Commit c1012277 authored by Chris Jewell's avatar Chris Jewell
Browse files

Fixed missing right bracket

parent 5a30dccb
......@@ -32,7 +32,7 @@ def chain_binomial_propagate(h, time_step):
probs = markov_transition[..., :, i]
binom = tfd.Binomial(
total_count=total_count,
probs=tf.clip_by_value(probs / (1. - prev_probs), 0., 1.)
probs=tf.clip_by_value(probs / (1. - prev_probs), 0., 1.))
sample = binom.sample()
counts = tf.concat([counts, sample[..., tf.newaxis]], axis=-1)
total_count -= sample
......@@ -47,7 +47,6 @@ def chain_binomial_propagate(h, time_step):
def chain_binomial_simulate(hazard_fn, state, start, end, time_step):
propagate = chain_binomial_propagate(hazard_fn, time_step)
times = tf.range(start, end, time_step)
print(times.shape[0])
output = tf.TensorArray(state.dtype, size=times.shape[0])
output = output.write(0, state)
......
......@@ -101,7 +101,7 @@ class CovidUK:
def create_initial_state(self, init_matrix=None):
if init_matrix is None:
I = np.zeros(self.N.shape, dtype=np.float64)
I[149*17+10] = 30. # Middle-aged in Surrey
I[149*17+10] = 30. # Middle-aged in Surrey
else:
np.testing.assert_array_equal(init_matrix.shape, [self.n_lads, self.n_ages],
err_msg=f"init_matrix does not have shape [<num lads>,<num ages>] \
......@@ -224,7 +224,7 @@ class CovidUKStochastic(CovidUK):
return rate_matrix
return h
@tf.function(autograph=False)
@tf.function(autograph=False, experimental_compile=True)
def simulate(self, param, state_init):
"""Runs a simulation from the epidemic model
......
......@@ -32,7 +32,7 @@ def sum_total_removals(sim):
def final_size(sim):
remove = sim[:, 3, :]
remove = sim[:, :, 3]
remove = remove.reshape([remove.shape[0], 152, 17])
fs = remove[-1, :, :].sum(axis=0)
return fs
......@@ -186,7 +186,7 @@ if __name__ == '__main__':
seeding = seed_areas(N, n_names) # Seed 40-44 age group, 30 seeds by popn size
state_init = model.create_initial_state(init_matrix=seeding)
with tf.device('/CPU:0'):
with tf.device('CPU'):
start = time.perf_counter()
t, sim = model.simulate(param, state_init)
end = time.perf_counter()
......
......@@ -18,7 +18,7 @@ parameter:
settings:
inference_period:
- 2020-02-19
- 2020-09-01
- 2020-04-01
holiday:
- 2020-03-23
- 2020-10-01
......
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