def Ham_yy(N = 12,g2=2.0): Ilist = [sigmai] * N Ham_yy = 0.0 for loopi in range(N-1): Ilist[loopi] = g2*sigmay Ilist[loopi+1] = sigmay Ham_yy = kron(Ilist)+Ham_yy Ilist = [sigmai] * N return Ham_yy sigmax = np.array([[0, 1], [1, 0]]) sigmay = np.array([[0, -1j], [1j, 0]]) sigmaz = np.array([[1, 0j], [0, -1]]) sigmai = np.array([[1, 0j], [0, 1]])代码的作用

Ham_chat：:hamburger:Ham_chat客户

xillinux-eval-zedboard-2.0c.zip_GPS_grasspzo_iran

标题中的"xillinux-eval-zedboard-2.0c.zip_GPS_grasspzo_iran"揭示了这个压缩包可能包含一个与Linux操作系统相关的项目，特别是针对Zynq ZedBoard开发板的一个评估版本，版本号为2.0c。同时，它还提到了GPS（全球...

解释这段代码def Ham_yy(N = 12,g2=2.0): Ilist = [sigmai] * N Ham_yy = 0.0 for loopi in range(N-1): Ilist[loopi] = g2sigmay Ilist[loopi+1] = sigmay Ham_yy = kron(Ilist)+Ham_yy Ilist = [sigmai] N return Ham_yy sigmax = np.array([[0, 1], [1, 0]]) sigmay = np.array([[0, -1j], [1j, 0]]) sigmaz = np.array([[1, 0j], [0, -1]]) sigmai = np.array([[1, 0j], [0, 1]])

这段代码是一个用于生成量子哈密顿量的函数，其中包含...在这个函数中，使用了 sigmax、sigmay、sigmaz 和 sigmai 四个矩阵来表示量子比特的不同状态，而 kron 函数则用于计算这些矩阵的张量积，最终生成整个哈密顿量。

详解def Ham_z(N=12,g1=1.0): Ilist = [sigmai]N Ham_z = np.zeros((2N,2N),dtype=np.complex128) for loopi in range(N): Ilist[loopi] = g1sigmaz Ham_z = kron(Ilist)+Ham_z Ilist = [sigmai] * N return Ham_z

这是一个关于哈密顿量的 Python 函数，用于计算一个由 N 个自旋组成的系统的哈密顿量，其中 g1 是一个常数，sigmai 和 sigmaz 是泡利矩阵。函数使用了 numpy 库中的 kron 函数来计算 Kronecker 积。

matlab2017a代码-Fibonacci_Ham_code_web:Matlab代码：斐波那契准晶体模型的哈密顿蒙特卡罗模拟

import random # sandwich ingredients and their associated calories ingredients = { "Bread": 100, "Ham": 150, "Lettuce": 10, "Tomato": 20, "Mayonnaise": 50, "Cheese": 120 } # prompt user for calorie range min_calories = int(input("Enter minimum calories: ")) max_calories = int(input("Enter maximum calories: ")) # calculate the minimum and maximum calories for the sandwich min_sandwich_calories = 2 * ingredients["Bread"] + min(ingredients.values()) * 2 max_sandwich_calories = 2 * ingredients["Bread"] + max(ingredients.values()) * 2 # check if the calorie range is valid if max_calories < min_sandwich_calories: print("Sorry, it is impossible to create a sandwich within the given calorie range.") else: # create the sandwich sandwich = ["Bread", "Bread"] sandwich_calories = 2 * ingredients["Bread"] while sandwich_calories < min_calories: # add random ingredient ingredient = random.choice(list(ingredients.keys())) sandwich.append(ingredient) sandwich_calories += ingredients[ingredient] while sandwich_calories <= max_calories: # add random ingredient ingredient = random.choice(list(ingredients.keys())) # check if the ingredient is the same as the previous one if len(sandwich) >= 3 and ingredient == sandwich[-2]: continue sandwich.append(ingredient) sandwich_calories += ingredients[ingredient] # check if the sandwich is already at the maximum calorie limit if sandwich_calories == max_sandwich_calories: break # add the last slice of bread sandwich.append("Bread") # print the sandwich and its total calories print("Your sandwich:", sandwich) print("Total calories:", sandwich_calories)

