static private void UseEngine(double[,] ar, ref Array cr, ref Array ci)
{
// Instantiate MATLAB Engine Interface through com
Console.WriteLine("Matlab Startup...\r\n");
MLApp.MLAppClass matlab = new MLApp.MLAppClass();
// Make imaginary matricies
double[] a_d = new double[] { 68.8, 120, 110, 118, 150, 179, 180, 150, 178, 152, 163, 107, 149, 97.8, 90.1, 94.3, 90.9, 89.9, 53.4 };
matlab.PutFullMatrix("a", "base", a_d, new double[19]);
String MyDocs = Environment.GetFolderPath(Environment.SpecialFolder.Personal);
String ProjectLocation = "Visual Studio 2010\\Projects\\Kinect-Tracking-Project\\MatlabPrototypes\\FeatureDetection";
String matFileCD_command = String.Format("cd '{0}';", System.IO.Path.Combine(MyDocs, ProjectLocation));
Console.WriteLine(matFileCD_command);
matlab.Execute(matFileCD_command);
// matlab.Execute("open testBayes.m");
// matlab.Execute("dbstop in math_on_numbers.m");
Console.WriteLine("Matlab Processing...\r\n");
matlab.Execute("c = transpose(testBayes(a));");
//matlab.Execute("com.mathworks.mlservices.MLEditorServices.closeAll");
//matlab.Execute("dbquit all");
try {
matlab.GetFullMatrix("c", "base", ref cr, ref ci);
} catch (Exception) {
Console.WriteLine("someErr");
}
}
static void Main(string[] args)
{
MLApp.MLAppClass matlab = new MLApp.MLAppClass();
// create variables: a_0, a_1, ..., a_4
for (int k = 0; k < 5; k++)
{
matlab.Execute(string.Format("a_{0} = rand(2);", k));
}
// retrieve variables from MATLAB and print their contents
object a;
for (int k = 0; k < 5; k++)
{
// current variable name
string varname = string.Format("a_{0}", k);
// get data array
a = null; // without this line, an exception is thrown!
matlab.GetWorkspaceData(varname, "base", out a);
// print contents
var arr = (double[, ])a;
Console.WriteLine("\nndims(a) = {0}, numel(a) = {1}", arr.Rank, arr.Length);
for (int i = 0; i < arr.GetLength(0); i++)
{
for (int j = 0; j < arr.GetLength(1); j++)
{
Console.WriteLine("{0}[{1},{2}] = {3}", varname, i, j, arr[i, j]);
}
}
}
}
static void Main(string[] args)
{
// Start the Configuration Manager
//ConfigurationManager configManager = new ConfigurationManager(@"D:\Work\Research\PhD\Implementation\Diactrization\Preprocessing\Preprocessing\Configurations.xml");
ConfigurationManager configManager = new ConfigurationManager(args[0]);
// Start the logger
Logger logger = new Logger(configManager);
// Start the parser
Parser parser = new Parser(configManager, logger);
// Start Train Set parsing from root directory
parser.Parse(configManager.rootTrainDirectory, "Train");
// Start Test Set parsing from root directory
parser.Parse(configManager.rootTestDirectory, "Test");
// Copy files to configuration environment if required
if (configManager.configEnvDirectory != "")
{
MLApp.MLAppClass matlab = new MLApp.MLAppClass();
matlab.Execute(@"load('" + configManager.rootTrainDirectory + @"\input_data');");
matlab.Execute(@"load('" + configManager.rootTestDirectory + @"\input_data');");
matlab.Execute(@"save('" + configManager.configEnvDirectory + @"\input_data');");
}
}
public static void seeGraph(string folderName, int args)
{
MLApp.MLAppClass matlab = new MLApp.MLAppClass();
