public BaseModule()
{
InitializeComponent();
// -------------- Load data --------------
dataLoad dl = new dataLoad();
Dictionary <String, double[]> pMat_raw = dl.LoadMatrix(controlDataFile);
Dictionary <String, double[]> rMat_raw = dl.LoadMatrix(caseDataFile);
Dictionary <String, List <String> > allPathways = dl.LoadPathways(pathwaysFile);
// -------------- Discretized case (Resistant) data matrix --------------
processData pD = new processData(th_dic_high, th_dic_low);
Dictionary <String, int[]> rMat_discritized = pD.getDiscritizedMat(pMat_raw, rMat_raw);
//Dictionary<String, double[]> rMat_standardizedOnly = pD.getStandardizedVal(pMat_raw, rMat_raw);
//printData(rMat_standardizedOnly);
Dictionary <String, Dictionary <String, int[]> > pathwayMats = pD.getDiscritizedMatForPathways(allPathways, rMat_discritized);
// -------------- Infer BN strucrure (for each pathway) --------------
//Parallel.ForEach(allPathways.Keys, pathway =>{
//foreach (string pathway in allPathways.Keys)
//{
//int nNodes = pathwayMats[pathway].Count;
string pathway = "TGF-BETA_SIGNALING_PATHWAY";
//Dictionary<String, double[]> pathwayStdData = pD.getStandardizedDataforAPathway(pathway, allPathways, rMat_standardizedOnly);
int nNodes = pathwayMats[pathway].Count;
// -- prepare MCMC sampling config settings
SetMCMCconfig();
NameValueCollection InputSettings = new NameValueCollection();
// ------------ input settings
InputSettings.Add("nNodes", nNodes.ToString());
InputSettings.Add("nIteration", nSamplingIter.ToString()); // [Tasaki et al.] sampling iteration: 5000, burn-in (inclusive): 1000 (20%)
InputSettings.Add("nBurnIn", nburnIn.ToString()); // http://www.genetics.org/content/early/2015/01/28/genetics.114.172619
InputSettings.Add("nMaxOutdegree", nMaxOutdegree.ToString());
InputSettings.Add("nMaxIndegree", nMaxIndegree.ToString());
InputSettings.Add("pathway", pathway);
// -- NS algorithm (comment out when need to use)
Adaptive_NS obj_NS = new Adaptive_NS(InputSettings, MethodSettings, OutputSettings, pathwayMats[pathway]);
List <String> BNs_NS = obj_NS.StartAnalysis();
string bn_NS = Find_CombinedBN(BNs_NS);
List <double[, ]> bnMats = getNetworkSfromString(bn_NS, nNodes);
printNet(bn_NS, obj_NS.nodeNameList, pathway, "NS");
bugsSampling bugs_NS = new bugsSampling(bnMats[0],
bnMats[1],
nNodes,
pathway,
obj_NS.nodeNameList,
"NS",
nburnIn,
nSamplingIter,
gamma_prior_a,
gamma_prior_b); // [0] original matrix, [1] complementary matrix
// -----------
// -- HAR algorithm (comment out when need to use)
Adaptive_HAR obj_HAR = new Adaptive_HAR(InputSettings, MethodSettings, OutputSettings, pathwayMats[pathway]);
List <String> BNs_HAR = obj_HAR.StartAnalysis();
string bn_HAR = Find_CombinedBN(BNs_HAR);
List <double[, ]> bnMats_HAR = getNetworkSfromString(bn_HAR, nNodes);
printNet(bn_HAR, obj_HAR.nodeNameList, pathway, "HAR");
bugsSampling bugs_HAR = new bugsSampling(bnMats_HAR[0],
bnMats_HAR[1],
nNodes,
pathway,
obj_HAR.nodeNameList,
"HAR",
nburnIn,
nSamplingIter,
gamma_prior_a,
gamma_prior_b); // [0] original matrix, [1] complementary matrix
// -----------
// -- MH algorithm (comment out when need to use)
Adaptive_MH obj_MH = new Adaptive_MH(InputSettings, MethodSettings, OutputSettings, pathwayMats[pathway]);
List <String> BNs_MH = obj_MH.StartAnalysis();
string bn_MH = Find_CombinedBN(BNs_MH);
List <double[, ]> bnMats_MH = getNetworkSfromString(bn_MH, nNodes);
