def arrayBST(l,start,end): mid=(start+end)/2 if start<=end: tree=BinaryTree(l[mid]) tree.left=arrayBST(l,start,mid-1) tree.right=arrayBST(l,mid+1,end) return tree
def arrayBST(l, start, end):
mid = (start + end) / 2
if start <= end:
tree = BinaryTree(l[mid])
tree.left = arrayBST(l, start, mid - 1)
tree.right = arrayBST(l, mid + 1, end)
return tree
def in_pre(inorder, preorder, start, end):
if start > end:
return None
tree = BinaryTree(preorder[in_pre.index])
in_pre.index += 1
if start == end:
return tree
i = inorder.index(tree.key)
tree.left = in_pre(inorder, preorder, start, i - 1)
tree.right = in_pre(inorder, preorder, i + 1, end)
return tree
def _build_tree(self, graph):
print("Building the tree")
# for clusters in each upper level besides 0
print ("The height of clusters is ", self._height_of_cluster)
uncontained_clusters = []
for i in (range(2, self._height_of_cluster+1)):
# for i in (range(2, 3)):
# for each cluster in this level
print (i)
# print("At level ", 3, " there are ", len(self._network.clusters_by_level(3)), " clusters.")
# self._network.print_cluster_by_level(3)
# print("At level ", 4, " there are ", len(self._network.clusters_by_level(4)), " clusters.")
# self._network.print_cluster_by_level(4)
# print("At level ", 5, " there are ", len(self._network.clusters_by_level(5)), " clusters.")
# self._network.print_cluster_by_level(5)
# at first, gather bottom level clusters which are contained in the upper level
cluster_contain_in_upper_level_map = {}
# list of clusters that were added to some other parent level clusters
ignore_cluster_list = []
for current_cluster in self._network.clusters_by_level(i):
# just checking
clustered_peers_list = []
print("Just checking Assert redundant clusters BEFORE")
for cluster in self._network.clusters_by_level(i):
for node in cluster.graph.nodes:
clustered_peers_list.append(node)
print(clustered_peers_list)
assert (self.at_least_one(Counter(clustered_peers_list)) < 0)
assert (self.at_most_logn(Counter(clustered_peers_list)) < 0)
# ignore intermediate clusters
print(current_cluster.cluster_id)
cluster_contain_in_upper_level_map[current_cluster.cluster_id] = []
skip_cluster = False
lower_level_clusters = self._network.clusters_by_level(i - 1)
print(len(lower_level_clusters))
print(len(uncontained_clusters))
for uncontained_cluster in uncontained_clusters:
lower_level_clusters.append(uncontained_cluster)
print(len(lower_level_clusters))
print("LOWER LEVEL CLUSTERS",lower_level_clusters)
# for lower_level_cluster in self._network.clusters_by_level(i - 1):
for lower_level_cluster in lower_level_clusters:
if lower_level_cluster.intermediate:
print(current_cluster.cluster_id)
print(current_cluster.peers)
else:
if lower_level_cluster.cluster_id not in ignore_cluster_list:
# check to see for duplicates
for key in cluster_contain_in_upper_level_map[current_cluster.cluster_id]:
# print(Counter(self._network.find_cluster_by_id(key).graph.nodes) == Counter(lower_level_cluster.graph.nodes))
if Counter(self._network.find_cluster_by_id(key).graph.nodes) == Counter(
lower_level_cluster.graph.nodes):
print("DUPLICATE FOUND")
skip_cluster = True
continue
for cluster_to_ignore in ignore_cluster_list:
# print("CLUSTER TO IGNORE ID")
# print(cluster_to_ignore)
# print(lower_level_cluster.cluster_id)
if Counter(lower_level_cluster.graph.nodes) == Counter(
self._network.find_cluster_by_id(cluster_to_ignore).graph.nodes):
print("DUPLICATE ENTRIES FOUND")
skip_cluster = True
continue
