mport requests from bs4 import BeautifulSoup import csv def get_top250_movies(): url = 'https://movie.douban.com/top250' headers = { 'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/58.0.3029.110 Safari/537.3'} movie_info_list = [] for i in range(0, 250, 25): params = {'start': str(i)} res = requests.get(url, headers=headers, params=params) soup = BeautifulSoup(res.text, 'html.parser') movie_list = soup.find_all('div', class_='info') for movie in movie_list: title = movie.find('span', class_='title').text info = movie.find('div', class_='bd').p.text.strip().split('\n') director = info[0][4:] actors = info[1][3:] year = info[1][-5:-1] rating = movie.find('span', class_='rating_num').text comment_num = movie.find('div', class_='star').find_all('span')[3].text[:-3] movie_info_list.append([title, director, actors, year, rating, comment_num]) return movie_info_list def save_to_csv(movie_info_list): with open('movie_info.csv', 'w', newline='', encoding='utf-8-sig') as f: writer = csv.writer(f) writer.writerow(['电影名称', '导演', '演员', '上映年份', '评分', '评论数']) for movie_info in movie_info_list: writer.writerow(movie_info) if name == 'main': movie_info_list = get_top250_movies() save_to_csv(movie_info_list) print('电影信息保存成功！') 在此代码的基础上对爬取的电影类型进行生成按照评分生成词云

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mport requests from lxml import etree import csv import time import pyecharts from pyecharts import options as opts from pyecharts.options import * #导入的是一个模块中的所有类 from pyecharts.charts import Bar from pyecharts.globals import ThemeType from bs4 import BeautifulSoup from selenium.webdriver import Chrome

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mport jieba import jieba.posseg as pseg from utils.dbutils import * from question_answer.获取天气情况 import * import re from utils.user_base import * jieba.enable_paddle() def get_loc_list(text): per_list = [] # 人名列表 word_list = jieba.lcut(text) # print(word_list) for word in word_list: if len(word)==1: # 不加判断会爆 continue words = pseg.cut(word, use_paddle=True) # paddle模式 # print(list(words)) word, flag = list(words)[0] if flag=='LOC': # 这里写成LOC是地名 per_list.append(word) per_list = list(set(per_list)) print(per_list) if len(per_list)==0: per_list.append(word_list[0]) return per_list

这段代码是一个函数，用于从文本中提取出地名列表。它使用了jieba库对文本进行分词，并使用了PaddlePaddle深度学习库来进行词性标注。具体来说，它首先将文本进行分词，然后对每个词进行词性标注，最后将词性为“LOC...

2023/5/31 16:01:36 mport requests url = "https://swa.apple.com:443/api/v1.0/external/authEntityRequests" pem = r"D:\bob_porject\ibeelink-apple-mfi\ibeelink-application\src\main\java\com\bob\ibeelink\apple\mfi\application\service\impl\MFi-gSccDMIpR_e_vXRs9QEXDA.cert.pem" key = r"D:\bob_porject\ibeelink-apple-mfi\ibeelink-application\src\main\java\com\bob\ibeelink\apple\mfi\application\service\impl\test.key" data = { "ppid": "988616-546718", "requested_auth_entity_count": 10 } headers = {"Accept": "application/json", "Content-Type": "application/json"} res = requests.post(url, json=data, headers=headers, cert=(pem, key), ) print(res.text) 请将上述代码转为springboot2.5.14与JDK1.8版本的代码

String url = "https://swa.apple.com:443/api/v1.0/external/authEntityRequests"; String pemPath = "D:\\bob_porject\\ibeelink-apple-mfi\\ibeelink-application\\src\\main\\java\\...

