def test_leftsibling():
"""leftsibling."""
dan = Node("Dan")
jet = Node("Jet", parent=dan)
jan = Node("Jan", parent=dan)
joe = Node("Joe", parent=dan)
eq_(leftsibling(dan), None)
eq_(leftsibling(jet), None)
eq_(leftsibling(jan), jet)
eq_(leftsibling(joe), jan)
def test_get_previous_sibling():
template = XMLTemplateParser("""
<template name="root" sizing="auto">
<area name="child0" size="4"></area>
<area name="child1" size="4"></area>
<area name="child2" size="4"></area>
<area name="child3" size="4"></area>
<area name="child4" size="4"></area>
</template>
""").parse()
assert template.child1 == leftsibling(template.child2)
assert None == leftsibling(template.child0)
assert template.child3 == leftsibling(template.child4)
def build_linkage(self):
# get a tuple of node at each level
levels = []
for group in LevelOrderGroupIter(self):
levels.append(group)
# just find how many nodes are leaves
# this is necessary only because we need to add n to non-leaf clusters
num_leaves = 0
for node in PostOrderIter(self):
if not node.children:
num_leaves += 1
link_count = 0
node_index = 0
linkages = []
labels = []
for g, group in enumerate(
levels[::-1][:-1]): # reversed and skip the last
for i in range(len(group) // 2):
# get partner nodes
left_node = group[2 * i]
right_node = group[2 * i + 1]
# just double check that these are always partners
assert leftsibling(right_node) == left_node
# check if leaves, need to add some new fields to track for linkage
if not left_node.children:
left_node._ind = node_index
left_node._n_clusters = 1
node_index += 1
labels.append(left_node.name)
if not right_node.children:
right_node._ind = node_index
right_node._n_clusters = 1
node_index += 1
labels.append(right_node.name)
# find the parent, count samples
parent_node = left_node.parent
n_clusters = left_node._n_clusters + right_node._n_clusters
parent_node._n_clusters = n_clusters
# assign an ind to this cluster for the dendrogram
parent_node._ind = link_count + num_leaves
link_count += 1
distance = g + 1 # equal height for all links
# add a row to the linkage matrix
linkages.append(
[left_node._ind, right_node._ind, distance, n_clusters])
labels = np.array(labels)
linkages = np.array(linkages,
dtype=np.double) # needs to be a double for scipy
return (linkages, labels)
def getneighbour(self, left_right: str) -> ProofNode:
"""Zwraca prawego, lub lewego sąsiada danego węzła.
Równie dobrze można używać `anytree.util.rightsibling(self)` oraz `anytree.util.leftsibling(self)`
:param left_right: 'L/Left' lub 'R/Right'
:type left_right: str
:raises ProofNodeError: Podano niepoprawny kierunek
:return: Sąsiad
:rtype: ProofNode
"""
if not self.parent:
return None
if left_right.upper() in ('R', 'RIGHT'):
return util.rightsibling(self)
elif left_right.upper() in ('L', 'LEFT'):
return util.leftsibling(self)
else:
raise ProofNodeError(f"'{left_right}' is not a valid direction")
def is_right_argument(child_node):
return(leftsibling(child_node) != None)
def build_linkage(self, bic_distance=False):
# get a tuple of node at each level
levels = []
for group in LevelOrderGroupIter(self):
levels.append(group)
# just find how many nodes are leaves
# this is necessary only because we need to add n to non-leaf clusters
num_leaves = 0
for node in PostOrderIter(self):
if not node.children:
num_leaves += 1
link_count = 0
node_index = 0
linkages = []
labels = []
for g, group in enumerate(levels[::-1][:-1]): # reversed and skip the last
for i in range(len(group) // 2):
# get partner nodes
left_node = group[2 * i]
right_node = group[2 * i + 1]
# just double check that these are always partners
assert leftsibling(right_node) == left_node
# check if leaves, need to add some new fields to track for linkage
if not left_node.children:
left_node._ind = node_index
left_node._n_clusters = 1
node_index += 1
labels.append(left_node.name)
if not right_node.children:
right_node._ind = node_index
right_node._n_clusters = 1
node_index += 1
labels.append(right_node.name)
# find the parent, count samples
parent_node = left_node.parent
n_clusters = left_node._n_clusters + right_node._n_clusters
parent_node._n_clusters = n_clusters
# assign an ind to this cluster for the dendrogram
parent_node._ind = link_count + num_leaves
link_count += 1
if not bic_distance:
distance = g + 1 # equal height for all links
else:
raise NotImplementedError()
# tried to use BIC as linkage distance, but not monotonic.
# would need to sort somehow by BIC ratios, but this may not be
# possible while preserving splitting nature of the tree
# self.cum_dist_ += (left_node.cum_dist_ + right_node.cum_dist_) / 2
# self.cum_dist_ += parent_node.bic_ratio_ - 1
# distance = self.cum_dist_
distance = parent_node.bic_ratio_ - 1
# add a row to the linkage matrix
linkages.append([left_node._ind, right_node._ind, distance, n_clusters])
labels = np.array(labels)
linkages = np.array(linkages, dtype=np.double) # needs to be a double for scipy
return (linkages, labels)