code: from sklearn.svm import LinearSVC
  import numpy as np
  
  
  def apply_threshold(data, threshold):
      data[np.where(np.abs(data) < threshold)] = 0
  
  def train_one(data, loss, C, verbose, max_iter, threshold, dual, tol):
      def _get_features(obj):
          # Index samples iff they are required
          # Helful in reducing memory footprint
          if obj['ind'] is None:
              return obj['data']
          else:
              return obj['data'].take(obj['ind'], axis=0)
      X, y = _get_features(data), data['Y']
      clf = LinearSVC(tol=tol,
                      loss=loss,
                      dual=dual,
                      C=C,
                      multi_class='ovr',
                      fit_intercept=True,
                      intercept_scaling=1,
                      class_weight=None,
                      verbose=verbose,
                      random_state=0,
                      max_iter=max_iter)
      try:
          clf.fit(X, y)
          weight, bias = clf.coef_, clf.intercept_
      except ValueError:
          # TODO Find a solution for this; choose randomly may be?
          weight, bias = np.zeros((1, X.shape[1]), dtype=np.float32), np.zeros(
              (1), dtype=np.float32)
      del clf
      apply_threshold(weight, threshold)
      return weight, bias
      
apis: sklearn.svm.LinearSVC numpy.abs numpy.zeros