def make_clusters(adjacency, alpha_breaks = [0,1,2], seed = 0):
graph = np.array(adjacency)
degrees = np.sum(graph, axis = 1)
n = len(graph)
all_nodes = np.eye(n)
clusters = [Cluster(all_nodes[i],graph[i].astype(float),degrees) for i in range(n)]
#clustered = [0 for i in range(n)]
my_alpha_clusters = alpha_clusters(alpha_breaks)
binary_cluster = [t_node([i]) for i in range(n)]
where_we_are = [seed]
for i in range(2*n+2):
here = where_we_are[-1]
#print("here",here,clusters[here].readable_nodes())
if(clusters[here].parent):
print("this is probably a bug")
if(sum(clusters[here].nodes)==n):
print("Finished after", i, "iterations")
break
if(i == 2*n):
print("bad break....")
break
next_node, not_using, not_using2, alpha = clusters[here].get_closest()
#print("next_node", next_node, "alpha", alpha)
while(clusters[next_node].parent):
next_node = clusters[next_node].parentId
next_node_best, not_using, not_using2, not_using3 = clusters[next_node].get_closest()
if(clusters[here].nodes[next_node_best] == 1):
#print("match!", next_node)
where_we_are = [here]
clusters[here].add_nodes(clusters[next_node].nodes,clusters[next_node].weights)
clusters[next_node].parent = clusters[here]
clusters[next_node].parentId = here
#print(here, clusters[here].readable_nodes())
binary_cluster[here].union(binary_cluster[next_node])
#my_alpha_clusters.add_cluster(here,clusters[here].nodes.astype(float),alpha)
else:
if(next_node in where_we_are):
#print("found a loop:", where_we_are, next_node)
for i in range(len(where_we_are)-1):
spot = where_we_are[i]
while(clusters[spot].parent):
spot = clusters[spot].parentId
clusters[here].add_nodes(clusters[spot].nodes,clusters[spot].weights)
clusters[spot].parent = clusters[here]
clusters[spot].parentId = here
binary_cluster[here].union(binary_cluster[spot])
where_we_are = [here]
else:
where_we_are.append(next_node)
#return my_alpha_clusters
#print(clusters[here].nodes)
return binary_cluster[0].get_parent()
def make_clusters(adjacency, alpha_breaks=[0, 1, 2], seed=0):
graph = np.array(adjacency)
degrees = np.sum(graph, axis=1)
n = len(graph)
all_nodes = np.eye(n)
clusters = [
Cluster(all_nodes[i], graph[i].astype(float), degrees)
for i in range(n)
]
#clustered = [0 for i in range(n)]
my_alpha_clusters = alpha_clusters(alpha_breaks)
binary_cluster = [t_node([i]) for i in range(n)]
where_we_are = [seed]
for i in range(2 * n + 2):
here = where_we_are[-1]
#print("here",here,clusters[here].readable_nodes())
if (clusters[here].parent):
print("this is probably a bug")
if (sum(clusters[here].nodes) == n):
print("Finished after", i, "iterations")
break
if (i == 2 * n):
print("bad break....")
