def generateRandomTestData(self, number):
outputs = np.random.random((number, 2))
for i in xrange(len(outputs)):
outputs[i, 1] = np.random.randint(0, 2) #inclusive, exclusive
timeslots = generate_timeslots(outputs)
return (outputs, timeslots)
def experiment(net, P, T, vP, vT, filename, epochs, learning_rate):
logger.info("Running experiment for: " + filename + ' ' + str(epochs) + ", rate: " + str(learning_rate))
print("Number of patients with events: " + str(T[:, 1].sum()))
print("Number of censored patients: " + str((1 - T[:, 1]).sum()))
timeslots = generate_timeslots(T)
try:
net = traingd(net, (P, T), (vP, vT), epochs, learning_rate, block_size = 100, error_module = cox_error)
except FloatingPointError:
print('Aaawww....')
outputs = net.sim(P)
c_index = get_C_index(T, outputs)
logger.info("C index = " + str(c_index))
#plot_network_weights(net)
kaplanmeier(time_array = T[:, 0], event_array = T[:, 1], output_array = outputs[:, 0])
if vP is not None and len(vP) > 0:
outputs = net.sim(vP)
kaplanmeier(time_array = vT[:, 0], event_array = vT[:, 1], output_array = outputs[:, 0])
return net
