def test_delete_and_pickle():
'''
Deleting an element doesn't affect the remaining elements'
indices.
'''
s = RecyclingSet(10, ['dog','cat','banana'])
del s[1]
eq_(s[1], None)
eq_(s.index('banana'), 2)
# Pickling doesn't change things.
s2 = pickle.loads(pickle.dumps(s))
eq_(s, s2)
eq_(s2[1], None)
eq_(s2.index('banana'), 2)
assert None not in s2
assert None not in s2
class CCIPCA(object):
"""A Candid Covariance-free Incremental Principal Component Analysis implementation"""
def __init__(self,
k,
ev=None,
i=0,
bootstrap=20,
amnesia=3.0,
remembrance=100000.0,
vector_size=10000,
auto_baseline=True):
"""Construct a CCIPCA computation with k initial eigenvectors ev at iteration i, using simple averaging until the iteration given by bootstrap, afterward using CCIPCA given amnesic parameter amnesia, rememberance parameter remembrance, and a weight vector and subspace criteria for simultaneous vector presentation"""
if ev is not None: self._v = ev
else:
self._v = np.zeros((k, vector_size))
self._k = k
self._amnesia = amnesia
self._iteration = i
self._bootstrap = bootstrap
self._remembrance = remembrance
self._vector_size = vector_size
self._labels = RecyclingSet(self._vector_size)
self._labels.listen_for_drops(self.forget_column)
self._auto_baseline = auto_baseline
def zerovec(self):
return np.zeros((self._vector_size, ))
def compute_attractor(self, k, u, v_hat=None):
"""Compute the attractor vector for eigenvector k with vector u"""
if k == 0: return u
if v_hat is None: v_hat = hat(self._v[k])
partial = u * np.dot(u, v_hat)
return partial
def update_eigenvector(self, k, u, learn=False):
"""Update eigenvector k with vector u, returning pair
containing magnitude of eigenvector component and residue vector"""
if learn:
# Handle elementary cases
if self._iteration < k:
return 0.0, self.zerovec()
if self._iteration == k:
self._v[k, :] = u
mag = np.linalg.norm(self._v[k])
return mag, self.zerovec()
# Compute weighting factors
n = min(self._iteration, self._remembrance)
if n < self._bootstrap:
w_old = float(n - 1) / n
w_new = 1.0 / n
else:
l = self._amnesia
w_old = float(n - l) / n
w_new = float(l) / n
# Compute the attractor
attractor = self.compute_attractor(k, u)
# Approach attractor
self._v[k] *= w_old
self._v[k] += attractor * w_new
# Calculate component magnitudes
v_hat = hat(self._v[k])
if k == 0:
if self._auto_baseline: base_mag = np.linalg.norm(self._v[k])
else: base_mag = 0.0
else: base_mag = np.dot(u, v_hat)
u_residue = u - (v_hat * base_mag)
if k == 0:
logger.debug('e0: %s' % str(self._v[k]))
logger.debug('u: %s' % str(u))
if learn:
logger.debug('attractor: %s' % str(attractor))
logger.debug('residue: %s' % str(u_residue))
logger.debug('')
return base_mag, u_residue
def iteration(self, u_tensor, learn=False):
"""Train the eigenvector table with new vector u"""
print "iteration = ", self._iteration
mags = []
u_copy = self.zerovec()
if learn:
for (key, ), value in u_tensor.iteritems():
u_copy[self._labels.add(key)] = value
else:
for (key, ), value in u_tensor.iteritems():
if key in self._labels:
u_copy[self._labels.index(key, touch=False)] = value
if np.linalg.norm(u_copy) == 0:
return np.zeros((self._v.shape[0], ))
assert np.linalg.norm(u_copy) > 0
for k in xrange(min(self._k, self._iteration + 1)):
mag, new_u = self.update_eigenvector(k, u_copy, learn)
u_copy = new_u
mags.append(mag)
if learn:
self._iteration += 1
# Sort all but the 0th vector by their sum of squares
#print "magnitudes:", np.sum(self._v[1:] * self._v[1:], axis=1)
sort_order = np.argsort(-np.sum(self._v[1:] * self._v[1:], axis=1))
self._v[1:] = self._v[1:][sort_order]
return mags
def fixed_iteration(self, column, learn=False):
"""Train the eigenvector table with new vector u"""
print "iteration = ", self._iteration
mags = []
u_copy = column[:]
assert np.linalg.norm(u_copy) > 0
for k in xrange(min(self._k, self._iteration + 1)):
mag, new_u = self.update_eigenvector(k, u_copy, learn)
u_copy = new_u
mags.append(mag)
if learn:
self._iteration += 1
# Sort all but the 0th vector by their sum of squares
sort_order = np.argsort(-np.sum(self._v[1:] * self._v[1:], axis=1))
self._v[1:] = self._v[1:][sort_order]
return mags
def reconstruct_array(self, weights):
# Create a linear combination of the eigenvectors
sum = self.zerovec()
for index, w in enumerate(weights):
sum += self._v[index] * w
return sum
def reconstruct(self, weights):
"""
Get a linear combination of the eigenvectors, and re-express it as
a Divisi tensor (a dense labeled vector).
