def hcluster(blogwords):
biclusters = [
bicluster(vec=blogwords[i], id=i) for i in range(len(blogwords))
]
distances = {}
flag = None
currentclusted = -1
while (len(biclusters) > 1):
min_val = 2
biclusters_len = len(biclusters)
for i in range(biclusters_len - 1):
for j in range(i + 1, biclusters_len):
if distances.get((biclusters[i].id, biclusters[j].id)) == None:
distances[(biclusters[i].id,
biclusters[j].id)] = pearson_distance(
biclusters[i].vec, biclusters[j].vec)
d = distances[(biclusters[i].id, biclusters[j].id)]
if d < min_val:
min_val = d
flag = (i, j)
bic1, bic2 = flag
newvec = [(biclusters[bic1].vec[i] + biclusters[bic2].vec[i]) / 2
for i in range(len(biclusters[bic1].vec))]
newbic = bicluster(newvec,
left=biclusters[bic1],
right=biclusters[bic2],
distance=min_val,
id=currentclusted)
currentclusted -= 1
del biclusters[bic2]
del biclusters[bic1]
biclusters.append(newbic)
return biclusters[0]
def kmeans(blogwords, k):
min_max_per_word = [[
min([row[i] for row in blogwords]),
max([row[i] for row in blogwords])
] for i in range(len(blogwords[0]))]
# generate k clusters randomly
clusters = []
for i in range(k):
cluster = []
for min_, max_ in min_max_per_word:
cluster.append(random.random() * (max_ - min_) + min_)
clusters.append(cluster)
lables = []
matchs = [[] for i in range(k)]
lastmatchs = [[] for i in range(k)]
rounds = 100
while rounds > 0:
matchs = [[] for i in range(k)]
print 'round \t', rounds
for i in range(len(blogwords)):
bestmatch_cluster = None
min_distance = 2.1
for j in range(k):
dis = pearson_distance(clusters[j], blogwords[i])
if dis < min_distance:
min_distance = dis
bestmatch_cluster = j
matchs[bestmatch_cluster].append(i)
print_matchs(matchs)
print_matchs(lastmatchs)
if matchs == lastmatchs: break
lastmatchs = [[item for item in matchs[i]] for i in range(k)]
#move the centroids to the average of their members
for j in range(k):
avg = [0.0 for i in range(len(blogwords[0]))]
for m in matchs[j]:
vec = blogwords[m]
for i in range(len(blogwords[0])):
avg[i] += vec[i]
avg = [item / len(blogwords[0]) for item in avg]
clusters[j] = avg
rounds -= 1
def totalcost(blogwords, costf, medoids_idx) :
size = len(blogwords)
total_cost = 0.0
medoids = {}
for idx in medoids_idx :
medoids[idx] = []
for i in range(size) :
choice = None
min_cost = 2.1
for m in medoids :
tmp = distances_cache.get((m,i),None)
if tmp is None :
tmp = pearson_distance(blogwords[m],blogwords[i])
distances_cache[(m,i)] = tmp
if tmp < min_cost :
choice = m
min_cost = tmp
medoids[choice].append(i)
total_cost += min_cost
return total_cost, medoids
def totalcost(blogwords, costf, medoids_idx):
size = len(blogwords)
total_cost = 0.0
medoids = {}
for idx in medoids_idx:
medoids[idx] = []
for i in range(size):
choice = None
min_cost = 2.1
for m in medoids:
tmp = distances_cache.get((m, i), None)
if tmp == None:
tmp = pearson_distance(blogwords[m], blogwords[i])
distances_cache[(m, i)] = tmp
if tmp < min_cost:
choice = m
min_cost = tmp
medoids[choice].append(i)
total_cost += min_cost
return total_cost, medoids
def kmeans(blogwords, k) :
min_max_per_word = [ [min([row[i] for row in blogwords]), max([row[i] for row in blogwords])] for i in range(len(blogwords[0]))]
# generate k clusters randomly
clusters = []
for i in range(k) :
cluster = []
for min_, max_ in min_max_per_word :
cluster.append(random.random() * (max_ - min_) + min_)
clusters.append(cluster)
lables = []
matchs = [ [] for i in range(k)]
lastmatchs = [ [] for i in range(k)]
rounds = 100
while rounds > 0 :
matchs = [ [] for i in range(k)]
print 'round \t',rounds
for i in range(len(blogwords)) :
bestmatch_cluster = None
min_distance = 2.1
for j in range(k) :
dis = pearson_distance(clusters[j], blogwords[i])
if dis < min_distance :
min_distance = dis
bestmatch_cluster = j
matchs[bestmatch_cluster].append(i)
print_matchs(matchs)
print_matchs(lastmatchs)
if matchs == lastmatchs : break
lastmatchs = [[ item for item in matchs[i] ] for i in range(k)]
#move the centroids to the average of their members
for j in range(k) :
avg = [0.0 for i in range(len(blogwords[0])) ]
for m in matchs[j] :
vec = blogwords[m]
for i in range(len(blogwords[0])) :
avg[i] += vec[i]
avg = [ item / len(blogwords[0]) for item in avg]
clusters[j] = avg
rounds -= 1
def hcluster(blogwords,blognames) :
biclusters = [ bicluster(vec = blogwords[i], id = i ) for i in range(len(blogwords)) ]
distances = {}
flag = None;
currentclusted = -1
while(len(biclusters) > 1) :
min_val = 2;
biclusters_len = len(biclusters)
for i in range(biclusters_len-1) :
for j in range(i + 1, biclusters_len) :
if distances.get((biclusters[i].id,biclusters[j].id)) == None:
distances[(biclusters[i].id,biclusters[j].id)] = pearson_distance(biclusters[i].vec,biclusters[j].vec)
d = distances[(biclusters[i].id,biclusters[j].id)]
if d < min_val :
min_val = d
flag = (i,j)
bic1,bic2 = flag
newvec = [(biclusters[bic1].vec[i] + biclusters[bic2].vec[i])/2 for i in range(len(biclusters[bic1].vec))]
newbic = bicluster(newvec, left=biclusters[bic1], right=biclusters[bic2], distance=min_val, id = currentclusted)
currentclusted -= 1
del biclusters[bic2]
del biclusters[bic1]
biclusters.append(newbic)
return biclusters[0]
