, false);
ton8(250, 3f, false);
ton8(300, 3f, true);
visaj(300, 3f);
ton16(350, 3f, true);
ton16(400, 3f, false);
pavza1(550);
pavza2(600);
pavza4(650);
pavza8(700);
sistem = 4;
melodijaPt();
ton1(900, 1.5f);
visaj(900, 1.5f);
ton2(50, 2f, false);
ton2(150, 2f, true);
ton4(100, 3f, true);
ton4(200, 3f, false);
ton8(250, 3f, false);
ton8(300, 3f, true);
visaj(300, 3f);
ton16(350, 3f, true);
ton16(400, 3f, false);
melodijaP.Image = bmp;
}*/
//razpoznaj
private void button2_Click_1(object sender, EventArgs e)
{
try
{
brisiPrikaz();
Praktikium.Matlab razp = new Praktikium.Matlab();
MWArray argin = datoteka;
MWArray argout = new MWNumericArray();
argout = razp.razpoznajMatlab(argin);
//izhod
MWNumericArray tmpArray = (MWNumericArray)argout;
double[, ,] izhodnaMatrika = (double[, ,])tmpArray.ToArray(MWArrayComponent.Real);
izhodnaMatrika[0, 0, 2] = izhodnaMatrika[0, 0, 2] + 1000;
int[,] rezultat = new int[izhodnaMatrika.Length / 3, 3];
for (int i = 0; i < izhodnaMatrika.Length / 3; i++)
{
rezultat[i, 0] = Convert.ToInt32(izhodnaMatrika[i, 0, 0]);
rezultat[i, 1] = Convert.ToInt32(izhodnaMatrika[i, 0, 1]);
rezultat[i, 2] = Convert.ToInt32(izhodnaMatrika[i, 0, 2]);
}
String[] toni = { "A0", "Ais", "H0", "C1", "Cis1", "D1", "Dis1", "E1", "F1", "Fis1", "G1", "Gis1", "A1", "Ais1", "H1", "C2", "Cis2", "D2", "Dis2", "E2", "F2", "Fis2", "G2", "Gis2", "A2", "Ais2", "H2", "C3", "Cis3", "D3", "Dis3", "E3", "F3", "Fis3", "G3", "Gis3", "A3", "Ais3", "H3", "C4", "Cis4", "D4", "Dis4", "E4", "F4", "Fis4", "G4", "Gis4", "A4", "Ais4", "H4", "C5", "Cis5", "D5", "Dis5", "E5", "F5", "Fis5", "G5", "Gis5", "A5", "Ais5", "H5", "C6", "Cis6", "D6", "Dis6", "E6", "F6", "Fis6", "G6", "Gis6", "A6", "Ais6", "H6", "C7", "Cis7", "D7", "Dis7", "E7", "F7", "Fis7", "G7", "Gis7", "A7", "Ais7", "H7", "C8" };
double[] frekvence = { 27.5000, 29.1352, 30.8677, 32.7032, 34.6478, 36.7081, 38.8909, 41.2034, 43.6535, 46.2493, 48.9994, 51.9131, 55.0000, 58.2705, 61.7354, 65.4064, 69.2957, 73.4162, 77.7817, 82.4069, 87.3071, 92.4986, 97.9989, 103.826, 110.000, 116.541, 123.471, 130.813, 138.591, 146.832, 155.563, 164.814, 174.614, 184.997, 195.998, 207.652, 220.000, 233.082, 246.942, 261.626, 277.183, 293.665, 311.127, 329.628, 349.228, 369.994, 391.995, 415.305, 440.000, 466.164, 493.883, 523.251, 554.365, 587.330, 622.254, 659.255, 698.456, 739.989, 783.991, 830.609, 880.000, 932.328, 987.767, 1046.50, 1108.73, 1174.66, 1244.51, 1318.51, 1396.91, 1479.98, 1567.98, 1661.22, 1760.00, 1864.66, 1975.53, 2093.00, 2217.46, 2349.32, 2489.02, 2637.02, 2793.83, 2959.96, 3135.96, 3322.44, 3520.00, 3729.31, 3951.07, 4186.01 };
List<String> najdeniToni = new List<string>();
List<int> dolzineTonov = new List<int>();
int dolzinaCelotna = rezultat[0, 0];
int najkrajsi = 1000000;
int najdaljsi = 0;