{ "Ham", 150 }, { "Lettuce", 10 }, { "Tomato", 20 }, { "Mayonnaise", 50 }, { "Cheese", 120 } }; // prompt user for calorie range Console.Write("Enter minimum calories: "); int min_calories =...

namespace SandwichCalories { class Program { static void Main(string[] args) { // sandwich ingredients and their associated calories Dictionary<string, int> ingredients = new Dictionary<string, int>() { { "Bread", 100 }, { "Ham", 150 }, { "Lettuce", 10 }, { "Tomato", 20 }, { "Mayonnaise", 50 }, { "Cheese", 120 } }; // prompt user for calorie range Console.Write("Enter minimum calories: "); int min_calories = int.Parse(Console.ReadLine()); Console.Write("Enter maximum calories: "); int max_calories = int.Parse(Console.ReadLine()); // calculate the minimum and maximum calories for the sandwich int min_sandwich_calories = 2 * ingredients["Bread"] + ingredients.Values.Min() * 2; int max_sandwich_calories = 2 * ingredients["Bread"] + ingredients.Values.Max() * 2; // check if the calorie range is valid if (max_calories < min_sandwich_calories) { Console.WriteLine("Sorry, it is impossible to create a sandwich within the given calorie range."); } else { // create the sandwich List<string> sandwich = new List<string> { "Bread", "Bread" }; int sandwich_calories = 2 * ingredients["Bread"]; while (sandwich_calories < min_calories) { // add random ingredient string ingredient = ingredients.Keys.ElementAt(new Random().Next(ingredients.Count)); sandwich.Add(ingredient); sandwich_calories += ingredients[ingredient]; } while (sandwich_calories <= max_calories) { // add random ingredient string ingredient = ingredients.Keys.ElementAt(new Random().Next(ingredients.Count)); // check if the ingredient is the same as the previous one if (sandwich.Count >= 3 && ingredient == sandwich[sandwich.Count - 2]) { continue; } sandwich.Add(ingredient); sandwich_calories += ingredients[ingredient]; // check if the sandwich is already at the maximum calorie limit if (sandwich_calories == max_sandwich_calories) { break; } } // add the last slice of bread sandwich.Add("Bread"); // print the sandwich and its total calories Console.WriteLine("Your sandwich: " + string.Join(", ", sandwich)); Console.WriteLine("Total calories: " + sandwich_calories); } } } } 改进代码

new Ingredient("Ham", 150), new Ingredient("Lettuce", 10), new Ingredient("Tomato", 20), new Ingredient("Mayonnaise", 50), new Ingredient("Cheese", 120) }; // prompt user for calorie range ...

不使用LINQ查询和操作集合改进代码 namespace SandwichCalories { class Program { static void Main(string[] args) { // sandwich ingredients and their associated calories Dictionary<string, int> ingredients = new Dictionary<string, int>() { { "Bread", 100 }, { "Ham", 150 }, { "Lettuce", 10 }, { "Tomato", 20 }, { "Mayonnaise", 50 }, { "Cheese", 120 } }; // prompt user for calorie range Console.Write("Enter minimum calories: "); int min_calories = int.Parse(Console.ReadLine()); Console.Write("Enter maximum calories: "); int max_calories = int.Parse(Console.ReadLine()); // calculate the minimum and maximum calories for the sandwich int min_sandwich_calories = 2 * ingredients["Bread"] + ingredients.Values.Min() * 2; int max_sandwich_calories = 2 * ingredients["Bread"] + ingredients.Values.Max() * 2; // check if the calorie range is valid if (max_calories < min_sandwich_calories) { Console.WriteLine("Sorry, it is impossible to create a sandwich within the given calorie range."); } else { // create the sandwich List<string> sandwich = new List<string> { "Bread", "Bread" }; int sandwich_calories = 2 * ingredients["Bread"]; while (sandwich_calories < min_calories) { // add random ingredient string ingredient = ingredients.Keys.ElementAt(new Random().Next(ingredients.Count)); sandwich.Add(ingredient); sandwich_calories += ingredients[ingredient]; } while (sandwich_calories <= max_calories) { // add random ingredient string ingredient = ingredients.Keys.ElementAt(new Random().Next(ingredients.Count)); // check if the ingredient is the same as the previous one if (sandwich.Count >= 3 && ingredient == sandwich[sandwich.Count - 2]) { continue; } sandwich.Add(ingredient); sandwich_calories += ingredients[ingredient]; // check if the sandwich is already at the maximum calorie limit if (sandwich_calories == max_sandwich_calories) { break; } } // add the last slice of bread sandwich.Add("Bread"); // print the sandwich and its total calories Console.WriteLine("Your sandwich: " + string.Join(", ", sandwich)); Console.WriteLine("Total calories: " + sandwich_calories); } } } }

