def make_graphs(min_edges, max_edges):
graphs = []
for j in range(min_edges, max_edges + 1):
for sparse6 in generate_multigraphs(j):
G = Graph(sparse6)
G.build_img()
graphs.append(G)
return graphs
def main():
grid = Graph(Vector2(7, 7)) #Graph size
grid.make_nodes() #makes nodes
grid.nodes[23].can_traverse = False #wall 1
grid.nodes[24].can_traverse = False #wall 2
grid.nodes[25].can_traverse = False #wall 3
s = grid.nodes[10] #starting node
g = grid.nodes[38] #goal node
p = algorithm(s, g, grid) # astar where the start, goal, and graph are taken in
def testing_astar():
'''Algorithm testing'''
grid = Graph(Vector2(7, 7))
grid.make_nodes()
grid.nodes[23].can_traverse = False
grid.nodes[24].can_traverse = False
grid.nodes[25].can_traverse = False
s = grid.nodes[10]
g = grid.nodes[38]
p = algorithm(s, g, grid)
a = 0
def init_graph_mfriend(k):
jfile = mfriend(k)
vertices = [Vertex(j.name) for j in jfile]
edges = []
for j in jfile:
for u in vertices:
if j.name == u.name:
for f in j.friend_list:
for v in vertices:
if v.name == f:
uv = Edge((u, v), directed=False)
edges.append(uv)
for e in edges:
if (uv.source.name, uv.target.name) == (
e.source.name, e.target.name) or (
uv.target.name,
uv.source.name) == (e.source.name,
e.target.name):
pass
graph = Graph()
init_graph(graph, vertices, edges)
return graph
def random_model(n1, n2, k, cap):
"""
create a graph with the partition A of size n1
and partition B of size n2 using the random model
:param n1: size of partition A
:param n2: size of partition B
:param k: length of preference list for vertices in A
:param cap: capacity of a vertex in partition B
:return: bipartite graph with above properties
"""
def order_by_master_list(l, master_list):
return sorted(l, key=master_list.index)
g = Graph()
# create the sets R and H, r_1 ... r_n1, h_1 .. h_n2
R = set('r{}'.format(i) for i in range(1, n1 + 1))
H = set('h{}'.format(i) for i in range(1, n2 + 1))
for res in R:
g.residents.append(Resident(res))
for hosp in H:
g.hospitals.append(Hospital(hosp, 0, cap))
# prepare a master list
# master_list = list(h for h in H)
# random.shuffle(master_list)
pref_lists_H, pref_lists_R = collections.defaultdict(list), {}
for resident in R:
r_ind = resident[1:]
pref_list = random.sample(H, min(
len(H), k)) # random.randint(1, len(H))) # sample houses
pref_lists_R[
resident] = pref_list # order_by_master_list(pref_list, master_list)
# add these residents to the preference list for the corresponding hospital
for hospital in pref_list:
h_ind = hospital[1:]
pref_lists_H[hospital].append(resident)
g.edges.append(Edge(r_ind, h_ind))
res = g.get_resident(resident)
for hosp in pref_lists_R[resident]:
res.pref.append(g.get_hospital(hosp))
for hospital in H:
random.shuffle(pref_lists_H[hospital])
hosp = g.get_hospital(hospital)
for res in pref_lists_H[hospital]:
hosp.pref.append(g.get_resident(res))
return g
def main():
nodelist = [(8, 2), (5, 9), (4, 3), (2, 6), (1, 7), (9, 2), (5, 5), (8, 3),
(11, 8), (6, 14)]
n = len(nodelist)
edges = edge_list(nodelist, n)
g = Graph(n)
for i in range(0, edges.shape[0]):
g.add_edge(int(edges[i][0]), int(edges[i][1]), edges[i][2])
g.toString()
start_node = 0
global visited
visited = [False] * n