"""
array = self.reconstruct_array(weights)
assert str(array[0]) != 'nan'
return LabeledView(DenseTensor(array), [self._labels])
def get_array(self):
return self._v
def get_labels(self):
return self._labels
def smooth(self, u, k_max=None, learn=False):
mags = self.iteration(u, learn)
if k_max is not None:
mags = mags[:k_max]
vec = self.reconstruct(mags)
if not learn:
logger.debug("decomp: %s" % str(mags))
return vec
def forget_column(self, slot, label):
logger.debug("forgetting column %d" % slot)
self._v[:, slot] = 0
class CCIPCA(object):
"""A Candid Covariance-free Incremental Principal Component Analysis implementation"""
def __init__(
self, k, ev=None, i=0, bootstrap=20, amnesia=3.0, remembrance=100000.0, vector_size=10000, auto_baseline=True
):
"""Construct a CCIPCA computation with k initial eigenvectors ev at iteration i, using simple averaging until the iteration given by bootstrap, afterward using CCIPCA given amnesic parameter amnesia, rememberance parameter remembrance, and a weight vector and subspace criteria for simultaneous vector presentation"""
if ev is not None:
self._v = ev
else:
self._v = np.zeros((k, vector_size))
self._k = k
self._amnesia = amnesia
self._iteration = i
self._bootstrap = bootstrap
self._remembrance = remembrance
self._vector_size = vector_size
self._labels = RecyclingSet(self._vector_size)
self._labels.listen_for_drops(self.forget_column)
self._auto_baseline = auto_baseline
def zerovec(self):
return np.zeros((self._vector_size,))
def compute_attractor(self, k, u, v_hat=None):
"""Compute the attractor vector for eigenvector k with vector u"""
if k == 0:
return u
if v_hat is None:
v_hat = hat(self._v[k])
partial = u * np.dot(u, v_hat)
return partial
def update_eigenvector(self, k, u, learn=False):
"""Update eigenvector k with vector u, returning pair
containing magnitude of eigenvector component and residue vector"""
if learn:
# Handle elementary cases
if self._iteration < k:
return 0.0, self.zerovec()
if self._iteration == k:
self._v[k, :] = u
mag = np.linalg.norm(self._v[k])
return mag, self.zerovec()
# Compute weighting factors
n = min(self._iteration, self._remembrance)
if n < self._bootstrap:
w_old = float(n - 1) / n
w_new = 1.0 / n
else:
l = self._amnesia
w_old = float(n - l) / n
w_new = float(l) / n
# Compute the attractor
attractor = self.compute_attractor(k, u)
# Approach attractor
self._v[k] *= w_old
self._v[k] += attractor * w_new
# Calculate component magnitudes
v_hat = hat(self._v[k])
if k == 0:
if self._auto_baseline:
base_mag = np.linalg.norm(self._v[k])
else:
base_mag = 0.0
else:
base_mag = np.dot(u, v_hat)
u_residue = u - (v_hat * base_mag)
if k == 0:
logger.debug("e0: %s" % str(self._v[k]))
logger.debug("u: %s" % str(u))
if learn:
logger.debug("attractor: %s" % str(attractor))
logger.debug("residue: %s" % str(u_residue))
logger.debug("")
return base_mag, u_residue
def iteration(self, u_tensor, learn=False):
"""Train the eigenvector table with new vector u"""
print "iteration = ", self._iteration
mags = []
u_copy = self.zerovec()
if learn:
for (key,), value in u_tensor.iteritems():
u_copy[self._labels.add(key)] = value
else:
for (key,), value in u_tensor.iteritems():
if key in self._labels:
u_copy[self._labels.index(key, touch=False)] = value
if np.linalg.norm(u_copy) == 0:
return np.zeros((self._v.shape[0],))
assert np.linalg.norm(u_copy) > 0
for k in xrange(min(self._k, self._iteration + 1)):
mag, new_u = self.update_eigenvector(k, u_copy, learn)
u_copy = new_u
mags.append(mag)
if learn:
self._iteration += 1
# Sort all but the 0th vector by their sum of squares
# print "magnitudes:", np.sum(self._v[1:] * self._v[1:], axis=1)
sort_order = np.argsort(-np.sum(self._v[1:] * self._v[1:], axis=1))
self._v[1:] = self._v[1:][sort_order]
return mags
def fixed_iteration(self, column, learn=False):
"""Train the eigenvector table with new vector u"""
print "iteration = ", self._iteration
mags = []
u_copy = column[:]
assert np.linalg.norm(u_copy) > 0
for k in xrange(min(self._k, self._iteration + 1)):
mag, new_u = self.update_eigenvector(k, u_copy, learn)
u_copy = new_u
mags.append(mag)
if learn:
self._iteration += 1
# Sort all but the 0th vector by their sum of squares
sort_order = np.argsort(-np.sum(self._v[1:] * self._v[1:], axis=1))
self._v[1:] = self._v[1:][sort_order]
return mags
def reconstruct_array(self, weights):
# Create a linear combination of the eigenvectors
sum = self.zerovec()
for index, w in enumerate(weights):
sum += self._v[index] * w
return sum
def reconstruct(self, weights):
"""
Get a linear combination of the eigenvectors, and re-express it as
a Divisi tensor (a dense labeled vector).
"""
array = self.reconstruct_array(weights)
assert str(array[0]) != "nan"
return LabeledView(DenseTensor(array), [self._labels])
def get_array(self):
return self._v
def get_labels(self):
return self._labels
def smooth(self, u, k_max=None, learn=False):
mags = self.iteration(u, learn)
if k_max is not None:
mags = mags[:k_max]
vec = self.reconstruct(mags)
if not learn:
logger.debug("decomp: %s" % str(mags))
return vec
def forget_column(self, slot, label):
logger.debug("forgetting column %d" % slot)
self._v[:, slot] = 0