//polje za iskanje pavz
int[] kjesopavze = new int[rezultat[0, 2]];
for (int i = 0; i < kjesopavze.Length; i++)
{
kjesopavze[i] = 0;
}
for (int i = 1; i < rezultat.Length / 3; i++)
{
for (int j = rezultat[i, 1]; j < rezultat[i, 2]; j++)
{
kjesopavze[j] = 1;
}
}
List<List<double>> pavze = new List<List<double>>();
bool zacF = false;
int zac = 0;
int kon = 0;
for (int i = 0; i < kjesopavze.Length; i++)
{
if (kjesopavze[i] == 0 && !zacF)
{
zac = 0;
kon = 0;
zac = i;
zacF = true;
}
if (kjesopavze[i] == 1 && zacF)
{
zacF = false;
kon = i;
List<double> lista = new List<double>();
lista.Add(zac);
lista.Add(kon);
pavze.Add(lista);
}
}
//dodam pavze - njihovo trajanje za kasnejsi rmz
int ij = 0;
for (int j = 1; j < rezultat.Length / 3; j++)
{
if (rezultat[j, 1] > ij && j == 1)
{
najdeniToni.Add("p-");
dolzineTonov.Add(rezultat[j, 1]);
ij = rezultat[j, 1];
}
else if (rezultat[j, 1] > rezultat[j - 1, 2])
{
najdeniToni.Add("p-");
dolzineTonov.Add(rezultat[j, 1] - rezultat[j - 1, 2]);
ij = rezultat[j, 1];
}
if (rezultat[j, 1] <= ij) //smo v tonu
{
for (int k = 0; k < frekvence.Length; k++)
{
if (rezultat[j, 0] == Math.Round(frekvence[k], 0))
{
int trenutna = rezultat[j, 2] - rezultat[j, 1];
najdeniToni.Add(toni[k] + "-");
dolzineTonov.Add(trenutna);
if (trenutna < najkrajsi) { najkrajsi = trenutna; }
if (trenutna > najdaljsi) { najdaljsi = trenutna; }
}
ij = rezultat[j, 2];
}
}
}
//grem cez cel zapis in pisem tone in pavze
int celinka = najkrajsi * 4;
//algoritem za postavljanje dob tonov
List<List<double>> vseDobe = new List<List<double>>();
for (int i = 0; i < dolzineTonov.Count; i++)
{
List<double> dobe = new List<double>();
int dol = dolzineTonov[i];
int ost = 1;
int trenutno = celinka;
int tonTempo = 1;
for (int t = 0; t < 6; t++)
{
if (dol > 4000)
{
if (dol >= trenutno)
{
ost = dol / trenutno;
for (int j = 0; j < ost; j++)
{
dobe.Add(tonTempo);
}
dol = dol - (ost * trenutno);
}
trenutno = trenutno / 2; //celinka->polovinka
tonTempo++;
}
}
vseDobe.Add(dobe);
}
//algoritem za izdelavo rmz zapisa
//
//vseDobe vsebujejo tempo
//najdeniToni vsebujejo imena tonov
//G3-4;A3-4;H3-4;C4-4;D4-4;G4-16;A4-1;p-4;D4-4;F4-8;p-1;
for (int i = 0; i < najdeniToni.Count; i++)
{
for (int j = 0; j < vseDobe[i].Count; j++)
{
double doba = vseDobe[i][j];
if (doba == 3) { doba = 4; }
else if (doba == 3) { doba = 4; }
else if (doba == 4) { doba = 8; }
else if (doba == 5) { doba = 16; }
else if (doba == 6) { doba = 32; }
melodija += najdeniToni[i] + doba + ";";
}
}
narisi();
}
catch (Exception er)
{
MessageBox.Show("Napaka pri iskanju frekvenc...");
}
}
private void StartCalibration()
{
Thread.CurrentThread.CurrentCulture = CultureInfo.CreateSpecificCulture("en-US");
ss.K = Double.Parse(this.labelk.Text, NumberStyles.AllowDecimalPoint | NumberStyles.Float);
