How to use generate_new_examples method in hypothesis

Best Python code snippet using hypothesis

generate_sessions.py

Source:generate_sessions.py Github

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1import json2import random3import pickle4import generate as generate5with open('regexp/corpus.json') as corpus_f:6 corpus = json.load(corpus_f)['train']7n_sessions = 58examples_per_instruction = 10009instructions_per_session = 1010allow_noop = False11generate_new_examples = True12sessions = [[] for _ in range(n_sessions)]13i = 014while i < n_sessions*instructions_per_session:15 annotation = corpus[i]16 text = ' '.join(annotation['hint'])17 if generate_new_examples:18 before, after = annotation['re'][1:-1].split('@')19 before = before.replace("C", "[^aeiou]").replace("V", "[aeiou]")20 examples = generate.generate_examples(before, after, examples_per_instruction*2)21 if examples is None:22 print('warning: skipping', annotation['re'], 'due to lack of examples')23 continue24 else:25 examples = [(o[1:-1],r[1:-1]) for o,r in annotation['examples']]26 n = 027 for original, result in examples:28 if original == result and not allow_noop:29 continue30 sessions[i%n_sessions].append((original, text, result))31 n += 132 if n >= examples_per_instruction:33 break34 i += 135for session in sessions:36 random.shuffle(session)...

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random_prediction_file_ex8b.py

Source:random_prediction_file_ex8b.py Github

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1from random import uniform, choice2generate_training_examples = 03generate_new_examples = 14if generate_new_examples:5 myfile = open("predict_500", 'w')6 for i in range(500):7 myfile.write("0:" + str(uniform(-0.7, 0.4)) + " 1:" + str(uniform(-0.6, 0.6)) + "\n")8 myfile.close()9if generate_training_examples:10 myfile1 = open("training_100", 'w')11 for i in range(100):12 myfile1.write(str(choice([-1, 1])) + " 0:" + str(uniform(-0.7, 0.4)) + " 1:" + str(uniform(-0.6, 0.6)) + "\n")...

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