lay<-create_layout(g,"manual",node.positions=node_pos) Error in layout_fun(graph, circular = circular, ...) : 参数没有用(node.positions = list(c(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 13, 16, 17, 18, 20, 21, 22, 23, 24, 25, 27, 28, 30, 31, 33, 34, 35, 37, 39, 40, 41, 42, 43, 44, 46, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 65, 67, 68, 69, 70, 71, 72, 74, 75, 76, 77, 78, 79, 84, 85, 88, 89, 90, 95, 114), c(-63.616672, 133.77514, 25.48583, -51.92528, -106.346771, -71.542969, 104.195397, -74.297333, -83.753428, 15.472962, -70.162651, 30.8025, 113.921327, 78.96288, -4.54806, 36.238414, 138.252924, 66.923684, 127.51009, 23.881275, 24.6

torch_cluster-1.6.3+pt20cpu-cp311-cp311-linux_x86_64whl.zip

edge_index = knn_graph(node_positions, k=10) 这里，node_positions是节点的位置向量，k表示每个节点的邻居数量。总结来说，torch_cluster库是PyTorch生态中的重要组成部分，为图神经网络的开发提供...

WSN.zip_CoCMA_Energy-Coverage_energy efficient_sink wsn_sleeping

In this paper we have proposed an energy-efficient coverage-preserving algorithm, called CoCMA, which encompasses optimization for initial deployments using a MA and a wake-up scheme. The CoCMA turns ...

Error in layout_fun(graph, circular = circular, ...) : 参数没有用(node.positions = list(c(-63.616672, 133.77514, 25.48583, -51.92528, -106.346771, -71.542969, 104.195397, -74.297333, -83.753428, 15.472962, -70.162651, 30.8025, 113.921327, 78.96288, -4.54806, 36.238414, 138.252924, 66.923684, 127.51009, 23.881275, 24.603189, -102.552784, 101.975766, 174.885971, 69.345116, -75.015152, 121.774017, 19.145136, 61.52401, 14.995463, 100.992541, -61.222503, 35.243322, 120.960515, 31.16558, -95.712891, 108.277199, 22.937506, 10.451526, 4.469936, 14.550072, 25.748151, 19.503304, -3.74922, 114.109497, 5.291266, 2.213749, 12.56738, 18.643501, -2.742777, 24.96676, 19.699024, 55.923255, 80.771797, 45.079162, -8.24389, 9.

lon <- c(-63.616672, 133.77514, 25.48583, -51.92528, -106.346771, -71.542969) lat <- c(41.878113, -25.274398, 42.360081, -14.235004, 56.130366, 37.774929) data <- data.frame(lon, lat) # 创建地理布局 ...

results.type = 'rect'; results.res = OTB_rect_positions; results.fps = num_frames/(elapsed_time - t_imread);

这段代码中，变量results.type被赋值为'rect'，变量results.res被赋值为OTB_rect_positions，变量results.fps被赋值为num_frames除以(elapsed_time - t_imread)的结果。其中，num_frames表示帧数，elapsed_time表示...

observation, legal_action, sub_action_mask, lstm_hidden, lstm_cell = [], [], [[]], [], [] pos_norm = req_pb.ai_req.frame_state.features.positions.pos_norm pos_polar = req_pb.ai_req.frame_state.features.positions.pos_polar list_treasure = req_pb.ai_req.frame_state.features.treasure

接下来的代码是从 req_pb.ai_req.frame_state.features 中获取了一些属性，并将它们赋值给 pos_norm、pos_polar 和 list_treasure 变量。请注意，这段代码没有进行任何操作或逻辑处理，只是初始化了一些...