ss.Lambda_X = Double.Parse(this.labellambdax.Text, NumberStyles.AllowDecimalPoint | NumberStyles.Float);
ss.Sigma_x = Double.Parse(this.labelsigma_x.Text, NumberStyles.AllowDecimalPoint | NumberStyles.Float);
ss.Mu_e = Double.Parse(this.labelmu_e.Text, NumberStyles.AllowDecimalPoint | NumberStyles.Float);
ss.Sigma_e = Double.Parse(this.labelsigma_e.Text, NumberStyles.AllowDecimalPoint | NumberStyles.Float);
ss.Lambda_e = Double.Parse(this.labellambdae.Text, NumberStyles.AllowDecimalPoint | NumberStyles.Float);
ss.P_xe = Double.Parse(this.labelpxe.Text, NumberStyles.AllowDecimalPoint | NumberStyles.Float);
ss.e0 = Double.Parse(this.labele0.Text, NumberStyles.AllowDecimalPoint | NumberStyles.Float);
ss.x0 = Double.Parse(this.labelx0.Text, NumberStyles.AllowDecimalPoint | NumberStyles.Float);
ss.MaxIter = Int32.Parse(this.labelMaxIter.Text);
try
{
MWNumericArray maxitera = new MWNumericArray(ss.MaxIter);
MWNumericArray e0a = new MWNumericArray(ss.e0);
MWNumericArray x0a = new MWNumericArray(ss.x0);
MWNumericArray maturity = new MWNumericArray(maturities.ToArray());
MWNumericArray futures = new MWNumericArray(pcount, lcount - 1, futureList.ToArray());
MWNumericArray k = new MWNumericArray(ss.K);
MWNumericArray lambdax = new MWNumericArray(ss.Lambda_X);
MWNumericArray lambdae = new MWNumericArray(ss.Lambda_e);
MWNumericArray sigmax = new MWNumericArray(ss.Sigma_x);
MWNumericArray sigmae = new MWNumericArray(ss.Sigma_e);
MWNumericArray p_xe = new MWNumericArray(ss.P_xe);
MWNumericArray mu_e = new MWNumericArray(ss.Mu_e);
Schwartz.Schwartz s = null;
s = new Schwartz.Schwartz();
MWArray[] Results;
try
{
/** Call matlab */
Results = (MWArray[])s.SchwartzAndSmith(12,maxitera, x0a, e0a, maturity, futures, k, mu_e, sigmax, sigmae, p_xe, lambdae, lambdax);
}
catch
{
MessageBox.Show("Calibration aborted!");
return;
}
/** Spot vector */
double[,] ret = (double[,])Results[0].ToArray();
this.generatedSpot = new double[ret.Length];
for (int i = 0; i < ret.Length; i++)
this.generatedSpot[i] = ret[0, i];
ss.K = (double)((MWNumericArray)Results[1]).ToVector(MWArrayComponent.Real).GetValue(0);
ss.Mu_e = (double)((MWNumericArray)Results[2]).ToVector(MWArrayComponent.Real).GetValue(0);
ss.Sigma_x = (double)((MWNumericArray)Results[3]).ToVector(MWArrayComponent.Real).GetValue(0);
ss.Sigma_e = (double)((MWNumericArray)Results[4]).ToVector(MWArrayComponent.Real).GetValue(0);
ss.P_xe = (double)((MWNumericArray)Results[5]).ToVector(MWArrayComponent.Real).GetValue(0);
ss.Lambda_e = (double)((MWNumericArray)Results[6]).ToVector(MWArrayComponent.Real).GetValue(0);
ss.Lambda_X = (double)((MWNumericArray)Results[7]).ToVector(MWArrayComponent.Real).GetValue(0);
ss.x0 = (double)((MWNumericArray)Results[8]).ToVector(MWArrayComponent.Real).GetValue(0);
