Computes an FP-Growth model that contains frequent itemsets.

        :param data:
          The input data set, each element contains a transaction.
        :param minSupport:
          The minimal support level.
          (default: 0.3)
        :param numPartitions:
          The number of partitions used by parallel FP-growth. A value
          of -1 will use the same number as input data.
          (default: -1)

def train(cls, data, minSupport=0.3, numPartitions=-1):
        """
        Computes an FP-Growth model that contains frequent itemsets.

        :param data:
          The input data set, each element contains a transaction.
        :param minSupport:
          The minimal support level.
          (default: 0.3)
        :param numPartitions:
          The number of partitions used by parallel FP-growth. A value
          of -1 will use the same number as input data.
          (default: -1)
        """
        model = callMLlibFunc("trainFPGrowthModel", data, float(minSupport), int(numPartitions))
        return FPGrowthModel(model)