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.parse_stanford_nlp(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): #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.parse_stanford_nlp(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): 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.parse_stanford_nlp(problem) with open("s_data/" + str(k) + ".pickle", 'wb') as f: pickle.dump(story, f)
return sets def bug(): print("bug") ip = 0 while ip == 0: inp = input() if inp == 0: ip = 1 else: exec(inp) if __name__ == "__main__": q, a, e = utils.parse_inp(sys.argv[1]) wps = q while True: for i in range(len(q)): print(i, q[i]) k = input() k = int(k) problem = wps[k].lower() print(problem) story = utils.parse_stanford_nlp(problem) sets = makesets(story["sentences"]) for s in sets: s[1].details() input()