[~,P]=my_P(6,2);%P表示6个数里面选2个数的所有排列的可能

根据您提供的代码，我无法确定my_P函数的具体实现，但是可以推测出my_P函数的作用是返回在n个数中选取k个数的所有排列的可能性，并且返回的结果中包含一个无用的占位符。以下是可能的my_P函数实现示例：

方法一：

function [res, P] = my_P(n, k)
    P = perms(1:n);
    res = P(:, 1:k);
end

方法二：

function [res, P] = my_P(n, k)
    P = nchoosek(1:n, k);
    res = P;
end

这两个函数都可以实现在n个数中选取k个数的所有排列的可能性，并且返回的结果中包含一个无用的占位符。

相关问题

mx = 8; my = 8; % x轴和y轴阵元个数 sn = 4; % 信号个数 dw = 0.5; % 半径波长比 snr = 10; % 信噪比 N = 1000; % 采样点数 fangwei = [10, 25, 135, 170]; % 信号方位角 yangjiao = [60 80 20 10]; % 信号俯仰角 for i = 1:sn for m = 1:mx daoxiang1(m, i) = exp(-j * 2 * pi * dw * (m - 1) * cos(fangwei(i) * pi / 180) * cos(yangjiao(i) * pi / 180)); end for mm = 1:my daoxiang2(mm, i) = exp(-j * 2 * pi * dw * mm * sin(fangwei(i) * pi / 180) * cos(yangjiao(i) * pi / 180)); end ss(i, :) = randn(1, N); % 生成高斯白噪声 end daoxiang = [daoxiang1; daoxiang2]; Signal = daoxiang * ss; x = awgn(Signal, snr, 'measured'); % 加入高斯白噪声 R = x * x' / N; [tzxiangliang, tzzhi] = eig(R); Nspace = tzxiangliang(:, 1:mx + my - sn); % 噪声子空间对应小的特征值（从小到大排列） for azi = 1:180 for ele = 1:90 for m = 1:mx daoxiang3(m, 1) = exp(-j * 2 * pi * dw * (m - 1) * cos(azi * pi / 180) * cos(ele * pi / 180)); end for mm = 1:my daoxiang4(mm, 1) = exp(-j * 2 * pi * dw * mm * sin(azi * pi / 180) * cos(ele * pi / 180)); end AQ1 = [daoxiang3; daoxiang4]; Power = AQ1' * Nspace * Nspace' * AQ1; % 在1-180度范围内进行计算 P(ele, azi) = -10 * log10(abs(Power)); end end [ele_grid, azi_grid] = meshgrid(1:90, 1:180); [x, y, z] = sph2cart(azi_grid / 180 * pi, (90 - ele_grid) / 180 * pi, P); mesh(x, y, z); title('九元L阵;信噪比：[50,50,50,50]；距离波长比：0.5'); xlabel('x'); ylabel('y'); zlabel('z'); 把这段代码改成经过10次蒙特卡洛实验后，求出方位角和俯仰角的角度均值

以下是修改后的代码：

mx = 8; my = 8; % x轴和y轴阵元个数 
sn = 4; % 信号个数 
dw = 0.5; % 半径波长比 
snr = 10; % 信噪比 
N = 1000; % 采样点数 
fangwei = [10, 25, 135, 170]; % 信号方位角 
yangjiao = [60 80 20 10]; % 信号俯仰角 
num_monte_carlo = 10; % 蒙特卡洛实验次数 

azimuth_mean = zeros(num_monte_carlo, sn); % 存储方位角均值 
elevation_mean = zeros(num_monte_carlo, sn); % 存储俯仰角均值 

for monte_carlo_idx = 1:num_monte_carlo
    for i = 1:sn
        for m = 1:mx
            daoxiang1(m, i) = exp(-j * 2 * pi * dw * (m - 1) * cos(fangwei(i) * pi / 180) * cos(yangjiao(i) * pi / 180));
        end
        for mm = 1:my
            daoxiang2(mm, i) = exp(-j * 2 * pi * dw * mm * sin(fangwei(i) * pi / 180) * cos(yangjiao(i) * pi / 180));
        end
        ss(i, :) = randn(1, N); % 生成高斯白噪声 
    end
    daoxiang = [daoxiang1; daoxiang2];
    Signal = daoxiang * ss;
    x = awgn(Signal, snr, 'measured'); % 加入高斯白噪声 
    R = x * x' / N;
    [tzxiangliang, tzzhi] = eig(R);
    Nspace = tzxiangliang(:, 1:mx + my - sn); % 噪声子空间对应小的特征值（从小到大排列）

    P = zeros(90, 180); % 存储每个方位角和俯仰角的功率值
    for azi = 1:180
        for ele = 1:90
            for m = 1:mx
                daoxiang3(m, 1) = exp(-j * 2 * pi * dw * (m - 1) * cos(azi * pi / 180) * cos(ele * pi / 180));
            end
            for mm = 1:my
                daoxiang4(mm, 1) = exp(-j * 2 * pi * dw * mm * sin(azi * pi / 180) * cos(ele * pi / 180));
            end
            AQ1 = [daoxiang3; daoxiang4];
            Power = AQ1' * Nspace * Nspace' * AQ1; % 在1-180度范围内进行计算 
            P(ele, azi) = -10 * log10(abs(Power));
        end
    end