plot_graph(nodelist, start_node)
def init_graph_toy_clique_problem():
name = 'abcdef'
vertices = [Vertex(n) for n in name]
e1 = Edge((vertices[0], vertices[1]), directed=False)
e2 = Edge((vertices[1], vertices[2]), directed=False)
e3 = Edge((vertices[2], vertices[3]), directed=False)
e4 = Edge((vertices[3], vertices[0]), directed=False)
e5 = Edge((vertices[0], vertices[4]), directed=False)
e6 = Edge((vertices[4], vertices[5]), directed=False)
e7 = Edge((vertices[5], vertices[0]), directed=False)
graph = Graph()
edges = [e1, e2, e3, e4, e5, e6, e7]
init_graph(graph, vertices, edges)
return graph
def init_graph_johnson():
w = Vertex('w')
x = Vertex('x')
y = Vertex('y')
z = Vertex('z')
e1 = Edge((w, z), weight=2)
e2 = Edge((z, x), weight=-7)
e3 = Edge((x, w), weight=6)
e4 = Edge((x, y), weight=3)
e5 = Edge((y, z), weight=5)
e6 = Edge((z, y), weight=-3)
e7 = Edge((y, w), weight=4)
graph = Graph()
vertices = [w, x, y, z]
edges = [e1, e2, e3, e4, e5, e6, e7]
init_graph(graph, vertices, edges)
return graph
def earliest_ancestor(ancestors, starting_node):
graph = Graph()
# For each pair
for i in ancestors:
# Add a vertices
graph.add_vertex(i[0]) # Parent
graph.add_vertex(i[1]) # Child
# Add an edge between the parent and the child
graph.add_edge(i[1],i[0])
q = Queue()
visited = set()
longest_path = 1
parent_end = -1
q.enqueue([starting_node])
while q.size() > 0:
path = q.dequeue()
vertex = path[-1]
if len(path) >= longest_path and vertex < parent_end or len(path) > longest_path:
parent_end = vertex
visited.add(vertex)
for neighbor in graph.vertices[vertex]:
path_add = path.copy()
path_add.append(neighbor)
q.enqueue(path_add)
return parent_end
from dfs import dfs from graph_class import Graph from check_connected_dfs import find_all_group, check_connected_num_dfs G1 = Graph(13) print(G1.nodes) print(G1.adj_of_node) G1.build_undirected_edge(0, 1) G1.build_undirected_edge(0, 9) G1.build_undirected_edge(1, 0) G1.build_undirected_edge(1, 8) G1.build_undirected_edge(2, 3) G1.build_undirected_edge(3, 2) G1.build_undirected_edge(3, 4) G1.build_undirected_edge(3, 5) G1.build_undirected_edge(3, 7) G1.build_undirected_edge(4, 3) G1.build_undirected_edge(5, 3) G1.build_undirected_edge(5, 6) G1.build_undirected_edge(6, 5) G1.build_undirected_edge(6, 7) G1.build_undirected_edge(7, 3) G1.build_undirected_edge(7, 8) G1.build_undirected_edge(7, 10) G1.build_undirected_edge(7, 11) G1.build_undirected_edge(8, 1) G1.build_undirected_edge(8, 7) G1.build_undirected_edge(8, 9) G1.build_undirected_edge(9, 0) G1.build_undirected_edge(9, 8) G1.build_undirected_edge(10, 7)
def verification(directory):
g = Graph()
g.create_graph(directory)
generate_max_card_lp(g, directory + '/max_card_lp.txt')
print(len(g.edges))
# edges.append((node, neighbour))
# return edges
#
#print(generate_edges(graph))
#
#def find_isolated_nodes(graph):
# ''' return a list of isolated nodes '''
# isolated = []
# for node in graph:
# if not graph[node]:
# isolated += node
# return isolated
#
#print(find_isolated_nodes(graph))
graph = Graph(g)
print('All path from vertex "A" to vertex "D":')
paths = graph.find_all_paths("A", "D")
print(paths)
path_len = []
shortest_path = []
for path in paths:
path_len.append(len(path))
