def init_image_original(self):
# set init image
display_graph(self.residual_graph,
'dinic_init',
source=self.source,
sink=self.sink,
size_graph='s')
return mpimg.imread('dinic_init.png')
def init_image(self):
display_graph(self.graph, filename="blocking_flow_init", source=self.source, sink=self.sink)
# Find blocking_flow and store in self.block_flow
self.block_flow, self.block_flow_mat = self.blocking_flow()
self.idx = 0
self.current_flow = 0
print 'found blockflow', self.block_flow
return mpimg.imread('blocking_flow_init.png')
def next_image(self):
if self.done:
return None
else:
# self.idx keeps track of current image displayed in GUI
if self.idx >= len(self.states):
self.done = True
return None
# Display the sequence of images
display_graph(self.states[self.idx][1],
filename="blocking_flow_next",
highlight_path=self.states[self.idx][0],
capacities=self.adj_matrix_capacitites,
source=self.source, sink=self.sink)
self.current_flow = self.states[self.idx][2]
print 'curr state', self.states[self.idx]
self.idx += 1
if self.idx >= len(self.states):
self.done = True
return mpimg.imread('blocking_flow_next.png')
def display_from(email_addr):
emails = Message.objects(from_str=email_addr).all()
count = emails.count()
print "%s emails found authored by '%s'" % (count, email_addr)
g = graph.generate_graph(emails)
graph.display_graph(g)
def show_chat_freq_graph(event):
chat_graph.display_graph()
def main() -> None:
xa = list()
ya = list()
length = int()
lagrange = Polynomial()
newton = Polynomial()
square = Polynomial()
print(color.Fore.CYAN + "\t\t\t\t\tЛабораторная работа №1")
while True:
try:
print(color.Fore.CYAN + "Введите значения Х (через пробел): " +
color.Fore.RESET,
end='')
xa = dispenser(input())
print(color.Fore.CYAN + "Введите значения Y (через пробел): " +
color.Fore.RESET,
end='')
ya = dispenser(input())
if len(xa) != len(ya):
raise RuntimeError
print(color.Fore.CYAN + "Введите значения M: " + color.Fore.RESET,
end='')
ma = int(input())
if ma >= len(xa):
raise ReferenceError
lagrange = lagrange_polynomial(xa, ya)
print(color.Fore.CYAN + "Полином Лагранжа: " + color.Fore.RESET,
end='')
print_polynomial(lagrange)
newton = newton_polynomial(xa, ya)
print(color.Fore.CYAN + "Полином Ньютона: " + color.Fore.RESET,
end='')
print_polynomial(newton)
square = square_polynomial(xa, ya, ma)
print(color.Fore.CYAN + f"Полином среднеквад. co степ {ma}: " +
color.Fore.RESET,
end='')
print_polynomial(square)
graph.display_graph(xa, ya, (lagrange, newton, square), ma)
except ReferenceError:
print(color.Fore.RED +
"Значение m > n. Введите значения повторно." +
color.Fore.RESET)
except RuntimeError:
print(
color.Fore.RED +
"Количество значений Х и Y не совпадает. Введите значения повторно."
+ color.Fore.RESET)
except ValueError:
print(color.Fore.RED +
"Вы ввели неправильные значения Х или Y. Попробуйте снова." +
color.Fore.RESET)
except:
print(color.Fore.RED + "Неизвестная ошибка." + color.Fore.RESET)
def find_important(G, n):
scores = nx.betweenness_centrality(G).items()
l = [ ]
for (item, score) in scores:
l.append((score, item))
l.sort(reverse=True)
important = []
for i in range(n):
important.append(l[i][1])
return important
if __name__ == '__main__':
g = graph.get_graph('/data/512/10000-twitter.gexf', limit=10000)
top_subgraphs = graph.get_subgraphs(g, k=1)
g = top_subgraphs[0]
graph.display_graph(g)
#t = find_best_params(g)
#t2 = find_best_weighted_params(g)
labels, score_1 = scluster(g, 4, 3)
graph.display_graph_clusters(g, labels)
important = find_important(g, 4)
labels, score_2 = weighted_scluster(g, 4, 3, important)
graph.display_graph_clusters(g, labels)
print score_1, score_2
# Sample
# g = sample_graph()
# important = find_important(g, 2)
def init_image_original(self):
# set init image
display_graph(self.residual_graph, 'dinic_init', source=self.source, sink=self.sink, size_graph = 's')
return mpimg.imread('dinic_init.png')
