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changing entropy for future prediction calculation #12

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changing entropy for future prediction calculation #12

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13 changes: 8 additions & 5 deletions bubblewrap.py
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Original file line number Diff line number Diff line change
Expand Up @@ -141,6 +141,7 @@ def init_nodes(self):
self.time_grad_Q = []
self.time_pred = []
self.entropy_list = []
self.entropy_far = []
self.loss = []

self.t = 1
Expand Down Expand Up @@ -185,8 +186,10 @@ def single_e_step(self, x):
if not self.go_fast:
new_log_pred = self.log_pred_prob(self.B, self.A, self.alpha)
self.pred.append(new_log_pred)
ent = entropy(self.A, self.alpha)
self.entropy_list.append(ent)
ent_present = entropy(self.A, self.alpha, future_distance=1)
ent_future = entropy(self.A, self.alpha, self.future_distance)
self.entropy_list.append(ent_present)
self.entropy_far.append(ent_future)
if self.future_x is not None:
future_B = self.logB_jax(self.future_x, self.mu, self.L, self.L_diag)
pred_far = self.pred_ahead(future_B, self.A, self.alpha, self.future_distance)
Expand Down Expand Up @@ -390,9 +393,9 @@ def pred_ahead(B, A, alpha, future_distance):
AT = np.linalg.matrix_power(A,future_distance)
return np.log(alpha @ AT @ np.exp(B) + 1e-16)

@jit
def entropy(A, alpha):
AT = np.linalg.matrix_power(A,1)
# @jit
def entropy(A, alpha, future_distance):
AT = np.linalg.matrix_power(A,future_distance)
one = alpha @ AT
return - np.sum(one.dot(np.log2(alpha @ AT)))

Expand Down