real_seqlen: real_batch[1]})
fake_batch = fake_iterator.next_batch(BATCH_SIZE)
G_loss_curr, Summary_curr, _ = sess.run([G_loss, merged, gen_train_op],
feed_dict={Z: fake_batch[0],
fake_seqlen: fake_batch[
1]})
# train_writer.add_summary(Summary_curr,global_step=it)
if it == 0:
if REAL_DATA:
pass
# integral_intensity = get_integral_empirical(real_sequences, intensity_real,T,n_t)
elif DATA != "rmtpp":
integral_intensity = get_integral(real_sequences, DATA)
integral_intensity = np.asarray(integral_intensity)
fig = sm.qqplot(integral_intensity, stats.expon,
distargs=(), loc=0, scale=1, line='45')
plt.grid()
fig.savefig('logs/out/{}/real.png'.format(saved_file))
plt.close()
if it % 1000 == 0:
sequences_generator = []
for _ in range(int(1000 / BATCH_SIZE)):
sequences_gen = sess.run(fake_data, feed_dict={
Z: fake_batch[0], fake_seqlen: fake_batch[1]})
shape_gen = sequences_gen.shape
sequences_gen = np.reshape(
sequences_gen, (shape_gen[0], shape_gen[1]))
deviation = np.linalg.norm(
intensity_gen - intensity_real) / np.linalg.norm(intensity_real)
# can use correlation or other metric
print('Iter: {}; deviation: {}'.format(it, deviation))
plt.plot(ts_real, intensity_real, label='real')
plt.plot(ts_gen, intensity_gen, label='generated')
plt.legend(loc=1)
plt.xlabel('time')
plt.ylabel('intensity')
plt.savefig('out/{}/{}_{}.png'.format(saved_file,
str(it).zfill(3), deviation),
bbox_inches='tight')
plt.close()
if not REAL_DATA and DATA != "rmtpp":
integral_intensity = get_integral(generated_sequences, DATA)
integral_intensity = np.asarray(integral_intensity)
fig = plt.figure()
left = -1.8 #x coordinate for text insert
ax1 = fig.add_subplot(1, 2, 1)
fig = sm.qqplot(integral_intensity,
stats.expon,
distargs=(),
loc=0,
scale=1,
line='45',
ax=ax1)
plt.grid()
ax2 = fig.add_subplot(1, 2, 2)
top = ax2.get_ylim()[1] * 0.75
res, slope_intercept = stats.probplot(integral_intensity,