def buildProductClusters():
global transpose
transpose = c.matrix.transpose()
cl.__init__(transpose, p.products)
catNum = len(p.products)/8 + 1
outputs = cl.kMeans(catNum,8)
return outputs
def main():
auxiliar = input(
"Qual o arquivo que deseja inserir?\n 1 = c2ds1-2sp\n 2 = c2ds3-2g\n 3 = monkey\n"
)
if auxiliar == "1":
p = "c2ds1-2sp"
elif auxiliar == '2':
p = "c2ds3-2g"
elif auxiliar == '3':
p = "monkey"
path = "bases/" + p + ".txt"
# file = pandas.read_csv(path, sep='\t')
# data = file.iloc[:, :].values
data = readDataSet(path)
nClusters = int(input("Quantos clusters voce deseja utilizar?\n"))
nIterations = int(input("Quantas iteracoes voce deseja realizar?\n"))
# incializa o kmeans
result = clustering.kMeans(nClusters, nIterations, data, p)
# itera o kmeans
result.fit()
#plots.kMeansPlot(result)
pat = "generated/" + p + "kMeans" + \
"C" + str(nClusters) + "_" + "I" + str(nIterations) + '.clu'
if (auxiliar == '3'):
for aaa in result.data:
aaa.cluster += 1
printPartition(pat, result.data)
def createSubcluster(indexMap, subMatrix, aMap):
cl.__init__(subMatrix, c.customers, aMap)
clust = []
results = cl.kMeans(25,8)
clusters = results[0]
clust.append(clusters)
centroids = results[1]
clust.append(centroids)
clust.append(cl.clusterMap)
clust.append(indexMap)
clust.append(s.averageSilhouettes(clust[0], subMatrix))
clust.append(aMap)
return clust
def run(names):
global products
products = p.products
results = [names, c.customersMap]
global transpose
transpose = c.matrix.transpose()
cl.__init__(transpose, p.products)
catNum = len(p.products)/8 + 1
outputs = cl.kMeans(catNum,8)
prodClusters = outputs[0]
centroids = outputs[1]
inputs = st.subMatrices(prodClusters)
prodClusters = n.normalizeProdClusters(prodClusters, centroids, inputs[0], inputs[1], 0.2, 0.4)
results.append(prodClusters)
inputs = st.subMatrices(prodClusters)
subMats = inputs[0]
maps = inputs[1]
indexMap = inputs[2]
subClusters = []
for i in range(0, len(subMats)):
subCluster = st.createSubcluster(indexMap[i], subMats[i], maps[i])
subCluster.append(r.buildRecommendations(names, [subCluster]))
subClusters.append(subCluster)
totCluster = st.createSubcluster(p.products, c.matrix, p.productsMap)
totCluster.append(r.buildRecommendations(names,[totCluster]))
powerClusters = []
powerSil = []
results.append('unfiltered results: ' + str(totCluster[4]))
for i in range(0, len(subClusters)):
if subClusters[i][4] >= totCluster[4]:
powerClusters.append(subClusters[i])
powerSil.append(subClusters[i][4])
if(len(powerSil) == 0):
return 'again'
else:
results.append('filtered average: ' + str(sum(powerSil)/len(powerSil)))
powerClusters.append(totCluster)
recommendationMatrix = r.buildRecommendations(names, powerClusters)
results.append(recommendationMatrix)
results.append(powerClusters)
results.append(subClusters)
return results
def main():
dataSet = []
nomeArquivo = ''
while True:
opcao = int(input('Entre com a opcao:\n[1] - Escolher o arquivo de dados \n[2] - Aplicar kMeans\n[3] - Aplicar Single-Link\n[4] - Aplicar Avg-Link\n[0] - Sair\n'))
if(opcao == 1):
oparquivo = int(input('[1] - c2ds1-2sp.txt\n[2] - c2ds3-2g.txt\n[3] - monkey.txt\n'))
if(oparquivo == 1):
nomeArquivo = 'c2ds1-2sp.txt'
elif(oparquivo == 2):
nomeArquivo = 'c2ds3-2g.txt'
elif(oparquivo == 3):
nomeArquivo = 'monkey.txt'
dataSet = readDataSet(nomeArquivo)
elif(opcao == 0):
break
elif(nomeArquivo == ''):
print('Nenhum arquivo selecionado.')