mport numpy as np from scipy.optimize import linprog def integer_cutting_plane(c, A, b, bound): # 初始化整数规划问题的线性规划松弛问题 relaxed_c = c relaxed_A = A relaxed_b = b bounds = bound while True: # 解决线性规划松弛问题 res = linprog(c=relaxed_c, A_ub=relaxed_A, b_ub=relaxed_b, bounds=bounds) x = res.x result = res.fun print(x) print(result) # 检查解是否为整数解 if all(int(val) == val for val in x): return x.astype(int) # 添加割平面 new_constraint = (relaxed_A @ x <= relaxed_b) # 添加割平面约束 relaxed_A = np.vstack((relaxed_A, new_constraint)) # relaxed_b = np.append(relaxed_b, np.floor(relaxed_A @ x)) # 定义目标函数系数 # 定义决策变量取值范围 def get_bounds(): x1 = (0, None) x2 = (0, None) return x1, x2 # 定义目标函数系数 def get_c(): c = np.array([40, 90]) return c # 定义不等式约束条件左边系数 def get_A(): A = np.array([[-9, -7], [-7, -20]]) return A # 定义不等式约束条件右边系数 def get_b(): b = np.array([-56, -70]) return b # 主程序 if name == 'main': integer_cutting_plane(get_c(), get_A(), get_b(), get_bounds()) 优化这段代码

4. 添加割平面的策略：添加割平面是整数切割平面方法的关键步骤。你可以尝试不同的割平面生成策略，例如Gomory切割、线性规划松弛问题的最优解等。 5. 并行化计算：如果你的机器有多个处理器或多个计算核心，你可以...

mport socket from collections import OrderedDict import numpy as np import tensorflow as tf import utils.io.image import utils.io.landmark import utils.io.text import utils.np_image import utils.sitk_image import utils.sitk_np from dataset import Dataset from datasets.pyro_dataset import PyroClientDataset from network import Unet from spine_localization_postprocessing import bb, bb_iou from tensorflow.keras.mixed_precision import experimental as mixed_precision from tensorflow_train_v2.dataset.dataset_iterator import DatasetIterator from tensorflow_train_v2.train_loop import MainLoopBase from tensorflow_train_v2.utils.data_format import get_batch_channel_image_size from tensorflow_train_v2.utils.loss_metric_logger import LossMetricLogger from tensorflow_train_v2.utils.output_folder_handler import OutputFolderHandler from tqdm import tqdm

这段代码看起来是一个Python脚本，导入了一些需要的模块和库，包括socket、collections、numpy、tensorflow等。它还引用了一些自定义的模块和类，如utils.io、dataset、network等。代码中还使用了一些函数和类，如...

mport numpy as np import tensorflow as tf from keras.models import Sequential from keras.layers import Dense, Activation, Dropout, Flatten from keras.layers.convolutional import Conv2D, MaxPooling2D from keras.utils import np_utils from keras.datasets import mnist from keras import backend as K from keras.optimizers import Adam import skfuzzy as fuzz import pandas as pd from sklearn.model_selection import train_test_split # 绘制损失曲线 import matplotlib.pyplot as plt import time from sklearn.metrics import accuracy_score data = pd.read_excel(r"D:\pythonProject60\filtered_data1.xlsx") # 读取数据文件 # Split data into input and output variables X = data.iloc[:, :-1].values y = data.iloc[:, -1].values X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42) # 导入MNIST数据集 # 数据预处理 y_train = np_utils.to_categorical(y_train, 3) y_test = np_utils.to_categorical(y_test, 3) # 创建DNFN模型 start_time=time.time() model = Sequential() model.add(Dense(64, input_shape=(11,), activation='relu')) model.add(Dense(128, activation='relu')) model.add(Dropout(0.5)) model.add(Dense(3, activation='softmax')) # 编译模型 model.compile(loss='categorical_crossentropy', optimizer=Adam(), metrics=['accuracy']) # 训练模型 history = model.fit(X_train, y_train, validation_data=(X_test, y_test), epochs=10, batch_size=128) # 使用DNFN模型进行预测 y_pred = model.predict(X_test) y_pred= np.argmax(y_pred, axis=1) print(y_pred) # 计算模糊分类 fuzzy_pred = [] for i in range(len(y_pred)): fuzzy_class = np.zeros((3,)) fuzzy_class[y_pred[i]] = 1.0 fuzzy_pred.append(fuzzy_class) fuzzy_pred = np.array(fuzzy_pred) end_time = time.time() print("Total time taken: ", end_time - start_time, "seconds")获得运行结果并分析

根据代码，这是一个使用Keras创建DNFN模型，对数据进行分类的代码。数据集使用的是一个Excel文件，通过pandas读取数据，然后分为训练集和测试集。模型使用了三层Dense层，其中第一层有64个神经元，第二层有128个神经...