ss.e0 = (double)((MWNumericArray)Results[9]).ToVector(MWArrayComponent.Real).GetValue(0);
likelihood = (double)((MWNumericArray)Results[10]).ToVector(MWArrayComponent.Real).GetValue(0);
nbiter = (int)((double)((MWNumericArray)Results[11]).ToVector(MWArrayComponent.Real).GetValue(0));
}
catch (Exception)
{
MessageBox.Show("It seems your computer does not have a working copy of the Matlab Compiler Runtime\n Calibration cannot be done without, but you can still use the rest of the program.");
this.Invoke(new CalibrationDoneCallback(CalibrationDone), this);
return;
}
this.Invoke(new CalibrationDoneCallback(CalibrationDone), this);
}
public Image(double[,] values)
{
Array = new ImageArray(values);
Height = Array.Array.GetLength(0);
Width = Array.Array.GetLength(1);
GetBitmap();
/*
string tp = "C:\\" + GetRandomName()+".bmp";
Bitmap.Save(tp,);
Loader tmp = new Loader();
image = tmp.loadimage(new MWCharArray(tp));
tmp.Dispose();
File.Delete(tp);
*/
MWNumericArray tmp = new MWNumericArray(values);
image = (MWArray)tmp;
tmp = null;
GC.Collect();
}
//@2BTESTED
/// <summary>
/// For an effectivly use of the matlab function, this method is designed for 1-step generating a
/// cloud's probablity. So when using this method, you need to format your current positions in
/// 2 arrays and an strength array additionaly.
/// </summary>
/// <param name="apId">Ap id</param>
/// <param name="x">X array</param>
/// <param name="y">Y array</param>
/// <param name="strength">Current signal strength</param>
/// <returns>The probablities</returns>
public double[] GetProbalities(string apId, double[] x, double[] y, double strength)
{
var mX = new MWNumericArray(x.Length, 1, x);
var mY = new MWNumericArray(y.Length, 1, y);
var mTrainX = new MWNumericArray(_trainX[apId].Count, 1, _trainX[apId].ToArray());
var mTrainY = new MWNumericArray(_trainY[apId].Count, 1, _trainY[apId].ToArray());
var mStrength = new MWNumericArray(_trainStrength[apId].Count, 1, _trainStrength[apId].ToArray());
MWArray[] outPut = _gt.Predict(2, _paras[apId], 1, mTrainX, mTrainY, mStrength, mX, mY);
var resM = (double[]) (((MWNumericArray) outPut[0]).ToVector(MWArrayComponent.Real));
var resV = (double[]) (((MWNumericArray) outPut[1]).ToVector(MWArrayComponent.Real));
var res = new List<double>();
for (int i = 0; i < x.Length; i++)
{
res.Add(BasicMath.GetGaussianProbability(resM[i], resV[i], strength));
}
return res.ToArray(); //The results' consequence needs checkout.
}
/// <summary>
/// When training points are all recorded or to a certain number,
/// the model training is run and the paras are renew then restored into _paras.
/// But we'll decide when and why we need to train the model outside this method,
/// and it's actually really important.
/// So the logic will be added soon.
