def ksc(tseries, num_clusters, n_iters=-1, n_runs=10):
'''
This method will make `n_runs` call to `_base_ksc` returning the results
from the run with the lowest over-all clustering cost. In each run,
a random initialization of centroids is performed. This is done by assigning
time series to clusters in a uniform random manner and then computing the
centroid of each cluster.
Please refer to the documentation of `_base_ksc` for a detailed summary
of the KSC algorithm.
Arguments
---------
tseries: a matrix of shape (number of time series, size of each series)
The time series to cluster
n_iters: int
The number of iterations which the algorithm will run
n_runs: int
The number of times to run the KSC algorithm
Returns
-------
centroids: a matrix of shape (num. of clusters, size of time series)
The final centroids found by the algorithm
assign: an array of num. series size
The cluster id which each time series belongs to
best_shift: an array of num. series size
The amount shift amount performed for each time series
cent_dists: a matrix of shape (num. centroids, num. series)
The distance of each centroid to each time series
References
----------
.. [1] J. Yang and J. Leskovec,
"Patterns of Temporal Variation in Online Media" - WSDM'11
http://dl.acm.org/citation.cfm?id=1935863
'''
min_cost = float('+inf')
best_cents = None
best_assign = None
best_shift = None
best_dist = None
for _ in xrange(n_runs):
assign = np.random.randint(0, num_clusters, tseries.shape[0])
cents = _compute_centroids(tseries, assign, num_clusters)
cents, assign, series_shift, dists = _base_ksc(tseries, cents, n_iters)
clust_cost = cost(tseries, assign, cents, dists)
if clust_cost < min_cost:
min_cost = clust_cost
best_cents = cents
best_assign = assign
best_shift = series_shift
best_dist = dists
return best_cents, best_assign, best_shift, best_dist
def ksc(tseries, num_clusters, n_iters=-1, n_runs=10):
'''
This method will make `n_runs` call to `_base_ksc` returning the results
from the run with the lowest over-all clustering cost. In each run,
a random initialization of centroids is performed. This is done by assigning
time series to clusters in a uniform random manner and then computing the
centroid of each cluster.
Please refer to the documentation of `_base_ksc` for a detailed summary
of the KSC algorithm.
Arguments
---------
tseries: a matrix of shape (number of time series, size of each series)
The time series to cluster
n_iters: int
The number of iterations which the algorithm will run
n_runs: int
The number of times to run the KSC algorithm
Returns
-------
centroids: a matrix of shape (num. of clusters, size of time series)
The final centroids found by the algorithm
assign: an array of num. series size
The cluster id which each time series belongs to
best_shift: an array of num. series size
The amount shift amount performed for each time series
cent_dists: a matrix of shape (num. centroids, num. series)
The distance of each centroid to each time series
References
----------
.. [1] J. Yang and J. Leskovec,
"Patterns of Temporal Variation in Online Media" - WSDM'11
http://dl.acm.org/citation.cfm?id=1935863
'''
min_cost = float('+inf')
best_cents = None
best_assign = None
best_shift = None
best_dist = None
for _ in range(n_runs):
assign = np.random.randint(0, num_clusters, tseries.shape[0])
cents = _compute_centroids(tseries, assign, num_clusters)
cents, assign, series_shift, dists = _base_ksc(tseries, cents, n_iters)
clust_cost = cost(tseries, assign, cents, dists)
if clust_cost < min_cost:
min_cost = clust_cost
best_cents = cents
best_assign = assign
best_shift = series_shift
best_dist = dists
return best_cents, best_assign, best_shift, best_dist
def run_clustering(X, k, dists_all):
cent, assign, shift, dists_cent = ksc.inc_ksc(X, k)
intra = metrics.avg_intra_dist(X, assign, dists_all)[0]
inter = metrics.avg_inter_dist(X, assign, dists_all)[0]
bcv = metrics.beta_cv(X, assign, dists_all)
cost = metrics.cost(X, assign, None, dists_cent)
return intra, inter, bcv, cost
def run_clustering(X, k, dists_all):
cent, assign, shift, dists_cent = ksc.inc_ksc(X, k)
intra = metrics.avg_intra_dist(X, assign, dists_all)[0]
inter = metrics.avg_inter_dist(X, assign, dists_all)[0]
bcv = metrics.beta_cv(X, assign, dists_all)
cost = metrics.cost(X, assign, None, dists_cent)
return intra, inter, bcv, cost
