def make_eq(q, a, VERBOSE, TRAIN):
bigtexamples = {x: ([], []) for x in ["+", "*", '/', '-', '=']}
#wps = open(q).readlines()
#answs = open(a).readlines()
#VERBOSE=True
wps = q
for k in range(len(wps)):
if VERBOSE:
for i in range(len(wps)):
print(i, wps[i])
k = int(input())
print(k)
problem = wps[k]
#First preprocessing, tokenize slightly
problem = problem.strip().split(" ")
for i, x in enumerate(problem):
if len(x) == 0: continue
if x[-1] in [',', '.', '?']:
problem[i] = x[:-1] + " " + x[-1]
problem = ' '.join(problem)
problem = " " + problem + " "
print(problem)
story = nlp.parse(problem)
sets = makesets.makesets(story['sentences'])
EF.main(sets, k, a[k], sys.argv[1])
sets = [
x for x in sets if makesets.floatcheck(x[1].num) or x[1].num == 'x'
]
print(sets)
for z in sets:
z[1].details()
def make_eq(q,a,VERBOSE,TRAIN):
bigtexamples = {x:([],[]) for x in ["+","*",'/','-','=']}
#wps = open(q).readlines()
#answs = open(a).readlines()
#VERBOSE=True
wps = q
for k in range(len(wps)):
if VERBOSE:
for i in range(len(wps)):
print(i,wps[i])
k = int(input())
print(k)
problem = wps[k]
#First preprocessing, tokenize slightly
problem = problem.strip().split(" ")
for i,x in enumerate(problem):
if len(x)==0:continue
if x[-1] in [',','.','?']:
problem[i] = x[:-1]+" "+x[-1]
problem = ' '.join(problem)
problem = " " + problem + " "
print(problem)
story = read_parse(k)
sets = makesets.makesets(story['sentences'])
pickle.dump(sets, open('madesets/'+str(k)+'.pickle','wb'))
EF.main(sets,k,a[k],sys.argv[1])
sets = [x for x in sets if makesets.floatcheck(x[1].num) or x[1].num == 'x']
print(sets)
for z in sets:
z[1].details()
def make_eq(q, a, VERBOSE, TRAIN):
#wps = open(q).readlines()
#answs = open(a).readlines()
#VERBOSE=True
wps = q
for k in range(len(wps)):
if VERBOSE:
for i in range(len(wps)):
print(i, wps[i])
k = int(input())
print(k)
#First preprocessing, tokenize slightly
problem = utils.preprocess_problem(wps[k])
print(problem)
story = utils.read_parse(k)
sets = makesets.makesets(story['sentences'])
EF.main(sets, k, a[k], sys.argv[1])
sets = [
x for x in sets if makesets.floatcheck(x[1].num) or x[1].num == 'x'
]
print(sets)
for z in sets:
z[1].details()
def make_eq(q, a, VERBOSE, TRAIN):
#wps = open(q).readlines()
#answs = open(a).readlines()
#VERBOSE=True
wps = q
for k in range(len(wps)):
if VERBOSE:
for i in range(len(wps)):
print(i, wps[i])
k = int(input())
print(k)
#First preprocessing, tokenize slightly
problem = utils.preprocess_problem(wps[k])
print(problem)
story = utils.read_parse(k)
sets = makesets.makesets(story['sentences'])
EF.main(sets, k, a[k], sys.argv[1])
sets = [x for x in sets
if makesets.floatcheck(x[1].num)
or x[1].num == 'x']
print(sets)
for z in sets:
z[1].details()
def make_eq(q, a, VERBOSE, TRAIN):
wps = q
for k in range(len(wps)):
if VERBOSE:
for i in range(len(wps)):
print(i, wps[i])
k = int(input())
print("Index: " + str(k))
problem = wps[k]
#First preprocessing, tokenize slightly
problem = problem.strip().split(" ")
for i, x in enumerate(problem):
if len(x) == 0: continue
if x[-1] in [',', '.', '?']:
problem[i] = x[:-1] + " " + x[-1]
problem = ' '.join(problem)
problem = " " + problem + " "
print("Problem: " + problem)
print("Answer: " + a[k].strip())
story = nlp.parse(problem)
sets = makesets.makesets(story['sentences'])
sets = [
x for x in sets if makesets.floatcheck(x[1].num) or x[1].num == 'x'
]
ents = [x[1].entity for x in sets]
print("--- SETS ---")
for z in sets:
z[1].details()
def make_eq(q, a, VERBOSE, TRAIN):
wps = q
replacements = {
' two ': ' 2 ',
" three ": ' 3 ',
' four ': ' 4 ',
' five ': ' 5 ',
' six ': ' 6 ',
' seven ': ' 7 ',
' eight ': ' 8 ',
' nine ': ' 9 ',
' ten ': ' 10 ',
' eleven ': ' 11 ',
' twice ': ' 2 '
}
for k in range(len(wps)):
if VERBOSE:
for i in range(len(wps)):
print(i, wps[i])
k = int(input())
print(k)
problem = wps[k].lower()
#First preprocessing, tokenize slightly
problem = problem.strip().split(" ")
for i, x in enumerate(problem):
if len(x) == 0: continue
if x[-1] in [',', '.', '?']:
problem[i] = x[:-1] + " " + x[-1]
problem = ' '.join(problem)
problem = " " + problem + " "
print(problem)
for r in replacements:
problem = problem.replace(r, replacements[r])
