def calculate_goal_success(self, epoch_range):
if os.path.isfile('batch_succeed.txt'):
pass
else:
with open('batch_succeed.txt', 'w+') as f:
pass
sucess_rate_epoch, sucess_rate_epoch_std = [], []
error_in_dist = (self.error_in_dist**2 * 2)**0.5 # euclidean distance
for epoch in epoch_range:
dist_from_goal_per_agent = torch.tensor(Plot.extract_data(
epoch, dir=self.dir + os.sep, calculate_dist='ON'),
dtype=torch.float)
sucess_rate_batch = torch.sum(
dist_from_goal_per_agent <= error_in_dist,
dim=1) / dist_from_goal_per_agent.shape[1]
idx_of_batches_suceeded = (sucess_rate_batch == 1).nonzero()
with open('batch_succeed.txt', 'a+') as f:
f.write(
'In Epoch {0} the Total num of batches whos agent succeeded is: {1}\n'
.format(epoch, idx_of_batches_suceeded.shape[0]))
f.write('The batchs whos agents succeeded are:/n')
for batch in idx_of_batches_suceeded:
f.write(str(batch.item()))
f.write(',')
f.write('\n')
sucess_rate_batch = sucess_rate_batch.type(torch.FloatTensor)
sucess_rate_epoch += [torch.mean(sucess_rate_batch)]
sucess_rate_epoch_std += [torch.std(sucess_rate_batch)]
Plot.create_sucees_reate_plot(sucess_rate_epoch, sucess_rate_epoch_std,
epoch_range)
def calculate(self, epoch_range, batch_range):
for epoch in epoch_range:
utterance = Plot.extract_data(epoch,
dir=self.dir,
calculate_utternace='ON')
total_iterations = utterance.shape[1]
for batch in batch_range:
for iteration in range(total_iterations):
self.counter += 1
cur_utter = utterance[batch, iteration, :, :]
for j in range(cur_utter.shape[0]):
for i in range(cur_utter.shape[1]):
if cur_utter[j, i] >= threshold:
self.utterance_stat[i] += 1
self.utterance_stat = np.true_divide(self.utterance_stat, self.counter)
print(self.utterance_stat)