matlab.PutWorkspaceData("Graphargs", "base", args);
matlab.PutWorkspaceData("folderName", "base", folderName);
matlab.Execute("cd 'D:\\Dropbox\\Research Assistant\\SIBI Data Feature\\Code\\kencoba\\'");
matlab.Execute("FeatureLooker(folderName,Graphargs);");
//matlab.Execute("com.mathworks.mlservices.MLEditorServices.closeAll");
}
public override void Run()
{
MLApp.MLAppClass matlab = new MLApp.MLAppClass();
matlab.Visible = 1; //会显示Command Window
matlab.Execute("clc; clear all; close all;");
Project project = Solution.Projects[0];
int line = 0;
for (int length1 = 3; length1 <= 7; length1++)
{
for (int length2 = 3; length2 <= 7; length2++)
{
if (length2 > length1)
{
// set new parameters
project.Parameters["Length1"].Value = length1;
project.Parameters["Length2"].Value = length2;
// print parameters
Console.Write("Length1 = " + length1 + " Length2 = " + length2);
// start backtest
Start();
// calculate objective function
double objective = Solution.Portfolio.GetValue();
// print objective
Console.WriteLine(" Objective = " + objective);
// check best objective
matlab.Execute(string.Format("x({0})={1}; y({0})={2}; z({0})={3};", ++line, length1, length2, objective));
}
}
}
matlab.Execute("t1=linspace(min(x),max(x),max(x)-min(x)+1); %% 如果数据不多，最后的max(x)-min(x)+1可以改成50等");
matlab.Execute("t2=linspace(min(y),max(y),max(y)-min(y)+1); %% 同上");
matlab.Execute("[X,Y]=meshgrid(t1,t2);");
matlab.Execute("Z=griddata(x,y,z,X,Y,'v4');");
matlab.Execute("figure,surfc(X,Y,Z),colorbar");
matlab.Execute("xlabel('x'); ylabel('y'); zlabel('z');");
matlab.Execute("title('Performance');");
matlab.Execute("save"); //保存一下，用于后期分析
// 执行完后暂时不退出
//matlab.Quit();
//matlab = null;
}
public override void Run()
{
MLApp.MLAppClass matlab = new MLApp.MLAppClass();
matlab.Visible = 1; //会显示Command Window
matlab.Execute("clc; clear all; close all;");
Project project = Solution.Projects[0];
int line = 0;
for (int length1 = 3; length1 <= 7; length1++)
{
for (int length2 = 3; length2 <= 7; length2++)
{
if (length2 > length1)
{
// set new parameters
project.Parameters["Length1"].Value = length1;
project.Parameters["Length2"].Value = length2;
// print parameters
Console.Write("Length1 = " + length1 + " Length2 = " + length2);
// start backtest
Start();
// calculate objective function
double objective = Solution.Portfolio.GetValue();
// print objective
Console.WriteLine(" Objective = " + objective);
// check best objective
matlab.Execute(string.Format("x({0})={1}; y({0})={2}; z({0})={3};", ++line, length1, length2, objective));
}
}
}
matlab.Execute("t1=linspace(min(x),max(x),max(x)-min(x)+1); %% 如果数据不多，最后的max(x)-min(x)+1可以改成50等");
matlab.Execute("t2=linspace(min(y),max(y),max(y)-min(y)+1); %% 同上");
matlab.Execute("[X,Y]=meshgrid(t1,t2);");
matlab.Execute("Z=griddata(x,y,z,X,Y,'v4');");
matlab.Execute("figure,surfc(X,Y,Z),colorbar");
matlab.Execute("xlabel('x'); ylabel('y'); zlabel('z');");
matlab.Execute("title('Performance');");
matlab.Execute("save"); //保存一下，用于后期分析
// 执行完后暂时不退出
//matlab.Quit();
//matlab = null;
}
static private double[] scores(DataTable kinectTable, double[] groundPlane, string exercise)
{
double[,] kinectData = new double[kinectTable.Rows.Count, kinectTable.Columns.Count];
