code: def predict(self, x):
          
          if isinstance(x, RDD):
              return x.map(lambda v: self.predict(v))
          return np.interp(x, self.boundaries, self.predictions)
docs: Predict labels for provided features.
          Using a piecewise linear function.
          1) If x exactly matches a boundary then associated prediction
          is returned. In case there are multiple predictions with the
          same boundary then one of them is returned. Which one is
          undefined (same as java.util.Arrays.binarySearch).
          2) If x is lower or higher than all boundaries then first or
          last prediction is returned respectively. In case there are
          multiple predictions with the same boundary then the lowest
          or highest is returned respectively.
          3) If x falls between two values in boundary array then
          prediction is treated as piecewise linear function and
          interpolated value is returned. In case there are multiple
          values with the same boundary then the same rules as in 2)
          are used.
  
          :param x:
            Feature or RDD of Features to be labeled.