public PoptvrModel PopLearn()
{
this.poptvr = new PoptvrModel(this.dataset, this.clusterSetting);
this.poptvr.initMembership(inputCluster, outputCluster);
this.poptvr.popLearn();
FileWriter.WritePopLearnOutput(this.poptvr);
return this.poptvr;
}
public static void WritePopLearnOutput(string filename, PoptvrModel poptvr)
{
Console.Write("Output InitialWeights.txt ... ");
FileWriter.WriteToFile(AppConfig.getOutputFolder() + filename + "InitialWeights.txt", poptvr.InitialWeightsString);
Console.WriteLine("Done!");
Console.Write("Output SelectedWeights.txt ... ");
FileWriter.WriteToFile(AppConfig.getOutputFolder() + filename + "SelectedWeights.txt", poptvr.SelectedWeightsString);
Console.WriteLine("Done!");
Console.Write("Output FuzzyRulesString.txt ... ");
FileWriter.WriteToFile(AppConfig.getOutputFolder() + filename + "FuzzyRulesString.txt", poptvr.FuzzyRulesString);
Console.WriteLine("Done!");
}
public static string POPTest(PoptvrModel poptvr, DataSet dataset)
{
Console.WriteLine("POPTest ....");
string resultString = "";
int correct = 0;
int[] ruleNodes = new int[(int)Math.Pow(poptvr.inputClusterSize, dataset.NumberOfInputNodes)];
int[] classTotal = new int[dataset.NumberOfOutputNodes];
int[] classCorrect = new int[dataset.NumberOfOutputNodes];
for (int i = 0; i < dataset.TotalNumberOfRecords; i++)
{
bool correctFlag = false;
poptvr.forwardFeed(SystemFunctions.getArrayAtRow(dataset.Inputdata, i));
poptvr.maxRuleNode(ruleNodes);
double max = poptvr.OutputLayer[0].Output;
int maxIndex = 0;
for (int j = 0; j < dataset.NumberOfOutputNodes; j++)
{
resultString += String.Format("{0:F3}\t", poptvr.OutputLayer[j].Output);
if (max < poptvr.OutputLayer[j].Output)
{
max = poptvr.OutputLayer[j].Output;
maxIndex = j;
}
}
for (int j = 0; j < dataset.NumberOfOutputNodes; j++)
{
// calculate the total no. in each class
if (dataset.DesiredOutputs[i, j] == 1.0)
{
classTotal[j]++;
}
// calculate the correct no. in each class, overall correct
if (j == maxIndex)
{
if (dataset.DesiredOutputs[i, j] == 1.0)
{
correct++;
classCorrect[j]++;
correctFlag = true;
}
}
}
if (correctFlag)
{
resultString += "\n";
}
else
{
resultString += "X\n";
}
}
for (int i = 0; i < dataset.NumberOfOutputNodes; i++)
{
resultString += String.Format("Class [{0:D}]:\t{1:F3}\tpercent\n", i, ((double)classCorrect[i] * 100.0) / (double)classTotal[i]);
}
resultString += String.Format("Overall:\t {0:F3}\tpercent\n", (correct * 100.0) / dataset.TotalNumberOfRecords);
int totalRulesFired = 0;
for (int i = 0; i < ruleNodes.Length; i++)
{
if (ruleNodes[i] != 0)
{
totalRulesFired++;
}
resultString += String.Format("ruleNode {0:D}:\t{1:D}\n", i, ruleNodes[i]);
}
resultString += String.Format("Total Rules Fired:\t{0:D}\n", totalRulesFired);
return resultString;
}
public static int POPTest(PoptvrModel poptvr, double[] data, int numberOfOutputNodes)
{
int[] ruleNodes = new int[(int)Math.Pow(poptvr.inputClusterSize, data.Length)];
poptvr.forwardFeed(data);
poptvr.maxRuleNode(ruleNodes);
double max = poptvr.OutputLayer[0].Output;
int maxIndex = 0;
for (int j = 0; j < numberOfOutputNodes; j++)
{
if (max < poptvr.OutputLayer[j].Output)
{
max = poptvr.OutputLayer[j].Output;
maxIndex = j;
}
}
return maxIndex;
}
public static void WritePopLearnOutput(PoptvrModel poptvr)
{
WritePopLearnOutput("", poptvr);
}
public static PoptvrModel RuleReduction(PoptvrModel poptvr, DataSet dataset, AppConfig appConfig)
{
Console.WriteLine("Start RSPOP reduction ...");
double[] CO = new double[dataset.NumberOfOutputNodes * poptvr.OutputClusterSize];
double[] COr = new double[dataset.NumberOfOutputNodes * poptvr.OutputClusterSize];
string res = "Removed from total " + poptvr.RuleLayer.Length + "\n";
for (int k = 0; k < poptvr.RuleLayer.Length; k++)
{
for (int i = 0; i < dataset.TotalNumberOfRecords; i++)
{
Console.Write("at condition " + k + "\t" + "record " + i + "\n");
poptvr.forwardFeed(SystemFunctions.getArrayAtRow(dataset.Inputdata, i));
for (int j = 0; j < dataset.NumberOfOutputNodes * poptvr.OutputClusterSize; j++)
{
CO[j] = poptvr.ConsequenceLayer[j].Output;
}
for (int l = 0; l < poptvr.InputClusterSize; l++)
{
bool deterioration = false;
poptvr.RuleLayer[k].PointBlocked = true;
poptvr.RuleLayer[k].PointBlockedAt = l;
poptvr.forwardFeed(SystemFunctions.getArrayAtRow(dataset.Inputdata, i));
for (int j = 0; j < dataset.NumberOfOutputNodes * poptvr.OutputClusterSize; j++)
{
COr[j] = poptvr.ConsequenceLayer[j].Output;
if (COr[j] < CO[j])
{
deterioration = true;
}
else
{
for (int m = 0; m < CO.Length; m++)
{
if (COr[j] > CO[m])
{
deterioration = true;
break;
}
}
}
if (deterioration)
{
break;
}
}
if (!deterioration)
{
res += (l + " is remove at " + k + "\n");
poptvr.RuleLayer[k].PointBlocked = false;
}
if (deterioration)
{
poptvr.RuleLayer[k].PointBlocked = false;
}
}
}
}
FileWriter fileWriter = new FileWriter(appConfig);
fileWriter.WriteToFile(appConfig.getOutputFolder() + "Removed Rule.txt", res);
return poptvr;
}