double[,] kinectZeros = new double[kinectTable.Rows.Count, kinectTable.Columns.Count];
double[] groundPlaneZeros = new double[4];
System.Array cr = new double[3];
System.Array ci = new double[3];
for (int r = 0; r < kinectTable.Rows.Count; r++)
{
for (int c = 0; c < kinectTable.Columns.Count; c++)
{
kinectData[r, c] = (double)kinectTable.Rows[r][c];
}
}
MLApp.MLAppClass matlab = new MLApp.MLAppClass();
matlab.PutFullMatrix("CS_kinectData", "base", kinectData, kinectZeros);
matlab.PutFullMatrix("CS_groundPlane", "base", groundPlane, groundPlaneZeros);
String MyDocs = Environment.GetFolderPath(Environment.SpecialFolder.Personal);
String ProjectLocation = "Visual Studio 2010\\Projects\\Kinect-Tracking-Project\\MatlabPrototypes\\FeatureDetection";
String matFileCD_command = String.Format("cd '{0}';", System.IO.Path.Combine(MyDocs, ProjectLocation));
matlab.Execute(matFileCD_command);
if (exercise == "squats")
{
matlab.Execute("c = cs_matlab_classifier(CS_kinectData, CS_groundPlane, 'squats');");
}
else if (exercise == "arm raise")
{
matlab.Execute("c = transpose(testBayes(CS_kinectData, CS_groundPlane, 'arm raise'));");
}
else if (exercise == "leg raise")
{
matlab.Execute("c = transpose(testBayes(CS_kinectData, CS_groundPlane, 'leg raise'));");
}
else if (exercise == "leg extension")
{
matlab.Execute("c = transpose(testBayes(CS_kinectData, CS_groundPlane, 'leg extension'));");
}
matlab.GetFullMatrix("c", "base", cr, ci);
double[] cr_d = new double[3];
cr_d = (double[])cr;
return(cr_d);
}
public static void testOutput()
{
MLApp.MLAppClass matlab = new MLApp.MLAppClass();
int a = 2;
int b = 6;
matlab.PutWorkspaceData("a", "base", a);
matlab.PutWorkspaceData("b", "base", b);
matlab.Execute("cd 'D:\\Dropbox\\Research Assistant\\SIBI Data Feature\\Code\\kencoba'");
matlab.Execute("c = a + b");
matlab.Execute("com.mathworks.mlservices.MLEditorServices.closeAll");;
}
public override void Run()
{
MLApp.MLAppClass matlab = new MLApp.MLAppClass();
matlab.Visible = 1; //会显示Command Window
matlab.Execute("clc; clear all; close all;");
WatcherStrat(@"D:\test", "*.*");
Project project = Solution.Projects[0];
//int line = 0;
for (int length1 = 3; length1 <= 7; length1++)
{
for (int length2 = 3; length2 <= 7; length2++)
{
if (length2 > length1)
{
// set new parameters
project.Parameters["Length1"].Value = length1;
project.Parameters["Length2"].Value = length2;
// print parameters
Console.Write("Length1 = " + length1 + " Length2 = " + length2);
// start backtest
// 这句话要写到matlab中的函数中去
matlab.Execute(@"save D:\test\" + Clock.Now.Ticks);
// 等待策略跑完一次
_autoResetEvent.WaitOne();
// calculate objective function
double objective = Solution.Portfolio.GetValue();
// print objective
Console.WriteLine(" Objective = " + objective);
// check best objective
}
}
}
// 执行完后暂时不退出
//matlab.Quit();
//matlab = null;
WatcherStop();
}
public override void Run()
{
MLApp.MLAppClass matlab = new MLApp.MLAppClass();
matlab.Visible = 1; //会显示Command Window
matlab.Execute("clc; clear all; close all;");
WatcherStrat(@"D:\test", "*.*");
Project project = Solution.Projects[0];
//int line = 0;
for (int length1 = 3; length1 <= 7; length1++)
{
for (int length2 = 3; length2 <= 7; length2++)
