def show_all_graphs():
"""
[show_all_graphs ()] displays all the significant graphs generated to
get [union_graph]
"""
# [graph]
graph_fig = plot.graph(graph)
graph_fig.show()
# [dense_graph]
dense_graph_fig = plot.graph(dense_graph)
dense_graph_fig.show()
# [unconnected_graph]
unconnected_graph_fig = plot.graph(union_graph, sub)
unconnected_graph_fig.show()
# [union_graph]
union_graph_fig = plot.graph(union_graph, sub)
union_graph_fig.show()
# # The :mod:`~.apps.data` module has pre-generated GBS samples from this graph. Let's load them, # postselect on samples with a large number of clicks, and convert them to subgraphs: planted = data.Planted() postselected = sample.postselect(planted, 16, 30) pl_graph = nx.to_networkx_graph(planted.adj) samples = sample.to_subgraphs(postselected, pl_graph) print(len(samples)) ############################################################################## # Not bad! We have more than 2000 samples to play with 😎. The planted subgraph is actually easy to # identify; it even appears clearly from the force-directed Kamada-Kawai algorithm that is used to # plot graphs in Strawberry Fields: sub = list(range(20, 30)) plot_graph = plot.graph(pl_graph, sub) plotly.offline.plot(plot_graph, filename="planted.html") ############################################################################## # .. raw:: html # :file: ../../examples_apps/planted.html # # .. note:: # The command ``plotly.offline.plot()`` is used to display plots in the documentation. In # practice, you can simply use ``plot_graph.show()`` to view the graph. ############################################################################## # A more interesting challenge is to find dense subgraphs of different sizes; it is often # useful to identify many high-density subgraphs, not just the densest ones. This is the purpose of # the :func:`~.subgraph.search` function in the :mod:`~.apps.subgraph` module: to identify # collections of dense subgraphs for a range of sizes. The output of this function is a
The first step is to import the Strawberry Fields ``apps`` module and external dependencies: """ from strawberryfields.apps import data, plot, sample, clique import numpy as np import networkx as nx import plotly ############################################################################## # The adjacency matrix of the TACE-AS graph can be loaded from the :mod:`~.apps.data` module and the # graph can be visualized using the :mod:`~.apps.plot` module: TA = data.TaceAs() A = TA.adj TA_graph = nx.Graph(A) plot_graph = plot.graph(TA_graph) plotly.offline.plot(plot_graph, filename="TACE-AS.html") ############################################################################## # .. raw:: html # :file: ../../examples_apps/TACE-AS.html # # .. note:: # The command ``plotly.offline.plot()`` is used to display plots in the documentation. In # practice, you can simply use ``plot_graph.show()`` to view your graph. ############################################################################## # Can you spot any cliques in the graph? It's not so easy using only your eyes! The TACE-AS graph # is sufficiently small that all cliques can be found by performing an exhaustive search over # all subgraphs. For example, below we highlight a small *maximal* clique, i.e., a clique # not contained inside another clique:
import networkx as nx
import plotly
adj = np.array(
[
[0, 1, 0, 0, 1, 1],
[1, 0, 1, 0, 1, 1],
[0, 1, 0, 1, 1, 0],
[0, 0, 1, 0, 1, 0],
[1, 1, 1, 1, 0, 1],
[1, 1, 0, 0, 1, 0],
]
)
graph = nx.Graph(adj)
plot_graph = plot.graph(graph)
plotly.offline.plot(plot_graph, filename="random_graph.html")
##############################################################################
# .. raw:: html
# :file: ../../examples_apps/random_graph.html
#
# .. note::
# The command ``plotly.offline.plot()`` is used to display plots in the documentation. In
# practice, you can simply use ``plot_graph.show()`` to view your graph.
#
# This is a 6-node graph with the nodes ``[0, 1, 4, 5]`` fully connected to each other. We expect
# to be able to sample dense subgraphs with high probability.
#
# Samples can be generated from this graph through GBS using the :func:`~.apps.sample.sample`
def show_union_graph():
"""
[show_union_graph] displays the final conjoined graph [union_graph]
"""
union_graph_fig = plot.graph(union_graph, sub)
union_graph_fig.show()
--------------------------------------------------------------------
# import packages
from strawberryfields.apps import data, plot, sample, clique
import numpy as np
import networkx as nx
import plotly
---------------------------------------------------------------------
# plot graph (randomly generated)
PH = data.PHat()
A = PH.adj
PH_graph = nx.Graph(A)
plot_graph = plot.graph(PH_graph)
plot_graph.show()
# Complete GBS (the mean density of the subgraphs samples by GBS > the uniform mean density! This means less searching for the classical algorithms)
GBS_dens = []
u_dens = []
for s in samples:
uniform = list(np.random.choice(16, 20))
GBS_dens.append(nx.density(PH_graph.subgraph(s)))
u_dens.append(nx.density(PH_graph.subgraph(uniform)))
print("GBS mean density = {:.4f}".format(np.mean(GBS_dens)))
print("Uniform mean density = {:.4f}".format(np.mean(u_dens)))
# GBS mean density = 0.3714
# Uniform mean density = 0.1673
m2_a = m2.adj m3_a = m3.adj ############################################################################## # Samples from these graphs can be accessed by indexing: print(m0[0]) ############################################################################## # We can now plot the four graphs using the :mod:`~.apps.plot` module. To use this module, # we need to convert the adjacency matrices into NetworkX Graphs: import networkx as nx import plotly plot_mutag_0 = plot.graph(nx.Graph(m0_a)) plot_mutag_1 = plot.graph(nx.Graph(m1_a)) plot_mutag_2 = plot.graph(nx.Graph(m2_a)) plot_mutag_3 = plot.graph(nx.Graph(m3_a)) plotly.offline.plot(plot_mutag_0, filename="MUTAG_0.html") ############################################################################## # .. raw:: html # :file: ../../examples_apps/MUTAG_0.html # # .. note:: # The command ``plotly.offline.plot()`` is used to display plots in the documentation. In # practice, you can simply use ``plot_mutag_0.show()`` to view your graph. plotly.offline.plot(plot_mutag_1, filename="MUTAG_1.html")