def get_clusters(x_original, axis=['row', 'column'][0]):
"""Performs UPGMA clustering using euclidean distances"""
x = x_original.copy()
if axis == 'column':
x = x.T
nr = x.shape[0]
metric_f = get_nonphylogenetic_metric('euclidean')
row_dissims = DistanceMatrix(metric_f(x), map(str, range(nr)))
# do upgma - rows
# Average in SciPy's cluster.heirarchy.linkage is UPGMA
linkage_matrix = linkage(row_dissims.condensed_form(), method='average')
tree = TreeNode.from_linkage_matrix(linkage_matrix, row_dissims.ids)
row_order = [int(tip.name) for tip in tree.tips()]
return row_order
def get_clusters(x_original, axis=['row', 'column'][0]):
"""Performs UPGMA clustering using euclidean distances"""
x = x_original.copy()
if axis == 'column':
x = x.T
nr = x.shape[0]
metric_f = get_nonphylogenetic_metric('euclidean')
row_dissims = DistanceMatrix(metric_f(x), map(str, range(nr)))
# do upgma - rows
# Average in SciPy's cluster.heirarchy.linkage is UPGMA
linkage_matrix = linkage(row_dissims.condensed_form(), method='average')
tree = TreeNode.from_linkage_matrix(linkage_matrix, row_dissims.ids)
row_order = [int(tip.name) for tip in tree.tips()]
return row_order
def get_clusters(x_original, axis=['row','column'][0]):
"""Performs UPGMA clustering using euclidean distances"""
x = x_original.copy()
if axis=='column':
x = x.T
nr = x.shape[0]
metric_f = get_nonphylogenetic_metric('euclidean')
row_dissims = metric_f(x)
# do upgma - rows
BIG = 1e305
row_nodes = map(PhyloNode, map(str,range(nr)))
for i in range(len(row_dissims)):
row_dissims[i,i] = BIG
row_tree = UPGMA_cluster(row_dissims, row_nodes, BIG)
row_order = [int(tip.Name) for tip in row_tree.iterTips()]
return row_order
def get_clusters(x_original, axis=['row', 'column'][0]):
"""Performs UPGMA clustering using euclidean distances"""
x = x_original.copy()
if axis == 'column':
x = x.T
nr = x.shape[0]
metric_f = get_nonphylogenetic_metric('euclidean')
row_dissims = metric_f(x)
# do upgma - rows
BIG = 1e305
row_nodes = map(PhyloNode, map(str, range(nr)))
for i in range(len(row_dissims)):
row_dissims[i, i] = BIG
row_tree = UPGMA_cluster(row_dissims, row_nodes, BIG)
row_order = [int(tip.Name) for tip in row_tree.iterTips()]
return row_order