import numpy as np import pandas as pd import talib def initialize(context): context.symbol = 'BTCUSDT' context.window_size = 5 context.deviation = 1 context.trade_size = 0.01 context.stop_loss = 0.05 context.take_profit = 0.1 schedule_function(rebalance, date_rules.every_day(), time_rules.market_open()) def rebalance(context, data): price = data.history(context.symbol, 'close', context.window_size + 1, '1d') signal = mean_reversion_signal(price, context.window_size, context.deviation) current_position = context.portfolio.positions[context.symbol].amount if signal[-1] == 1 and current_position <= 0: target_position_size = context.trade_size / data.current(context.symbol, 'close') order_target_percent(context.symbol, target_position_size) elif signal[-1] == -1 and current_position >= 0: order_target(context.symbol, 0) elif current_position > 0: current_price = data.current(context.symbol, 'close') stop_loss_price = current_price * (1 - context.stop_loss) take_profit_price = current_price * (1 + context.take_profit) if current_price <= stop_loss_price or current_price >= take_profit_price: order_target(context.symbol, 0) def moving_average(x, n): ma = talib.SMA(x, timeperiod=n) return ma def std_deviation(x, n): std = talib.STDDEV(x, timeperiod=n) return std def mean_reversion_signal(price, window_size, deviation): ma = moving_average(price, window_size) std = std_deviation(price, window_size) upper_band = ma + deviation * std lower_band = ma - deviation * std signal = np.zeros_like(price) signal[price > upper_band] = -1 # 卖出信号 signal[price < lower_band] = 1 # 买入信号 return signal ''' 运行回测 ''' start_date = pd.to_datetime('2019-01-01', utc=True) end_date = pd.to_datetime('2021-01-01', utc=True) results = run_algorithm( start=start_date, end=end_date, initialize=initialize, capital_base=10000, data_frequency='daily', bundle='binance' ) ''' 查看回测结果 ''' print(results.portfolio_value)格式错误

if current_price <= stop_loss_price or current_price >= take_profit_price: order_target(context.symbol, 0) def moving_average(x, n): ma = talib.SMA(x, timeperiod=n) return ma def std_deviation(x...

import numpy as np import matplotlib.pyplot as plt # 设置模拟参数 num_boids = 50 # 粒子数 max_speed = 0.03 # 最大速度 max_force = 0.05 # 最大受力 neighborhood_radius = 0.2 # 邻域半径 separation_distance = 0.05 # 分离距离 alignment_distance = 0.1 # 对齐距离 cohesion_distance = 0.2 # 凝聚距离 # 初始化粒子位置和速度 positions = np.random.rand(num_boids, 2) velocities = np.random.rand(num_boids, 2) * max_speed # 模拟循环 for i in range(1000): # 计算邻域距离 distances = np.sqrt(np.sum(np.square(positions[:, np.newaxis, :] - positions), axis=-1)) neighbors = np.logical_and(distances > 0, distances < neighborhood_radius) # 计算三个力 separation = np.zeros_like(positions) alignment = np.zeros_like(positions) cohesion = np.zeros_like(positions) for j in range(num_boids): # 计算分离力 separation_vector = positions[j] - positions[neighbors[j]] separation_distance_mask = np.linalg.norm(separation_vector, axis=-1) < separation_distance separation_vector = separation_vector[separation_distance_mask] separation[j] = np.sum(separation_vector, axis=0) # 计算对齐力 alignment_vectors = velocities[neighbors[j]] alignment_distance_mask = np.linalg.norm(separation_vector, axis=-1) < alignment_distance alignment_vectors = alignment_vectors[alignment_distance_mask] alignment[j] = np.sum(alignment_vectors, axis=0) # 计算凝聚力 cohesion_vectors = positions[neighbors[j]] cohesion_distance_mask = np.linalg.norm(separation_vector, axis=-1) < cohesion_distance cohesion_vectors = cohesion_vectors[cohesion_distance_mask] cohesion[j] = np.sum(cohesion_vectors, axis=0) # 计算总受力 total_force = separation + alignment + cohesion total_force = np.clip(total_force, -max_force, max_force) # 更新速度和位置 velocities += total_force velocities = np.clip(velocities, -max_speed, max_speed) positions += velocities # 绘制粒子 plt.clf() plt.scatter(positions[:, 0], positions[:, 1], s=5) plt.xlim(0, 1) plt.ylim(0, 1) plt.pause(0.01)

