def properSelection(Agents,AgentScores,numSurvivors,maxScore):
surAgents = []
surAgents = bs.sort_rev(AgentScores,Agents)
lenAg = len(surAgents)
normScores = surAgents
accumScores = []
normScore = 0
#Step 1 - Normalise The Results, so they add up to 1.
for score in AgentScores:
normScore = normScore + score
for agentId in range(lenAg):
normScores[agentId] = float(AgentScores[agentId] / normScore)
#print(normScores)
#Step 2 - Sort Agents (They are already sorted)
#Step 3 - Accumulated Results
accumScores = normScores
for i in range(1,len(normScores)):
accumScores[i] = accumScores[i-1] + normScores[i]
#print(accumScores)
return Agents
movieResults = []
for movie in data:
movieId = movieId + 1
agentId = -1
AgentResults = []
for agent in Agents:
agentId = agentId + 1
result = (nn.runThroughNetworkOnce(movie, agent, layoutOfNetwork))
AgentScores[agentId] = AgentScores[agentId] + nn.workOutResult(
fullData, resultPos, endFireFactor, movieId, result)
AgentResults.append(nn.getActualResult(endFireFactor, result))
movieResults.append(
[fullData[movieId][resultPos], movieId, AgentResults])
#Sort Agents First
Agents = bs.sort_rev(AgentScores, Agents)
#Kill off poor performing Agents
[Agents, cut, acScores] = ga.properSelection(Agents, AgentScores,
len(data), minC, maxC)
numBreed = int(ga.numToBreed(numAgents, len(Agents)))
numRandomAgents = numAgents - len(Agents) - 2 * numBreed
bestAgent = Agents[0:2]
#Unsort the Agents
Agents = ga.bs.unsort(Agents, numSwaps)
#Surviving Agents Breed to create more
cutPoint = int(random.random() * ((maxCut - minCut) + minCut) *
len(Agents[0]))
Agents = ga.singleCrossover(Agents, numBreed, cutPoint)
def naturalSelection(Agents,AgentScores,numSurvivors):
survivingAgents = []
survivingAgents = bs.sort_rev(AgentScores,Agents)[0:numSurvivors]
return survivingAgents
def naturalSelection(Agents, AgentScores, numSurvivors):
"""Literally only Keep agents in the Agent array from index 0 to numSurvivors"""
survivingAgents = []
survivingAgents = bs.sort_rev(AgentScores, Agents)[0:numSurvivors]
return survivingAgents