text: # https://scikit-learn.org/stable/modules/generated/sklearn.svm.OneClassSVM.html
  from sklearn.svm import OneClassSVM
  X = [[0], [0.44], [0.45], [0.46], [1]]
  clf = OneClassSVM(gamma='auto').fit(X)
  print('predict\n', clf.predict(X))
  yy = clf.fit_predict(X)
  print('fit_predict\n', yy)
  yy [yy == -1] = 0
  print('fit_predict changed\n', yy)
  print('score_samples\n', clf.score_samples(X))
  # Offset used to define the decision function from the raw scores.
  # We have the relation: decision_function = score_samples - offset_.
  # The offset is the opposite of intercept_ and is provided for consistency with other
  # outlier detection algorithms.
  print('offset_\n', clf.offset_)
  # Signed distance to the separating hyperplane.
  # Signed distance is positive for an inlier and negative for an outlier.
  print('decision_function\n', clf.decision_function(X))
  print('decision_function(x) = score_samples(x) - offset_\n', clf.score_samples(X)-clf.offset_)
  print('sum(decision_function(x))\n', clf.decision_function(X).sum())
  print('n_support_\n', clf.n_support_)
  
  
  
  # from sklearn.datasets import load_iris
  # from sklearn.linear_model import LogisticRegression
  # X, y = load_iris(return_X_y=True)
  # clf = LogisticRegression(random_state=0).fit(X, y)
  # clf.predict(X[:2, :])
  #
  # clf.predict_proba(X[:2, :])
  
  
  # clf.score(X, y)
  