/// </summary>
/// <param name="apId">The AP id</param>
public void TrainModel(string apId)
{
try
{
MWNumericArray mParas = _paras[apId];
var mTrainX = new MWNumericArray(_trainX[apId].Count, 1, _trainX[apId].ToArray());
var mTrainY = new MWNumericArray(_trainY[apId].Count, 1, _trainY[apId].ToArray());
var mStrength = new MWNumericArray(_trainStrength[apId].Count, 1, _trainStrength[apId].ToArray());
MWArray[] outPut = _gt.Optimization(2, mParas, _ncg, _meanType, mTrainX, mTrainY, mStrength);
_paras[apId] = (MWNumericArray) outPut[0];
IsGaussianSupportOn = true;
}
catch (Exception)
{
GaussianSupportList[apId] = false;
}
GaussianSupportList[apId] = true;
}
private MWNumericArray[][] wrap_func(int num, int ray_num, Transmitter.EMIT_OPTION emit_opt, Signal_Type signal_type, int sample_cnt)
{
MWNumericArray[][] signals = new MWNumericArray[num][];
while (num-- > 0)
{
foreach (Transmitter trans in this.viz_rps.Map.Transmitters)
{
trans.Reset();
trans.EmitRays(ray_num, emit_opt); //各发射机辐射波束
foreach (Receiver rec in this.viz_rps.Map.Receivers)
{
rec.Reset();
if (this.viz_rps.Map.IsLOS(trans.Location, rec.Location)) //若存在LOS情况
{
LineSegment segLOS = new LineSegment(trans.Location, rec.Location);
Ray rayLOS = new Ray(trans, new LineSegment[] { segLOS }, segLOS.DirectionRadian, trans.CenterFrequency, trans.EmitPower * trans.Gain, trans.EmitPower * trans.Gain, true);
rec.AcceptRay(rayLOS, this.viz_rps.Map);
}
}
foreach (Ray ray in trans.Rays) //更新每条ray, 若被接收，则更新接收它的接收机
{
Receiver rec = null;
while (!ray.Update(this.viz_rps.Map, out rec)) ;
if (rec != null)
rec.AcceptRay(ray, this.viz_rps.Map);
}
}
FingerprintGenerator fg = new FingerprintGenerator();
Dictionary<Fingerprint_Name, Fingerprint> result = new Dictionary<Fingerprint_Name, Fingerprint>();
List<double>[] ray_angles = new List<double>[this.viz_rps.Map.ReceiverCount];
List<double>[] ray_strengths = new List<double>[this.viz_rps.Map.ReceiverCount];
List<double>[] ray_taus = new List<double>[this.viz_rps.Map.ReceiverCount];
int kk = 0;
foreach (Receiver rec in this.viz_rps.Map.Receivers)
{
ray_angles[kk] = new List<double>();
ray_strengths[kk] = new List<double>();
ray_taus[kk] = new List<double>();
foreach (Ray ray in rec.Rays)
{
ray_angles[kk].Add(ray.CurrentDirection);
ray_strengths[kk].Add(ray.FadedPower(this.viz_rps.Map.MeterPixelRatio));
ray_taus[kk].Add(ray.GetLifeSpan(this.viz_rps.Map.MeterPixelRatio));
}
kk++;
}
double[] args = null;
switch (signal_type)
{
case Signal_Type.PSEUDO_DOPPLER:
//if (this.receivers.First().Value.Antenna.AntType != AntennaType.PSEUDO_DOPPLER)
// return null;
Antenna_PseudoDoppler ant = this.viz_rps.Map.receivers.First().Value.Antenna as Antenna_PseudoDoppler;
args = fg.PackArgs_PseudoDoppler(this.viz_rps.Map.receivers.First().Value.FrequencyLow, this.viz_rps.Map.receivers.First().Value.CenterFrequency,
this.viz_rps.Map.receivers.First().Value.SampleFrequency, ant.SwitchFrequency, ant.Radius, ant.AntennaNumber);
break;
}
signals[num - 1] = new MWNumericArray[ray_angles.GetLength(0)];
for (int i = 0; i < signals.Length; i++)
{
signals[num - 1][i] = new FingerprintGenerator().GenerateSignal_MW(signal_type, ray_angles[i].ToArray(), ray_strengths[i].ToArray(), ray_taus[i].ToArray(), sample_cnt, args);
}
}
return signals;
}
private void AutoTest(object sender, DoWorkEventArgs e)
{
object[] param = (object[])e.Argument;
int avg_cnt = (int)param[0];
int KNN_K = (int)param[1];
int KNNHC_K = (int)param[2];
string db_file_name = (string)param[3];
string data_file_name = (string)param[4];
double start_snr = (double)param[5];
