这个 SVM 预测器期望输入一个子类型的 Vector . 因此，有两种方法可以解决这个问题：
创建子类型 Vector 它包含作为标记的tweet文本。然后它将通过预测器循环。这种方法的优点是不需要额外的操作。但是，需要定义新的类和实用程序来用标记表示不同的向量类型：

val env = ExecutionEnvironment.getExecutionEnvironment

val input = env.fromElements("foobar", "barfo", "test")

val vectorizedInput = input.map(word => {
  val value = word.chars().sum()
  new DenseVectorWithTag(Array(value), word)
})

val svm = SVM().setBlocks(env.getParallelism)

val weights = env.fromElements(DenseVector(1.0))

svm.weightsOption = Option(weights) // skipping the training here

val predictionResult: DataSet[(DenseVectorWithTag, Double)] = svm.predict(vectorizedInput)

class DenseVectorWithTag(override val data: Array[Double], tag: String)
  extends DenseVector(data) {
  override def toString: String = "(" + super.toString + ", " + tag + ")"
}

加入预测 DataSet 与输入 DataSet 关于矩阵的向量化表示 tweets . 这种方法的优点是我们不需要引入新的类。我们为此付出的代价是额外的联接操作，这可能会很昂贵：

val input = env.fromElements("foobar", "barfo", "test")

val vectorizedInput = input.map(word => {
  val value = word.chars().sum()
  (DenseVector(value), word)
})

val svm = SVM().setBlocks(env.getParallelism)

val weights = env.fromElements(DenseVector(1.0))

svm.weightsOption = Option(weights) // skipping the training here

val predictionResult = svm.predict(vectorizedInput.map(a => a._1))
val inputWithPrediction: DataSet[(String, Double)] = vectorizedInput
  .join(predictionResult)
  .where(0)
  .equalTo(0)
  .apply((t, p) => (t._2, p._2))