printNet(bn_MH, obj_MH.nodeNameList, pathway, "MH");
bugsSampling bugs_MH = new bugsSampling(bnMats_MH[0],
bnMats_MH[1],
nNodes,
pathway,
obj_MH.nodeNameList,
"MH",
nburnIn,
nSamplingIter,
gamma_prior_a,
gamma_prior_b); // [0] original matrix, [1] complementary matrix
// -----------
//}
//});
MessageBox.Show("complete");
}
public void DO_Operation(object sender, DoWorkEventArgs e)
{
Session["OuterWorker"] = bgWorker1;
#region Prepare input settings and call the required analysis function constructor
NameValueCollection inputSettings = new NameValueCollection();
inputSettings.Add("nNodes", txt_nNodes.Text);
inputSettings.Add("nIteration", txt_nIteration.Text);
inputSettings.Add("nBurnIn", txt_nBurnIn.Text);
inputSettings.Add("nMaxOutdegree", txt_maxOutDegree.Text);
inputSettings.Add("nMaxIndegree", txt_maxInDegree.Text);
//inputSettings.Add("nSSDstepsize", txt_SSD_Step.Text);
Session["iterationCount"] = txt_nIteration.Text.ToString().Trim();
#endregion
#region Prepare Method settings and call the required analysis function constructor
List <string> MethodSettings = new List <string>();
string initialNetwork = "";
string SamplingDistribution = "";
string AcceptanceRatio = "";
List <string> MCMC_methods = new List <string>();
if (radioButton_InitNet_FixedorRandom.SelectedIndex > -1)
{
if (radioButton_InitNet_FixedorRandom.SelectedIndex == 0)
{
initialNetwork = "Fixed";
}
else if (radioButton_InitNet_FixedorRandom.SelectedIndex == 1)
{
initialNetwork = "Random";
}
}
SamplingDistribution = "DirMul";
AcceptanceRatio = "MH";
//if (radioButton_SamplingDist_Uniform.Checked)
// SamplingDistribution = "Uniform";
//else if (radioButton_SamplingDist_DirMul.Checked)
// SamplingDistribution = "DirMul";
//if (radioButton_AccepRatio_Approximation.Checked)
// AcceptanceRatio = "Approximation";
//else if (radioButton_AccepRatio_MH.Checked)
// AcceptanceRatio = "MH";
//if (radioButton_MCMC_MetroPolish.Checked)
// MCMC_methods = "Metropolis";
if (chk_method_MH.Checked)
{
MCMC_methods.Add("MH");
}
if (chk_method_NS.Checked)
{
MCMC_methods.Add("NS");
}
if (chk_method_HAR.Checked)
{
MCMC_methods.Add("HAR");
}
MethodSettings.Add(initialNetwork);
MethodSettings.Add(SamplingDistribution);
MethodSettings.Add(AcceptanceRatio);
#endregion
#region Prepare Numerical Output settings
List <string> NumerialOutputSettings = new List <string>();
string SampledNetwithFreq = "false";
string LogPosteriorFunction = "false";
string SSD_values = "false";
string Mean_Posterior = "false";
if (checkBox_SelectedNets_with_Freq.Checked)
{
SampledNetwithFreq = "true";
}
if (checkBox_LogPosterior.Checked)
{
LogPosteriorFunction = "true";
}
//if (checkBox_SSD_values.Checked)
// SSD_values = "true";
if (checkBox_MeanPosterior.Checked)
{
Mean_Posterior = "true:" + txt_MeanPosterior_Th.Text;
}
NumerialOutputSettings.Add(SampledNetwithFreq);
NumerialOutputSettings.Add(LogPosteriorFunction);
NumerialOutputSettings.Add(SSD_values);
NumerialOutputSettings.Add(Mean_Posterior);
#endregion
#region Prepare Graphical Output settings
List <string> GraphicalOutputSettings = new List <string>();
string LogPosterior_VS_iteration = "false";
string SSD_VS_StepSize = "false";
string TrueDist_VS_EstimatedDist = "false";
string Mean_Posterior_Net = "false";
string Highest_Freq_Net = "false";
if (checkBox_LogPosterior_VS_iteration.Checked)
{
LogPosterior_VS_iteration = "true";
}
//if (checkBox_SSD_VS_stepSize.Checked)
