def normalizeProdClusters(clusts, centroids, mats, maps, floor, ceil):
displacement = 0
again = False
for i in range(0,len(clusts)):
ratio = len(clusts[i - displacement])/len(clusts)
if ratio < floor:
again = True
merge(clusts, centroids, mats, maps, i - displacement)
displacement += 1
mats = st.subMatrices(clusts)[0]
displacement = 0
for i in range(0,len(clusts)):
t1 = len(clusts[i])/(0.0 + len(p.products)) > ceil
t2 = len(clusts[i]) > 8
if (t1 and t2):
again = True
dissolve(clusts, centroids, mats, maps, i - displacement)
displacement += 1
subs = st.subMatrices(clusts)
if(again):
clusts = normalizeProdClusters(clusts, centroids, subs[0], subs[1], floor, ceil)
else:
displacement = 0
for i in range(0,len(clusts)):
if not isinstance(clusts[i-displacement],list):
clusts.pop(i - displacement)
displacement += 1
return clusts
def normalizeProdClusters(clusts, centroids, mats, maps, floor, ceil):
displacement = 0
again = False
for i in range(0, len(clusts)):
ratio = len(clusts[i - displacement]) / len(clusts)
if ratio < floor:
again = True
merge(clusts, centroids, mats, maps, i - displacement)
displacement += 1
mats = st.subMatrices(clusts)[0]
displacement = 0
for i in range(0, len(clusts)):
t1 = len(clusts[i]) / (0.0 + len(p.products)) > ceil
t2 = len(clusts[i]) > 8
if (t1 and t2):
again = True
dissolve(clusts, centroids, mats, maps, i - displacement)
displacement += 1
subs = st.subMatrices(clusts)
if (again):
clusts = normalizeProdClusters(clusts, centroids, subs[0], subs[1],
floor, ceil)
else:
displacement = 0
for i in range(0, len(clusts)):
if not isinstance(clusts[i - displacement], list):
clusts.pop(i - displacement)
displacement += 1
return clusts
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 run(nameList, matrix):
global products
products = p.products
global names
names = nameList
outputs = buildProductClusters()
productClusters = outputs[0]
centroids = outputs[1]
inputs = st.subMatrices(productClusters)
productClusters = n.normalizeProdClusters(productClusters, centroids, inputs[0], inputs[1], 0.2, 0.4)
inputs = st.subMatrices(productClusters)
subMats = inputs[0]
maps = inputs[1]
indexMap = inputs[2]
customerClustersHelpers = buildCustomerHelpers()
subClustersHelpers = buildSubHelpers(indexMap, subMats, maps)
powerups = buildPowerHelpers(subClustersHelpers,customerClustersHelpers)
powerClustersHelpers = powerups[0]
productClusterLocator = powerups[1]
powerRecMatrix = r.buildRecommendations(names, powerClustersHelpers)
productClustersMap = st.createClusterMap(productClusters)
customerMatrix = c.customerMatrixiser()
productMatrix = p.productMatrixiser()
hasPurchased = matrix
if(len(powerClustersHelpers) == 0):
return 'again'
else:
results = []
# index 0
results.append(names)
# index 1
results.append(c.customersMap)
# index 2
results.append(productClusters)
# index 3
results.append(p.productsMap)
# index 4
results.append(products)
# 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
results.append(productClustersMap)
# index 11
results.append(productClusterLocator)
# index 12
results.append(customerMatrix)
# index 13
results.append(productMatrix)
# index 14
results.append(hasPurchased)
return results
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
