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- import numpy as np
- import matplotlib.pyplot as plt
- import networkx as nx
- from scipy.spatial import distance_matrix
- import itertools
- from networkx.drawing.layout import fruchterman_reingold_layout
- def add_unique_vector(vectors, selected_vectors, remaining_indices):
- # Выбираем случайный индекс из оставшихся
- new_index = np.random.choice(remaining_indices)
- # Добавляем новый выбранный вектор
- selected_vectors.append(vectors[new_index])
- remaining_indices = list(remaining_indices - new_index)
- return selected_vectors, remaining_indices
- # Расчет энергии вершины
- def calculate_vertex_energy(vertex, vectors, k, l):
- # Вычисляем расстояния от вершины до каждого вектора
- distances = [np.linalg.norm(vertex - vector) for vector in vectors]
- # Получаем индексы векторов, отсортированных по расстоянию
- sorted_indices = np.argsort(distances)
- # Выбираем num_nearest ближайших векторов
- nearest_vector = sorted_indices[0]
- # Вычисляем энергию вершины
- energy = 0
- energy += k * (1 / l) * np.linalg.norm(vertex - nearest_vector) ** 2
- return energy
- # Расчет энергии ребра
- def calculate_edge_energy(edge, vectors, k, s):
- v1, v2 = vectors[edge[0]], vectors[edge[1]]
- return k * s**2 * np.linalg.norm(v1 - v2) ** 2
- # Расчет энергии клина
- def calculate_wedge_energy(wedge, vectors, k, r):
- v1, v2, v3 = vectors[wedge[0]], vectors[wedge[1]], vectors[wedge[2]]
- return k * r**4 * np.linalg.norm(v1 + v3 - 2 * v2) ** 2
- # Расчет энергии графа
- def calculate_energy(
- graph,
- vectors,
- selected_vectors,
- max_vertex_energy,
- max_edge_energy,
- max_wedge_energy,
- max_energy_vertex,
- max_energy_edge,
- max_energy_wedge,
- edge_combination,
- ):
- # vertex_k = 0.5 # Замените на ваше значение для коэффициента аппроксимации вершин
- # edge_k = 0.5 # Замените на ваше значение для коэффициента растяжения ребер
- # wedge_k = 0.5 # Замените на ваше значение для коэффициента изгиба клиньев
- max_wedge_energy = (
- -np.inf
- ) # Инициализируем максимальную энергию клина как минус бесконечность
- max_energy_wedge = None # Инициализируем клин с максимальной энергией как None
- edge_combination = 0
- max_vertex_energy = -np.inf
- max_edge_energy = -np.inf
- max_energy_vertex = 0
- max_energy_edge = None
- energy = 0
- for node in graph.nodes:
- vertex_energy = calculate_vertex_energy(
- selected_vectors[int(node)], vectors, vertex_k, len(selected_vectors)
- )
- if vertex_energy > max_vertex_energy:
- max_vertex_energy = vertex_energy
- max_energy_vertex = node
- energy += vertex_energy
- for edge in graph.edges:
- edge_energy = calculate_edge_energy(edge, selected_vectors, edge_k)
- if edge_energy > max_edge_energy:
- max_edge_energy = edge_energy
- max_energy_edge = edge
- energy += edge_energy
- # Добавляем энергию клина для всех подграфов из трех вершин
- for nodes in itertools.combinations(graph.nodes, 3):
- # print(nodes)
- if graph.has_edge(nodes[0], nodes[1]) and graph.has_edge(nodes[0], nodes[2]):
- wedge_energy = calculate_wedge_energy(nodes, selected_vectors, wedge_k)
- # print("Энергия клина:")
- # print(p1)
- if wedge_energy > max_wedge_energy:
- max_wedge_energy = wedge_energy
- max_energy_wedge = nodes
- edge_combination = 1
- energy += wedge_energy
- elif graph.has_edge(nodes[0], nodes[1]) and graph.has_edge(nodes[1], nodes[2]):
- wedge_energy = calculate_wedge_energy(nodes, selected_vectors, wedge_k)
- if wedge_energy > max_wedge_energy:
- max_wedge_energy = wedge_energy
- max_energy_wedge = nodes
- edge_combination = 2
- # print("Энергия клина:")
- # print(p1)
- energy += wedge_energy
- elif graph.has_edge(nodes[0], nodes[2]) and graph.has_edge(nodes[1], nodes[2]):
- wedge_energy = calculate_wedge_energy(nodes, selected_vectors, wedge_k)
- if wedge_energy > max_wedge_energy:
- max_wedge_energy = wedge_energy
- max_energy_wedge = nodes
- edge_combination = 3
- # print("Энергия клина:")
- # print(p1)
- energy += wedge_energy
- return (
- energy,
- max_vertex_energy,
- max_edge_energy,
- max_wedge_energy,
- max_energy_vertex,
- max_energy_edge,
- max_energy_wedge,
- edge_combination,
- )
- # Расчет энергии графа
- def calculate_energy1(graph, vectors, selected_vectors):
- vertex_k = 2 # Замените на ваше значение для коэффициента аппроксимации вершин
- edge_k = 1 # Замените на ваше значение для коэффициента растяжения ребер
- wedge_k = 0.25
- energy = 0
- for node in graph.nodes:
- vertex_energy = calculate_vertex_energy(
- selected_vectors[int(node)], vectors, vertex_k, len(graph.nodes)
- )
- energy += vertex_energy
- for edge in graph.edges:
- edge_energy = calculate_edge_energy(
- edge, selected_vectors, edge_k, len(graph.edges)
- )
- energy += edge_energy
- r = 0
- # Добавляем энергию клина для всех подграфов из трех вершин
- for nodes in itertools.combinations(graph.nodes, 3):
- # print(nodes)
- if graph.has_edge(nodes[0], nodes[1]) and graph.has_edge(nodes[0], nodes[2]):
- r = r + 1
- elif graph.has_edge(nodes[0], nodes[1]) and graph.has_edge(nodes[1], nodes[2]):
- r = r + 1
- elif graph.has_edge(nodes[0], nodes[2]) and graph.has_edge(nodes[1], nodes[2]):
- r = r + 1
- # Добавляем энергию клина для всех подграфов из трех вершин
- for nodes in itertools.combinations(graph.nodes, 3):
- # print(nodes)
- if graph.has_edge(nodes[0], nodes[1]) and graph.has_edge(nodes[0], nodes[2]):
- wedge_energy = calculate_wedge_energy(nodes, selected_vectors, wedge_k, r)
- energy += wedge_energy
- elif graph.has_edge(nodes[0], nodes[1]) and graph.has_edge(nodes[1], nodes[2]):
- wedge_energy = calculate_wedge_energy(nodes, selected_vectors, wedge_k, r)
- energy += wedge_energy
- elif graph.has_edge(nodes[0], nodes[2]) and graph.has_edge(nodes[1], nodes[2]):
- wedge_energy = calculate_wedge_energy(nodes, selected_vectors, wedge_k, r)
- energy += wedge_energy
- return energy
- def visualize_graph(vectors, selected_vectors, G):
- fig = plt.figure()
- ax = fig.add_subplot(111, projection="3d")
- # Отображаем все векторы на графике
- for vector in vectors:
- ax.scatter(vector[0], vector[1], vector[2], color="b")
- # Отображаем выбранные векторы на графике
- for vector in selected_vectors:
- ax.scatter(vector[0], vector[1], vector[2], color="r")
- # Добавляем ребра между вершинами
- for edge in G.edges:
- ax.plot(
- [selected_vectors[edge[0]][0], selected_vectors[edge[1]][0]],
- [selected_vectors[edge[0]][1], selected_vectors[edge[1]][1]],
- [selected_vectors[edge[0]][2], selected_vectors[edge[1]][2]],
- "r-",
- )
- # Добавляем оси
- ax.set_xlabel("X")
- ax.set_ylabel("Y")
- ax.set_zlabel("Z")
- plt.show()
- def modify_graph(
- G,
- selected_vectors,
- max_vertex_energy,
- max_edge_energy,
- max_wedge_energy,
- max_energy_wedge,
- edge_combination,
- vectors,
- remaining_indices,
- ):
- if max_vertex_energy >= max_edge_energy and max_vertex_energy >= max_wedge_energy:
- print("hello1")
- # Добавляем новую вершину к вершине с наибольшей энергией
- # max_energy_vertex = vertex_energies.index(max_vertex_energy)
- G.add_node(len(selected_vectors))
- G.add_edge(max_energy_vertex, len(selected_vectors))
- selected_vectors, remaining_indices = add_unique_vector(
- vectors, selected_vectors, remaining_indices
- )
- # selected_vectors.append(np.random.randint(-6, 7, 3)) # Добавляем новый вектор
- elif max_edge_energy >= max_vertex_energy and max_edge_energy >= max_wedge_energy:
- print("hello2")
- # Разбиваем ребро с наибольшей энергией новой вершиной
- # edges = list(G.edges) # Получаем список всех ребер
- # max_energy_edge = edges[edge_energies.index(max_edge_energy)] # Находим ребро с максимальной энергией
