code: def create_sparse_instance(cls, values, max_values, classname="weka.core.SparseInstance", weight=1.0):
          
          jni_classname = classname.replace(".", "/")
          indices = []
          vals = []
          for (i, v) in values:
              indices.append(i)
              vals.append(float(v))
          indices = numpy.array(indices, dtype=numpy.int32)
          vals = numpy.array(vals)
          return Instance(
              javabridge.make_instance(
                  jni_classname, "(D[D[II)V",
                  weight, javabridge.get_env().make_double_array(vals),
                  javabridge.get_env().make_int_array(indices), max_values))
docs: Creates a new sparse instance.
  
          :param values: the list of tuples (0-based index and internal format float). The indices of the
                         tuples must be in ascending order and "max_values" must be set to the maximum
                         number of attributes in the dataset.
          :type values: list
          :param max_values: the maximum number of attributes
          :type max_values: int
          :param classname: the classname of the instance (eg weka.core.SparseInstance).
          :type classname: str
          :param weight: the weight of the instance
          :type weight: float