def smythEmissionDistribution(pair): """ Given a pair (S: list of sequences, target_m: int), get the emission distribution for Smyth's "default" HMM. target_m is an upper bound on the number of states -- if we can only have m' distinct observation values, then the distribution for a m' state HMM is returned. @param pair: A tuple of the form (S: list of sequences, m: int) @return: The corresponding emission distribution encoded as a list of (mu, stddev) pairs """ S, target_m = pair merged, distinct = prepareSeqs(S) m_prime = min(target_m, len(distinct)) centroids, labels, inertia = k_means(merged, m_prime, init='k-means++') clusters = partition(merged, labels) B = [] has_zero = False for cluster in clusters: assert len(cluster) > 0 mu = mean(cluster) stddev = std(cluster) B.append((mu, stddev)) if stddev < 0.001: has_zero = True return (B, labels, has_zero)
def smythEmissionDistribution(pair):
"""
Given a pair (S: list of sequences, target_m: int), get the emission
distribution for Smyth's "default" HMM. target_m is an upper bound on the
number of states -- if we can only have m' distinct observation values, then
the distribution for a m' state HMM is returned.
@param pair: A tuple of the form (S: list of sequences, target_m: int)
@return: (B, labels, has_zero), where:
* S', obs = concat(S), set(S)
* m' = min(target_m, len(obs))
* [C_0,...,C_{m'-1}] = result of clustering S' with k-means.
* labels: tells which cluster each item in merged goes into; i.e.,
labels[i] = j, where S'[i] belongs to cluster C_j.
* B[i] = (mean(C_i), stddev(C_i)).
* has_zero = True if there is i such that B[i][1] ~= 0.0.
"""
S, target_m = pair
# merged list of 1d vectors, set of distinct observation values
merged, distinct = prepareSeqs(S)
# m_prime is min of either target_m or the number of distinct obs values
m_prime = min(target_m, len(distinct))
# k-means partitions merged into m_prime clusters [C_0,...,C_{m'-1}].
# centroids = [c_0,...,c_{m'-1}]: cluster centers; i.e., c_i is the center
# of C_j.
# labels: tells which cluster each item in merged goes into; i.e.,
# labels[i] = j, where merged[i] belongs to cluster C_j.
# inertia: sum of distances of samples to closest cluster center
# inertia = sum_{i=0}^{m'-1}(sum_{x in C_i} dist(x, c_i)).
centroids, labels, inertia = k_means(merged, m_prime, init='k-means++')
# takes labels and arranges merged into
# a list of lists, each of which contains the series from one cluster
# clusters = [C_0,..,C_{m'-1}]
clusters = partition(merged, labels)
# Compute (B, labels, has_zero), where
# B[i] = (mean(C_i), stddev(C_i)).
# has_zero = True if there is i such that B[i][1] ~= 0.0.
B = []
has_zero = False
for cluster in clusters:
assert len(cluster) > 0
mu = mean(cluster)
stddev = std(cluster)
B.append((mu, stddev))
if stddev < 0.001:
has_zero = True
return (B, labels, has_zero)
def _kMedoids(self):
"""
Create multiple partitions for k values in [self.min_k... self.max_k]
via k-medoids.
"""
self.dist_matrix = self._getDistMatrix()
batch_items = ((self.dist_matrix, k, 10) for k in self.k_values)
printAndFlush("K-medoids clustering (parallel)...")
results = self._doMap(kMedoids, batch_items)
printAndFlush("done")
for i in xrange(0, len(self.k_values)):
k, result = self.k_values[i], results[i]
labels, error, nfound = result
self.labelings[k] = labels
clusters = partition(self.S, labels)
self.partitions[k] = (clusters)
def model(self): """ With the user specified k range, clustering algorithm, HMM intialization, and distance function, create a set of HMM mixtures modeling the sequences in self.S. When finished, self.components is populated with a dict mapping k values to HMM triples. """ start = clock() # self._cluster() for k in self.k_values: clusters = partition(self.S, self.labelings[k]) self.partitions[k] = (clusters) self._trainModels() self.times['total'] = clock() - start if not self.single_threaded: self.pool.close()
def _hierarchical(self):
"""
Create multiple partitions for k values in [self.min_k... self.max_k]
via hierarchical, agglomerative clustering.
"""
self.dist_matrix = self._getDistMatrix()
printAndFlush("Hierarchical clustering (serial)...")
# tree = treecluster(distancematrix=self.dist_matrix, method='m')
linkage_matrix = linkage(self.dist_matrix, method='complete')
for k in self.k_values:
# labels = tree.cut(k)
labels = fcluster(linkage_matrix, k, 'maxclust')
self.labelings[k] = labels
clusters = partition(self.S, labels)
# Technically, scipy's tree cutting function isn't guaranteed to
# produce k clusters. It only seems to do this when there's a very
# lopsided distance matrix, as was the case before we used log
# observations. With log observations, it's been fine, and it
# performs better than Pycluster's analogous routine.
if len(clusters) != k:
raise ValueError("fcluster could only produce %i clusters!" %
len(clusters))
self.partitions[k] = clusters
printAndFlush("done")
seq = list(model.sampleSingle(LEN, seed=j))
create = 0
destroy = LEN*WINDOW_SIZE
records.append({
'ident': (i, i),
'create': create,
'destroy': destroy,
'relays_in': [],
'relays_out': map(lambda o: max(0, exp(o)-1), seq)
})
elif mode == "-clusters":
data_path = sys.argv[5]
with open(data_path) as data_file:
orig_records = cPickle.load(data_file)['records']
labels = results['labelings'][k]
clusters = partition(orig_records, labels)
sampled = map(lambda c: sample(c, 100) if len(c) > 100 else c, clusters)
for i, cluster in enumerate(sampled):
for record in cluster:
record['ident'] = (i, i)
records.append(record)
for record in records:
if record['ident'] == (6, 6):
print len(record['relays_out'])
output = {
'window_size': WINDOW_SIZE,
'records': records
}
with open(out_path, 'w') as outfile:
cPickle.dump(output, outfile, protocol=2)