elif(opcao == 2):
name = nomeArquivo
nClusters = int(input('Digite a quantidade de clusters: '))
times = int(input('Digite a quantidade de iteracoes: '))
partition = cl.kMeans(dataSet,nClusters,times)
rmkPartition = outPartition(dataSet,partition)
complete = 'k'+str(nClusters)+'t'+str(times)+'.clu'
printPartition('kMeans/'+name.replace('.txt', complete), rmkPartition)
#pl.plotKMeansMonkey(dataSet, rmkPartition)
elif(opcao == 3):
name = nomeArquivo
kMin = int(input('Digite a quantidade minima de clusters: '))
kMax = int(input('Digite a quantidade maxima de clusters: '))
partition = cl.singleLink(dataSet, kMin, kMax)
k = kMax
for each in partition:
rmkPartition = outPartition(dataSet,each)
complete = 'k'+str(k)+'.clu'
printPartition('singleLink/'+name.replace('.txt', complete), rmkPartition)
pl.plotSingleLinkMonkey(dataSet, rmkPartition, k)
k-=1
elif(opcao == 4):
kMin = int(input('Digite a quantidade minima de clusters: '))
kMax = int(input('Digite a quantidade maxima de clusters: '))
partition = cl.avgLink(dataSet, kMin, kMax)
def createClusterHelpers(indexMap, subMatrix, aMap):
cl.__init__(subMatrix, c.customers, aMap)
clust = []
results = cl.kMeans(25,8)
clusters = results[0]
# index 0
clust.append(clusters)
centroids = results[1]
# index 1
clust.append(centroids)
# index 2
clust.append(cl.clusterMap)
# index 3
clust.append(indexMap)
avgSils = s.averageSilhouettes(clust[0], subMatrix, centroids)
# index 4
clust.append(s.silhouettesList)
# index 5
clust.append(avgSils)
return clust
def dissolve(clusts, centroids, mats, maps, i):
trans = mats[i].transpose()
cl.__init__(trans, clusts[i], maps[i])
num = len(clusts[i])/8+1
results = cl.kMeans(num, 20)
pClusts = results[0]
pCents = results[1]
clusts.pop(i)
centroids.pop(i)
mats.pop(i)
maps.pop(i)
for j in range(0, len(pClusts)):
clusts.append(pClusts[j])
centroids.append(pCents[j])
newMat = []
newMap = {}
st.redoMatrix(clusts,len(clusts)-1,newMat, newMap)
mats.append(newMat)
maps.append(newMap)
def dissolve(clusts, centroids, mats, maps, i):
trans = mats[i].transpose()
cl.__init__(trans, clusts[i], maps[i])
num = len(clusts[i]) / 8 + 1
results = cl.kMeans(num, 20)
pClusts = results[0]
pCents = results[1]
clusts.pop(i)
centroids.pop(i)
mats.pop(i)
maps.pop(i)
for j in range(0, len(pClusts)):
clusts.append(pClusts[j])
centroids.append(pCents[j])
newMat = []
newMap = {}
st.redoMatrix(clusts, len(clusts) - 1, newMat, newMap)
mats.append(newMat)
maps.append(newMap)
def run(names):
global products
products = p.products
# indexes 0 and 1
results = [names, c.customersMap]
global transpose
transpose = c.matrix.transpose()
cl.__init__(transpose, p.products)
catNum = len(p.products)/8 + 1
outputs = cl.kMeans(catNum,8)
productClusters = outputs[0]
centroids = outputs[1]
inputs = st.subMatrices(productClusters)
productClusters = n.normalizeProdClusters(productClusters, centroids, inputs[0], inputs[1], 0.2, 0.4)
# index 2
results.append(productClusters)
# index 3
results.append(p.productsMap)
# index 4
results.append(products)
inputs = st.subMatrices(productClusters)
subMats = inputs[0]
maps = inputs[1]
indexMap = inputs[2]
subClustersHelpers = []
for i in range(0, len(subMats)):
subCluster = st.createSubclustersHelpers(indexMap[i], subMats[i], maps[i])
subCluster.append(r.buildRecommendations(names, [subCluster]))
subClustersHelpers.append(subCluster)
customerClustersHelpers = st.createSubclustersHelpers(p.products, c.matrix, p.productsMap)
customerClustersHelpers.append(r.buildRecommendations(names,[customerClustersHelpers]))
powerClustersHelpers = []
powerI = []
powerCount = 0
productClusterLocator = []
for i in range(0, len(subClustersHelpers)):
if subClustersHelpers[i][5] >= customerClustersHelpers[5]:
powerClustersHelpers.append(subClustersHelpers[i])
powerI.append(i)
productClusterLocator.append(['power', powerCount])
powerCount += 1
else:
productClusterLocator.append(['sub', i - powerCount])
if(len(powerClustersHelpers) == 0):
return 'again'
displacement = 0
for i in range(0,len(powerI)):
subClustersHelpers.pop(powerI[i]-displacement)
displacement += 1
powerRecMatrix = r.buildRecommendations(names, powerClustersHelpers)
# index 5
results.append(powerRecMatrix)
# index 6
results.append([customerClustersHelpers])
# index 7
results.append(subClustersHelpers)
# index 8
results.append(powerClustersHelpers)
# index 9
results.append(c.matrix)
# index 10
productClustersMap = st.createClusterMap(productClusters)
results.append(productClustersMap)
results.append(productClusterLocator)
return results