{
if (length2 > length1)
{
// set new parameters
project.Parameters["Length1"].Value = length1;
project.Parameters["Length2"].Value = length2;
// print parameters
Console.Write("Length1 = " + length1 + " Length2 = " + length2);
// start backtest
// 这句话要写到matlab中的函数中去
matlab.Execute(@"save D:\test\" + Clock.Now.Ticks);
// 等待策略跑完一次
_autoResetEvent.WaitOne();
// calculate objective function
double objective = Solution.Portfolio.GetValue();
// print objective
Console.WriteLine(" Objective = " + objective);
// check best objective
}
}
}
// 执行完后暂时不退出
//matlab.Quit();
//matlab = null;
WatcherStop();
}
/// <summary>
/// 构造函数
/// </summary>
/// <param name="data">数据集</param>
/// <param name="conv">kmeans时的收敛值</param>
/// <param name="addr">邻接表的地址</param>
public SpectralClusteringGenerator(Dataset data, double conv, PrintLogFunction PrintLog, string addr)
{
dataset = data;
arrData = data.Data.ToArray();
convergence = conv;
PrintLog("初始化MATLAB组件...");
matlab = new MLApp.MLAppClass();
matlab.Visible = 0;
PrintLog("初始化结束...");
if (addr == null /*|| File.Exists(addr)==false*/)
{
PrintLog("初始化Spectral邻居点图, 正在寻找每个点相邻最近的数个点...");
FindNeighbours(9, PrintLog);
PrintLog("初始化结束...");
}
else
{
addrNeighbours = addr;
}
}
static void Main(string[] args)
{
MLApp.MLAppClass matlab = new MLApp.MLAppClass();
System.Array pr = new double[4];
pr.SetValue(11, 0);
pr.SetValue(12, 1);
pr.SetValue(13, 2);
pr.SetValue(14, 3);
System.Array pi = new double[4];
pi.SetValue(1, 0);
pi.SetValue(2, 1);
pi.SetValue(3, 2);
pi.SetValue(4, 3);
matlab.PutFullMatrix("a", "base", pr, pi);
System.Array prresult = new double[4];
System.Array piresult = new double[4];
matlab.GetFullMatrix("a", "base", ref prresult, ref piresult);
}
// Calling MATLAB function file to perform Gaussian fitting
private void GaussianFitting(int n, double[,] arrSamplesR, double[,] arrSamplesI, ref Array arrModes, ref Array arrMUs, ref Array arrSigmaXSigmaY, ref Array junkModes, ref Array junkMUs, ref Array junkSigmaXSigmaY)
{
// Instantiate MATLAB Engine Interface through com
MLApp.MLAppClass matlab = new MLApp.MLAppClass();
//DateTime startTime = DateTime.Now;
// Set input matrices
matlab.PutFullMatrix("samples", "base", arrSamplesR, arrSamplesI);
//DateTime stopTime = DateTime.Now;
//TimeSpan duration = stopTime - startTime;
//double RunTime = duration.TotalSeconds;
//System.Windows.Forms.MessageBox.Show("Set input paramters " + RunTime + " seconds!");
double[] NR = new double[1];
double[] NI = new double[1];
NR[0] = n;
NI[0] = 0;
matlab.PutFullMatrix("N", "base", NR, NI);
//startTime = DateTime.Now;
// Using Engine Interface, execute ML script file
string appPath = System.IO.Path.GetDirectoryName(System.Windows.Forms.Application.ExecutablePath);
matlab.Execute("cd " + appPath);
matlab.Execute("cd ..\\..\\..\\Scripts");
matlab.Execute("[modes, MUs, SigmaXSigmaY] = IPPAGaussianFitting(samples, N);");
//stopTime = DateTime.Now;
//duration = stopTime - startTime;
//RunTime = duration.TotalSeconds;
//System.Windows.Forms.MessageBox.Show("Execute ML Script " + RunTime + " seconds!");
//startTime = DateTime.Now;
// Using Engine Interface, get matrices from the base workspace.
