text: import pandas as pd
  import numpy as np
  from sklearn import svm
  np.set_printoptions(suppress=True, threshold=np.inf)
  
  def classify(train_file, test_file):
  
      if not isinstance(train_file,str) or not isinstance(test_file,str):
          raise Exception("File names must be entered as string")
      elif train_file[-4:] != '.csv' or test_file[-4:] != '.csv':
          raise Exception(
              "Files must be .csv files and be entered with a .csv extension")
  
      #load training dataset
      data = pd.read_csv(train_file)
  
      #Parse labels and features from training set
      train_labels = data.signal_type
      labels = list(set(train_labels))
      train_labels = np.array([labels.index(x) for x in train_labels])
  
      train_features = data.iloc[:,1:]
      train_features = np.array(train_features)
  
      #Begin training and testing
      classifier = svm.SVC(kernel='linear')
      classifier.fit(train_features, train_labels)
  
      #load testing dataset
      test_dataframe = pd.read_csv(test_file)
  
      #Parse labels and features from test set
      test_labels = test_dataframe.signal_type
      labels = list(set(test_labels))
      test_labels = np.array([labels.index(x) for x in test_labels])
  
      test_features = test_dataframe.iloc[:,1:]
      test_features = np.array(test_features)
  
      #Model testing
      print(classifier.predict(test_features))
      return classifier.score(test_features,test_labels)*100
  