def cluster_kmeans(tica_dir, data_dir, traj_dir, n_clusters, lag_time):
clusterer_dir = "%s/clusterer_%dclusters.h5" % (tica_dir, n_clusters)
if (os.path.exists(clusterer_dir)):
print("Already clustered")
else:
print("Clustering by KMeans")
reduced_data = verboseload(data_dir)
trajs = np.concatenate(reduced_data)
clusterer = KMeans(n_clusters=n_clusters, n_jobs=-1)
clusterer.fit_transform(reduced_data)
verbosedump(clusterer, clusterer_dir)
def cluster_kmeans(tica_dir, data_dir, traj_dir, n_clusters, lag_time): clusterer_dir = "%s/clusterer_%dclusters.h5" %(tica_dir, n_clusters) if (os.path.exists(clusterer_dir)): print "Already clustered" else: print "Clustering by KMeans" reduced_data = verboseload(data_dir) trajs = np.concatenate(reduced_data) clusterer = KMeans(n_clusters = n_clusters, n_jobs=-1) clusterer.fit_transform(reduced_data) verbosedump(clusterer, clusterer_dir)
def cluster_features(features, clusterer, n_clusters=8):
'''
Input
features : list of arrays, length n_trajs, each of shape (n_samples, n_features)
Output
clst : msmbuilder.cluster object, with attributes
cluster_centers_ : (n_clusters, n_features)
labels_ : list of arrays, each of shape (n_samples, )
'''
if clusterer == 'KMeans':
from msmbuilder.cluster import KMeans
clst = KMeans(n_clusters=n_clusters)
elif clusterer == 'KCenters':
from msmbuilder.cluster import KCenters
clst = KCenters(n_clusters=n_clusters)
elif clusterer == 'KMedoids':
from msmbuilder.cluster import KMedoids
clst = KMedoids(n_clusters=n_clusters)
elif clusterer == 'MiniBatchKMeans':
from msmbuilder.cluster import MiniBatchKMeans
clst = MiniBatchKMeans(n_clusters=n_clusters)
elif clusterer == 'MiniBatchKMedoids':
from msmbuilder.cluster import MiniBatchKMedoids
clst = MiniBatchKMedoids(n_clusters=n_clusters)
clusters = clst.fit_transform(features)
return clst
