/// <summary>
/// Generate a selection of randomly generated individuals.
/// low range 0-0.5, high range 0.5-1.0.
/// </summary>
public void InitiatePopulation()
{
Random random = new Random();
for (int i = 0; i < populationSize; i++)
{
population.Add(new IndividualFP());
List <RuleFP> rulesToAdd = new List <RuleFP>();
for (int r = 0; r < numberOfRules; r++)
{
var currentRule = new RuleFP();
for (int c = 0; c < boundryLength; c++)
{
RuleFPBoundry conditionToAdd = null;
var low = random.NextDouble() / 2; // 0 - 0.5
var high = 0.5 + random.NextDouble() / 2; // 0.5 - 1.0
conditionToAdd = new RuleFPBoundry((float)low, (float)high);
currentRule.condBoundry.Add(conditionToAdd);
}
// Output (0,1)
currentRule.output = random.Next(2);
rulesToAdd.Add(currentRule);
}
population[i].Rulebase = rulesToAdd;
}
}
/// <summary>
/// Used for cloning purposes.
/// </summary>
/// <param name="clonedRule"></param>
public RuleFP(RuleFP clonedRule)
{
this.condBoundry = new List <RuleFPBoundry>();
this.output = clonedRule.output;
this.fitness = clonedRule.fitness;
foreach (var condBoundry in clonedRule.condBoundry)
{
this.condBoundry.Add(new RuleFPBoundry(condBoundry));
}
}
/// <summary>
/// Checks that Each RuleFPBoundry's range is inclusive of the supplied Data's condition.
/// </summary>
/// <param name="rule"></param>
/// <param name="inputData"></param>
/// <returns></returns>
private bool ConditionsMatch(RuleFP rule, DataFP inputData)
{
for (int i = 0; i < rule.condBoundry.Count; i++)
{
if (rule.condBoundry[i].low > inputData.cond[i] || rule.condBoundry[i].high < inputData.cond[i])
{
return(false);
}
}
return(true);
}