def eval(self,q:Queue,inner_q:Queue):
running_mean = RunningMeanWelford()
# activation_sum=0
n=0
for transformation in self.queue_as_generator(q):
for activations in self.queue_as_generator(inner_q):
if self.sign != 1:
activations *= self.sign
activated = (activations > self.threshold) * 1
running_mean.update_all(activated)
self.l=running_mean.mean()
def distance(self,batch:np.ndarray,batch_inverted:np.ndarray,mean_running:RunningMeanWelford):
n_shape=len(batch.shape)
assert n_shape>=2
n,f=batch.shape[0],batch.shape[1]
if n_shape>2 and self.normalize:
# normalize all extra dimensions
for i in range(n):
for j in range(f):
batch[i,j,:]/=np.linalg.norm(batch[i,j,:])
batch_inverted[i,j,:]/=np.linalg.norm(batch_inverted[i,j,:])
# ssd of all values
distances = (batch-batch_inverted)**2
n_shape=len(batch.shape)
if n_shape>2:
# aggregate extra dims to keep only the feature dim
distances= distances.mean(axis=tuple(range(2,n_shape)))
distances = np.sqrt(distances)
assert len(distances.shape)==2
mean_running.update_all(distances)