matlab.GetFullMatrix("modes", "base", ref arrModes, ref junkModes);
matlab.GetFullMatrix("MUs", "base", ref arrMUs, ref junkMUs);
matlab.GetFullMatrix("SigmaXSigmaY", "base", ref arrSigmaXSigmaY, ref junkSigmaXSigmaY);
//stopTime = DateTime.Now;
//duration = stopTime - startTime;
//RunTime = duration.TotalSeconds;
//System.Windows.Forms.MessageBox.Show("Retrieve matrices " + RunTime + " seconds!");
}
static private void UseEngine(double[,] ar, ref Array cr, ref Array ci) {
// Instantiate MATLAB Engine Interface through com
Console.WriteLine("Matlab Startup...\r\n");
MLApp.MLAppClass matlab = new MLApp.MLAppClass();
// Make imaginary matricies
double[] a_d = new double[] { 68.8, 120, 110, 118, 150, 179, 180, 150, 178, 152, 163, 107, 149, 97.8, 90.1, 94.3, 90.9, 89.9, 53.4 };
matlab.PutFullMatrix("a", "base", a_d, new double[19]);
String MyDocs = Environment.GetFolderPath(Environment.SpecialFolder.Personal);
String ProjectLocation = "Visual Studio 2010\\Projects\\Kinect-Tracking-Project\\MatlabPrototypes\\FeatureDetection";
String matFileCD_command = String.Format("cd '{0}';", System.IO.Path.Combine(MyDocs, ProjectLocation));
Console.WriteLine(matFileCD_command);
matlab.Execute(matFileCD_command);
// matlab.Execute("open testBayes.m");
// matlab.Execute("dbstop in math_on_numbers.m");
Console.WriteLine("Matlab Processing...\r\n");
matlab.Execute("c = transpose(testBayes(a));");
//matlab.Execute("com.mathworks.mlservices.MLEditorServices.closeAll");
//matlab.Execute("dbquit all");
try {
matlab.GetFullMatrix("c", "base", ref cr, ref ci);
} catch (Exception) {
Console.WriteLine("someErr");
}
}
/*
* All input and output arguments must have been allocated
* prior to calling these functions
*
* Input:
* ar real part of a
* ai imaginary part of a
* br real part of b
* bi imaginary part of b
*
* Output:
* cr real part of c
* ci imaginary part of c
* dr real part of d
* di imaginary part of d
*/
static private void UseEngine(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 routine inside
* MATLAB using the MATLAB Engine's com interface
*/
// Instantiate MATLAB Engine Interface through com
MLApp.MLAppClass matlab = new MLApp.MLAppClass();
// Using Engine Interface, put the matrix "a" into
// the base workspace.
// "a" is a complex variable with a real part of ar,
// and an imaginary part of ai
matlab.PutFullMatrix("a", "base", ar, ai);
// Using Engine Interface, put the matrix "b" into
// the base workspace.
// "b" is a complex variable with a real part of br,
// and an imaginary part of bi
matlab.PutFullMatrix("b", "base", br, bi);
// Using Engine Interface, execute the ML command
// contained in quotes.
matlab.Execute("cd c:\\demos\\CSharp_MATLAB\\mcode;");
matlab.Execute("open math_on_numbers.m");
matlab.Execute("dbstop in math_on_numbers.m");
matlab.Execute("[c, d] = math_on_numbers(a,b);");
matlab.Execute("com.mathworks.mlservices.MLEditorServices.closeAll");
//matlab.Execute("dbquit all");
// Using Engine Interface, get the matrix "c" from
// the base workspace.
// "c" is a complex variable with a real part of cr,
// and an imaginary part of ci
matlab.GetFullMatrix("c", "base", ref cr, ref ci);
// using engine interface, get the matrix "c" from
// the base workspace.
// "d" is a complex variable with a real part of dr,
// and an imaginary part of di
matlab.GetFullMatrix("d", "base", ref dr, ref di);
}
//// Print out the permutations of the input
//static string ShowPermutations<T>(IEnumerable<T> input, int count)
//{
// string s = "";
// foreach (IEnumerable<T> permutation in PermuteUtils.Permute<T>(input, count))
// {
// foreach (T i in permutation)
// {
// s += " " + i.ToString();
// }
// s += "\n";
// }
// return s;
//}
private static void UseEngine(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 routine inside
* MATLAB using the MATLAB Engine's com interface
*/
// Instantiate MATLAB Engine Interface through com
MLApp.MLAppClass matlab = new MLApp.MLAppClass();
// Using Engine Interface, put the matrix "a" into
// the base workspace.