for path in paths:
if len(path) == min(path_len):
shortest_path.append(path)
print("The shortest paths are ", shortest_path)
#print('The path from vertex "A" to vertex "F":')
#path = graph.find_path("A", "F")
#print(path)
# +
# # matrix genaration
# matrix1 = graph1.create_matrix(amount_nodes)
# edges1 = graph1.generate_edges(amount_nodes, percent_edges)
# matrix1 = graph1.add_edges(matrix1, edges1)
# list_edges = graph1.make_list_edges_distances(matrix1)
# netxgraph1 = graph1.build_networkx_graph(list_edges)
# +
# matrix1
# -
# This initializes regular SOM grid matrix, it needs to be passes instead of matrix1 for it to work
# Also one needs to experiment with sigma to achieve good results learning rate
graph2 = Graph()
matrix2 = graph2.standard_som_distance_matrix(length, width)
list_edges = graph2.make_list_edges_distances(matrix2)
netxgraph2 = graph2.build_networkx_graph(list_edges)
map1 = MapClass(data, length, width, learning_rate, number_epochs, matrix2,
sigma, data_lables, batch_size, shuffle, netxgraph2,
special_shape_map)
# +
# ### Drawing configuration
# drawtype="rbg" tries to draw colors on map - needs an input data with 3 dimensions or columns (colors)
# drawtype="black-white" draws black-white - needs an input data with one dimension or column (gray_colors)
# drawtype="networkx" graph drawing using the networkx library
def seat_model_generator(n1, n2, k_low, k_up):
"""
create a graph with the partition R of size n1 and
partition H of size n2
:param n1: size of partition R
:param n2: size of partition H
:param k_low, k_up: range for length of resident preference lists
"""
def order_by_master_list(l, master_list):
return sorted(l, key=master_list.index)
possible_credits = [5, 10, 15, 20]
# set up geometric distribution among above possible hospital credits
probs = np.random.geometric(p=0.10, size=len(possible_credits))
probs = probs / np.sum(probs)
def get_hosp_credits():
return list(
np.random.choice(possible_credits, size=1, replace=False,
p=probs))[0]
def get_hosp_capacity_uniform():
# returns the average hospital capacity based on resident, hospital credits
res_cap_sum = 0
hosp_cred_sum = 0
for r in g.residents:
res_cap_sum += r.uq
for h in g.hospitals:
hosp_cred_sum += h.credits
return int(np.ceil((1.5 * res_cap_sum) / hosp_cred_sum))
def get_hosp_capacity_non_uniform(cap):
low = int(np.ceil(0.3 * cap))
high = int(np.ceil(1.7 * cap))
return random.randint(low, high)
g = Graph()
# default hospital capacity
cap = 60
# create the sets R and H, r_1 ... r_n1, h_1 .. h_n2
R = list('r{}'.format(i) for i in range(1, n1 + 1))
H = list('h{}'.format(i) for i in range(1, n2 + 1))
for res in R:
g.residents.append(Resident(res))
for hosp in H:
g.hospitals.append(Hospital(hosp, 0, cap, get_hosp_credits()))
# prepare a master list
master_list = list(r for r in R)
random.shuffle(master_list)
# setup a probability distribution over the hospitals
p = np.random.geometric(p=0.10, size=len(H))
# normalize the distribution
p = p / np.sum(p) # p is a ndarray, so this operation is correct
prob_dict = dict(zip(H, p))
master_list_h = sorted(H, key=lambda h: prob_dict[h], reverse=True)
pref_H, pref_R = collections.defaultdict(list), {}
for r in R:
# sample hospitals according to the probability distribution and without replacement
k = random.randint(k_low, k_up)
pref_R[r] = list(
np.random.choice(H, size=min(len(H), k), replace=False, p=p))
# add these residents to the preference list for the corresponding hospitals
for h in pref_R[r]:
pref_H[h].append(r)
for r in R:
pref_R[r] = order_by_master_list(pref_R[r], master_list_h)
# random.shuffle(pref_R[r])
res = g.get_resident(r)
for hosp in pref_R[r]:
res.pref.append(g.get_hospital(hosp))
for h in H:
pref_H[h] = order_by_master_list(pref_H[h], master_list)
# random.shuffle(pref_H[h])
hosp = g.get_hospital(h)
for res in pref_H[h]:
hosp.pref.append(g.get_resident(res))
g.init_resident_capacities()
# get average capacity for hospitals
cap = get_hosp_capacity_uniform()
for h in g.hospitals:
h.uq = get_hosp_capacity_non_uniform(cap)
# initialize class constraints for residents
g.init_all_resident_class()
# initialize master class constraints applicable to all residents
g.init_master_classes_disjoint()
return g
for i in G.adj_of_node[out_element]:
indegree_list[i] -= 1
if indegree_list[i] == 0:
no_incoming_set.add(i)
if all((G.nodes_visited)):
return toporder_list
else:
return 'NOT A VALID DAG'
#print(str(all_set))
#print(str(edge_to_set))
#print(str(no_incoming_set))
#print(str(indegree_list))
#print(toporder_list)
DAG1 = Graph(8)
DAG1.build_directed_edge(1, 4)
DAG1.build_directed_edge(1, 6)
DAG1.build_directed_edge(2, 7)
DAG1.build_directed_edge(3, 4)
DAG1.build_directed_edge(3, 7)
DAG1.build_directed_edge(4, 5)
DAG1.build_directed_edge(7, 5)
DAG1.build_directed_edge(7, 6)
#print(topsort_dfs(DAG1))
print(topsort_kahn(DAG1))
DAG2 = Graph(8)
DAG2.build_directed_edge(0, 6)
DAG2.build_directed_edge(1, 2)
DAG2.build_directed_edge(1, 4)
##una_cmap = [colormap_builder(1,unacceptable[0],6),colormap_builder(1,unacceptable[1],7)]
##
##build_average_graph(7, 1)
##build_average_graph(31, 1)
##build_average_graph(93, 1)
##build_average_graph(186, 1)
##build_bearing_graph(7, 1)
#build_fft_graph(filenames,48,1/500)
#print(cbar)
frame = Graph_Frame(r'/usr/local/RVMD/GUI/Ch3_Graphs/')
#graph_X = Graph(ID, *kwargs)
graph_1 = Graph('Ch3_AVG', has_cbar=True, has_gradient=True)
graph_2 = Graph('Ch3_AVG', has_cbar=True, has_gradient=True)
graph_3 = Graph('Ch3_AVG', has_cbar=True, has_gradient=True)
graph_4 = Graph('Ch3_AVG', has_cbar=True, has_gradient=True)
graph_5 = Graph('Ch3_LOOSE', fill_color='lightsteelblue')
graph_6 = Graph('Ch3_LOOSE', fill_color='lightsteelblue')
graph_7 = Graph('Ch3_LOOSE', fill_color='lightsteelblue')
graph_8 = Graph('Ch3_LOOSE', fill_color='lightsteelblue')
graph_9 = Graph('Ch3_MISA', fill_color='honeydew')
graph_10 = Graph('Ch3_MISA', fill_color='honeydew')
graph_11 = Graph('Ch3_MISA', fill_color='honeydew')
graph_12 = Graph('Ch3_MISA', fill_color='honeydew')
graph_13 = Graph('Ch3_BEAR', fill_color='limegreen')
gray_colors_lables = [[0.1], ["black"], ["white"], [0.125], [0.529], [0.9],
[0.33], [0.4], [0.67], [.33], [.5]]
# +
# Graph setup
# -
# for now amount_verticies has to be a multiplication of length and width
amount_nodes = 100
percent_edges = 0.2
# +
# using graph class to generate matrix
graph1 = Graph()
# +
# matrix genaration
matrix1 = graph1.create_matrix(amount_nodes)
