def generate_parameters(args):
"""Generates some random parameters for the program to use
Args:
args: The arguments supplied to the program
Returns: Randomly generate parameters
"""
p = Parameters(args)
p.k = np.random.randint(1, 100) if not args.k else args.k
p.delta = np.random.randint(1, 100) if not args.delta else args.delta
p.beta = np.random.randint(1, 100) if not args.beta else args.beta
p.mu = np.random.randint(1, 100) if not args.mu else args.mu
p.l = np.random.randint(1, 10) if not args.l else args.l
return p
def test_beta_mu(file_name, beta_list, mu_list):
frame = pd.read_csv(file_name)
headers = list(frame.columns.values)[1:-1]
info_loss = []
for mu in mu_list:
print("mu: {}".format(mu))
avg_loss_list = []
for beta in beta_list:
print("beta: {}".format(beta))
params = Parameters()
params.k = 10
params.delta = 200
params.beta = beta
params.mu = mu
params.l = 1
params.dp = False
stream = CASTLE(handler, headers, "FareAmount", params)
for (_, row) in frame.iterrows():
stream.insert(row)
clusters = stream.big_gamma
cum_loss = 0
for cluster in clusters:
cum_loss += cluster.information_loss(stream.global_ranges)
avg_loss = cum_loss / len(clusters)
avg_loss_list.append(avg_loss)
info_loss.append(np.array(avg_loss_list))
X, Y = np.meshgrid(beta_list, mu_list)
plot_average_loss_2D(np.array(info_loss), X, "Beta", Y, "Mu")