// "a" is a complex variable with a real part of ar,
// and an imaginary part of ai
matlab.PutFullMatrix("a", "base", ar, ai);
// Using Engine Interface, put the matrix "b" into
// the base workspace.
// "b" is a complex variable with a real part of br,
// and an imaginary part of bi
matlab.PutFullMatrix("b", "base", br, bi);
// Put test array into base workspace
double[,] SR = new double[2, 2];
double[,] SI = new double[2, 2];
SR[0, 0] = 1;
SR[0, 1] = 2;
SR[1, 0] = 3;
SR[1, 1] = 4;
SI[0, 0] = 0;
SI[0, 1] = 0;
SI[1, 0] = 0;
SI[1, 1] = 0;
System.Array TR = new double[2];
System.Array TI = new double[2];
TR.SetValue(1, 0);
TR.SetValue(2, 1);
TI.SetValue(0, 0);
TI.SetValue(0, 1);
// Test List
List<double[]> samples = new List<double[]>();
double[] aaa = new double[2];
double[] bbb = new double[2];
aaa[0] = 5;
aaa[1] = 6;
bbb[0] = 7;
bbb[1] = 8;
samples.Add(aaa);
samples.Add(bbb);
double[,] arrSamplesR = new double[samples.Count, 2];
double[,] arrSamplesI = new double[samples.Count, 2];
for (int i = 0; i < samples.Count; i++)
{
double[] b = samples[i];
arrSamplesR[i, 0] = b[0];
arrSamplesR[i, 1] = b[1];
arrSamplesI[i, 0] = 0;
arrSamplesI[i, 1] = 0;
}
matlab.PutFullMatrix("ss", "base", arrSamplesR, arrSamplesI);
// Using Engine Interface, execute the ML command
// contained in quotes.
matlab.Execute("cd 'C:\\Lanny\\MAMI\\ZPlayGround\\C# Calling MATLAB\\CSharp_MATLAB\\CSharp_MATLAB\\mcode';");
matlab.Execute("open math_on_numbers.m");
matlab.Execute("dbstop in math_on_numbers.m");
matlab.Execute("[c, d] = math_on_numbers(a,b);");
matlab.Execute("com.mathworks.mlservices.MLEditorServices.closeAll");
//matlab.Execute("dbquit all");
// Using Engine Interface, get the matrix "c" from
// the base workspace.
// "c" is a complex variable with a real part of cr,
// and an imaginary part of ci
matlab.GetFullMatrix("c", "base", ref cr, ref ci);
// using engine interface, get the matrix "c" from
// the base workspace.
// "d" is a complex variable with a real part of dr,
// and an imaginary part of di
matlab.GetFullMatrix("d", "base", ref dr, ref di);
System.Array resultsR = new double[2, 2];
System.Array resultsI = new double[2, 2];
matlab.GetFullMatrix("ss", "base", ref resultsR, ref resultsI);
Console.WriteLine(resultsR.GetValue(0, 0).ToString() + " "
+ resultsR.GetValue(0, 1).ToString() + " "
+ resultsR.GetValue(1, 0).ToString() + " "
+ resultsR.GetValue(1, 1).ToString());
}
static void Main(string[] args)
{
// Start the Configuration Manager
//ConfigurationManager configManager = new ConfigurationManager(@"D:\Work\Research\PhD\Implementation\Diactrization\Preprocessing\Preprocessing\Configurations.xml");
ConfigurationManager configManager = new ConfigurationManager(args[0]);
// Start the logger
Logger logger = new Logger(configManager);
// Start the train parser
Parser trainParser;
switch (configManager.trainInputFormat)
{
case "ReadyFeatures":
trainParser = new ReadyFeaturesParser(configManager, logger);
break;
case "RawTxt":
trainParser = new RawTxtParser(configManager, logger);
break;
default:
trainParser = new ReadyFeaturesParser(configManager, logger);
break;
}
// Start Train Set parsing from root directory
trainParser.Parse(configManager.rootTrainDirectory, "Train", configManager.trainInputParsingMode, configManager.trainInputFormat);
// Start the test parser
Parser testParser;