edges1 = graph1.generate_edges(amount_nodes, percent_edges)
matrix1 = graph1.add_edges(matrix1, edges1)
list_edges = graph1.make_list_edges_distances(matrix1)
netxgraph1 = graph1.build_networkx_graph(list_edges)
# -
# +
# matrix1
# +
def mahadian_model(n1, n2, k, cap):
"""
create a graph with the partition R of size n1 and
partition H of size n2 using the model as described in
Marriage, Honesty, and Stability
Immorlica, Nicole and Mahdian, Mohammad
Sixteenth Annual ACM-SIAM Symposium on Discrete Algorithms
:param n1: size of partition R
:param n2: size of partition H
:param k: length of preference list for the residents
:param cap: capacity of the hospitals
:return: bipartite graph with above properties
"""
def order_by_master_list(l, master_list):
return sorted(l, key=master_list.index)
g = Graph()
# create the sets R and H, r_1 ... r_n1, h_1 .. h_n2
R = list('r{}'.format(i) for i in range(1, n1 + 1))
H = list('h{}'.format(i) for i in range(1, n2 + 1))
# prepare a master list
master_list = list(r for r in R)
random.shuffle(master_list)
for res in R:
g.residents.append(Resident(res))
for hosp in H:
g.hospitals.append(Hospital(hosp, 0, cap))
# setup a probability distribution over the hospitals
p = np.random.geometric(p=0.10, size=len(H))
# normalize the distribution
p = p / np.sum(p) # p is a ndarray, so this operation is correct
prob_dict = dict(zip(H, p))
master_list_h = sorted(H, key=lambda h: prob_dict[h], reverse=True)
# print(prob_dict, master_list_h, sep='\n')
pref_H, pref_R = collections.defaultdict(list), {}
for r in R:
# sample women according to the probability distribution and without replacement
pref_R[r] = list(
np.random.choice(H, size=min(len(H), k), replace=False, p=p))
# add these man to the preference list for the corresponding women
r_ind = r[1:]
for h in pref_R[r]:
h_ind = h[1:]
pref_H[h].append(r)
g.edges.append(Edge(r_ind, h_ind))
for r in R:
pref_R[r] = order_by_master_list(pref_R[r], master_list_h)
res = g.get_resident(r)
for hosp in pref_R[r]:
res.pref.append(g.get_hospital(hosp))
for h in H:
pref_H[h] = order_by_master_list(pref_H[h], master_list)
#random.shuffle(pref_H[h])
hosp = g.get_hospital(h)
for res in pref_H[h]:
hosp.pref.append(g.get_resident(res))
return g
gray_colors_lables = [[0.1], ["black"], ["white"], [0.125], [0.529], [0.9],
[0.33], [0.4], [0.67], [.33], [.5]]
# +
# Graph setup
# -
# for now amount_verticies has to be a multiplication of length and width
amount_nodes = 400
percent_edges = 0.5
# +
# using graph class to generate matrix
graph1 = Graph()
# +
# matrix genaration
matrix1 = graph1.create_matrix(amount_nodes)
edges1 = graph1.generate_edges(amount_nodes, percent_edges)
matrix1 = graph1.add_edges(matrix1, edges1)
list_edges = graph1.make_list_edges_distances(matrix1)
netxgraph1 = graph1.build_networkx_graph(list_edges)
# +
# matrix1
# -
# This initializes regular SOM grid matrix, it needs to be passes instead of matrix1 for it to work
"""Exercise 2: Get all unique nodes using a Graph class (2 way Digraph Class)
The below code is set to test our understanding of what is a unique node, knowing that the
Graph class sets to Edge classes in a two way fashion (bi-directional).