One possible way to improve the code without using LINQ and manipulating collections is to simplify the while loop that adds random ingredients to the sandwich. Instead of generating a random index ...

from sklearn.naive_bayes import BernoulliNB,MultinomialNB from sklearn.feature_extraction.text import CountVectorizer from sklearn.model_selection import train_test_split import pandas as pd path = 'E:/Python_file/zuoye/SMSSpamCollection.txt' Cnames=['labels','messages'] data = pd.read_csv(path,sep='\t', header=None, names=Cnames) #读取数据集，分隔符是\t data=data.replace({'ham':0,'spam':1}) #替换标签值 print('数据集展示：') print(data) print('\n----------------------------------\n') X=data['messages'] y=data['labels'] x_train,x_test,y_train,y_test=train_test_split(X,y,train_size=0.8,random_state=123) vector_nomial=CountVectorizer() #实现词袋模型 vector_bernou=CountVectorizer() #多项式模型分类垃圾短信 train_matrix=vector_nomial.fit_transform(x_train) test_matrix=vector_nomial.transform(x_test) polynomial=MultinomialNB() clm_nomial=polynomial.fit(train_matrix,y_train) result_nomial=clm_nomial.predict(test_matrix) #伯努利模型分类垃圾短信 train_matrix=vector_bernou.fit_transform(x_train) test_matrix=vector_bernou.transform(x_test) Bernoulli=BernoulliNB() clm_bernoulli=Bernoulli.fit(train_matrix,y_train) result_bernou=clm_bernoulli.predict(test_matrix) print('多项式模型的预测结果,类型，长度：') print(result_nomial,type(result_nomial),result_nomial.shape) print('多项式模型的前一百个预测结果：') print(result_nomial[0:100]) print('多项式模型模型R²评分：'+ str(clm_nomial.score(test_matrix,y_test))) print('\n----------------------------------\n') print('伯努利模型的预测结果,类型，长度：') print(result_bernou,type(result_bernou),result_bernou.shape) print('伯努利模型的前一百个预测结果：') print(result_bernou[0:100]) print('伯努利模型R²评分：'+ str(clm_bernoulli.score(test_matrix,y_test)))

print('\n----------------------------------\n') print('伯努利模型的预测结果,类型，长度：') print(result_bernou, type(result_bernou), result_bernou.shape) print('伯努利模型的前一百个预测结果：') print...

帮我修改此代码 % 计算窗口长度和重叠长度的样点数 nwinlen = round(winlenfs); % 窗口长度（样点数） nwinshift = round(winshiftfs); % 重叠长度（样点数） % 分帧加窗 nx = length(x); nframe = floor((nx-nwinlen)/nwinshift)+1; % 帧数 w = rectwin(nwinlen); % 矩形窗 win = repmat(w, 1, nframe); % 窗矩阵 x_seg_rect = x(1:nwinlen+(nframe-1)nwinshift) . win; % 分帧加窗（矩形窗） w = hamming(nwinlen); % 汉明窗 win = repmat(w, 1, nframe); % 窗矩阵 x_seg_ham = x(1:nwinlen+(nframe-1)nwinshift) . win; % 分帧加窗（汉明窗） % 时域波形 t = (0:nx-1)/fs; % 时间轴 subplot(2,2,1); plot(t,x,'k');title('原始波形'); subplot(2,2,2); plot(t(1:nwinlen+(nframe-1)nwinshift),x_seg_rect,'k'); title('加矩形窗后波形'); subplot(2,2,3); plot(t(1:nwinlen+(nframe-1)nwinshift),x_seg_ham,'k'); title('加汉明窗后波形'); % 短时频谱 nfft = 512; % FFT点数 f = (0:nfft/2-1)fs/nfft; % 频率轴 subplot(2,2,4); spectrogram(x_seg_rect,winlenfs,winshiftfs,nfft,fs,'yaxis');title('加矩形窗后短时频谱'); subplot(2,2,5); spectrogram(x_seg_ham,winlenfs,winshift*fs,nfft,fs,'yaxis'); title('加汉明窗后短时频谱');

