本文整理汇总了Python中Pycluster.somcluster方法的典型用法代码示例。如果您正苦于以下问题：Python Pycluster.somcluster方法的具体用法？Python Pycluster.somcluster怎么用？Python Pycluster.somcluster使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类Pycluster的用法示例。
在下文中一共展示了Pycluster.somcluster方法的3个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: som_cluster_test
# 需要导入模块: import Pycluster [as 别名]
# 或者: from Pycluster import somcluster [as 别名]
def som_cluster_test(data,real_labels, outputfile = None):
if outputfile != None:
f = open(outputfile,'w')
f.write(out_result_header())
start = time.time()
ks = range(6,40)
for k in ks:
print 'som clustering when k=%d' % k
predicted = Pycluster.somcluster(data,nxgrid=k,nygrid=1, niter=5, dist='u')[0]
predicted = [xy[0] for xy in predicted.tolist()]
cata = tuple(set(predicted))
for i in range(0,len(predicted)):
predicted[i]=cata.index(predicted[i])
if outputfile != None:
f.write(out_result(predicted, k, real_labels))
elasped = time.time() - start
print 'som clustering time: %.3f' % (elasped/float(len(ks)))示例2: self_organizing_map
# 需要导入模块: import Pycluster [as 别名]
# 或者: from Pycluster import somcluster [as 别名]
def self_organizing_map(flat_data, data):
""" """
# Self-organizing maps
clusterid, celldata = pc.somcluster(
data=flat_data.values(),
transpose=0,
nxgrid=5,
nygrid=5,
inittau=0.02,
niter=100,
dist='e')
# load clusters into dictionary
clusters = defaultdict(list)
for i, j in zip(clusterid, data):
clusters[tuple(i)].append(j)
make_plots('SOM (c=%s, m=%s, d=%s)' % (nclusters, method, distance),
clusters, flat_data)示例3:
# 需要导入模块: import Pycluster [as 别名]
# 或者: from Pycluster import somcluster [as 别名]
input_vecs = utils.make_prices_diffs_vecs(data)
else:
input_vecs = utils.make_prices_vecs(data)
# Run clustering algorithm.
if algorithm_type == ClusterAlg.KMEANS:
labels, wcss, n = Pycluster.kcluster(input_vecs, number_of_clusters,
dist = dist_measure, npass = number_of_iters,
method = dist_method)
elif algorithm_type == ClusterAlg.HIERARCHICAL:
tree = Pycluster.treecluster(input_vecs, method = dist_method,
dist = dist_method)
labels = tree.cut(number_of_clusters)
elif algorithm_type == ClusterAlg.SELFORGMAPS:
labels, celldata = Pycluster.somcluster(input_vecs, nxgrid = xgrid,
nygrid = ygrid, niter = number_of_iters)
# If algorithm is self-organizing maps each item is assigned to
# a particular 2D point, so we need to create groups from 2D points.
# See implementation of making groups from labels for details.
if algorithm_type == ClusterAlg.SELFORGMAPS:
clusters = utils.make_groups_from_labels(labels, data, True)
else:
clusters = utils.make_groups_from_labels(labels, data)
# Check with which type of key we have to deal with.
# Any better idea how to check if object is a pair? :)
keys_are_2D_points = True
sample_key = clusters.keys()[0]