Run the following code by running the file with `python exercise_2_graphs.py`
"""
from graph_class import Node, Graph, Edge
nodes = []
nodes.append(Node("ABC")) # nodes[0]
nodes.append(Node("ACB")) # nodes[1]
nodes.append(Node("BAC")) # nodes[2]
nodes.append(Node("BCA")) # nodes[3]
nodes.append(Node("CAB")) # nodes[4]
nodes.append(Node("CBA")) # nodes[5]
g = Graph()
for n in nodes:
g.addNode(n)
g.addEdge(Edge(g.getNode('ABC'), g.getNode('ACB')))
g.addEdge(Edge(g.getNode('ABC'), g.getNode('BAC')))
g.addEdge(Edge(g.getNode('ACB'), g.getNode('CAB')))
g.addEdge(Edge(g.getNode('BAC'), g.getNode('BCA')))
g.addEdge(Edge(g.getNode('BCA'), g.getNode('CBA')))
g.addEdge(Edge(g.getNode('CAB'), g.getNode('CBA')))
if __name__ == "__main__":
print(g)
##
#CREATE GRAPH FRAME OBJECTS THAT WILL STORE THE GRAPH OBJECTS
##
frame_0 = Graph_Frame(r'/usr/local/RVMD/GUI/Ch0_Graphs/')
frame_1 = Graph_Frame(r'/usr/local/RVMD/GUI/Ch1_Graphs/')
frame_2 = Graph_Frame(r'/usr/local/RVMD/GUI/Ch2_Graphs/')
frame_3 = Graph_Frame(r'/usr/local/RVMD/GUI/Ch3_Graphs/')
#graph_X = Graph(ID, *kwargs)
##
#CREATE GRAPH OBJECTS FOR FRAME_0
##
graph_1_f0 = Graph('Ch0_AVG', has_cbar=True, has_gradient=True)
graph_2_f0 = Graph('Ch0_AVG', has_cbar=True, has_gradient=True)
graph_3_f0 = Graph('Ch0_AVG', has_cbar=True, has_gradient=True)
graph_4_f0 = Graph('Ch0_AVG', has_cbar=True, has_gradient=True)
graph_5_f0 = Graph('Ch0_LOOSE', fill_color='lightsteelblue')
graph_6_f0 = Graph('Ch0_LOOSE', fill_color='lightsteelblue')
graph_7_f0 = Graph('Ch0_LOOSE', fill_color='lightsteelblue')
graph_8_f0 = Graph('Ch0_LOOSE', fill_color='lightsteelblue')
graph_9_f0 = Graph('Ch0_MISA', fill_color='lightsteelblue')
graph_10_f0 = Graph('Ch0_MISA', fill_color='lightsteelblue')
graph_11_f0 = Graph('Ch0_MISA', fill_color='lightsteelblue')
graph_12_f0 = Graph('Ch0_MISA', fill_color='lightsteelblue')
graph_13_f0 = Graph('Ch0_BEAR', fill_color='lightsteelblue')
exit("Program closed!")
def loop(tkroot):
"""
Main loop of program
"""
global element_indexes
element_indexes = draw_graph(graph, graph_canvas, element_indexes)
tkroot.mainloop()
if __name__ == "__main__":
focused_object_index = None # For deleting elements and moving vertices
dragged_object_index = None # For creating edges
element_indexes = dict() # Dictionary of IDs of elements on canvas and object instances of graph components
graph = Graph() # Main graph instance for visualization
# Example of adding components to graph
_test_dict = {
"A": ["B"],
"B": ["D"],
"C": [],
"D": ["A", "C"]
}
graph.add_from_neighbors_list(_test_dict)
root = setup()
loop(root)
[.33, .33, .33], [.5, .5, .5], [.66, .66, .66]]
color_names = \
['black', 'blue', 'darkblue', 'skyblue',
'greyblue', 'lilac', 'green', 'red',
'cyan', 'violet', 'yellow', 'white',
'darkgrey', 'mediumgrey', 'lightgrey']
# +
# Graph setup
# -
amount_vertecies = 100
percent_edges = 0.5
graph1 = Graph()
matrix1 = graph1.create_matrix(amount_vertecies)
edges1 = graph1.generate_edges(amount_vertecies, percent_edges)
matrix1 = graph1.add_edges(matrix1, edges1)
# +
# Network configuration
data = rgb_colors
data_lables = color_names
batch_size = 2
length = 10
from graph_class import Vertex
from graph_class import Graph
g = Graph()
for i in range(6):
g.addVertex(i)
g.vertList
g.addEdge(0, 1, 5)
g.addEdge(0, 5, 2)
g.addEdge(1, 2, 4)
g.addEdge(2, 3, 9)
g.addEdge(3, 4, 7)
g.addEdge(3, 5, 3)
g.addEdge(4, 0, 1)
g.addEdge(5, 4, 8)
g.addEdge(5, 2, 1)
for v in g:
for w in v.getConnections():
print("( %s , %s )" % (v.getId(), w.getId()))