def test_generate():
FIVE_MB = 5 * 1024 * 1024
eq(len(''.join(utils.generate_random(0))), 0)
eq(len(''.join(utils.generate_random(1))), 1)
eq(len(''.join(utils.generate_random(FIVE_MB - 1))), FIVE_MB - 1)
eq(len(''.join(utils.generate_random(FIVE_MB))), FIVE_MB)
eq(len(''.join(utils.generate_random(FIVE_MB + 1))), FIVE_MB + 1)
def setup(self, input_n, output, hidden_size):
self.input_n = input_n
self.input_cells = [1.0] * input_n
self.output_m = output
self.output_cells = [0.0] * output
self.hidden_set = hidden_size
self.hidden_result = [0.0] * hidden_size
self.input_w = utils.generate_random(input_n, hidden_size)
self.output_w = utils.generate_random(hidden_size, output)
self.input_b = numpy.random.random(hidden_size)
self.output_b = numpy.random.random(output)
self.input_delta = [0.0] * self.input_n
self.output_delta = [0.0] * self.hidden_set
def panel_vouchers():
form = get_voucher_form(request.form, ShopData.query.all())
if request.method == 'POST' and form.validate():
for_id = form.data['id']
try:
uses = int(form.data['uses'])
amount = int(form.data['amount'])
except Exception as e:
flash('Invalid input (did you provide text instead of numbers?)', 'danger')
return redirect(url_for('panel_vouchers'))
vouchers = []
for i in range(amount):
random = generate_random(8)
vouchers.append(random)
voucher = Voucher()
voucher.key = random
voucher.offer = for_id
voucher.uses = uses
database.session.add(voucher)
database.session.commit()
return render_template('views/panel.html', tab='vouchers', admins=admins, form=form, vouchers=vouchers)
return render_template('views/panel.html', tab='vouchers', admins=admins, form=form)
def __init__(self, name, email):
self.name = name
self.email = email
self.exp_time = get_epoch_time() + (30 * 60 * 1000) # 30 minutes
self.secret_key = str(hashlib.sha256(bytes(generate_random(32), 'utf-8')).hexdigest())
def main(_):
tf.logging.set_verbosity(_verbosity_levels[tf.flags.FLAGS.verbosity])
params = {
'criterion': tf.flags.FLAGS.criterion,
'max_iter': tf.flags.FLAGS.maxiter,
'kernel': tf.flags.FLAGS.kernel,
'bandwidth': tf.flags.FLAGS.bandwidth,
'n_clusters': tf.flags.FLAGS.nclusters,
'batch_size': tf.flags.FLAGS.batchsize
}
data = generate_random(100, 500)
# assert os.path.exists(tf.flags.FLAGS.data)
if tf.flags.FLAGS.method in methods:
cl = methods[tf.flags.FLAGS.method](**remove_empty(params))
labels = cl.fit(data)
centroids = cl.centroids
history = cl.history
else:
history = load(os.path.join(tf.flags.FLAGS.save, 'history.npy'))
centroids = load(os.path.join(tf.flags.FLAGS.save, 'centroids.npy'))
labels = load(os.path.join(tf.flags.FLAGS.save, 'labels.npy'))
if tf.flags.FLAGS.method == 'visualize':
assert len(history) > 1 \
and history[0].shape[0] == labels.shape[0], 'Invalid ' \
'history'
plot(history, data, labels, centroids, draw_lines=False)
elif tf.flags.FLAGS.method == 'visualize_animated':
assert len(history) > 1 \
and history[0].shape[0] == labels.shape[0], 'Invalid ' \
'history'
animated_plot(history, labels)
else:
raise ValueError('--mode parameter must either '
'be < means_shift >'
'< mini_batch_mean_shift >,'
'< kmeans > or < mini_batch_kmeans >.')
return
if history is None:
tf.logging.warn('Data is too large to visualize.')
elif data.shape[1] != 2:
tf.logging.warn('Data must be 2 dimensional to visualize.')
else:
tf.logging.info('Creating plot for history visualization.')
plot(history, data, labels, centroids, draw_lines=False)
save(os.path.join(tf.flags.FLAGS.save, 'history.npy'), history)
save(os.path.join(tf.flags.FLAGS.save, 'centroids.npy'), centroids)
save(os.path.join(tf.flags.FLAGS.save, 'labels.npy'), labels)
def get_neighbor(result,start,end,step,typ,p,conf):
xi = utils.generate_random(result,start,end,step,typ,p,conf)
while not in_neighborhood(result, xi, end, start, r, len(conf), step, typ, conf):
xi = utils.generate_random(result,start,end,step,typ,p,conf)
return xi
# funcs = (bubble_sort, selection_sort, insertion_sort, quick_sort,
# quick_sort_3partition, heap_sort, merge_sort, merge_sort_fastest,
# counting_sort, bucket_sort, bucket_sort_array, radix_sort)
times = []
# for f in funcs:
# n, t = utils.print_func_run_time(length, f, utils.generate_random(int(n))[0])
# # times.append((n, t))
# # times.sort(key = lambda x: x[1])
# # for n, t in times:
# # print(n, t)
times.append(
utils.print_func_run_time(length, bubble_sort,
utils.generate_random(n)[0]))
times.append(
utils.print_func_run_time(length, selection_sort,
utils.generate_random(n)[0]))
times.append(
utils.print_func_run_time(length, insertion_sort,
utils.generate_random(n)[0]))
times.append(
utils.print_func_run_time(length, quick_sort,
utils.generate_random(n)[0]))
times.append(
utils.print_func_run_time(length, quick_sort_3partition,
utils.generate_random(n)[0]))
times.append(
utils.print_func_run_time(length, heap_sort,
utils.generate_random(n)[0]))