# check if same members
if skip_cluster == True:
continue
# print("Current cluster id ", current_cluster.cluster_id, " with nodes ", current_cluster.graph.nodes)
# print("Lower level cluster id ", lower_level_cluster.cluster_id, " with nodes ", lower_level_cluster.graph.nodes)
result = set(lower_level_cluster.graph.nodes).issubset(current_cluster.graph.nodes)
# print("CONTAIN ", result)
# print("THE MAP IS ", cluster_contain_in_upper_level_map)
if result:
cluster_contain_in_upper_level_map[current_cluster.cluster_id].append(
lower_level_cluster.cluster_id)
ignore_cluster_list.append(lower_level_cluster.cluster_id)
print("SIZE LOWER LEVEL CLUSTERS ", len(self._network.clusters_by_level(i-1)))
print("IGNORE CLUSTER LIST SIZE",len(ignore_cluster_list))
print (ignore_cluster_list)
# delete all uncontained_clusters that are in ignore_cluster_list
temp_uncontained_clusters = copy.deepcopy(uncontained_clusters)
print("BEFORE",uncontained_clusters)
print(len(uncontained_clusters))
for cluster in uncontained_clusters:
print("PRINTING A CLUSTER")
print(cluster.cluster_id)
print(len(uncontained_clusters))
for cluster in uncontained_clusters:
print("CHECKING CHECKING")
print(cluster.cluster_id)
print(ignore_cluster_list)
print(cluster.cluster_id in ignore_cluster_list)
if cluster.cluster_id in ignore_cluster_list:
print("FOUND CLUSTER", cluster.cluster_id)
print("REMOVING")
index_to_delete = None
for index in range(0,len(temp_uncontained_clusters)):
if temp_uncontained_clusters[index].cluster_id == cluster.cluster_id:
index_to_delete = index
print("index_TO_DELETE IS ", index_to_delete)
break
del(temp_uncontained_clusters[index_to_delete])
# temp_uncontained_clusters.remove(cluster)
uncontained_clusters = copy.deepcopy(temp_uncontained_clusters)
print("AFTERE",uncontained_clusters)
for cluster in uncontained_clusters:
print(cluster.cluster_id)
for lower_level_cluster in self._network.clusters_by_level(i-1):
if lower_level_cluster.cluster_id not in ignore_cluster_list:
if not lower_level_cluster.intermediate:
uncontained_clusters.append(lower_level_cluster)
print("PREPARED CLUSTER CONTAIN MAP", cluster_contain_in_upper_level_map)
# build the binary trees
current_tree = BinaryTree('temp')
for root_cluster_key in cluster_contain_in_upper_level_map:
tree_leafs = []
current_tree = BinaryTree(root_cluster_key)
print("ROOT CLUSTER IS ", root_cluster_key)
print (len(cluster_contain_in_upper_level_map[root_cluster_key]))
height_of_tree = int(math.log(1 if len(cluster_contain_in_upper_level_map[root_cluster_key]) == 0 else 2**(len(cluster_contain_in_upper_level_map[root_cluster_key]) - 1).bit_length(), 2) )
if len(cluster_contain_in_upper_level_map[root_cluster_key])== 1:
height_of_tree = 1
print("HEIGHT OF TREE IS ", height_of_tree + 1)
tree_nodes = cluster_contain_in_upper_level_map[root_cluster_key]
lower_level_trees = []
for bottommmost_level_cluster in cluster_contain_in_upper_level_map[root_cluster_key]:
print("REACHED and count is ")
print(bottommmost_level_cluster)
temp_tree = BinaryTree(bottommmost_level_cluster)
temp_tree.set_data(self._network.find_cluster_by_id(bottommmost_level_cluster).peers)
lower_level_trees.append(temp_tree)
for level in range(height_of_tree):
# for level in range(1):
new_lower_level_trees = []
print ("LEVEL ",level, "of the tree")
print("NEW LEVEL NODE COUNT ", len(lower_level_trees))
total_nodes_in_this_level = len(lower_level_trees)
for node in range(total_nodes_in_this_level):
if len(lower_level_trees) >= 2:
# name the cluster root different
tree_name = ''
if level + 1 == height_of_tree:
tree_name = root_cluster_key
else:
tree_name = lower_level_trees[0].getNodeValue() + "_" + lower_level_trees[1].getNodeValue()
temp_tree = BinaryTree(tree_name)
temp_data = []
temp_data.extend(lower_level_trees[0].get_data())
temp_data.extend(lower_level_trees[1].get_data())
temp_tree.set_data(temp_data)
temp_tree.left = lower_level_trees[0]
temp_tree.right = lower_level_trees[1]
# set the parents of lower level trees
lower_level_trees[0].set_parent(temp_tree)
lower_level_trees[1].set_parent(temp_tree)
lower_level_trees.pop(0)
lower_level_trees.pop(0)
# create a cluster out of the tree
if level+1 != height_of_tree:
# no cluster for the root cluster
print("KOKOKOKOKOKO",temp_tree.get_data())
cluster_graph = graph.subgraph([str(s) for s in temp_tree.get_data()])
cluster = Cluster(tree_name, cluster_graph, i)
cluster.set_intermediate()
self._network.add_cluster(i, cluster)
new_lower_level_trees.append(temp_tree)
print(level+1)
print(height_of_tree)
if level+1 == height_of_tree:
current_tree = temp_tree
print("PRINTING TREE")
printTree(temp_tree)
print("printed tree")
elif len(lower_level_trees) == 1:
# todo ignore same single cluster in multiple levels
if level + 1 == height_of_tree:
print("SHOULD REACH ONCE")
print(lower_level_trees[0].getNodeValue())
tree_name = ''
if level + 1 == height_of_tree:
tree_name = root_cluster_key
else:
tree_name = lower_level_trees[0].getNodeValue()
temp_tree = BinaryTree(tree_name)
temp_tree.set_data(lower_level_trees[0].get_data())
temp_tree.left = lower_level_trees[0]
# set the parents of lower level trees
# set parent only if not same node in lower level
lower_level_trees[0].set_parent(temp_tree)
lower_level_trees.pop(0)
# create a cluster out of the tree
if level + 1 != height_of_tree:
# no cluster for the root cluster
cluster_graph = graph.subgraph([str(s) for s in temp_tree.get_data()])
cluster = Cluster(tree_name, cluster_graph, i)
cluster.set_intermediate()
self._network.add_cluster(i, cluster)
new_lower_level_trees.append(temp_tree)
if level + 1 == height_of_tree:
current_tree = temp_tree
else:
new_lower_level_trees.append(lower_level_trees.pop(0))
print("AND NEW LEVEL NODE COUNT ", len(lower_level_trees))
print("NEW LEVEL NODE COUNT ", len(new_lower_level_trees))
lower_level_trees = new_lower_level_trees
print("Tree for cluster ", root_cluster_key, " is ")
(self._network.find_cluster_by_id(root_cluster_key)).set_tree(current_tree)
# set the tree children here, traversing starts from the children
printTree(current_tree)
(self._network.find_cluster_by_id(root_cluster_key)).set_tree_leafs(get_leafs(current_tree))
print("KO")
for clus in self._network.clusters_by_level(i):
clus.print()
# clus.tree.display_tree()
print("UNCONTAINED CLUSTERS ")
for cluster in uncontained_clusters:
cluster.print()
assert len(uncontained_clusters) == 0
# def _sort_clusters_in_a_level(self):
# for i in (range(0, self._height_of_cluster + 1)):
# index = 0
# for clus in self._network.clusters_by_level(i):
# clus.setIndex(index)
# index = index+1
# print("PRINTING CLUSTERS")
# for i in (range(0, self._height_of_cluster + 1)):
# for clus in self._network.clusters_by_level(i):
# clus.print()
print("Height of clusters", self._height_of_cluster)
for i in range(0,self._height_of_cluster+1):
print("At level ", i, " there are ", len(self._network.clusters_by_level(i)), " clusters.")
self._network.print_cluster_by_level(i)