def test_sdm_prototype_n(params, kp=1, ke=1, noise=0, iters=100):
n, m, D = params
mem = sdm.SDM(n, m, D)
corruption = np.empty((iters, kp))
bits = int(n * noise)
# store the same number of items multiple times
for i in xrange(iters):
# generate random prototype and exemplars
vecs = util.random_input(n, kp)
cvecs = vecs[..., None] * np.ones((n, kp, ke), dtype='i4')
cvecs = util.corrupt(
cvecs.reshape((n, kp*ke)),
bits, with_replacement=True)
ex = util.corrupt(vecs, bits)
# reset the memory to its original state
mem.reset()
# write random inputs to memory
mem.writeM(cvecs, cvecs)
# read the items back out
r = mem.readM(ex)
# find the fraction of corrupted bits
corruption[i] = np.mean(r ^ vecs, axis=0)
return corruption
def test_hopfield_capacity_n(n, k=1, iters=100):
corruption = np.empty((iters, k))
# store the same number of items multiple times
for i in xrange(iters):
# generate random inputs
vecs = util.random_input(n, k)
# create hopfield net
mem = hop.hopnet(vecs)
# read the items backout
r = mem.readM(vecs, 1000)
# find the largest fraction of corrupted bits
corruption[i] = np.mean(r ^ vecs, axis=0)
return corruption
def test_hopfield_capacity_n(n, k=1, iters=100):
corruption = np.empty((iters, k))
# store the same number of items multiple times
for i in xrange(iters):
# generate random inputs
vecs = util.random_input(n, k)
# create hopfield net
mem = hop.hopnet(vecs)
# read the items backout
r = mem.readM(vecs, 1000)
# find the largest fraction of corrupted bits
corruption[i] = np.mean(r ^ vecs, axis=0)
return corruption
def test_sdm_capacity_n(params, k=1, iters=100):
n, m, D = params
mem = sdm.SDM(n, m, D)
corruption = np.empty((iters, k))
# store the same number of items multiple times
for i in xrange(iters):
# generate random inputs
vecs = util.random_input(n, k)
# reset the memory to its original state
mem.reset()
# write random inputs to memory
mem.writeM(vecs, vecs)
# read the items back out
r = mem.readM(vecs)
# find the largest fraction of corrupted bits
corruption[i] = np.mean(r ^ vecs, axis=0)
return corruption
def test_sdm_capacity_n(params, k=1, iters=100):
n, m, D = params
mem = sdm.SDM(n, m, D)
corruption = np.empty((iters, k))
# store the same number of items multiple times
for i in xrange(iters):
# generate random inputs
vecs = util.random_input(n, k)
# reset the memory to its original state
mem.reset()
# write random inputs to memory
mem.writeM(vecs, vecs)
# read the items back out
r = mem.readM(vecs)
# find the largest fraction of corrupted bits
corruption[i] = np.mean(r ^ vecs, axis=0)
return corruption
def test_hopfield_prototype_n(n, kp=1, ke=1, noise=0, iters=100):
corruption = np.empty((iters, kp))
bits = int(n * noise)
# store the same number of items multiple times
for i in xrange(iters):
# generate random inputs
vecs = util.random_input(n, kp)
cvecs = vecs[..., None] * np.ones((n, kp, ke), dtype="i4")
cvecs = util.corrupt(cvecs.reshape((n, kp * ke)), bits, with_replacement=True)
ex = util.corrupt(vecs, bits)
# create hopfield net
mem = hop.hopnet(cvecs)
# read the items backout
r = mem.readM(ex, 1000)
# find the largest fraction of corrupted bits
corruption[i] = np.mean(r ^ vecs, axis=0)
return corruption
def test_hopfield_noise_tolerance_n(n, k=1, noise=0, iters=100):
if noise == 0:
return test_hopfield_capacity_n(n, k=k, iters=iters)
corruption = np.empty((iters, k))
bits = int(n * noise)
# store the same number of items multiple times
for i in xrange(iters):
# generate random inputs
vecs = util.random_input(n, k)
cvecs = util.corrupt(vecs, bits)
# create hopfield net
mem = hop.hopnet(vecs)
# read the items backout
r = mem.readM(cvecs, 1000)
# find the largest fraction of corrupted bits
corruption[i] = np.mean(r ^ vecs, axis=0)
return corruption
def test_hopfield_noise_tolerance_n(n, k=1, noise=0, iters=100):
if noise == 0:
return test_hopfield_capacity_n(n, k=k, iters=iters)
corruption = np.empty((iters, k))
bits = int(n * noise)
# store the same number of items multiple times
for i in xrange(iters):
# generate random inputs
vecs = util.random_input(n, k)
cvecs = util.corrupt(vecs, bits)
# create hopfield net
mem = hop.hopnet(vecs)
# read the items backout
r = mem.readM(cvecs, 1000)
# find the largest fraction of corrupted bits
corruption[i] = np.mean(r ^ vecs, axis=0)
return corruption
def test_hopfield_prototype_n(n, kp=1, ke=1, noise=0, iters=100):
corruption = np.empty((iters, kp))
bits = int(n * noise)
# store the same number of items multiple times
for i in xrange(iters):
# generate random inputs
vecs = util.random_input(n, kp)
cvecs = vecs[..., None] * np.ones((n, kp, ke), dtype='i4')
cvecs = util.corrupt(
cvecs.reshape((n, kp*ke)),
bits, with_replacement=True)
ex = util.corrupt(vecs, bits)
# create hopfield net
mem = hop.hopnet(cvecs)
# read the items backout
r = mem.readM(ex, 1000)
# find the largest fraction of corrupted bits
corruption[i] = np.mean(r ^ vecs, axis=0)
return corruption
def test_sdm_prototype_n(params, kp=1, ke=1, noise=0, iters=100):
n, m, D = params
mem = sdm.SDM(n, m, D)
corruption = np.empty((iters, kp))
bits = int(n * noise)
# store the same number of items multiple times
for i in xrange(iters):
# generate random prototype and exemplars
vecs = util.random_input(n, kp)
cvecs = vecs[..., None] * np.ones((n, kp, ke), dtype="i4")
cvecs = util.corrupt(cvecs.reshape((n, kp * ke)), bits, with_replacement=True)
ex = util.corrupt(vecs, bits)
# reset the memory to its original state
mem.reset()
# write random inputs to memory
mem.writeM(cvecs, cvecs)
# read the items back out
r = mem.readM(ex)
# find the fraction of corrupted bits
corruption[i] = np.mean(r ^ vecs, axis=0)
return corruption
