def question_candidates(q_id):
#Incomplete
'''Select some useful subset of the candidates for a particular question.
Return them in a list.
'''
all_chunks = chunker.run(q_id)
return all_chunks[:40] # need better way to filter
def write(start, stop, filename):
d = {}
for x in range(start, stop):
all_chunks = chunker.run(x)
print x
d[x] = all_chunks
cache_chunkers.cache_chunks(d, open(filename, "r+"))
def write (start,stop,filename):
d = {}
for x in range(start,stop):
all_chunks = chunker.run(x)
print x
d[x] = all_chunks
cache_chunkers.cache_chunks(d, open(filename, "r+"))
def question_candidates(q_id): #Incomplete '''Select some useful subset of the candidates for a particular question. Return them in a list. ''' all_chunks = chunker.run(q_id) return all_chunks[:40] # need better way to filter
def filter_chunks(q_id):
answers = chunker.run(q_id)
my_answers = []
qList = read_questions.read_questions_no_answers()
qDict = {}
b = 0
for q in qList:
for q2 in q:
if b == 0:
qN = q2
b = 1
else:
qDict[qN] = q2
b = 0
for key in qDict:
if key == str(q_id):
for ansCandidate in answers:
print ansCandidate
passed = pos.pos_test(qDict[key], ansCandidate)
if (passed != 0):
my_answers.append(ansCandidate)
return my_answers
def filter_chunks(q_id):
answers = chunker.run(q_id)
my_answers = []
qList = read_questions.read_questions_no_answers()
qDict = {}
b = 0
for q in qList:
for q2 in q:
if b == 0:
qN = q2
b = 1
else:
qDict[qN] = q2
b = 0
for key in qDict:
if key == str(q_id):
for ansCandidate in answers:
print ansCandidate
passed = pos.pos_test(qDict[key], ansCandidate)
if (passed !=0):
my_answers.append(ansCandidate)
return my_answers
# Turns out I don't see any questions where a VP is likely, so I made VP = 0.1, 0 might even be better.
def pos_test(question, (answer, doc_num, index, features, q_id)):
result = 0
if features == "NP":
result = 1
if features == "VP":
result = 0.1
return result
if __name__ == "__main__":
testQ = 213
answers = run(testQ)
#print answers
#for ansCandidate in answers:
#print ansCandidate[0], ansCandidate[3]
# Again I stick the questions into a dictionary
qList = read_questions.read_questions_no_answers()
qDict = {}
b = 0
for q in qList:
for q2 in q:
if b == 0:
qN = q2
b = 1
else:
qDict[qN] = q2
# PP and S get 0 so they will not be part of our answer.
# Turns out I don't see any questions where a VP is likely, so I made VP = 0.1, 0 might even be better.
def pos_test(question, (answer, doc_num, index, features,q_id)):
result = 0
if features == "NP":
result = 1
if features == "VP":
result = 0.1
return result
if __name__ == "__main__":
testQ = 213
answers = run(testQ)
#print answers
#for ansCandidate in answers:
#print ansCandidate[0], ansCandidate[3]
# Again I stick the questions into a dictionary
qList = read_questions.read_questions_no_answers()
qDict = {}
b = 0
for q in qList:
for q2 in q:
if b == 0:
qN = q2
b = 1
else:
qDict[qN] = q2
def run(dataset_dir, madfile, stages):
# verify that carving directory exists, which will contain all
# intermediary and output files for this code
carvedir = dataset_filepaths.get_carving_dir(dataset_dir)
if not os.path.exists(carvedir):
os.makedirs(carvedir)
# filter the raw range scans to include statistical information
# about the scanner intrinsics
if 1 in stages:
print ""
print "STAGE 1 - GENERATING SCAN STATISTICS"
print ""
ret = filter_urg_scans.run(dataset_dir)
if ret != 0:
return -1 # an error occurred
ret = filter_tango_scans.run(dataset_dir)
if ret != 0:
return -1 # an error occurred
# convert the mad file into a noisypath file
if 2 in stages:
print ""
print "STAGE 2 - GENERATING PATH STATISTICS"
print ""
ret = noisypath_gen.run(dataset_dir, madfile)
if ret != 0:
return -2 # an error occurred
# run the wedge generation program on input scans
if 3 in stages:
print ""
print "STAGE 3 - GENERATING WEDGES"
print ""
path_file = dataset_filepaths.get_noisypath_file(dataset_dir)
ret = wedge_gen.run(dataset_dir, path_file)
if ret != 0:
return -3; # an error occurred
# run the chunker program on the resulting wedge file
if 4 in stages:
print ""
print "STAGE 4 - RUNNING CHUNKER"
print ""
ret = chunker.run(dataset_dir)
if ret != 0:
return -4 # an error occurred
# run the procarve program on the output chunks
if 5 in stages:
print ""
print "STAGE 5 - CARVING"
print ""
ret = procarve.run(dataset_dir)
if ret != 0:
return -5 # an error occurred
# generate wall samples and floorplan from carving
if 6 in stages:
print ""
print "STAGE 6 - GENERATING FLOORPLAN"
print ""
ret = oct2fp.run(dataset_dir, madfile);
if ret != 0:
return -6 # an error occurred
# run the fp optimization program on resulting octree
if 7 in stages:
print ""
print "STAGE 7 - ALIGNING FLOORPLAN TO CARVING"
print ""
ret = fp_optimizer.run(dataset_dir)
if ret != 0:
return -7 # an error occurred
# merge the floorplans and carving into one file
if 8 in stages:
print ""
print "STAGE 8 - MERGING CARVING AND FLOORPLAN"
print ""
ret = merge_fp_oct.run(dataset_dir)
if ret != 0:
return -8 # an error ocurred
# run the octsurf program to generate mesh
if 9 in stages:
print ""
print "STAGE 9 - GENERATING SURFACE"
print ""
ret = octsurf.run(dataset_dir)
if ret != 0:
return -9 # an error occurred
# success
return 0