加注释 <c:when test="${pageInfo.pages <= 3 }"> <c:set var="begin" value="1"/> <c:set var="end" value="${pageInfo.pages }"/> </c:when> <c:otherwise> <c:set var="begin" value="${pageInfo.pageNum -1 }"/> <c:set var="end" value="${pageInfo.pageNum + 2}"/> <c:if test="${begin < 2 }"> <c:set var="begin" value="1"/> <c:set var="end" value="3"/> </c:if> <c:if test="${end > pageInfo.pages }"> <c:set var="begin" value="${pageInfo.pages-2}"/> <c:set var="end" value="${pageInfo.pages }"/> </c:if> </c:otherwise>

<c:when test="${pageInfo.pages <= 3 }">  <c:set var="begin" value="1"/> <c:set var="end" value=...

修改代码为找出区间[-2,40]之间的曲率极大值点的横坐标：mport numpy as np # 定义高斯核函数 def gkernel(x, x0, sig): return np.exp(-(x-x0)**2/(2*sig2)) # 定义曲率函数 def curvature(x, y): dy = np.gradient(y, x) ddy = np.gradient(dy, x) k = np.abs(ddy) / (1 + dy2)**1.5 return k # 定义参数和数组 x1 = np.linspace(-2, 40, 10) x2 = np.linspace(-2, 40, 100) sig = 1 w = 1 y_rec = np.zeros_like(x2) curv_list = [] # 计算曲率值 for xi in x2: y = y_rec.copy() for k, xk in enumerate(x1): y += w * gkernel(xi, xk, sig) curv = curvature(x2, y) curv_list.append(curv[0]) # 找到曲率值最大的四个点 idx_max = np.argsort(curv_list)[-10:] x_max = x2[idx_max] x_max_diff = np.diff(x_max) while np.any(x_max_diff < 2): idx = np.argmin(x_max_diff) x_max[idx+1] += 1 x_max_diff = np.diff(x_max) print("曲率最大的十个点的横坐标为：", x_max)

import numpy as np import matplotlib.pyplot as plt # 定义高斯核函数 def gkernel(x, x0, sig): return np.exp(-(x-x0)**2/(2*sig**2)) # 定义曲率函数 def curvature(x, y): dy = np.gradient(y, x) ddy = ...

mport numpy as np def rts_smooth(data, window_size, smooth_factor): assert window_size % 2 == 1, "Window size must be odd" assert 0 <= smooth_factor <= 1, "Smooth factor must be between 0 and 1" half_window = (window_size - 1) // 2 data_length = len(data) smoothed_data = np.zeros(data_length) for i in range(half_window, data_length - half_window): window = data[i - half_window:i + half_window + 1] median = np.median(window) deviation = np.abs(window - median) threshold = smooth_factor * np.median(deviation) if np.abs(data[i] - median) > threshold: smoothed_data[i] = median else: smoothed_data[i] = data[i] # 处理首尾值 smoothed_data[:half_window] = data[:half_window] smoothed_data[data_length - half_window:] = data[data_length - half_window:] return smoothed_data 使用示例 data = [10, 15, 20, 12, 18, 22, 25, 16, 14, 23] window_size = 3 smooth_factor = 0.6 smoothed_data = rts_smooth(data, window_size, smooth_factor) print(smoothed_data)这段代码如果输入数据有很多维度怎么改

import numpy as np def rts_smooth(data, window_size, smooth_factor): assert window_size % 2 == 1, "Window size must be odd" assert 0 <= smooth_factor <= 1, "Smooth factor must be between 0 and 1" ...