x_seg_ham = x(1:nwinlen+(nframe-1)*nwinshift) .* win_ham; % 分帧加窗（汉明窗） % 时域波形 t = (0:nx-1)/fs; % 时间轴 subplot(2,2,1); plot(t,x,'k');title('原始波形'); subplot(2,2,2); plot(t(1:nwinlen+...

不使用LINQ查询和操作集合改进代码 namespace SandwichCalories { class Program { static void Main(string[] args) { // sandwich ingredients and their associated calories Dictionary<string, int> ingredients = new Dictionary<string, int>() { { "Bread", 66 }, { "Ham", 72 }, { "Bologna", 57 }, { "Chicken", 17 }, { "Corned Beef", 53 }, { "Salami", 40 }, { "Cheese, American", 104 }, { "Cheese, Cheddar", 113 }, { "Cheese, Havarti", 105 }, { "Mayonnaise", 94 }, { "Mustard", 10 }, { "Butter", 102 }, { "Garlic Aioli", 100 }, { "Sriracha", 15 }, { "Dressing, Ranch", 73 }, { "Dressing, 1000 Island", 59 }, { "Lettuce", 5 }, { "Tomato", 4 }, { "Cucumber", 4 }, { "Banana Pepper", 10 }, { "Green Pepper", 3 }, { "Red Onion", 6 }, { "Spinach", 7 }, { "Avocado", 64 } }; // prompt user for calorie range Console.Write("Enter minimum calories: "); int min_calories = int.Parse(Console.ReadLine()); Console.Write("Enter maximum calories: "); int max_calories = int.Parse(Console.ReadLine()); // calculate the minimum and maximum calories for the sandwich int min_sandwich_calories = 2 * ingredients["Bread"] + ingredients.Values.Min() * 2; int max_sandwich_calories = 2 * ingredients["Bread"] + ingredients.Values.Max() * 2; // check if the calorie range is valid if (max_calories < min_sandwich_calories) { Console.WriteLine("Sorry, it is impossible to create a sandwich within the given calorie range."); } else { // create the sandwich List<string> sandwich = new List<string> { "Bread" }; int sandwich_calories = 1 * ingredients["Bread"]; while (sandwich_calories < min_calories) { // add random ingredient string ingredient = ingredients.Keys.ElementAt(new Random().Next(ingredients.Count)); sandwich.Add(ingredient); sandwich_calories += ingredients[ingredient]; } while (sandwich_calories <= max_calories) { // add random ingredient string ingredient = ingredients.Keys.ElementAt(new Random().Next(ingredients.Count)); // check if the ingredient is the same as the previous one if (sandwich.Count >= 3 && ingredient == sandwich[sandwich.Count - 2]) { continue; } sandwich.Add(ingredient); sandwich_calories += ingredients[ingredient]; // check if the sandwich is already at the maximum calorie limit if (sandwich_calories == max_sandwich_calories) { break; } } // add the last slice of bread sandwich.Add("Bread"); // print the sandwich and its total calories Console.WriteLine("Your sandwich: " + string.Join(", ", sandwich)); Console.WriteLine("Total calories: " + sandwich_calories); } } } }

{ "Ham", 72 }, { "Bologna", 57 }, { "Chicken", 17 }, { "Corned Beef", 53 }, { "Salami", 40 }, { "Cheese, American", 104 }, { "Cheese, Cheddar", 113 }, { "Cheese, Havarti", 105 }, { ...

