ClassifierPos.scala

      serialize(modelFile+"-iter-"+trainer.iteration)
      val other = ClassifierPos.load(modelFile+"-iter-"+trainer.iteration)
      val od = other.ClassifierPosFeatureDomain.dimensionDomain
      val s0 = testDocs.head.sentences.head
      val sd0 = new SentenceData(s0)
      val sd1 = new other.SentenceData(s0)
      sd0.lemmas.zipWithIndex.foreach(l => assert(sd1.lemmas(l._2) == l._1))
      val f0 = new SparseBinaryTensor1(ClassifierPosFeatureDomain.dimensionDomain.size)
      val f1 = new SparseBinaryTensor1(other.ClassifierPosFeatureDomain.dimensionDomain.size)
      assert(ClassifierPosFeatureDomain.dimensionDomain.size == other.ClassifierPosFeatureDomain.dimensionDomain.size)
      for (t <- 0 until s0.length) {
        addFeatures(sd0, t, f0)
        other.addFeatures(sd1, t, f1)
        val p00 = model.evidenceTemplate.weights * f0
        val p01 = model.evidenceTemplate.weights * f1
        val p10 = other.model.evidenceTemplate.weights * f0
        val p11 = other.model.evidenceTemplate.weights * f1
        for (i <- 0 until p00.length) {
          assert(p00(i) == p01(i), "values " + p00 + " " + p01)
          assert(p00(i) == p10(i), "values " + p00 + " " + p10)
          assert(p00(i) == p11(i), "values " + p00 + " " + p11)
        }
        assert(p00.maxIndex == p01.maxIndex)
        assert(p00.maxIndex == p10.maxIndex)
        assert(p00.maxIndex == p11.maxIndex)
        s0.tokens(t).posLabel.set(p00.maxIndex)(null)
      }
      testDocs.foreach(other.process(_))
      println("Other accuracy: " + HammingObjective.accuracy(testDocs.flatMap(t => t.tokens).map(t => t.attr[PosLabel])))
      testDocs.foreach(process(_))
      println("This accuracy: " + HammingObjective.accuracy(testDocs.flatMap(t => t.tokens).map(t => t.attr[PosLabel])))