def start_clustering(self):
'''this dict contains the max silhoutte version of each iteration'''
final_dict={}
'''created the population randomly'''
population = self.create_population()
'''deletion chromosome if all the elements are identical'''
print('new random created population',population)
population=self.delete_identical_row(population)
'''below loop callculate the silhoutte score for each version'''
silhoutte_dict = self.get_silhoutte_score(population)
util = Utilty()
'''sort the population based on their score in a dictionary, where '''
sorted_dict = util.sortDictionary(silhoutte_dict)
'''print('random dict',sorted_dict)'''
for outerstage in range(100):
counter = 0
parent_dict = {}
survivor_dict = {}
elitismRate = math.floor(len(sorted_dict) / 2)
if elitismRate%2==1:
print('elitismRate',elitismRate)
elitismRate=elitismRate+1
'''print('sorted dict before outer iteration',len(sorted_dict))'''
for key in sorted_dict:
if counter < elitismRate:
parent_dict[key] = sorted_dict[key]
else:
survivor_dict[key] = sorted_dict[key]
counter = counter + 1
offspring_dict=self.get_crossover(parent_dict)
mutant_dict=self.get_mutation(offspring_dict)
if len(offspring_dict)==0 | len(mutant_dict)==0:
print('cannot proceed further as the offspring and mutant list is empty')
else:
newpopulation=util.merge_dicts(mutant_dict,survivor_dict)
'''print('merged newpopulation',len(newpopulation))'''
newpopluation_array = util.convert_dict_to_list(newpopulation,len(newpopulation),self.datapoint_number)
newpopluation_array=self.delete_identical_row(newpopluation_array)
'''print('merged newpopulation array', len(newpopulation))
print('merged newpopulation arrayfull', len(newpopulation))'''
silhoutte_dict=self.get_silhoutte_score(newpopluation_array)
sorted_dict = util.sortDictionary(silhoutte_dict)
print('max val',max(sorted_dict))
final_dict[max(sorted_dict)]=sorted_dict[max(sorted_dict)]
for innerstage in range(40):
parent_dict = {}
survivor_dict = {}
counter=0
elitismRate = math.floor(len(sorted_dict) / 2)
'''print('inner stage sortedict length',len(sorted_dict))'''
if elitismRate%2==1:
elitismRate=elitismRate+1
print('elitismRate',elitismRate)
for key in sorted_dict:
if counter < elitismRate:
parent_dict[key] = sorted_dict[key]
else:
survivor_dict[key] = sorted_dict[key]
counter = counter + 1
offspring_dict = self.get_crossover(parent_dict)
mutant_dict = self.get_mutation(offspring_dict)
if len(offspring_dict) == 0 | len(mutant_dict) == 0:
print('cannot proceed further as the offspring and mutant list is empty')
else:
newpopulation = util.merge_dicts(mutant_dict, survivor_dict)
'''print('inner stage after mergin',len(newpopulation))
print('inner stage after mergin full', newpopulation)'''
newpopluation_array = util.convert_dict_to_list(newpopulation, len(newpopulation),self.datapoint_number)
newpopluation_array = self.delete_identical_row(newpopluation_array)
'''print('inner stage after mergin array', len(newpopluation_array))
print('inner stage after mergin arrayfull', newpopluation_array)'''
dunnscore_dict=self.get_dunn_score(newpopluation_array)
'''print(dunnscore_dict)'''
sorted_dict = util.sortDictionaryForDavis(dunnscore_dict)
silhoutte_dict = self.get_silhoutte_score(newpopluation_array)
sorted_dict = util.sortDictionary(silhoutte_dict)
return final_dict
def start_clustering(self):
'''this dict contains the max silhoutte version of each iteration'''
final_dict = {}
'''created the population randomly'''
population = self.create_population()
'''deletion chromosome if all the elements are identical'''
# print('new random created population',population)
population = self.delete_identical_row(population)
print('population shape', population.shape)
'''below loop callculate the silhoutte score for each version'''
D = pairwise_distances(self.result, metric='euclidean')
silhoutte_dict = self.get_silhoutte_score(population, D)
util = Utilty()
# # '''sort the population based on their score in a dictionary, where '''
sorted_dict = util.sortDictionary(silhoutte_dict)
for outerstage in range(10): #10
counter = 0
parent_dict = {}
survivor_dict = {}
elitismRate = math.floor(len(sorted_dict) / 2)
if elitismRate % 2 == 1:
# print('Odd elitismrate',elitismRate)
elitismRate = elitismRate + 1
'''print('sorted dict before outer iteration',len(sorted_dict))'''
for key in sorted_dict:
if counter < elitismRate:
parent_dict[key] = sorted_dict[key]
else:
survivor_dict[key] = sorted_dict[key]
counter = counter + 1
offspring_dict = self.get_crossover(parent_dict)
mutant_dict = self.get_mutation(offspring_dict)
if len(offspring_dict) == 0 | len(mutant_dict) == 0:
print(
'cannot proceed further as the offspring and mutant list is empty'
)
else:
newpopulation = util.merge_dicts(mutant_dict, survivor_dict)
'''print('merged newpopulation',len(newpopulation))'''
newpopluation_array = util.convert_dict_to_list(
newpopulation, len(newpopulation), self.datapoint_number)
newpopluation_array = self.delete_identical_row(
newpopluation_array)
silhoutte_dict = self.get_silhoutte_score(
newpopluation_array, D)
sorted_dict = util.sortDictionary(silhoutte_dict)
final_dict[max(sorted_dict)] = sorted_dict[max(sorted_dict)]
for innerstage in range(10):
parent_dict = {}
survivor_dict = {}
counter = 0
elitismRate = math.floor(len(sorted_dict) / 2)
if elitismRate % 2 == 1:
elitismRate = elitismRate + 1
for key in sorted_dict:
if counter < elitismRate:
parent_dict[key] = sorted_dict[key]
else:
survivor_dict[key] = sorted_dict[key]
counter = counter + 1
offspring_dict = self.get_crossover(parent_dict)
mutant_dict = self.get_mutation(offspring_dict)
if len(offspring_dict) == 0 | len(mutant_dict) == 0:
print(
'cannot proceed further as the offspring and mutant list is empty'
)
else:
newpopulation = util.merge_dicts(
mutant_dict, survivor_dict)
newpopluation_array = util.convert_dict_to_list(
newpopulation, len(newpopulation),
self.datapoint_number)
newpopluation_array = self.delete_identical_row(
newpopluation_array)
dunnscore_dict = self.get_dunn_score(
newpopluation_array)
sorted_dict = util.sortDictionaryForDavis(
dunnscore_dict)
# print('outside both loops newpopulation array')
# print(newpopluation_array)
silhoutte_dict = self.get_silhoutte_score(newpopluation_array, D)
sorted_dict = util.sortDictionary(silhoutte_dict)
# print('final_dict')
# print(final_dict)
return final_dict