    % 求出方位角和俯仰角的均值
    for i = 1:sn
        [ele_idx, azi_idx] = find(P == max(P(:)));
        azimuth_mean(monte_carlo_idx, i) = mean(azi_idx);
        elevation_mean(monte_carlo_idx, i) = mean(ele_idx);
        P(ele_idx, azi_idx) = -100; % 将已经找到的最大值置为极小值，以便寻找下一个最大值
    end
end

% 显示结果
disp(['方位角均值：', num2str(mean(azimuth_mean)), ' 度']);
disp(['俯仰角均值：', num2str(mean(elevation_mean)), ' 度']);

以上代码进行了10次蒙特卡洛实验，每次实验都会生成不同的随机信号和高斯白噪声，以求得方位角和俯仰角的角度均值。最终输出结果显示方位角和俯仰角的角度均值。

import numpy as np import csv import pandas as pd import numpy as npjk import matplotlib.pyplot as plt plt.rcParams['font.sans-serif']=['SimHei']#解决图标中汉字显示问题 plt.rcParams['axes.unicode_minus']=False#解决图标中汉字显示问题 from urllib.request import urlopen,Request from bs4 import BeautifulSoup #云计算2113方宇-2021058226 headers = {'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/96.0.4664.45 Safari/537.36'} url = 'https://search.jd.com/Search?keyword=%E8%93%9D%E7%89%99%E9%BC%A0%E6%A0%87&enc=utf-8&wq=%E8%93%9D%E7%89%99%E9%BC%A0%E6%A0%87&pvid=405a663911e84dd3822389ef5b97c147' response = Request(url,headers=headers) res = urlopen(response) data = res.read().decode('utf-8') soup = BeautifulSoup(data,'html.parser') ddd=soup.find('ul',class_="gl-warp clearfix") bbb=ddd.select('li>.gl-i-wrap>.p-price>strong>i')#价格 ccc=ddd.select('li>.gl-i-wrap>.p-shop>span>a')#店名 #云计算2113方宇2021058226 get_rmb_date = [] for i in bbb: get_rmb_date.append(i.text) get_shop_date = [] for i in ccc: get_shop_date.append(i.text) data = [] for i in range(len(get_shop_date)): temp = [] temp.append(get_shop_date[i]) temp.append(get_rmb_date[i]) data.append(temp) print(data) #云计算2113-方宇2021058226 f = open('D:/mypython/mycode/2021058226.csv','w',encoding='utf-8') csv_write = csv.writer(f) csv_write.writerow(['商品店家','商品价格']) for i in data: csv_write.writerow(i) f.close() #云计算2113方宇-2021058226 csv_file ='D:/mypython/mycode/2021058226.csv' csv_data=pd.read_csv(csv_file,low_memory=False) csv_df=pd.DataFrame(csv_data) dfl=csv_df.head(n=10) print(dfl) plt.figure(figsize = (10,6)) x = np.arange(10) y = np.array(dfl['商品店家']) xticks = list(dfl['商品价格']) print(x,y,xticks) p=[0,1,2,3,4,5,6,7,8,9] plt.xticks(p,y,rotation=20) plt.bar(p,xticks,color='red') plt.show()如何将词云柱状图按序排列

如果想将柱状图按照序排列，可以先对数据进行排序，再绘制柱状图。以下是修改后的代码：

import numpy as np
import csv
import pandas as pd
import numpy as npjk    
import matplotlib.pyplot as plt
from wordcloud import WordCloud
plt.rcParams['font.sans-serif']=['SimHei']#解决图标中汉字显示问题
plt.rcParams['axes.unicode_minus']=False#解决图标中汉字显示问题
from urllib.request import urlopen,Request
from bs4 import BeautifulSoup

headers = {'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/96.0.4664.45 Safari/537.36'}
url = 'https://search.jd.com/Search?keyword=%E8%93%9D%E7%89%99%E9%BC%A0%E6%A0%87&enc=utf-8&wq=%E8%93%9D%E7%89%99%E9%BC%A0%E6%A0%87&pvid=405a663911e84dd3822389ef5b97c147'
response = Request(url,headers=headers)
res = urlopen(response)
data = res.read().decode('utf-8')
soup = BeautifulSoup(data,'html.parser')
ddd=soup.find('ul',class_="gl-warp clearfix")
bbb=ddd.select('li>.gl-i-wrap>.p-price>strong>i')#价格
ccc=ddd.select('li>.gl-i-wrap>.p-shop>span>a')#店名

get_rmb_date = []
for i in bbb:
    get_rmb_date.append(float(i.text))

get_shop_date = []
for i in ccc:
    get_shop_date.append(i.text)

# 对数据进行排序
data = sorted(zip(get_shop_date, get_rmb_date), key=lambda x: x[1], reverse=True)
get_shop_date, get_rmb_date = zip(*data)

print(data)

f = open('D:/mypython/mycode/2021058226.csv','w',encoding='utf-8')
csv_write = csv.writer(f)

csv_write.writerow(['商品店家','商品价格'])

for i in data:
    csv_write.writerow(i)

f.close()

csv_file ='D:/mypython/mycode/2021058226.csv'
csv_data=pd.read_csv(csv_file,low_memory=False)
csv_df=pd.DataFrame(csv_data)

dfl=csv_df.head(n=10)
print(dfl)

plt.figure(figsize = (10,6))
x = np.arange(10)
y = np.array(dfl['商品店家'])
xticks = list(dfl['商品价格'])
print(x,y,xticks)

p = [0,1,2,3,4,5,6,7,8,9]

plt.xticks(p, y, rotation=20)
plt.bar(p, xticks, color='red')
plt.show()

这样，柱状图就会按照商品价格的高低进行排列。