public Data(DataTable dataSet)
{
this.OriginalDataSet = dataSet;
this.DataSet = ConvertNumericData(dataSet.Copy());
this.Decisions = FindDecisions();
AttributeValuePairs = AttributeValuePair.GetAttributeValuePairs(DataSet);
Characteristic = new Characteristic(this);
MaximalConsistent = new MaximalConsistent(this);
ConditionalProbability = ProbApprox.GetConditionalProbability(this);
}
static void Main(string[] args)
{
//var dataTable = FileOperation.ReadDataFile(@"C:\Users\kumar\OneDrive\Documents\Projects\CharMax\CharMax\Datasets\breast-35.d");
var files = Directory.GetFiles(ConfigurationManager.AppSettings["DataSetFolder"], "*.d", SearchOption.TopDirectoryOnly);
ConcurrentBag <Rules> AllRules = new ConcurrentBag <Rules>();
List <List <int> > temp = new List <List <int> >();
temp.Add(new List <int> {
1, 2, 3, 4
});
temp.Add(new List <int> {
3, 4
});
temp.Add(new List <int> {
4, 3, 3, 4
});
var t = temp.IntersectAll().ToList();
//Parallel.ForEach(files, file =>
//{
// var dataTable = FileOperation.ReadDataFile(file);
// Data data = new Data(dataTable);
// var alphaValue = 0.0f;
// Parallel.For(0, 12, alpha =>
// {
// alphaValue = (float)Math.Round(alphaValue + 0.10f, 1);
// RuleInduction ruleInductionCharacteristic = new RuleInduction();
// RuleInduction ruleInductionMCB = new RuleInduction();
// var charTask = Task.Factory.StartNew(() =>
// {
// var charApprox = ProbApprox.GetConceptApprox(data.Characteristic, data.ConditionalProbability.CharacteristicCondProb, alphaValue);
// return ruleInductionCharacteristic.ComputeRules(data, data.AttributeValuePairs, charApprox);
// });
// var mcBTask = Task.Factory.StartNew(() =>
// {
// var mcbApprox = ProbApprox.GetConceptApprox(data.MaximalConsistent, data.ConditionalProbability.MaximalCondProb, alphaValue);
// return ruleInductionMCB.ComputeRules(data, data.AttributeValuePairs, mcbApprox);
// });
// Task.WaitAll(charTask, mcBTask);
// var charRule = charTask.Result;
// var mcbRule = mcBTask.Result;
// Rules rules = new Rules(Path.GetFileNameWithoutExtension(file), alphaValue, charRule, mcbRule);
// AllRules.Add(rules);
// });
//for (float alphaValue = 0.0f; alphaValue <= 1.0f; alphaValue = (float)Math.Round(alphaValue + 0.10f, 1))
//{
// //RuleInduction ruleInductionCharacteristic = new RuleInduction();
// //RuleInduction ruleInductionMCB = new RuleInduction();
// //var charTask = Task.Factory.StartNew(() =>
// //{
// // var charApprox = ProbApprox.GetConceptApprox(data.Characteristic, data.ConditionalProbability.CharacteristicCondProb, alphaValue);
// // return ruleInductionCharacteristic.ComputeRules(data, data.AttributeValuePairs, charApprox);
// //});
// //var mcBTask = Task.Factory.StartNew(() =>
// //{
// // var mcbApprox = ProbApprox.GetConceptApprox(data.MaximalConsistent, data.ConditionalProbability.MaximalCondProb, alphaValue);
// // return ruleInductionMCB.ComputeRules(data, data.AttributeValuePairs, mcbApprox);
// //});
// //Task.WaitAll(charTask, mcBTask);
// //var charRule = charTask.Result;
// //var mcbRule = mcBTask.Result;
// //Rules rules = new Rules(Path.GetFileNameWithoutExtension(file), alphaValue, charRule, mcbRule);
// //AllRules.Add(rules);
//}
//});
foreach (var file in files)
{
var dataTable = FileOperation.ReadDataFile(file);
Data data = new Data(dataTable);
//var alphaValue = 0.0f;
for (float alphaValue = 0.01f; alphaValue <= 1.0f; alphaValue = (float)Math.Round(alphaValue + 0.10f, 1))
{
RuleInduction ruleInductionCharacteristic = new RuleInduction();
RuleInduction ruleInductionMCB = new RuleInduction();
var charTask = Task.Factory.StartNew(() =>
{
var charApprox = ProbApprox.GetConceptApprox(data.Characteristic, data.ConditionalProbability.CharacteristicCondProb, alphaValue);
return(ruleInductionCharacteristic.ComputeRules(data, data.AttributeValuePairs, charApprox));
});
var mcBTask = Task.Factory.StartNew(() =>
{
var mcbApprox = ProbApprox.GetConceptApprox(data.MaximalConsistent, data.ConditionalProbability.MaximalCondProb, alphaValue);
return(ruleInductionMCB.ComputeRules(data, data.AttributeValuePairs, mcbApprox));
});
//Task.WaitAll(charTask, mcBTask);
var charRule = charTask.Result;
var mcbRule = mcBTask.Result;
//var charApprox = ProbApprox.GetConceptApprox(data.Characteristic, data.ConditionalProbability.CharacteristicCondProb, alphaValue);
//var r = ruleInductionCharacteristic.ComputeRules(data, data.AttributeValuePairs, charApprox);
//var mcbApprox = ProbApprox.GetConceptApprox(data.MaximalConsistent, data.ConditionalProbability.MaximalCondProb, alphaValue);
//var r1 = ruleInductionMCB.ComputeRules(data, data.AttributeValuePairs, mcbApprox);
Rules rules = new Rules(Path.GetFileNameWithoutExtension(file), alphaValue, charRule, mcbRule);
//Rules rules = new Rules(Path.GetFileNameWithoutExtension(file), alphaValue, r, r1);
AllRules.Add(rules);
}
}
RuleStat ruleStat = new RuleStat();
ruleStat.SaveRuleStat(AllRules.ToList());
//Console.WriteLine("Rules - Characteristic Set");
//PrintRules(charRule);
//Console.WriteLine("\n\n\n End of Characteristic Rules \n \n");
//Console.WriteLine("Rules - MCB");
//PrintRules(mcbRule);
}