这段代码是一个基于群体智能的仿真模型，用于模拟粒子的运动行为。该模型使用numpy和matplotlib库来实现。主要步骤包括： 1. 设置模拟参数：定义粒子数、最大速度、最大受力、邻域半径、分离距离、对齐距离、凝聚...

def dijkstra_shortest_path(graph, start, end): # 使用Dijkstra算法获取最短路径 path = nx.dijkstra_path(graph, start, end) distance = nx.dijkstra_path_length(graph, start, end) return path, distance def visualize_graph(graph, path): pos = nx.spring_layout(graph) # 绘制边 nx.draw_networkx_edges(graph, pos, edge_color='gray') # 绘制节点 node_labels = {node: node for node in graph.nodes()} nx.draw_networkx_labels(graph, pos, labels=node_labels, font_color='black', font_size=8) # 绘制最短路径 path_edges = [(path[i], path[i + 1]) for i in range(len(path) - 1)] nx.draw_networkx_edges(graph, pos, edgelist=path_edges, edge_color='red', width=2) # 绘制路径点之间的连线 for i in range(len(path) - 1): start = path[i] end = path[i + 1] start_pos = node_positions[start] end_pos = node_positions[end] plt.plot([start_pos[0], end_pos[0]], [start_pos[1], end_pos[1]], color='red', linestyle='dashed') plt.axis('off') plt.show() 对上述代码进行解释

- 使用nx.spring_layout()函数计算节点的布局位置，并将结果赋值给变量pos。 - 使用nx.draw_networkx_edges()函数绘制图中的边，使用灰色表示。 - 使用nx.draw_networkx_labels()函数绘制节点的标签，...

上述代码的问题Traceback (most recent call last): File "1.py", line 14, in <module> C_positions += structure.positions[structure.get_chemical_symbols() == 'C'] ValueError: operands could not be broadcast together with shapes (0,) (0,35,3)

这个错误通常是因为某些POSCAR文件中没有C原子，导致structure.get_chemical_symbols() == 'C'的结果是一个空数组，从而无法将空数组与structure.positions相加。为了解决这个问题，可以在累加C原子坐标之前，先...

解释一下if SLAM_LOG.headtrack_Traj!=None: headtrack_traj_timestamps=SLAM_LOG.headtrack_Traj.timestamps headtrack_traj_xyz= SLAM_LOG.headtrack_Traj.positions_xyz headtrack_Traj_euler = SLAM_LOG.headtrack_Traj.get_orientations_euler(axes='sxyz') rows=np.where((headtrack_traj_timestamps>first_timeStamps)&(headtrack_traj_timestamps<last_timeStamps)) headtrack_traj_timestamps=headtrack_traj_timestamps[rows] headtrack_traj_xyz=headtrack_traj_xyz[rows] headtrack_Traj_euler=headtrack_Traj_euler[rows]

- 第五行rows=np.where((headtrack_traj_timestamps>first_timeStamps)&(headtrack_traj_timestamps<last_timeStamps))根据时间戳信息筛选出在指定时间范围内的头部追踪数据的行数，并将其保存到rows变量中。...

