Latex下代码的排版

Latex下代码的排版

文章目录

• 一、添加模板文件
• 二、显示效果

一、添加模板文件

latex模板文件中对应添加下列内容

documentclass{article}
usepackage{listings}
usepackage{xcolor}

definecolor{codegreen}{rgb}{0,0.6,0}
definecolor{codegray}{rgb}{0.5,0.5,0.5}
definecolor{codepurple}{rgb}{0.58,0,0.82}
definecolor{backcolour}{rgb}{0.95,0.95,0.92}

lstdefinestyle{mystyle}{
    backgroundcolor=color{backcolour},   
    commentstyle=color{codegreen},
    keywordstyle=color{magenta},
    numberstyle=tinycolor{codegray},
    stringstyle=color{codepurple},
    basicstyle=ttfamilyfootnotesize,
    breakatwhitespace=false,         
    breaklines=true,                 
    captionpos=b,                    
    keepspaces=true,                 
    numbers=left,                    
    numbersep=5pt,                  
    showspaces=false,                
    showstringspaces=false,
    showtabs=false,                  
    tabsize=2
}

lstset{style=mystyle}

begin{document}

%% 下面为具体代码

begin{lstlisting}[language=Python, caption= DataGen]
  def gen_datas(train, test):
  """生成数据
  参数:
      train: 原始训练数据集
      test: 原始验证数据集
  返回:
      X_train_r: 处理后训练数据集
      y_train: 处理后训练标签
      X_test_r: 处理后验证数据集
      y_test: 处理后验证标签
  """

  label_encoder_train = LabelEncoder().fit(train.Result)
  labels_train = label_encoder_train.transform(train.Result)
  label_encoder_test = LabelEncoder().fit(test.Result)
  labels_test = label_encoder_test.transform(test.Result)    
  classes = list(label_encoder_train.classes_)
  # classes_test = list(label_encoder_test.classes_)

  train = train.drop('Result', axis=1)
  test = test.drop('Result', axis=1)
  
  # 标签的种类 = NB_CLASS
  numb_class = len(classes)
  # 数据的归一化处理
  scaled_train = data_scaler(train)

  # 同一文件内数据集：10%作为测试集，90%作为训练集
  # random_state：随机数种子，和random中的seed种子一样，保证每次抽样到的数据一样，便于调试
  sss = StratifiedShuffleSplit(test_size=0.1, random_state=23)
  for train_index, test_index in sss.split(scaled_train, labels_train):
      X_train, X_test = scaled_train[train_index], scaled_train[test_index]
      y_train, y_test = labels_train[train_index], labels_train[test_index]

  # 不同文件作为训练集和验证集
  # y_train = labels_train
  # X_train = data_scaler(train)
  # y_test = labels_test
  # X_test = data_scaler(test)

  # reshape train data 
  # reshape 30*1
  # 也可以reshape 10*3
  X_train_r = np.zeros((len(X_train), NB_FEATURES, 1))
  X_train_r[:, :, 0] = X_train[:, :NB_FEATURES]
  # X_train_r[:, :, 1] = X_train[:, NB_PER_LAYER: NB_PER_LAYER * 2]
  # X_train_r[:, :, 2] = X_train[:, NB_PER_LAYER * 2:]

  # reshape test data
  X_test_r = np.zeros((len(X_test), NB_FEATURES, 1))
  X_test_r[:, :, 0] = X_test[:, :NB_FEATURES]
  # X_test_r[:, :, 1] = X_test[:, NB_PER_LAYER: NB_PER_LAYER * 2]
  # X_test_r[:, :, 2] = X_test[:, NB_PER_LAYER * 2:]

  y_train = np_utils.to_categorical(y_train, NB_CLASS)
  y_test = np_utils.to_categorical(y_test, NB_CLASS)

  return X_train_r, y_train, X_test_r, y_test

end{lstlisting}
%%
end{document}

二、显示效果

具体效果