def reduce(self, uid, values):
c = Cluster()
c.uid = uid
sqdist = 0.0
# TODO: update the cluster center
instances = []
for value in values:
doc = Document(value)
instances.append(doc)
for token in doc.tfidf.keys():
bool = token in c.tfidf.keys()
if bool:
c.tfidf[token] += doc.tfidf[token]
else:
c.tfidf[token] = doc.tfidf[token]
size = float(len(values))
for token in c.tfidf.keys():
c.tfidf[token] = c.tfidf[token] / size
#compute the distance
for instance in instances:
sqdist += MathUtil.compute_distance(map1=c.tfidf,
map2=instance.tfidf)
# Output the cluster center into file: clusteri
self.emit("cluster" + str(c.uid), str(c))
# Output the within distance into file: distancei
self.emit("distance" + str(c.uid), str(c.uid) + "|" + str(sqdist))
def reduce(self, uid, values):
values = [eval(text_dict) for text_dict in values]
c = Cluster()
c.uid = uid
c_total = len(values)
sqdist = 0.0
# set cluster center to sum of members
for doc in values:
for tokenid in doc:
if c.tfidf.has_key(tokenid):
c.tfidf[tokenid] += doc[tokenid]
else:
c.tfidf[tokenid] = doc[tokenid]
# set cluster center, currently the sum, to the mean
for tokenid in c.tfidf:
c.tfidf[tokenid] = c.tfidf[tokenid] / float(c_total)
# set sqdist to the squared sum of deviations from mean
for doc in values:
sqdist += MathUtil.compute_distance(c.tfidf, doc, squared=True)
# Output the cluster center into file: clusteri
self.emit("cluster" + str(c.uid), str(c))
# Output the within distance into file: distancei
self.emit("distance" + str(c.uid), str(c.uid) + "|" + str(sqdist))
def reduce(self, uid, values):
c = Cluster()
c.uid = uid
sqdist = 0.0
# TODO: update the cluster center
instances = []
for value in values:
doc = Document(value)
instances.append(doc)
for token in doc.tfidf.keys():
bool = token in c.tfidf.keys()
if bool:
c.tfidf[token] += doc.tfidf[token]
else:
c.tfidf[token] = doc.tfidf[token]
size = float(len(values))
for token in c.tfidf.keys():
c.tfidf[token] = c.tfidf[token]/size
#compute the distance
for instance in instances:
sqdist += MathUtil.compute_distance(map1 = c.tfidf, map2 = instance.tfidf)
# Output the cluster center into file: clusteri
self.emit("cluster" + str(c.uid), str(c))
# Output the within distance into file: distancei
self.emit("distance" + str(c.uid), str(c.uid) + "|" + str(sqdist))
def reduce(self, uid, values):
# values is a list of dictionaries
c = Cluster()
c.uid = int(uid)
sqdist = 0.0
# Compute new center
count = 0
for value in values:
count += 1
doc = Document(value)
for key, v in doc.tfidf.items():
c.tfidf[key] = c.tfidf.get(key, 0.0) + v
for key in c.tfidf:
c.tfidf[key] /= count
# Get within cluster distance
for value in values:
doc = Document(value)
sqdist += MathUtil.compute_distance(c.tfidf, doc.tfidf)
# Output the cluster center into file: clusteri
self.emit("cluster" + str(c.uid), str(c))
# Output the within distance into file: distancei
self.emit("distance" + str(c.uid), str(c.uid) + "|" + str(sqdist))
def reduce(self, uid, values):
values = [eval(text_dict) for text_dict in values]
c = Cluster()
c.uid = uid
c_total = len(values)
sqdist = 0.0
# set cluster center to sum of members
for doc in values:
for tokenid in doc:
if c.tfidf.has_key(tokenid):
c.tfidf[tokenid] += doc[tokenid]
else:
c.tfidf[tokenid] = doc[tokenid]
# set cluster center, currently the sum, to the mean
for tokenid in c.tfidf:
c.tfidf[tokenid] = c.tfidf[tokenid] / float(c_total)
# set sqdist to the squared sum of deviations from mean
for doc in values:
sqdist += MathUtil.compute_distance(c.tfidf, doc, squared=True)
# Output the cluster center into file: clusteri
self.emit("cluster" + str(c.uid), str(c))
# Output the within distance into file: distancei
self.emit("distance" + str(c.uid), str(c.uid) + "|" + str(sqdist))
def map(self, line):
# TODO: call `self.emit(key, value)`
instance = Document(line)
min_dist = sys.maxsize
key = -1
for cluster in self.clusters:
dist = MathUtil.compute_distance(map1 = cluster.tfidf, map2 = instance.tfidf)
if dist < min_dist:
key = cluster.uid
min_dist = dist
self.emit(key, line) #instance.__str__()
def map(self, line):
#find cluster assignment by brute force
doc = Document(line)
cluster_uid = None
sqdist_to_nearest = float('inf')
for cluster_k in self.clusters:
sqdist_k = MathUtil.compute_distance(map1 = cluster_k.tfidf, map2 = doc.tfidf, squared=True)
if sqdist_k <= sqdist_to_nearest:
cluster_uid = cluster_k.uid
#dutifully emit.
self.emit(key = cluster_uid, value = doc)
return
def map(self, line):
# TODO: call `self.emit(key, value)`
instance = Document(line)
min_dist = sys.maxsize
key = -1
for cluster in self.clusters:
dist = MathUtil.compute_distance(map1=cluster.tfidf,
map2=instance.tfidf)
if dist < min_dist:
key = cluster.uid
min_dist = dist
self.emit(key, line) #instance.__str__()
def map(self, line):
# Key is cluster id - clusters stored in self.clusters
# Value is the line
dist = float("inf")
temp_dist = float("inf")
doc = Document(line)
key = doc.uid
for c in self.clusters:
temp_dist = MathUtil.compute_distance(doc.tfidf,c.tfidf)
if temp_dist < dist:
dist = temp_dist
key = c.uid
self.emit(str(key),str(doc))