- G.add_node(len(selected_vectors))
- G.add_edge(max_energy_edge[0], len(selected_vectors))
- G.add_edge(max_energy_edge[1], len(selected_vectors))
- G.remove_edge(max_energy_edge[0], max_energy_edge[1])
- selected_vectors, remaining_indices = add_unique_vector(
- vectors, selected_vectors, remaining_indices
- ) # Добавляем новый вектор
- else:
- print("hello3")
- if edge_combination == 1:
- old_edge_length = np.linalg.norm(
- selected_vectors[max_energy_wedge[0]]
- - selected_vectors[max_energy_wedge[1]]
- )
- new_edge_length = np.linalg.norm(
- selected_vectors[max_energy_wedge[0]]
- - selected_vectors[max_energy_wedge[2]]
- )
- if new_edge_length < old_edge_length:
- # Удаляем старое ребро и добавляем новое
- G.remove_edge(max_energy_wedge[0], max_energy_wedge[1])
- G.add_edge(max_energy_wedge[2], max_energy_wedge[1])
- else:
- G.remove_edge(max_energy_wedge[0], max_energy_wedge[2])
- G.add_edge(max_energy_wedge[2], max_energy_wedge[1])
- elif edge_combination == 2:
- old_edge_length = np.linalg.norm(
- selected_vectors[max_energy_wedge[0]]
- - selected_vectors[max_energy_wedge[1]]
- )
- new_edge_length = np.linalg.norm(
- selected_vectors[max_energy_wedge[1]]
- - selected_vectors[max_energy_wedge[2]]
- )
- print(f"длина старого ребра: {old_edge_length}")
- print(f"длина нового ребра: {new_edge_length }")
- if new_edge_length < old_edge_length:
- # Удаляем старое ребро и добавляем новое
- G.remove_edge(max_energy_wedge[0], max_energy_wedge[1])
- G.add_edge(max_energy_wedge[0], max_energy_wedge[2])
- else:
- G.remove_edge(max_energy_wedge[1], max_energy_wedge[2])
- G.add_edge(max_energy_wedge[0], max_energy_wedge[2])
- elif edge_combination == 3:
- old_edge_length = np.linalg.norm(
- selected_vectors[max_energy_wedge[0]]
- - selected_vectors[max_energy_wedge[2]]
- )
- new_edge_length = np.linalg.norm(
- selected_vectors[max_energy_wedge[1]]
- - selected_vectors[max_energy_wedge[2]]
- )
- if new_edge_length < old_edge_length:
- # Удаляем старое ребро и добавляем новое
- G.remove_edge(max_energy_wedge[0], max_energy_wedge[2])
- G.add_edge(max_energy_wedge[0], max_energy_wedge[1])
- else:
- G.remove_edge(max_energy_wedge[2], max_energy_wedge[1])
- G.add_edge(max_energy_wedge[0], max_energy_wedge[1])
- # print(max_energy_wedge[0])
- # print(max_energy_wedge[1])
- # print(max_energy_wedge[2])
- return G, selected_vectors, remaining_indices
- # for edges in G.edges:
- # print(edges)
- def calculate_psi(g, NP, lambda_):
- return ((g - 1) * (NP + 1) + 1) ** (1 / lambda_)
- def calculate_probabilities(
- vectors, graphs, selected_vectors_list, vertex_k, edge_k, wedge_k, g, NP, lambda_
- ):
- # Вычисляем psi
- psi_g = calculate_psi(g, NP, lambda_)
- # Вычисляем энергии для всех векторов
- energies = [
- calculate_energy1(graphs[i], vectors, selected_vectors_list[i]) ** psi_g
- for i in range(len(selected_vectors_list))
- ]
- # Вычисляем сумму энергий
- sum_energies = sum(energies)
- # Вычисляем вероятности
- probabilities = [energy / sum_energies for energy in energies]
- # print(probabilities)
- return probabilities
- def select_support_vector(
- vectors, graphs, selected_vectors_list, vertex_k, edge_k, wedge_k, g, NP, lambda_
- ):
- # Вычисляем вероятности
- probabilities = calculate_probabilities(
- vectors,
- graphs,
- selected_vectors_list,
- vertex_k,
- edge_k,
- wedge_k,
- g,
- NP,
- lambda_,
- )
- # Выбираем опорный вектор на основе вероятностей
- id = np.random.choice(len(selected_vectors_list), p=probabilities)
- support_vector = selected_vectors_list[id]
- return support_vector
- def calculate_A(x_min, x_max, x_r, e):
- return (np.arctan((x_max - x_r) / e) - np.arctan((x_min - x_r) / e)) ** -1
- def calculate_e(i, g, NP, D):
- return 1 / ((g - 1) * (NP + 1) + i + 1) ** (1 / (2 * D))
- def generate_potential_offspring(x_r, e, A):