mport pandas as pd from sklearn.feature_extraction.text import CountVectorizer # 读取CSV文件 df = pd.read_csv(r'F:\RIOS\Patents\fillter.csv') # 获取要计算词向量的列 text = df['filtered_draw_desc_text'].values # 计算词向量 vectorizer = CountVectorizer() X = vectorizer.fit_transform(text) 阅读这个代码并在这个基础上生成一个新的表，这个表有两个attribution，一个是单词名称，一个是对应向量

df_words = pd.DataFrame(X.toarray(), columns=vectorizer.get_feature_names()) # 添加单词名称列 df_words.insert(0, 'word', vectorizer.get_feature_names()) # 打印结果 print(df_words) 这个代码将...

mport numpy as np import scipy.stats as ss import pandas as pd import matplotlib.pyplot as plt import seaborn as sns df=pd.read_excel("./tmp_apply.xlsx") sns.set_context(font_scale=20) plt.rcParams['font.family'] = 'FangSong' plv_tb=pd.pivot_table(df,values="sale",index=["FactoryName","JiJXH"],columns=["Xian"],aggfunc=np.mean) fig, ax = plt.subplots(figsize=(200,10)) sns.heatmap(plv_tb,vmin=plv_tb.min().min(), vmax=plv_tb.max().max(),cmap=sns.color_palette("RdYlBu",n_colors=10),annot=True, fmt='g', annot_kws={"size": 20}, ax=ax) plt.subplots_adjust(left=0.2, bottom=0.2) # 调整左边和底部的距离 plt.show() 怎么使热力图和色阶图距离变小

你可以通过调整 plt.subplots_adjust() 方法中的参数来调整热力图和色阶图之间的距离。具体来说，你可以将 left 和 bottom 的值减小，例如： python plt.subplots_adjust(left=0.1, bottom=0.1) ...

给代码注释 1 mport mathroot_(ab,c):D= b**2- 4aCif D >= 0:r1 = (-b + math.sqrt(D))/2/ar2 = (-b - math.sgrt(D))/2/aprint(f'root1 = (r1), root2 = ir2)')D=Dr1 _real = -b/2/ar1_imag = math.sgrt(D)/2/aprint(f'roots = {r1 real] +- i*{r1_imag)') root_(1,1,1)

import math # 定义函数名为 root_(a,b,c) def root_(a,b,c): # 计算判别式 D D = b**2 - 4*a*c # 判断 D 是否大于等于 0，如果是则存在实数根 if D >= 0: # 计算实数根 r1 = (-b + math.sqrt(D)) / (2*a)...

帮我看看下面代码哪里错了：mport os,sys,torch # sys.path.append(os.path.dirname()) import numpy as np import scipy from scipy import ndimage import tensorflow as tf import matplotlib.pyplot as plt from keras.applications.resnet50 import ResNet50 from tensorflow.keras.preprocessing import image from tensorflow.keras.applications.resnet50 import preprocess_input, decode_predictions from PIL import Image import random # from numba import cuda报错：在 'init.py' 中找不到引用 'resnet50'

从错误信息来看，可能是你的keras版本过低导致的。请尝试升级你的keras版本，可以通过以下命令升级到最新版本： pip install keras --upgrade 如果还是不行，可以尝试升级tensorflow和keras依赖的库： ...

python:mport string from zhon.hanzi import punctuation with open('shuihuzhuan.txt', 'r', encoding='utf-8') as f: txt = f.read() for fh in punctuation: txt = str(txt).replace(fh,' ') print(txt) import jieba # 使用jieba库进行中文分词 words = list(jieba.cut(txt)) # 统计每个两个字以上词语出现的次数 word_count = {} for word in words: if len(word) >= 2: word_count[word] = word_count.get(word, 0) + 1 # 按照词频从大到小排序 word_count_sorted = sorted(word_count.items(), key=lambda x: x[1], reverse=True) # 输出前20个结果 top_words = [x[0] for x in word_count_sorted[:20]] print('即将出现前20个词频') for word in top_words: print(f'{word}: {word_count[word]}') # 将结果保存到文件中 with open('shuihuzhuan_word_count.txt', 'w', encoding='utf-8') as f: for word, count in word_count_sorted: f.write(f"{word}: {count}\n") import matplotlib.pyplot as plt # 取出Top20的词和词频 from pylab import mpl mpl.rcParams["font.sans-serif"] = ["SimHei"] # 设置显示中文字体 mpl.rcParams["axes.unicode_minus"] = False # 设置正常显示符号 plt.figure(figsize=(10, 5)) plt.bar(range(len(top_words)), [word_count[word] for word in top_words], align='center') plt.xticks(range(len(top_words)), top_words, fontsize=12, rotation=45) plt.xlabel(' ',fontsize=14) plt.ylabel(' ',fontsize=14) plt.title('数据来自水浒传', fontsize=16) plt.show()如何理解如何写出来的

