def conf_interval(data):
N = len(data)
sorted_estimates = np.sort(np.array(data))
conf_interval = (np.abs(sorted_estimates[int(0.025 * N)]), np.abs(sorted_estimates[int(0.975 * N)]))
return conf_interval
data, targets = ReadLensesData.read_data()
data = np.array(data)
targets = np.array(targets)
errors = []
# Peform LOE Cross-Validation
for i in range(0, len(data)):
print(i, " : ", len(data))
data_p = data[np.arange(len(data)) != i]
targets1 = ConvertData.convert_to_binary(targets, 1)
targets2 = ConvertData.convert_to_binary(targets, 2)
targets3 = ConvertData.convert_to_binary(targets, 3)
targets1_p = targets1[np.arange(len(data)) != i]
targets2_p = targets2[np.arange(len(data)) != i]
targets3_p = targets3[np.arange(len(data)) != i]
hard = train_cnf_network(6, data_p, np.array(targets1_p), 90000)
print("Hard: ", hard[2])
soft = train_cnf_network(6, data_p, np.array(targets2_p), 90000)
print("Soft: ", soft[2])
no = train_cnf_network(6, data_p, np.array(targets3_p), 90000)
print("NO: ", no[2])
hard_er = run_cnf_network(6, data.tolist(), targets1, hard[1])