break
next_node, not_using, not_using2, alpha = clusters[here].get_closest()
#print("next_node", next_node, "alpha", alpha)
while (clusters[next_node].parent):
next_node = clusters[next_node].parentId
next_node_best, not_using, not_using2, not_using3 = clusters[
next_node].get_closest()
if (clusters[here].nodes[next_node_best] == 1):
#print("match!", next_node)
where_we_are = [here]
clusters[here].add_nodes(clusters[next_node].nodes,
clusters[next_node].weights)
clusters[next_node].parent = clusters[here]
clusters[next_node].parentId = here
#print(here, clusters[here].readable_nodes())
binary_cluster[here].union(binary_cluster[next_node])
#my_alpha_clusters.add_cluster(here,clusters[here].nodes.astype(float),alpha)
else:
if (next_node in where_we_are):
#print("found a loop:", where_we_are, next_node)
for i in range(len(where_we_are) - 1):
spot = where_we_are[i]
while (clusters[spot].parent):
spot = clusters[spot].parentId
clusters[here].add_nodes(clusters[spot].nodes,
clusters[spot].weights)
clusters[spot].parent = clusters[here]
clusters[spot].parentId = here
binary_cluster[here].union(binary_cluster[spot])
where_we_are = [here]
else:
where_we_are.append(next_node)
#return my_alpha_clusters
#print(clusters[here].nodes)
return binary_cluster[0].get_parent()
def make_clusters(adjacency, seed = 0):
graph = np.array(adjacency)
degrees = np.sum(graph, axis = 1)
n = len(graph)
all_nodes = np.eye(n)
clusters = [Cluster(i, all_nodes[i],graph[i].astype(float),degrees) for i in range(n)]
binary_cluster = [t_node([i]) for i in range(n)]
where_we_are = [seed]
where_we_are_alpha = []
for i in range(3*n+2):
here = where_we_are[-1]
#print("here",here,clusters[here].readable_nodes())
if(clusters[here].parent):
print("this is probably a bug")
if(sum(clusters[here].nodes)==n):
print("Finished after", i, "iterations")
break
if(i == 3*n):
print("bad break....")
break
next_node, alpha = clusters[here].get_closest()
#print("here", here, "next_node", next_node, "alpha", alpha)
#print(clusters[next_node].get_nodes())
#print(clusters[here].get_change(clusters[next_node].get_nodes()))
while(clusters[next_node].parent):
next_node = clusters[next_node].parentId
next_node_best, next_node_alpha = clusters[next_node].get_closest()
#print("alphas", alpha, next_node_alpha)
if(clusters[here].nodes[next_node_best] == 1):
#print("match!", next_node)
#print(clusters[next_node].get_change(clusters[here].get_nodes()))
where_we_are = [here]
where_we_are_alpha = []
clusters[here].add_nodes(clusters[next_node].nodes,clusters[next_node].weights)
clusters[next_node].parent = clusters[here]
clusters[next_node].parentId = here
binary_cluster[here].union(binary_cluster[next_node], alpha, next_node_alpha)
elif(alpha < next_node_alpha):
print("Takeover!!!", alpha, next_node_alpha, here, next_node)
#print(clusters[next_node].get_change(clusters[here].get_nodes()))
where_we_are = [here]
where_we_are_alpha = []
clusters[here].add_nodes(clusters[next_node].nodes,clusters[next_node].weights)
clusters[next_node].parent = clusters[here]
clusters[next_node].parentId = here
binary_cluster[here].union(binary_cluster[next_node], alpha, next_node_alpha)
elif(next_node in where_we_are):
where_we_are_alpha.append(alpha) #sort of messy, but we got to do it
print("found a loop:", where_we_are, next_node, where_we_are_alpha,"here", here)
backwards_alpha = []
max_alpha =[] #actually we are doing the average
for i in range(len(where_we_are)):
first = where_we_are[i]
second = where_we_are[(i+1)%len(where_we_are)]
t_t_alpha, next_ve = clusters[second].get_alpha(clusters[first].nodes)
backwards_alpha.append(t_t_alpha)
if(backwards_alpha[i] > where_we_are_alpha[i]):
max_alpha.append(t_t_alpha+where_we_are_alpha[i]) #average
else:
max_alpha.append(where_we_are_alpha[i]+t_t_alpha) #average
minimum_arg = np.argmin(np.array(max_alpha))