switch (configManager.testInputFormat)
{
case "ReadyFeatures":
testParser = new ReadyFeaturesParser(configManager, logger);
break;
case "RawTxt":
testParser = new RawTxtParser(configManager, logger);
break;
default:
testParser = new ReadyFeaturesParser(configManager, logger);
break;
}
// Start Test Set parsing from root directory
testParser.Parse(configManager.rootTestDirectory, "Test", configManager.testInputParsingMode, configManager.testInputFormat);
// Copy files to configuration environment if required
if (configManager.configEnvDirectory != "")
{
String s = String.Empty;
MLApp.MLAppClass matlab = new MLApp.MLAppClass();
s = matlab.Execute(@"load('" + configManager.rootTrainDirectory + @"\input_data');");
if (Regex.Match(s, "Error").Success)
{
logger.LogError(s, ErrorCode.MATLAB_ERROR);
}
s = matlab.Execute(@"load('" + configManager.rootTestDirectory + @"\input_data');");
if (Regex.Match(s, "Error").Success)
{
logger.LogError(s, ErrorCode.MATLAB_ERROR);
}
s = matlab.Execute(@"save('" + configManager.configEnvDirectory + @"\input_data');");
if (Regex.Match(s, "Error").Success)
{
logger.LogError(s, ErrorCode.MATLAB_ERROR);
}
}
}
static private string[] scores(DataTable kinectTable, double[] groundPlane, string exercise)
{
double[,] kinectData = new double[kinectTable.Rows.Count, kinectTable.Columns.Count];
double[,] kinectZeros = new double[kinectTable.Rows.Count, kinectTable.Columns.Count];
double[] groundPlaneZeros = new double[4];
//System.Array cr = new double[3];
//System.Array ci = new double[3];
for (int r = 0; r < kinectTable.Rows.Count; r++)
{
for (int c = 0; c < kinectTable.Columns.Count; c++)
{
kinectData[r, c] = (double)kinectTable.Rows[r][c];
}
}
MLApp.MLAppClass matlab = new MLApp.MLAppClass();
matlab.PutFullMatrix("CS_kinectData", "base", kinectData, kinectZeros);
matlab.PutFullMatrix("CS_groundPlane", "base", groundPlane, groundPlaneZeros);
String MyDocs = Environment.GetFolderPath(Environment.SpecialFolder.Personal);
String ProjectLocation = "Visual Studio 2010\\Projects\\Kinect-Tracking-Project\\MatlabPrototypes\\FeatureDetection";
String matFileCD_command = String.Format("cd '{0}';", System.IO.Path.Combine(MyDocs, ProjectLocation));
matlab.Execute(matFileCD_command);
matlab.Execute("initWorkspace();");
if (exercise == "Squat")
{
matlab.Execute("c = cs_matlab_classifier('squats', CS_kinectData, CS_groundPlane);");
}
else if (exercise == "Arm Raise")
{
matlab.Execute("c = cs_matlab_classifier('armRaise', CS_kinectData, CS_groundPlane);");
}
else if (exercise == "Leg Raise")
{
matlab.Execute("c = cs_matlab_classifier('legRaise', CS_kinectData, CS_groundPlane);");
}
else if (exercise == "Hip Abduction")
{
matlab.Execute("c = cs_matlab_classifier('legExt', CS_kinectData, CS_groundPlane);");
}
string[] cr_d = new string[30];
StreamReader FileStreamReader;
FileStreamReader = File.OpenText(System.IO.Path.Combine(MyDocs, ProjectLocation) + "\\results.csv");
int i = 0;
while (FileStreamReader.Peek() != -1)
{
string[] words;
words = FileStreamReader.ReadLine().Split(',');
cr_d[i] = words[0];
cr_d[i + 1] = words[1];
cr_d[i + 2] = words[2];
i = i + 3;
}
FileStreamReader.Close();
//matlab.GetFullMatrix("c", "base", cr, ci);
//double[] cr_d = new double[3];
//cr_d = (double[])cr;
return cr_d;
}
static void Main(string[] args)
{
// Start the Configuration Manager