- return x_r + e * np.tan((np.random.rand() - 0.5) * A)
- def crossover(selected_vectors_list, potential_offspring, q):
- # Создаем mutant_vectors, равный по размеру selected_vectors
- mutant_vectors = potential_offspring
- # Заменяем случайные q векторов mutant_vectors на соответствующие векторы из selected_vectors
- # Для оставшихся векторов
- for l in range(q, len(selected_vectors_list)):
- for j in range(len(selected_vectors_list[l])):
- # Выбираем случайное число
- p = np.random.rand()
- # Вычисляем CR_{l,g}
- CR_lg = 0.9 if p > 0.1 else p
- # Для каждой координаты j в векторе
- if np.random.rand() < 1.0 - CR_lg:
- mutant_vectors[l], [j] = selected_vectors_list[l], [j]
- return mutant_vectors
- def generate_offspring(selected_vectors_list, support_vector, g, NP):
- # Вычисляем x_min и x_max для каждой координаты
- x_min = []
- x_max = []
- for i in range(len(selected_vectors_list)):
- x_min_i = []
- x_max_i = []
- for j in range(len(selected_vectors_list[i])):
- x_min_i.append(np.min(selected_vectors_list[i][j]))
- x_max_i.append(np.max(selected_vectors_list[i][j]))
- x_min.append(x_min_i)
- x_max.append(x_max_i)
- # Создаем список для хранения потенциальных потомков
- potential_offspring = []
- # Для каждого вектора в selected_vectors
- for i in range(len(selected_vectors_list)):
- # Для каждой координаты в векторе
- potential_offspring_i = []
- for j in range(len(selected_vectors_list[i])):
- # Вычисляем x_r, e и A
- x_r = support_vector[j]
- e = calculate_e(i, g, NP, len(selected_vectors_list[i]))
- A = calculate_A(-1, 1, x_r, e)
- # Генерируем потенциального потомка
- x_star = generate_potential_offspring(x_r, e, A)
- # Добавляем потенциального потомка в список
- potential_offspring_i.append(x_star)
- potential_offspring.append(potential_offspring_i)
- return potential_offspring
- def selection(G, vectors, selected_vectors, mutant_vectors, energy):
- # Для каждого вектора в selected_vectors
- for i in range(len(selected_vectors)):
- vec = selected_vectors
- vec[i] = mutant_vectors[i]
- en = calculate_energy1(G, vectors, vec)
- if en < energy:
- selected_vectors[i] = mutant_vectors[i]
- return selected_vectors
- def data():
- n = 12
- # Создаем случайную последовательность n-2 от 0 до n-1
- prufer_sequence = np.random.randint(0, n, n - 2)
- # Восстанавливаем дерево по коду Прюфера
- G1 = nx.from_prufer_sequence(prufer_sequence)
- # Укладываем вершины графа на плоскость методом Фрухтермана-Рейнгольда
- pos = fruchterman_reingold_layout(G1, dim=3)
- # Берем точки на ребрах графа
- edge_points = [pos[node] for node in G1.nodes]
- # [(pos[edge[0]] + pos[edge[1]]) / 2 for edge in G.edges]
- # Смещаем каждую точку на случайный вектор с нормальным распределением
- vectors = []
- for point in edge_points:
- for _ in range(5): # Создаем три смещенные точки для каждой точки на ребре
- vectors.append(point + np.random.normal(size=3) * 0.05)
- return vectors
- def visualize_graphs(vectors, graphs, selected_vectors_list):
- fig = plt.figure()
- ax = fig.add_subplot(111, projection="3d")
- # Отображаем все векторы на графике
- for vector in vectors:
- ax.scatter(vector[0], vector[1], vector[2], color="b")
- color_i = [
- "red",
- "blue",
- "green",
- "yellow",
- "orange",
- "lightcoral",
- "darkorange",
- "olive",
- "teal",
- "violet",
- "skyblue",
- ]
- # Отображаем выбранные векторы на графике
- for i in range(len(selected_vectors_list)):
- for vector in selected_vectors_list[i]:
- ax.scatter(vector[0], vector[1], vector[2], color=color_i[i % len(color_i)])
- # Добавляем ребра между вершинами
- for i in range(len(graphs)):
- for edge in graphs[i].edges:
- ax.plot(
- [
- selected_vectors_list[i][edge[0]][0],
- selected_vectors_list[i][edge[1]][0],
- ],
- [
- selected_vectors_list[i][edge[0]][1],
- selected_vectors_list[i][edge[1]][1],
- ],
- [