解释一下下面这个代码 # 自己实现 # 计算每个词在垃圾邮件中出现的概率和在正常邮件中出现的概率 # 统计正常邮件和垃圾邮件的个数 def calEmailsFreq(Emails): WordsCount={} # 概率表：{词：[垃圾概率，正常概率]} HamCount = 0 # 正常邮件的个数 SpamCount = 0 # 垃圾邮件的个数 # 遍历邮件列表 for email in Emails: label = email[0] words = email[1] if label == "spam": SpamCount += 1 # 垃圾邮件计数 for word in words: if word in WordsCount: WordsCount[word][0] += 1 else: WordsCount[word] = [1,0] else: HamCount += 1 # 正常邮件计数 for word in words: if word in WordsCount: WordsCount[word][1] += 1 else: WordsCount[word] = [0,1] for word in WordsCount.keys(): WordsCount[word][0] /= SpamCount WordsCount[word][1]/= HamCount return WordsCount,HamCount,SpamCount

如果标签是"ham"，则类似地更新HamCount和WordsCount中每个词在正常邮件中出现的次数。接着，遍历WordsCount中的每个词，将它在垃圾邮件中出现的次数除以SpamCount，将它在正常邮件中出现的次数除以...

贝叶斯分类垃圾邮件def checkOneEmail(table,filename): emailwords = readEmail(filename) # 先验概率 Rate1 = 0.5 Rate2 = 0.5 for word in emailwords: if word in table: # 避免除零错误 if table[word][0] < 0.001 and table[word][1] < 0.001: continue Rate1 = table[word][0] #垃圾邮件中的概率 Rate2 = table[word][1] #正常邮件中的概率 # 小概率处理 if Rate1 <1e-50 or Rate2 <1e-50: break # 计算该邮件属于垃圾邮件的概率 SpamRate = Rate1 / (Rate1 + Rate2) if Rate1 > Rate2: return "spam" else: return "ham"

这段代码是一个简单的贝叶斯分类器，用于判断一封邮件是垃圾邮件还是正常邮件。它的输入是一个单词表和一个文件名，其中单词表是一个字典，包含每个单词在垃圾邮件和正常邮件中出现的次数。readEmail函数用于读取...

建立贝叶斯模型 NB_model = MultinomialNB(alpha=0.1, class_prior=None, fit_prior=True) 1 贝叶斯模型训练 NB_model.fit(x_train_df,y_train) 1 贝叶斯模型保存 from sklearn.externals import joblib joblib.dump(NB_model, "nb_model.m") 1 2 贝叶斯模型预测 prediction = dict() prediction["MultinomialNB"] = NB_model.predict(x_test_df) 1 2 3 贝叶斯模型评估 from sklearn.metrics import accuracy_score,f1_score,confusion_matrix,classification_report print(classification_report(y_test,prediction["MultinomialNB"],target_names=["Ham","Spam"]))

你提供的代码实现了一个基于MultinomialNB算法的朴素贝叶斯分类器...y_test是测试数据的真实标签，prediction["MultinomialNB"]是使用MultinomialNB模型预测出的标签，target_names=["Ham","Spam"]表示两个类别的名称。

df = pd.read_csv('./spam.csv', encoding="latin-1") df=df[['class','message']] df['label'] = df['class'].map({'ham': 0, 'spam': 1}) df.head() #%% #构造训练集和测试集 X = df['message'] y = df['label'] cv = CountVectorizer() X = cv.fit_transform(X) X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=42) #朴素贝叶斯分类器 clf = MultinomialNB() clf.fit(X_train,y_train) clf.score(X_test,y_test) y_pred = clf.predict(X_test) print(classification_report(y_test, y_pred))解释上述代码

3. 将“class”列中的“ham”和“spam”标签映射为数字0和1，并将结果存储到一个新列“label”中。 4. 对“message”列中的文本内容进行向量化处理，使用CountVectorizer函数将文本数据转换为数字向量。 5. 将数据...

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详解def Ham_z(N=12,g1=1.0): Ilist = [sigmai]N Ham_z = np.zeros((2N,2N),dtype=np.complex128) for loopi in range(N): Ilist[loopi] = g1sigmaz Ham_z = kron(Ilist)+Ham_z Ilist = [sigmai] * N return Ham_z