story = nlp.parse(problem)
sets = makesets.makesets(story['sentences'])
EF.main(sets, k, a[k])
sets = [
x for x in sets if makesets.floatcheck(x[1].num) or x[1].num == 'x'
]
print(sets)
ents = [x[1].entity for x in sets]
for z in sets:
z[1].details()
def make_eq(q, a, e, VERBOSE, TRAIN):
bigtexamples = {x: ([], []) for x in ["+", "*", '/', '-', '=']}
#wps = open(q).readlines()
#answs = open(a).readlines()
#VERBOSE=True
wps = q
for k in range(len(wps)):
if VERBOSE:
for i in range(len(wps)):
print(i, wps[i])
k = int(input())
print(k)
problem = wps[k]
#First preprocessing, tokenize slightly
problem = problem.strip().split(" ")
for i, x in enumerate(problem):
if len(x) == 0: continue
if x[-1] in [',', '.', '?']:
problem[i] = x[:-1] + " " + x[-1]
problem = ' '.join(problem)
problem = " " + problem + " "
print(problem)
story = read_parse(k)
sets = makesets.makesets(story['sentences'])
if e[k].strip() != '':
irrelev = e[k].strip().split(" ")
imap = [(x[-1], x) for x in irrelev]
if not all([x[0] for x in imap]) == '0':
print(imap)
for x in [x for x in imap if x[0] == '0']:
ent, num, v = x[1].split(',')
if ent in ["$", "dollar"]: continue
else:
sets = [x for x in sets if x[1].num != num]
pickle.dump(sets, open('madesets/' + str(k) + '.pickle', 'wb'))
EF.main(sets, k, a[k], sys.argv[1])
sets = [
x for x in sets if makesets.floatcheck(x[1].num) or x[1].num == 'x'
]
print(sets)
for z in sets:
z[1].details()
def make_eq(q, a, VERBOSE, TRAIN, fold):
bigtexamples = {x: ([], []) for x in ["+", "*", '/', '-', '=']}
#wps = open(q).readlines()
#answs = open(a).readlines()
#VERBOSE=True
wps = q
predicates = []
for k in range(len(wps)):
if VERBOSE:
for i in range(len(wps)):
print(i, wps[i])
k = int(input())
print(k)
problem = wps[k].lower()
#First preprocessing, tokenize slightly
problem = problem.strip().split(" ")
for i, x in enumerate(problem):
if len(x) == 0: continue
if x[-1] in [',', '.', '?']:
problem[i] = x[:-1] + " " + x[-1]
problem = ' '.join(problem)
problem = " " + problem + " "
print(problem)
story = nlp.parse(problem)
sets = makesets.makesets(story['sentences'])
#EF.main(sets,k)
sets = [
x for x in sets if makesets.floatcheck(x[1].num) or x[1].num == 'x'
]
print(sets)
for z in sets:
if z[1].verbs:
predicates.extend(z[1].verbs.split(" "))
predicates = list(set(predicates))
pickle.dump(predicates, open("data/predicates" + fold, 'wb'))
def make_eq(q,a,VERBOSE,TRAIN):
bigtexamples = {x:([],[]) for x in ["+","*",'/','-','=']}
#wps = open(q).readlines()
#answs = open(a).readlines()
#VERBOSE=True
wps = q
IRR = open("output_relevant.txt").readlines()
IRR = [x.strip().split(" ") for x in IRR]
IRR = [[y.split(",")[1:] for y in x] for x in IRR]
wps = pickle.load(open('irrelevSets.pickle','rb'))
for k, sets in wps:
print(k)
EF.main(sets,k,a[k])
sets = [x for x in sets if makesets.floatcheck(x[1].num) or x[1].num == 'x']
print(sets)
for z in sets:
z[1].details()
def make_eq(q, a, VERBOSE, TRAIN):
bigtexamples = {x: ([], []) for x in ["+", "*", '/', '-', '=']}
#wps = open(q).readlines()
#answs = open(a).readlines()
#VERBOSE=True
wps = q
replacements = {
' two ': ' 2 ',
" three ": ' 3 ',
' four ': ' 4 ',
' five ': ' 5 ',
' six ': ' 6 ',
' seven ': ' 7 ',
' eight ': ' 8 ',
' nine ': ' 9 ',
' ten ': ' 10 ',
' eleven ': ' 11 ',
' twice ': ' 2 '
}
for k in range(len(wps)):
if VERBOSE:
for i in range(len(wps)):
print(i, wps[i])
k = int(input())
print(k)
problem = wps[k].lower()
#First preprocessing, tokenize slightly
problem = problem.strip().split(" ")
for i, x in enumerate(problem):
if len(x) == 0: continue
if x[-1] in [',', '.', '?']:
problem[i] = x[:-1] + " " + x[-1]
problem = ' '.join(problem)
problem = " " + problem + " "
print(problem)
for r in replacements:
problem = problem.replace(r, replacements[r])
'''
if " how " in problem:
left,right = problem.split(" how ")
else: left = problem
for r in replacements:
left = left.replace(r,replacements[r])
if " how " in problem:
problem = left + ' how ' + right
else:
problem = left
'''
story = nlp.parse(problem)
sets = makesets.makesets(story['sentences'])
#EF.main(sets,k)
sets = [
x for x in sets if makesets.floatcheck(x[1].num) or x[1].num == 'x'
]
print(sets)
for z in sets:
z[1].details()