def run(self, itter):
bestFitness = np.inf
bestGen = []
bestCentroid = None
for a in range(itter):
i = 0
for ant in self.ants:
ant.init()
ant.run()
#print(ant.tabu)
if( ant.fitness<bestFitness):
bestFitness= ant.fitness
bestCentroid = ant.centroid
print ("=========================")
print ("ant number: "+str(i))
print ("fitness : " +str(ant.fitness))
i+=1
print("=====================================")
print("Itteration : %s"%(a))
print("=====================================")
print("bestfitness : %s"%(bestFitness))
bestGen.append(bestFitness)
for i in range(len(bestGen)):
print("best fitness at generation -%s : %s" %(i, bestGen[i]))
clust = KMeans(self.data.data,bestCentroid, self.ansdict)
clust.run(20)
print("===================================================")
print("====================K-Means========================")
print("===================================================")
print("SSE : %s"%(clust.getaccsse()))
print("SSE : %s"%(clust.getacc()))
print("Assigned data : %s"%(clust.assignedNum))
#print(self.pheromap)
return self.pheromap
import random
from dataset import Dataset
from KMeans import KMeans
Data = Dataset('nursery.txt')
Data.run()
numCen = 5
centroid = [
Data.data[random.randrange(0, len(Data.data))].copy()
for i in range(numCen)
]
Clust = KMeans(Data.data, centroid, 5)
Clust.run(10)
print(Clust.getaccsse())
print(Clust.getacc())
def Evolutionize(self):
bestGeneration = []
bestCentroid = None
bestFitness = np.inf
for i in range(self.itter):
tempBestGen = np.inf
for j in range(self.populationsize):
particles = [k for k in range(self.populationsize)
if k != j] #not current
randomparticle = []
for a in range(3):
ran = random.choice(particles)
randomparticle.append(ran)
particles.remove(ran)
a, b, c = randomparticle
mutant = []
for z in range(self.centroidNum):
mutant.append([])
for x in range(self.dimension):
#print(len(self.population[a].centroid))
#print(len(self.population[a].centroid[z]))
tempa = self.population[a].centroid[z][x]
tempb = self.population[b].centroid[z][x]
tempc = self.population[c].centroid[z][x]
temp = tempa - self.mut * (tempb - tempc)
mutant[z].append(temp)
#temp = [self.population[a].centroid[z][itt] +
# self.mut * (self.population[b].centroid[z][itt]
# - self.population[c].centroid[z][itt]) for itt in range(len(self.population[0].centroid[z])-1)]
#print (temp)
#print(mutant)
for a in range(len(mutant)):
for b in range(len(mutant[a])):
mutant[a][b] = np.clip(mutant[a][b], self.bounddown[a],
self.boundup[a])
crossTruth = [
np.random.rand(self.dimension) < self.crossp
for a in range(self.centroidNum)
]
#print(crossTruth)
trial = self.population[j].centroid.copy()
for a in range(len(crossTruth)):
for b in range(len(crossTruth[a])):
if (crossTruth[a][b]):
trial[a][b] = mutant[a][b]
temp = self.population[j].getfitness(trial)
print("=========================")
print("particle number: " + str(j))
print("fitness trial : " + str(temp))
print("self best : " + str(self.population[j].lastfitness))
#print("%s, %s" %(self.population[j].lastfitness,tempBestGen))
#print("%s" %(self.population[j].lastfitness<tempBestGen))
if self.population[j].lastfitness < tempBestGen:
#recording best fitness each generation
tempBestGen = self.population[j].lastfitness
if (self.population[j].lastfitness < bestFitness):
bestFitness = self.population[j].lastfitness
bestCentroid = self.population[j].centroid
if temp < self.population[j].lastfitness:
self.population[j].lastfitness = temp
self.population[j].centroid = trial
bestGeneration.append(tempBestGen)
print("=====================================")
print("Itteration : %s" % (i))
print("=====================================")
print("bestfitness : %s" % (min(self.fitness)))
for i in range(len(bestGeneration)):
print("best fitness at generation -%s : %s" %
(i, bestGeneration[i]))
print("---------------------------------------------------")
print("Centroid")
print("---------------------------------------------------")
for i in range(len(bestCentroid)):
print("centroid-%s :%s" % (i, bestCentroid[i]))
clust = KMeans(self.data.data, bestCentroid, self.ansdict)
clust.run(self.itterkmeans)
print("===================================================")
print("====================K-Means========================")
print("===================================================")
print("SSE : %s" % (clust.getaccsse()))
print("Akurasi : %s" % (clust.getacc()))
print("Assigned data : %s" % (clust.assignedNum))
print("---------------------------------------------------")
print("Centroid")
print("---------------------------------------------------")
for i in range(len(clust.centroid)):
print("centroid-%s :%s" % (i, clust.centroid[i]))
clust.print_Output()
import numpy as np
import pandas as pd
import config
from KMeans import KMeans
from NumpyEncoder import NumpyEncoder
featuresNames = [
"acousticness", "danceability", "energy", "instrumentalness", "loudness",
"speechiness", "valence", "tempo"
]
audioFeatures = pd.read_json("json/audioFeatures.json")[featuresNames]
audioFeatures = (audioFeatures - audioFeatures.min()) / \
(audioFeatures.max() - audioFeatures.min())
X = audioFeatures.values
with open("json/centroids.json") as json_file:
centroids = json.load(json_file)
km = KMeans(X, config.nb_playlists)
km.centroids = np.array(centroids[config.nb_playlists - 1])
km.run()
print(len(km.idx))
with open("json/indexes.json", "w") as outfile:
json.dump(km.idx, outfile, cls=NumpyEncoder)
while(text.lower() != 'p'):
text = input("Para parar, digite p\n")
plotter.close()
if __name__ == '__main__':
n = 50000
NGroups = 6
timeVariable = math.log(n, 5) - 1.8
data = ListOfPoints(n, 2, 100)
data.points = [[random()*100, random()*100] for i in range(n)]
kmeans = KMeans(data, NGroups)
print('Iteração 1')
kmeans.run()
plotter = HP(kmeans.dataPoints, kmeans.centroids, kmeans.nearest, 1000 * timeVariable)
try:
#_thread.start_new_thread(executeCode, (kmeans, timeVariable))
executeCode(kmeans, timeVariable)
plotter.show()
#_thread.start_new_thread(hidePlotterOnDemand, (plotter, 0))
#plotter.show()
except Exception as e:
print(e)
data.append([float(p[0]), float(p[1])])
return np.array(data)
circle = ReadFile('circle.txt')
moon = ReadFile('moon.txt')
while 1:
s = input(
"1 : K-Means | 2 : Kernel K-Means | 3 : Spectral clustering | 4 : DBSCAN\n"
)
if s == "1":
t = input("1 : circle | 2 : moon\n")
if t == "1":
KC = KMeans("circle", circle, 2)
KC.run()
KC = None
elif t == "2":
KM = KMeans("moon", moon, 2)
KM.run()
KM = None
elif s == "2":
t = input("1 : circle | 2 : moon\n")
if t == "1":
i = input("1 : original | 2 : initial\n")
KKC = KernelKMeans('circle', circle, 2, 10, i == "2")
KKC.run()
KKC = None
if t == "2":
i = input("1 : original | 2 : initial\n")
KKM = KernelKMeans('moon', moon, 2, 10, i == "2")