double stop_snr = (double)param[6];
double step_snr = (double)param[7];
int ray_num = (int)param[8];
Transmitter.EMIT_OPTION emit_opt = (Transmitter.EMIT_OPTION)param[9];
Noise_Type noise_type = (Noise_Type)param[10];
double alpha = (double)param[11];
int sample_cnt = (int)param[12];
Signal_Type signal_type = (Signal_Type)param[13];
System.IO.FileStream data_fs = new FileStream(data_file_name, FileMode.Create, FileAccess.Write, FileShare.Read);
System.Runtime.Serialization.IFormatter formatter = new System.Runtime.Serialization.Formatters.Binary.BinaryFormatter();
FileStream fs = new FileStream(db_file_name, FileMode.Open, FileAccess.Read, FileShare.Read);
FingerprintDataBase fdb = (FingerprintDataBase)formatter.Deserialize(fs);
fs.Close();
Point pt_real = new Point();
foreach (Transmitter trans in this.viz_rps.Map.Transmitters)
pt_real = trans.Location;
double[] snrs = new double[(int)((stop_snr - start_snr) / step_snr) + 1];
for(int i = 0; i < snrs.Length; i++)
snrs[i] = start_snr + i * step_snr;
foreach (Transmitter trans in this.viz_rps.Map.Transmitters)
{
trans.Reset();
trans.EmitRays(ray_num, emit_opt); //各发射机辐射波束
foreach (Receiver rec in this.viz_rps.Map.Receivers)
{
rec.Reset();
if (this.viz_rps.Map.IsLOS(trans.Location, rec.Location)) //若存在LOS情况
{
LineSegment segLOS = new LineSegment(trans.Location, rec.Location);
Ray rayLOS = new Ray(trans, new LineSegment[] { segLOS }, segLOS.DirectionRadian, trans.CenterFrequency, trans.EmitPower * trans.Gain, trans.EmitPower * trans.Gain, true);
rec.AcceptRay(rayLOS, this.viz_rps.Map);
}
}
foreach (Ray ray in trans.Rays) //更新每条ray, 若被接收，则更新接收它的接收机
{
Receiver rec = null;
while (!ray.Update(this.viz_rps.Map, out rec)) ;
if (rec != null)
rec.AcceptRay(ray, this.viz_rps.Map);
}
}
FingerprintGenerator fg = new FingerprintGenerator();
Dictionary<Fingerprint_Name, Fingerprint> result = new Dictionary<Fingerprint_Name, Fingerprint>();
List<double>[] ray_angles = new List<double>[this.viz_rps.Map.ReceiverCount];
List<double>[] ray_strengths = new List<double>[this.viz_rps.Map.ReceiverCount];
List<double>[] ray_taus = new List<double>[this.viz_rps.Map.ReceiverCount];
int kk = 0;
foreach (Receiver rec in this.viz_rps.Map.Receivers)
{
ray_angles[kk] = new List<double>();
ray_strengths[kk] = new List<double>();
ray_taus[kk] = new List<double>();
foreach (Ray ray in rec.Rays)
{
ray_angles[kk].Add(ray.CurrentDirection);
ray_strengths[kk].Add(ray.FadedPower(this.viz_rps.Map.MeterPixelRatio));
ray_taus[kk].Add(ray.GetLifeSpan(this.viz_rps.Map.MeterPixelRatio));
}
kk++;
}
double[] args = null;
switch (signal_type)
{
case Signal_Type.PSEUDO_DOPPLER:
//if (this.receivers.First().Value.Antenna.AntType != AntennaType.PSEUDO_DOPPLER)
// return null;
Antenna_PseudoDoppler ant = this.viz_rps.Map.receivers.First().Value.Antenna as Antenna_PseudoDoppler;
args = fg.PackArgs_PseudoDoppler(this.viz_rps.Map.receivers.First().Value.FrequencyLow, this.viz_rps.Map.receivers.First().Value.CenterFrequency,
this.viz_rps.Map.receivers.First().Value.SampleFrequency, ant.SwitchFrequency, ant.Radius, ant.AntennaNumber);
break;
}
MWNumericArray[] signals = new MWNumericArray[ray_angles.GetLength(0)];
for (int i = 0; i < signals.Length; i++)
{
signals[i] = new FingerprintGenerator().GenerateSignal_MW(signal_type, ray_angles[i].ToArray(), ray_strengths[i].ToArray(), ray_taus[i].ToArray(), sample_cnt, args);
}
for (int n = 0; n < snrs.Length; n++)
{
double[] sum_errs = new double[6];//SA_single, SA_KNN, SA_KNNHC, S_single, S_KNN, S_KNNHC
for(int i = 0; i < avg_cnt; i++)
{
MWNumericArray[] n_signals = new MWNumericArray[signals.Length];