这段代码是一个Python程序，目的是对《水浒传》这本小说进行中文分词，并...此外，程序还使用matplotlib库绘制了Top20词语的条形图以可视化结果。整个程序的目的是为了研究《水浒传》这本小说中哪些词语使用频率较高。

mport pandas as pd import numpy as np import matplotlib.pyplot as plt import seaborn as sns df = pd.read_csv('data（北深）.csv') df['date'] = pd.to_datetime(df['date']) # 将日期字符串转换为日期格式 df['Month'] = df['date'].dt.month # 增加一列表示月份 df['days_to_departure'] = df['days_to_departure'].astype(int) # 将天数转换为整数类型 sns.set(style='whitegrid') fig, ax = plt.subplots(figsize=(10, 10)) sns.heatmap(df.pivot_table(index='days_to_departure', columns='date', values='lowest_price'), cmap='YlOrRd', ax=ax) ax.set_title('Flight Price Heatmap') ax.set_xlabel('Date') ax.set_ylabel('Days to Departure') plt.show()上述代码生成的热力图中将横轴的日期格式改为YYYY- MM- DD的形式

df = pd.read_csv('data（北深）.csv') df['date'] = pd.to_datetime(df['date']) df['Month'] = df['date'].dt.month df['days_to_departure'] = df['days_to_departure'].astype(int) sns.set(style='...

mport jieba import wordcloud import numpy as np from PIL import Image star_mask=np.array(Image.open("star.png")) f=open("text.txt","r",encoding="utf-8") t=f.read() f.close() ls=jieba.lcut(t) txt="".join(ls) w=wordcloud.WordCloud(width=1000,height=700, backgroud_color="white", font_path="simhei.ttf", stopwords =wordcloud.STOPWORDS.add("发展"), mask = star_mask) w.generate(txt) w.to_file("out2.png")错哪了

from wordcloud import WordCloud, STOPWORDS import numpy as np from PIL import Image # 加载图片和文本数据 star_mask = np.array(Image.open("star.png")) f = open("text.txt", "r", encoding="utf-8") t = f...

mport matplotlib.pyplot as plt#加载绘图工具 from mpl_toolkits.basemap import Basemap fig,ax = plt.subplots() ax.scatter(Ame['Lon'], Ame['Lat'], marker='o', color='red')# 添加标签 for i, row in Ame.iterrows(): ax.annotate((Ame['Lon'], Ame['Lat'])) #地图优化 map = Basemap(projection='merc', lat_0=0, lon_0=0, resolution='l') #加国界州界 map.drawcoastlines() map.drawcountries() map.drawstates() #更换站点样式 # 显示地图 plt.show()

这是一个Python模块，用于绘制数据可视化图形，常用于科学计算、数据分析和机器学习等领域。通过导入该模块并使用其中的pyplot子模块，可以方便地创建各种类型的图表，如折线图、散点图、柱状图、饼图等。

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给代码注释 1 mport mathroot_(ab,c):D= b**2- 4aCif D >= 0:r1 = (-b + math.sqrt(D))/2/ar2 = (-b - math.sgrt(D))/2/aprint(f'root1 = (r1), root2 = ir2)')D=Dr1 _real = -b/2/ar1_imag = math.sgrt(D)/2/aprint(f'roots = {r1 real] +- i*{r1_imag)') root_(1,1,1)