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How to use generate_new_examples method in hypothesis

Best Python code snippet using hypothesis

generate_sessions.py

Source:generate_sessions.py

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)...

random_prediction_file_ex8b.py

Source:random_prediction_file_ex8b.py

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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