- selected_vectors_list[i][edge[0]][2],
- selected_vectors_list[i][edge[1]][2],
- ],
- color_i[i % len(color_i)],
- )
- # Добавляем оси
- ax.set_xlabel("X")
- ax.set_ylabel("Y")
- ax.set_zlabel("Z")
- plt.show()
- def create_auxiliary_variables(NP):
- max_wedge_energy = [-np.inf] * NP
- max_energy_wedge = [None] * NP
- edge_combination = [0] * NP
- max_vertex_energy = [-np.inf] * NP
- max_edge_energy = [-np.inf] * NP
- max_energy_vertex = [0] * NP
- max_energy_edge = [None] * NP
- return (
- max_wedge_energy,
- max_energy_wedge,
- edge_combination,
- max_vertex_energy,
- max_edge_energy,
- max_energy_vertex,
- max_energy_edge,
- )
- def DFS(G, v, visited=None):
- if visited is None:
- visited = set()
- visited.add(v)
- for neighbour in G[v]:
- if neighbour not in visited:
- DFS(G, neighbour, visited)
- return visited
- def generate_graphs(vectors, NP):
- graphs = []
- selected_vectors_list = []
- remaining_indices_list = []
- for _ in range(NP):
- indices = np.random.choice(len(vectors), 3, replace=False)
- selected_vectors = [vectors[i] for i in indices]
- all_indices = set(range(len(vectors)))
- remaining_indices = list(all_indices - set(indices))
- distances = distance_matrix(selected_vectors, selected_vectors)
- np.fill_diagonal(distances, np.inf)
- dist_vector_pairs = [
- (np.min(dist), vec) for dist, vec in zip(distances, selected_vectors)
- ]
- dist_vector_pairs.sort(key=lambda x: x[0])
- sorted_vectors = [pair[1] for pair in dist_vector_pairs]
- selected_vectors = sorted_vectors
- distances = distance_matrix(selected_vectors, selected_vectors)
- np.fill_diagonal(distances, np.inf)
- G = nx.Graph()
- for i in range(len(selected_vectors)):
- G.add_node(i)
- visited = set()
- for i in range(len(selected_vectors)):
- nearest_vertex = np.argmin(distances[i])
- if i not in visited:
- G.add_edge(i, nearest_vertex)
- visited.add(nearest_vertex)
- connected = len(DFS(G, 0)) == len(G)
- if not connected:
- components = [list(c) for c in nx.connected_components(G)]
- for i in range(len(components) - 1):
- G.add_edge(components[i][0], components[i + 1][0])
- graphs.append(G)
- selected_vectors_list.append(selected_vectors)
- remaining_indices_list.append(remaining_indices)
- return graphs, selected_vectors_list, remaining_indices_list
- if __name__ == "__main__":
- vertex_k = 10 # Замените на ваше значение для коэффициента аппроксимации вершин
- edge_k = 100 # Замените на ваше значение для коэффициента растяжения ребер
- wedge_k = 10 # Замените на ваше значение для коэффициента изгиба клиньев
- NP = 5
- np.random.seed(8)
- vectors = []
- vectors = data()
- graphs, selected_vectors_list, remaining_indices_list = generate_graphs(vectors, NP)
- (
- max_wedge_energy,
- max_energy_wedge,
- edge_combination,
- max_vertex_energy,
- max_edge_energy,
- max_energy_vertex,
- max_energy_edge,
- ) = create_auxiliary_variables(NP)
- # visualize_graphs(vectors, graphs,selected_vectors_list)
- # print(calculate_probabilities(vectors,graphs, selected_vectors_list, vertex_k,edge_k,wedge_k, 1, NP, 1))
- for i in range(2250):
- support_vector = select_support_vector(
- vectors, graphs, selected_vectors_list, vertex_k, edge_k, wedge_k, 1, NP, 1
- )
- potential_offspring = generate_offspring(
- selected_vectors_list, support_vector, 1, NP
- )
- # visualize_graphs(vectors, graphs, potential_offspring)
- mutant_vectors = crossover(selected_vectors_list, potential_offspring, 3)
- for j in range(len(mutant_vectors)):
- energy_mutant = calculate_energy1(graphs[j], vectors, mutant_vectors[j])
- energy_selected = calculate_energy1(
- graphs[j], vectors, selected_vectors_list[j]
- )
- if energy_mutant < energy_selected:
- selected_vectors_list[j] = mutant_vectors[j]
- visualize_graphs(vectors, graphs, mutant_vectors)
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