//ConfigurationManager configManager = new ConfigurationManager(@"D:\Work\Research\PhD\Implementation\Diactrization\Preprocessing\Preprocessing\Configurations.xml");
ConfigurationManager configManager = new ConfigurationManager(args[0]);
// Start the logger
Logger logger = new Logger(configManager);
// Start the train parser
Parser trainParser;
switch (configManager.trainPOSInputFormat)
{
case "RDI":
trainParser = new RDIFormatParser(configManager, logger);
break;
case "Stanford":
trainParser = new StanfordFormatParser(configManager, logger);
break;
default:
trainParser = new RDIFormatParser(configManager, logger);
break;
}
// Start Train Set parsing from root directory
trainParser.Parse(configManager.rootTrainDirectory, "Train", configManager.trainPOSInputFormat);
// Start the test parser
Parser testParser;
switch (configManager.testPOSInputFormat)
{
case "RDI":
testParser = new RDIFormatParser(configManager, logger);
break;
case "Stanford":
testParser = new StanfordFormatParser(configManager, logger);
break;
default:
testParser = new RDIFormatParser(configManager, logger);
break;
}
// Start Train Set parsing from root directory
testParser.Parse(configManager.rootTestDirectory, "Test", configManager.testPOSInputFormat);
// Copy files to configuration environment if required
if (configManager.configEnvDirectory != "")
{
MLApp.MLAppClass matlab = new MLApp.MLAppClass();
matlab.Execute(@"load('" + configManager.rootTrainDirectory + @"\input_data');");
matlab.Execute(@"load('" + configManager.rootTestDirectory + @"\input_data');");
matlab.Execute(@"save('" + configManager.configEnvDirectory + @"\input_data');");
}
}
static private double[] scores(DataTable kinectTable, double[] groundPlane, string exercise)
{
double[,] kinectData = new double[kinectTable.Rows.Count, kinectTable.Columns.Count];
double[,] kinectZeros = new double[kinectTable.Rows.Count, kinectTable.Columns.Count];
double[] groundPlaneZeros = new double[4];
System.Array cr = new double[3];
System.Array ci = new double[3];
for (int r = 0; r < kinectTable.Rows.Count; r++)
{
for (int c = 0; c < kinectTable.Columns.Count; c++)
{
kinectData[r, c] = (double)kinectTable.Rows[r][c];
}
}
MLApp.MLAppClass matlab = new MLApp.MLAppClass();
matlab.PutFullMatrix("CS_kinectData", "base", kinectData, kinectZeros);
matlab.PutFullMatrix("CS_groundPlane", "base", groundPlane, groundPlaneZeros);
String MyDocs = Environment.GetFolderPath(Environment.SpecialFolder.Personal);
String ProjectLocation = "Visual Studio 2010\\Projects\\Kinect-Tracking-Project\\MatlabPrototypes\\FeatureDetection";
String matFileCD_command = String.Format("cd '{0}';", System.IO.Path.Combine(MyDocs, ProjectLocation));
matlab.Execute(matFileCD_command);
if (exercise == "squats")
{
matlab.Execute("c = cs_matlab_classifier(CS_kinectData, CS_groundPlane, 'squats');");
}
else if (exercise == "arm raise")
{
matlab.Execute("c = transpose(testBayes(CS_kinectData, CS_groundPlane, 'arm raise'));");
}
else if (exercise == "leg raise")
{
matlab.Execute("c = transpose(testBayes(CS_kinectData, CS_groundPlane, 'leg raise'));");
}
else if (exercise == "leg extension")
{
matlab.Execute("c = transpose(testBayes(CS_kinectData, CS_groundPlane, 'leg extension'));");
}
matlab.GetFullMatrix("c", "base", cr, ci);
double[] cr_d = new double[3];
cr_d = (double[])cr;
return cr_d;
}
