def load_clusters():
"""Load info from topics.txt file into Cluster, TermCluster tables"""
# Delete whatever's in the db already
Cluster.query.delete()
TermCluster.query.delete()
count_clusters = 0
for row in open("topics.csv"):
row = row.rstrip().split(",")
# Parse the txt into the appropriate data types for seeding
cluster = int(row[1][-3:])
word = row[3].strip()
# Check if word is in our list of key terms. If it is, add to
# TermCluster table to allow for lookup later (see seed.py for TODO)
if Term.check_for_term(word) is True:
term_cluster_to_add = TermCluster(word=word, cluster_id=cluster)
db.session.add(term_cluster_to_add)
db.session.commit()
# Check if a cluster is in our list of clusters. If it's not, add it.
if Cluster.check_for_cluster(cluster) is False:
cluster_to_add = Cluster(cluster_id=cluster)
db.session.add(cluster_to_add)
db.session.commit()
# Print where we are and increment counter
print "Topics.txt seeding row", count_clusters
count_clusters += 1
def computeNeighborhood(p, setPoints, typeDistance, minPoints, eps):
pointsOfCluster = []
for q in setPoints:
if typeDistance == 0:
if p.is_in_neighborhoodByEUSimple(q, eps):
pointsOfCluster.append(q)
elif typeDistance == 1:
if p.is_in_neighborhoodEURelativeSpeed(q, eps):
pointsOfCluster.append(q)
if len(pointsOfCluster) < minPoints:
return None
else:
return Cluster(None, pointsOfCluster)
def load_clusters():
"""Load info from topics.txt file into Cluster, TermCluster tables
File format: R row id,Topic XXX,R column ID,word
where XXX represents a number between 0-400
R ids can be discarded during seeding
Source: topic clustering data from Neurosynth, converted to long format
in R prior to seeding.
Notes: the words tracked in this clustering are not in perfect
alignment with those tracked in studies_terms.txt. Approximately 2000 of the
terms in studies_terms have a topical cluster, the remaining ~1000 do not.
This number could be improved by stemming. Many of the words not tracked
in clusters are multi-word phrases."""
# Delete whatever's in the db already
Cluster.query.delete()
TermCluster.query.delete()
count_clusters = 0
topics_fileobj = open('seed_data/topics.csv')
for row in topics_fileobj:
row = row.rstrip().split(',')
# Parse the txt into the appropriate data types for seeding
cluster = int(row[1][-3:])
word = row[3].strip()
# Check if word is in our list of key terms. If it is, add to
# TermCluster table to allow for lookup later (see model.py for TODO)
if Term.check_for_term(word) is True:
term_cluster_to_add = TermCluster(word=word, cluster_id=cluster)
db.session.add(term_cluster_to_add)
db.session.commit()
# Check if a cluster is in our list of clusters. If it's not, add it.
if Cluster.check_for_cluster(cluster) is False:
cluster_to_add = Cluster(cluster_id=cluster)
db.session.add(cluster_to_add)
db.session.commit()
# Print where we are and increment counter
print "Topics.txt seeding row", count_clusters
count_clusters += 1
topics_fileobj.close()
def dbscan(setPoints, eps, minPoints):
clusters = []
noises = []
IDs = []
classifications = {}
for pt in setPoints:
IDs.append(pt.id)
classifications[pt.id] = 0
for pt in setPoints:
if classifications[pt.id] == 0:
points = _expand_cluster(setPoints, pt, classifications, noises,
eps, minPoints)
if points:
clusters.append(Cluster(None, points))
# s=""
# for p in noises:
# s+=p.toString()+' '
# print(s)
# s=""
# for c in clusters:
# s += c.toString()+'\n'
# print(s)
return [clusters, noises, IDs]
import sys
sys.path.append("../src")
from simulator import Simulator
from model import Cluster
#import stacktracer
#stacktracer.trace_start("trace.html",interval=5,auto=True) # Set auto flag to always update file!
model = Cluster(2)
sim = Simulator(model)
sim.setVerbose(None)
#sim.setTerminationTime(10.0)
sim.setStateSaving("custom")
sim.simulate()
