update fitness

__main__.py

@@ -2,7 +2,7 @@
 import numpy as np
 import pandas as pd
 
-from cluster import Cluster
+from cluster import Clustering
 from genetic import Genetic
 from generation import Generation
 
@@ -23,7 +23,7 @@ def readVars(config_file):
 
 
 # minmax normalization
-def normalize(data):
+def minmax(data):
  normData = data
  data = data.astype(float)
  normData = normData.astype(float)
@@ -42,12 +42,11 @@ def normalize(data):
  normData.to_csv('result/norm_data.csv', index=None, header=None)
  return normData
 
-
 if __name__ == '__main__':
  config_file = "config.txt"
  if(NORMALIZATION):
- data = pd.read_csv('data/iris2.csv', header=None)
- data = normalize(data) # normalize
+ data = pd.read_csv('data/iris.csv', header=None)
+ data = minmax(data) # normalize
  else:
  data = pd.read_csv('result/norm_data.csv', header=None)
  # size of column
@@ -66,7 +65,8 @@ def normalize(data):
  print("Pc", Pc)
  print("---------------------------------------")
 
- chromosome_length = kmax + kmax * dim
+ # dim or pattern id 
+ chromosome_length = kmax * dim
 
  #-------------------------------------------------------#
  # main #
@@ -75,18 +75,18 @@ def normalize(data):
  initial.randomGenerateChromosomes(
  chromosome_length) # initial generate chromosome
 
- cluster = Cluster(initial, data, kmax) # eval fit of chromo
+ clustering = Clustering(initial, data, kmax) # eval fit of chromosomes
 
- # ------------------cal fitness------------------#
- generation, countFitTime = cluster.calcChromosomesFit()
+ # ------------------calc fitness------------------#
+ generation = clustering.calcChromosomesFit()
 
  # ------------------------GA----------------------#
- while countFitTime <= budget:
+ while generationCount <= budget:
  GA = Genetic(numOfInd, Ps, Pm, Pc, budget, data, generationCount, kmax)
- generation, countFitTime, generationCount = GA.geneticProcess(
- generation, countFitTime)
+ generation, generationCount = GA.geneticProcess(
+ generation)
  iBest = generation.chromosomes[0]
- cluster.printIBest(iBest, countFitTime)
+ clustering.printIBest(iBest)
 
  # ------------------output result-------------------#
- cluster.output_result(iBest, data)
+ clustering.output_result(iBest, data)