修改代码，错误如下：File "structure_analysis4.py", line 33, in <module> indices1, indices2, distances = neighbor_list('ijD', pos1, cutoff, self_interaction=False, bothways=True) TypeError: neighbor_list() got an unexpected keyword argument 'bothways'。。from ase import io from ase.build import sort from ase.visualize import view from ase.neighborlist import neighbor_list import numpy as np from ase import Atoms # 加载两个POSCAR文件 pos1 = io.read('POSCAR1') pos2 = io.read('POSCAR2') # 指定原子种类 atom_type = 'C' # 获得第一个POSCAR中指定原子的位置列表 #indices1 = [i for i, atom in enumerate(pos1) if atom.symbol == atom_type] #positions1 = sort(pos1.get_positions()[indices1]) indices1 = [i for i, atom in enumerate(pos1) if atom.symbol == atom_type] positions1 = pos1.get_positions()[indices1] atoms1 = Atoms(symbols=[atom_type]len(positions1), positions=positions1) sorted_atoms1 = sort(atoms1) # 获得第二个POSCAR中指定原子的位置列表 #indices2 = [i for i, atom in enumerate(pos2) if atom.symbol == atom_type] #positions2 = sort(pos2.get_positions()[indices2]) indices2 = [i for i, atom in enumerate(pos2) if atom.symbol == atom_type] positions2 = pos2.get_positions()[indices2] atoms2 = Atoms(symbols=[atom_type]len(positions2), positions=positions2) sorted_atoms2 = sort(atoms2) # 计算两个位置列表之间的距离矩阵 cutoff = 5.0 # 距离截断半径 indices1, indices2, distances = neighbor_list('ijD', pos1, cutoff, self_interaction=False, bothways=True) dist_matrix1 = np.zeros((len(positions1), len(positions1))) for i, j, d in zip(indices1, indices2, distances): if i in indices1 and j in indices1: dist_matrix1[indices1.index(i), indices1.index(j)] = d indices1, indices2, distances = neighbor_list('ijD', pos2, cutoff, self_interaction=False, bothways=True) dist_matrix2 = np.zeros((len(positions2), len(positions2))) for i, j, d in zip(indices1, indices2, distances): if i in indices2 and j in indices2: dist_matrix2[indices2.index(i), indices2.index(j)] = d # 计算两个距离矩阵之间的相似性 similarity = 1.0 - np.abs(dist_matrix1 - dist_matrix2).sum() / dist_matrix1.size print('Structure similarity: ', similarity)

另外，第一个 neighbor_list() 函数的输入参数应该是 positions1，而不是 pos1。修改后的代码如下： python from ase import io from ase.build import sort from ase.visualize import view from ase....

Traceback (most recent call last): File "E:\Duzhuan\anaconda\lib\site-packages\IPython\core\interactiveshell.py", line 3460, in run_code exec(code_obj, self.user_global_ns, self.user_ns) File "<ipython-input-2-eadcdc23616b>", line 1, in <module> runfile('C:\\Users\\zhangxiao\\Desktop\\Algorithm Integration\\d_1.py', wdir='C:\\Users\\zhangxiao\\Desktop\\Algorithm Integration') File "E:\Duzhuan\JetBrainsPyCharm 2023.1\PyCharm 2023.1\plugins\python\helpers\pydev\_pydev_bundle\pydev_umd.py", line 198, in runfile pydev_imports.execfile(filename, global_vars, local_vars) # execute the script File "E:\Duzhuan\JetBrainsPyCharm 2023.1\PyCharm 2023.1\plugins\python\helpers\pydev\_pydev_imps\_pydev_execfile.py", line 18, in execfile exec(compile(contents+"\n", file, 'exec'), glob, loc) File "C:\Users\zhangxiao\Desktop\Algorithm Integration\d_1.py", line 119, in <module> success_rate = cuckoo_search() File "C:\Users\zhangxiao\Desktop\Algorithm Integration\d_1.py", line 112, in cuckoo_search known_node_positions = np.concatenate((known_node_positions, np.array(unknown_node_positions)), axis=0)

unknown_node_positions = [position for position in unknown_node_positions if position is not None] known_node_positions = np.concatenate((known_node_positions, np.array(unknown_node_positions)), axis=...

