本文整理汇总了Python中sklearn.cluster.bicluster.SpectralCoclustering.get_shape方法的典型用法代码示例。如果您正苦于以下问题：Python SpectralCoclustering.get_shape方法的具体用法？Python SpectralCoclustering.get_shape怎么用？Python SpectralCoclustering.get_shape使用的例子？那么, 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sklearn.cluster.bicluster.SpectralCoclustering的用法示例。
在下文中一共展示了SpectralCoclustering.get_shape方法的2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: list
# 需要导入模块: from sklearn.cluster.bicluster import SpectralCoclustering [as 别名]
# 或者: from sklearn.cluster.bicluster.SpectralCoclustering import get_shape [as 别名]
col_complement = np.nonzero(np.logical_not(cocluster.columns_[i]))[0]
weight = X[rows[:, np.newaxis], cols].sum()
cut = (X[row_complement[:, np.newaxis], cols].sum() +
X[rows[:, np.newaxis], col_complement].sum())
return cut / weight
bicluster_ncuts = list(bicluster_ncut(i)
for i in xrange(len(newsgroups.target_names)))
best_idx = np.argsort(bicluster_ncuts)[:5]
print()
print("Best biclusters:")
print("----------------")
for idx, cluster in enumerate(best_idx):
n_rows, n_cols = cocluster.get_shape(cluster)
cluster_docs, cluster_words = cocluster.get_indices(cluster)
if not len(cluster_docs) or not len(cluster_words):
continue
# categories
cluster_categories = list(document_names[i] for i in cluster_docs)
counter = Counter(cluster_categories)
cat_string = ", ".join("{:.0f}% {}".format(float(c) / n_rows * 100,
name)
for name, c in counter.most_common()[:3])
# words
out_of_cluster_docs = cocluster.row_labels_ != cluster
out_of_cluster_docs = np.where(out_of_cluster_docs)[0]
word_col = X[:, cluster_words]示例2: range
# 需要导入模块: from sklearn.cluster.bicluster import SpectralCoclustering [as 别名]
# 或者: from sklearn.cluster.bicluster.SpectralCoclustering import get_shape [as 别名]
avg_data[row_sel, col_sel] = np.average(data[row_sel, col_sel])
avg_data = avg_data[np.argsort(model.row_labels_)]
avg_data = avg_data[:, np.argsort(model.column_labels_)]
plt.matshow(avg_data, cmap=plt.cm.Blues)
plt.title("Average cluster intensity")
plt.savefig('%s_averaged.png' % (identifier), bbox_inches='tight')
if args.write:
print "Writing clusters to database."
# No need to clean up here, just overwrite by _id.
for c in range(n_clusters):
(nr, nc) = model.get_shape(c)
(row_ind, col_ind) = model.get_indices(c)
cluster_val = None
if nr > 25 or nc > 50:
print "Nulling cluster %d: shape (%d, %d)" % (c, nr, nc)
else:
cluster_val = c
for ri in row_ind:
data_list[ri]['cluster'] = cluster_val
datastream.save(data_list[ri])
for ci in col_ind:
events_list[ci]['cluster'] = cluster_val
events.save(events_list[ci])