#print(where_we_are[minimum_arg], where_we_are[(minimum_arg+1)%len(where_we_are)])
here = where_we_are[minimum_arg]
spot = where_we_are[(minimum_arg +1)%len(where_we_are)]
if(clusters[here].parent or clusters[spot].parent):
raise SyntaxError
"""
size_of_cluster = []
for x in where_we_are:
size_of_cluster.append(sum(clusters[x].nodes))
minimum_arg = np.argmin(np.array(size_of_cluster))
here = where_we_are[minimum_arg]
spot = where_we_are[(minimum_arg +1)%len(where_we_are)]
"""
clusters[here].add_nodes(clusters[spot].nodes,clusters[spot].weights)
clusters[spot].parent = clusters[here]
clusters[spot].parentId = here
binary_cluster[here].union(binary_cluster[spot],\
where_we_are_alpha[minimum_arg],\
backwards_alpha[minimum_arg])
where_we_are = [here]
where_we_are_alpha = []
else:
#print("moving on")
#print(clusters[next_node].get_change(clusters[here].get_nodes()))
where_we_are.append(next_node)
where_we_are_alpha.append(alpha)
return binary_cluster[0].get_parent()
def make_clusters(adjacency, seed=0):
graph = np.array(adjacency)
degrees = np.sum(graph, axis=1)
n = len(graph)
all_nodes = np.eye(n)
clusters = [
Cluster(i, all_nodes[i], graph[i].astype(float), degrees)
for i in range(n)
]
binary_cluster = [t_node([i]) for i in range(n)]
where_we_are = [seed]
where_we_are_alpha = []
for i in range(3 * n + 2):
here = where_we_are[-1]
#print("here",here,clusters[here].readable_nodes())
if (clusters[here].parent):
print("this is probably a bug")
if (sum(clusters[here].nodes) == n):
print("Finished after", i, "iterations")
break
if (i == 3 * n):
print("bad break....")
break
next_node, alpha = clusters[here].get_closest()
#print("here", here, "next_node", next_node, "alpha", alpha)
while (clusters[next_node].parent):
next_node = clusters[next_node].parentId
next_node_best, next_node_alpha = clusters[next_node].get_closest()
if (clusters[here].nodes[next_node_best] == 1):
#print("match!", next_node)
where_we_are = [here]
where_we_are_alpha = []
clusters[here].add_nodes(clusters[next_node].nodes,
clusters[next_node].weights)
clusters[next_node].parent = clusters[here]
clusters[next_node].parentId = here
binary_cluster[here].union(binary_cluster[next_node], alpha,
next_node_alpha)
elif (next_node in where_we_are):
where_we_are_alpha.append(
alpha) #sort of messy, but we got to do it
#print("found a loop:", where_we_are, next_node, where_we_are_alpha,"here", here)
backwards_alpha = []
max_alpha = [] #actually we are doing the average
for i in range(len(where_we_are)):
first = where_we_are[i]
second = where_we_are[(i + 1) % len(where_we_are)]
t_t_alpha, next_ve = clusters[second].get_alpha(
clusters[first].nodes)
backwards_alpha.append(t_t_alpha)
if (backwards_alpha[i] > where_we_are_alpha[i]):
max_alpha.append(t_t_alpha +
where_we_are_alpha[i]) #average
else:
max_alpha.append(where_we_are_alpha[i] +
t_t_alpha) #average
minimum_arg = np.argmin(np.array(max_alpha))
#print(where_we_are[minimum_arg], where_we_are[(minimum_arg+1)%len(where_we_are)])
here = where_we_are[minimum_arg]
spot = where_we_are[(minimum_arg + 1) % len(where_we_are)]
if (clusters[here].parent or clusters[spot].parent):
raise SyntaxError
"""
size_of_cluster = []
for x in where_we_are:
size_of_cluster.append(sum(clusters[x].nodes))
minimum_arg = np.argmin(np.array(size_of_cluster))
here = where_we_are[minimum_arg]
spot = where_we_are[(minimum_arg +1)%len(where_we_are)]
"""
clusters[here].add_nodes(clusters[spot].nodes,
clusters[spot].weights)
clusters[spot].parent = clusters[here]
clusters[spot].parentId = here
binary_cluster[here].union(binary_cluster[spot],\
where_we_are_alpha[minimum_arg],\
backwards_alpha[minimum_arg])
where_we_are = [here]
where_we_are_alpha = []
else:
#print("moving on")
where_we_are.append(next_node)
where_we_are_alpha.append(alpha)
return binary_cluster[0].get_parent()