clear all; close all; num_nodes = 30; area_size = 100; simulation_time = 100; [node_positions, node_speeds] = nodes_initialization(num_nodes, area_size); for t = 1:simulation_time [node_positions, node_speeds] = mobility_model(node_positions, node_speeds, area_size); [source, destination, relay_nodes] = d2d_communication(num_nodes); distances = pdist2(node_positions', node_positions'); pow_range = 1:10; plr = zeros(size(pow_range)); for idx = 1:length(pow_range) plr(idx)=packet_loss_rate(pow_range(idx), distances([source, relay_nodes], destination)); end disp(['转发功率: ', num2str(pow_range)]); disp(['丢包率: ', num2str(plr)]); end

这是一个简单的无线传感器网络模拟程序，使用 MATLAB 实现。该程序包括以下步骤： 1. 初始化节点的位置和速度。 2. 在每个时间步长内，模拟节点的运动，更新节点的位置和速度。 3. 根据某种通信策略，选择源节点...

def initialize(context): # 设置回测日期区间 set_benchmark('000300.XSHG') set_option('use_real_price', True) # 设置买入的股票数量上限 g.max_stock_count = 5 def handle_data(context, data): # 获取当前日期 current_date = context.current_dt.date() # 获取股票池中的股票列表 stocks = get_index_stocks('000852.XSHG') # 按照股票池中的股票进行遍历 for stock in stocks: # 判断股票是否满足买入条件 if check_buy_condition(stock, current_date, context): buy_stock(stock, context) # 判断持有的股票是否满足卖出条件 if check_sell_condition(stock, current_date, context): sell_stock(stock, context) def check_buy_condition(stock, current_date, context): # 判断股票是否连续下跌三天 prices = attribute_history(stock, 3, '1d', ['close']) if len(prices) == 3 and prices['close'][-1] < prices['close'][-2] < prices['close'][-3]: return True else: return False def buy_stock(stock, context): # 判断当前持仓的股票数量是否已达上限 if len(context.portfolio.positions) >= g.max_stock_count: return buy_date = context.current_dt.date() # 买入股票 order_value(stock, context.portfolio.cash / g.max_stock_count) def check_sell_condition(stock, current_date, context): # 获取持有股票的买入日期 buy_date = context.current_dt.date() time_diff = current_date - buy_date threshold = timedelta(days=3) # 判断是否满足卖出条件 if time_diff >= threshold or ((context.portfolio.positions[stock].last_price - context.portfolio.positions[stock].avg_cost) / context.portfolio.positions[stock].avg_cost <= -0.05): # 判断是否亏损超过5% return order_target(stock, 0) 报错 type object 'UserObject' has no attribute 'getattr'

if time_diff >= threshold or ((context.portfolio.positions[stock].last_price - context.portfolio.positions[stock].avg_cost) / context.portfolio.positions[stock].avg_cost <= -0.05): # 判断是否亏损...

def data_processing(data): # 日期缺失，补充 data.fillna(method='ffill', inplace=True) date_history = pd.DataFrame(data.iloc[:, 0]) data_history = pd.DataFrame(data.iloc[:, 1]) date_history = np.array(date_history) data_history = [x for item in np.array(data_history).tolist() for x in item] # 缺失值处理 history_time_list = [] for date in date_history: date_obj = datetime.datetime.strptime(date[0], '%Y/%m/%d %H:%M') #将字符串转为 datetime 对象 history_time_list.append(date_obj) start_time = history_time_list[0] # 起始时间 end_time = history_time_list[-1] # 结束时间 delta = datetime.timedelta(minutes=15) #时间间隔为15分钟 time_new_list = [] current_time = start_time while current_time <= end_time: time_new_list.append(current_time) current_time += delta # 缺失位置记录 code_list = [] for i in range(len(time_new_list)): code_list = code_list history_time_list = history_time_list while (time_new_list[i] - history_time_list[i]) != datetime.timedelta(minutes=0): history_time_list.insert(i, time_new_list[i]) code_list.append(i) for i in code_list: data_history.insert(i, data_history[i - 1]) # 输出补充好之后的数据 data = pd.DataFrame({'date': time_new_list, 'load': data_history}) return data 代码优化

while current_time <= end_time: time_new_list.append(current_time) current_time += delta return time_new_list def find_missing_positions(time_new_list, history_time_list): """ 查找缺失位置 ""...