for (int l = 0; l < signals.Length; l++)
{
n_signals[l] = new FingerprintGenerator().AddNoise_MW(noise_type, signals[l], new double[] { snrs[n] });
}
Dictionary<Fingerprint_Name, Fingerprint> res = new Dictionary<Fingerprint_Name,Fingerprint>();
for (int j = 0; j < Enum.GetNames(new Fingerprint_Name().GetType()).Length; j++ )
res[(Fingerprint_Name)j] = new FingerprintGenerator().GenerateFingerprint(signal_type, (Fingerprint_Name)j, n_signals, args);
this.current_fingerprint = res;
//SA_single
PointF loc_SA_single = fdb.MatchLocation(this.current_fingerprint[Fingerprint_Name.SA], FingerprintDataBase.Match_Strategy.Single, null);
sum_errs[0] += new MathUtils.MathUtility().point_dist(pt_real, loc_SA_single);
//SA_KNN
PointF loc_SA_KNN = fdb.MatchLocation(this.current_fingerprint[Fingerprint_Name.SA], FingerprintDataBase.Match_Strategy.KNN, new double[] { KNN_K });
sum_errs[1] += new MathUtils.MathUtility().point_dist(pt_real, loc_SA_KNN);
//SA_KNNHC
PointF loc_SA_KNNHC = fdb.MatchLocation(this.current_fingerprint[Fingerprint_Name.SA], FingerprintDataBase.Match_Strategy.KNN_HC, new double[] { KNNHC_K });
sum_errs[2] += new MathUtils.MathUtility().point_dist(pt_real, loc_SA_KNNHC);
//S_single
PointF loc_S_single = fdb.MatchLocation(this.current_fingerprint[Fingerprint_Name.S], FingerprintDataBase.Match_Strategy.Single, null);
sum_errs[3] += new MathUtils.MathUtility().point_dist(pt_real, loc_S_single);
//S_KNN
PointF loc_S_KNN = fdb.MatchLocation(this.current_fingerprint[Fingerprint_Name.S], FingerprintDataBase.Match_Strategy.KNN, new double[] { KNN_K });
sum_errs[4] += new MathUtils.MathUtility().point_dist(pt_real, loc_S_KNN);
//S_KNNHC
PointF loc_S_KNNHC = fdb.MatchLocation(this.current_fingerprint[Fingerprint_Name.S], FingerprintDataBase.Match_Strategy.KNN_HC, new double[] { KNNHC_K });
sum_errs[5] += new MathUtils.MathUtility().point_dist(pt_real, loc_S_KNNHC);
bgAutoTest.ReportProgress((int)((n * avg_cnt + i + 1) / (double)(avg_cnt * snrs.Length) * 100));
System.Threading.Thread.Sleep(200);
}
for (int m = 0; m < sum_errs.Length; m++ )
{
double err = sum_errs[m] / avg_cnt;
byte[] tmp = Encoding.ASCII.GetBytes(err.ToString("0.00") + "\t");
data_fs.Write(tmp, 0, tmp.Length);
}
byte[] rn = Encoding.ASCII.GetBytes("\r\n");
data_fs.Write(rn, 0, rn.Length);
}
data_fs.Close();
e.Result = true;
}
public double[,] NetArrayFromMLArray(MWNumericArray matriz)
{
double[,] rdo = (double[,])matriz.ToArray(MWArrayComponent.Real);
return rdo;
}
public static List<PdchModel> PdchSimulink(List<PdchModel> input)
{
List<PdchModel> pm = new List<PdchModel>();
pm = input.OrderBy(e => e.PsPagingTime).ToList();
PdchNewclass pnc = new PdchNewclass();
int size = input.Count();
double[,] X = new double[size, 5];
double[,] Y = new double[size, 1];
for (int i = 0; i < size; i++)
{
X[i, 0] = pm[i].BCr;
X[i, 1] = pm[i].GDr;
X[i, 2] = pm[i].IMr;
X[i, 3] = pm[i].MMSr;
X[i, 4] = pm[i].OCr;
Y[i, 0] = pm[i].PdchUse;
}
MWNumericArray mtraffic = new MWNumericArray(X);
MWNumericArray mpdchuse = new MWNumericArray(Y);
MWArray mpdchnew = pnc.pdchsim(mtraffic, mpdchuse);
MWNumericArray mx_y1 = (MWNumericArray)mpdchnew;
double[,] csArray = (double[,])mx_y1.ToArray(MWArrayComponent.Real);
for (int i = 0; i < csArray.Length; i++)
pm[i].PdchSim = csArray[i, 0];
return pm;
}
static private void UseDotNET(Array ar, Array ai, Array br,
Array bi, ref Array cr, ref Array ci, ref Array dr, ref Array di)
{
/*
* This function calls the math_by_numbers method from
* inside a .NET assembly created with MATLAB using Builder
* for .NET.