// SSD_VS_StepSize = "true";
//if (checkBox_TrueDist_VS_EstimatedDist.Checked)
// TrueDist_VS_EstimatedDist = "true";
if (checkBox_MeanPosteriorNet.Checked)
{
Mean_Posterior_Net = "true";
}
if (checkBox_HighestFreqNet.Checked)
{
Highest_Freq_Net = "true";
}
GraphicalOutputSettings.Add(LogPosterior_VS_iteration);
GraphicalOutputSettings.Add(SSD_VS_StepSize);
GraphicalOutputSettings.Add(TrueDist_VS_EstimatedDist);
GraphicalOutputSettings.Add(Mean_Posterior_Net);
GraphicalOutputSettings.Add(Highest_Freq_Net);
#endregion
#region Call proper Class
inputFileInfo = (List <string>)Session["inputFileInfo"];
string outputDirectory = (string)Session["path"];
#region For Metropolis-Hastings Sampler
if (MCMC_methods.Contains("MH"))
{
Session["MCMC_method_MH"] = "true";
//Response.Write("<script type='text/javascript' src='Scripts/Progress.js'> GetStatus('MH');</script>");
//Response.Write("<script type='text/javascript' src='Scripts/Progress.js'> GetStatus_1();</script>");
//Response.Write("<script> window.onload=function(){GetStatus('MH');}</script>");
//Response.Write("<script> window.onload=function(){GetStatus_1();}</script>");
ClientScript.RegisterStartupScript(typeof(Page), "keyMH", "<script type=\"text/javascript\"> GetStatus_1(); </script>", false);
//Response.Write("<script> window.GetStatus('MH');</script>");
/* THE SAMPLER CLASS RUNNING STARTS HERE */
Adaptive_MH obj_MH = new Adaptive_MH(inputFileInfo, inputSettings, outputDirectory, MethodSettings, NumerialOutputSettings, GraphicalOutputSettings);
obj_MH.StartAnalysis(Session);
}
#endregion
#region For Neighbourhood Sampler
if (MCMC_methods.Contains("NS"))
{
Session["MCMC_method_NS"] = "true";
//Response.Write("<script type='text/javascript' src='Scripts/Progress.js'> GetStatus('NS'); </script>");
//Response.Write("<script type='text/javascript' src='Scripts/Progress.js'> GetStatus_2(); </script>");
//Response.Write("<script> window.onload=function(){GetStatus('NS');}</script>");
//Response.Write("<script> window.onload=function(){GetStatus_2();}</script>");
//Response.Write("<script> window.GetStatus('NS');</script>");
ClientScript.RegisterStartupScript(typeof(Page), "keyNS", "<script type=\"text/javascript\"> GetStatus_2(); </script>", false);
/* THE SAMPLER CLASS RUNNING STARTS HERE */
Adaptive_NS obj_NS = new Adaptive_NS(inputFileInfo, inputSettings, outputDirectory, MethodSettings, NumerialOutputSettings, GraphicalOutputSettings);
obj_NS.StartAnalysis(Session);
}
#endregion
#region For Hit-n-Run Sampler
if (MCMC_methods.Contains("HAR"))
{
Session["MCMC_method_HAR"] = "true";
//Response.Write("<script type='text/javascript' src='Scripts/Progress.js'> GetStatus('HAR');</script>");
//Response.Write("<script type='text/javascript' src='Scripts/Progress.js'> GetStatus_3();</script>");
//Response.Write("<script> window.onload=function(){GetStatus('HAR');}</script>");
//Response.Write("<script> window.onload=function(){GetStatus_3();}</script>");
//.Write("<script> window.GetStatus('HAR');</script>");
ClientScript.RegisterStartupScript(typeof(Page), "keyHAR", "<script type=\"text/javascript\"> GetStatus_3(); </script>", false);
/* THE SAMPLER CLASS RUNNING STARTS HERE */
Adaptive_HAR obj_HAR = new Adaptive_HAR(inputFileInfo, inputSettings, outputDirectory, MethodSettings, NumerialOutputSettings, GraphicalOutputSettings, txt_HAR_lambda.Text);
obj_HAR.StartAnalysis(Session);
}
#endregion
#endregion
}