import jqdata # 初始化聚宽账号密码 def initialize(context): # 设置回测日期区间 set_benchmark('000300.XSHG') set_option('use_real_price', True) # 设置买入的股票数量上限 g.max_stock_count = 5 def handle_data(context, data): # 获取当前日期 current_date = context.current_dt.date() # 获取股票池中的股票列表 stocks = get_index_stocks('000852.XSHG') # 按照股票池中的股票进行遍历 for stock in stocks: # 判断股票是否满足买入条件 if check_buy_condition(stock, current_date, context): buy_stock(stock, context) # 判断持有的股票是否满足卖出条件 if check_sell_condition(stock, current_date, context): sell_stock(stock, context) def check_buy_condition(stock, current_date, context): # 判断股票是否连续下跌三天 prices = attribute_history(stock, 3, '1d', ['close']) if len(prices) == 3 and prices['close'][-1] < prices['close'][-2] < prices['close'][-3]: return True else: return False def buy_stock(stock, context): # 判断当前持仓的股票数量是否已达上限 if len(context.portfolio.positions) >= g.max_stock_count: return # 买入股票 order_value(stock, context.portfolio.cash / g.max_stock_count) def check_sell_condition(stock, current_date, context): # 获取持有股票的买入日期 buy_date = context.portfolio.positions[stock].init_time.date() # 判断是否满足卖出条件 if current_date - buy_date >= 3: # 判断是否亏损超过5% if (context.portfolio.positions[stock].last_price - context.portfolio.positions[stock].avg_cost) / context.portfolio.positions[stock].avg_cost <= -0.05: return True return False def sell_stock(stock, context): # 卖出股票 order_target(stock, 0)当中buy_date = context.portfolio.positions[stock].init_time.date()报错'NoneType' object has no attribute 'date'

if (context.portfolio.positions[stock].last_price - context.portfolio.positions[stock].avg_cost) / context.portfolio.positions[stock].avg_cost <= -0.05: return True return False 这样就可以...

修改这串代码import jqdatasdk as jq from datetime import datetime # 设置聚宽账户信息 jq.auth('username', 'password') jq.set_token('API token') # 设置选股条件 price_limit = 20 # 股票价格上限 pe_limit = 50 # 市盈率上限 pb_limit = 5 # 市净率上限 # 获取当前时间 now = datetime.now().strftime('%H:%M:%S') # 判断是否处于集合竞价时段 if now >= '09:20:00' and now <= '09:25:00': # 获取股票池 stocks = jq.get_all_securities(types=['stock']).index.tolist() # 筛选符合条件的股票 q = jq.query(jq.valuation.code, jq.valuation.pe_ratio, jq.valuation.pb_ratio).filter( jq.valuation.pe_ratio < pe_limit, jq.valuation.pb_ratio < pb_limit) df = jq.get_fundamentals(q, date=None) stocks = df['code'].tolist() # 过滤股票价格 prices = jq.get_price(stocks, end_date=datetime.now(), frequency='minute', fields=['close']) stocks = prices[prices['close'] <= price_limit].index.tolist() # 获取持仓信息 positions = jq.get_position('stock') # 调整持仓 for stock in positions: if stock in stocks: # 如果当前持仓的股票仍符合条件，则继续持有 pass else: # 如果当前持仓的股票不符合条件，则卖出 jq.order(stock, amount=0, side='sell') # 集合竞价买入 for stock in stocks: jq.order(stock, amount=100, side='buy', style='limit', price=price_limit)

for stock in stocks: jq.order(stock, amount=100, side='buy', style='limit', price=price_limit) 其中，price_limit、pe_limit 和 pb_limit 分别对应股票价格、市盈率和市净率的上限值，可以根据自己的...

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