def extract(self, item, query, target_keys=None):
if target_keys:
self.print_instance_difficulty(item, query)
item_hrr = HRR(data=item)
query_hrr = HRR(data=query)
noisy_hrr = item_hrr.convolve(~query_hrr)
return self.associate(noisy_hrr.v, target_keys)
def extract(self, item, query, target_keys=None):
if target_keys:
self.print_instance_difficulty(item, query)
item_hrr = HRR(data=item)
query_hrr = HRR(data=query)
noisy_hrr = item_hrr.convolve(~query_hrr)
return self.associate(noisy_hrr.v, target_keys)
def print_instance_difficulty(self, item, query, target_keys):
if target_keys:
# Print data about how difficult the current instance is
correct_key = target_keys[0]
item_hrr = HRR(data=item)
query_hrr = HRR(data=query)
noisy_hrr = item_hrr.convolve(~query_hrr)
correct_hrr = HRR(data=self.index_vectors[correct_key])
sim = noisy_hrr.compare(correct_hrr)
dot = np.dot(noisy_hrr.v, correct_hrr.v)
norm = np.linalg.norm(noisy_hrr.v)
print "Statistics when extraction computed exactly:"
print(
"Cosine Similarity between extracted vector "
"(before assoc) and correct index vector: "), sim
print(
"Dot product between extracted vector (before assoc) "
"and correct index vector: "), dot
print "Norm of extracted vector (before assoc): ", norm
self.ideal_dot = dot
hrrs = [(key, HRR(data=iv))
for key, iv in self.index_vectors.iteritems()
if key != correct_key]
sims = [noisy_hrr.compare(h) for (k, h) in hrrs]
dots = [np.dot(noisy_hrr.v, h.v) for (k, h) in hrrs]
sim = max(sims)
dot = max(dots)
print "Cosine Similarity of closest incorrect index vector ", sim
print "Dot product of closest incorrect index vector ", dot
self.second_dot = dot
def print_instance_difficulty(self, item, query, target_keys):
if target_keys:
# Print data about how difficult the current instance is
correct_key = target_keys[0]
item_hrr = HRR(data=item)
query_hrr = HRR(data=query)
noisy_hrr = item_hrr.convolve(~query_hrr)
correct_hrr = HRR(data=self.index_vectors[correct_key])
sim = noisy_hrr.compare(correct_hrr)
dot = np.dot(noisy_hrr.v, correct_hrr.v)
norm = np.linalg.norm(noisy_hrr.v)
print "Statistics when extraction computed exactly:"
print ("Cosine Similarity between extracted vector "
"(before assoc) and correct index vector: "), sim
print ("Dot product between extracted vector (before assoc) "
"and correct index vector: "), dot
print "Norm of extracted vector (before assoc): ", norm
self.ideal_dot = dot
hrrs = [(key, HRR(data=iv))
for key, iv in self.index_vectors.iteritems()
if key != correct_key]
sims = [noisy_hrr.compare(h) for (k, h) in hrrs]
dots = [np.dot(noisy_hrr.v, h.v) for (k, h) in hrrs]
sim = max(sims)
dot = max(dots)
print "Cosine Similarity of closest incorrect index vector ", sim
print "Dot product of closest incorrect index vector ", dot
self.second_dot = dot