*/
// Instantiate our .NET class from the MATLAB created component
dotnetclass AClass = new dotnetclass();
// explicity convert our input arguments into MWArrays
// this can be done with implicit conversion
MWNumericArray a = new MWNumericArray(1, 2, (double[])ar, (double[])ai);
MWNumericArray b = new MWNumericArray(1, 2, (double[])br, (double[])bi);
// call math_on_method from Assembly specifying the number
// of return arguments expected and passing in a and b
MWArray[] RetVal = AClass.math_on_numbers(2, a, b);
// Unpack return values seperating the real and imaginary parts
// using the ToArray method of MWNummericArray. Since RetVal was
// declared as a MWArray above, it must be explicity typecast here.
cr = ((MWNumericArray) RetVal[0]).ToVector(MWArrayComponent.Real);
ci = ((MWNumericArray) RetVal[0]).ToVector(MWArrayComponent.Imaginary);
dr = ((MWNumericArray) RetVal[1]).ToVector(MWArrayComponent.Real);
di = ((MWNumericArray) RetVal[1]).ToVector(MWArrayComponent.Imaginary);
}
//计算关键层接口
private bool getKeyLayer(ref int[] bianHao, ref double[] pjxs)
{
double[,] data = getParams();
if (data == null)
{
MessageBox.Show("数据录入有误，请检查。(第一层应为地表，煤层应有底板。)");
return false;
}
double[] rowData = arrayTrans(data);
MWNumericArray mwdata = new MWNumericArray(rowData.Length / 11, 11, rowData);
try
{
MWArray[] result = logic.yancengzuhe(2, mwdata, FuYanXCL, CaiGao, SuiZhangXS, Mcqj);
MWNumericArray biaoHaoList = (MWNumericArray)result[0];
MWNumericArray pjxsList = (MWNumericArray)result[1];
double[] outBiaoHao = (double[])biaoHaoList.ToVector(MWArrayComponent.Real);
int length = outBiaoHao.Length;
bianHao = new int[length];
for (int i = 0; i < length; i++)
{
bianHao[i] = (int)outBiaoHao[i];
}
pjxs = (double[])pjxsList.ToVector(MWArrayComponent.Real);
return true;
}
catch (Exception e)
{
e.ToString();
MessageBox.Show("关键层计算出现错误，请检查数据合理性");
return false;
}
}
public Image RunFilter(Image src)
{
filterDataContext db = new filterDataContext();
double[] sigma = new double[] { 3.0, 3.0 };
MWNumericArray mw_sigma = new MWNumericArray(sigma);
MWLogicalArray mw_print = new MWLogicalArray(Form1.print);
MWArray[] Result = filter2(2, src.image, type[2], mw_print);//spfilt(2, src.image, "median");
MWNumericArray descriptor = null;
descriptor = (MWNumericArray)Result[0];
double[,] result = null;
result = (double[,])descriptor.ToArray(MWArrayComponent.Real);
Image res = new Image(result);
MWNumericArray e_descriptor = null;
e_descriptor = (MWNumericArray)Result[1];
WorkTime = (double)e_descriptor.ToScalarDouble();
//db.add_b_res(src.Height, src.Height / src.Width, WorkTime, w, sigma[0], sigma[1]);
Result = null;
e_descriptor = null;
result = null;
mw_sigma = null;
GC.Collect();
return res;
}