tutorial.scala

	val noseless = MeshIO.readMesh(new File("datasets/noseless.stl")).get

	show(noseless,"noseless")

	val littleModel = StatismoIO.readStatismoMeshModel(new File("datasets/model.h5")).get

	show(littleModel, "littleModel")

	val zeroMean = VectorField(RealSpace[_3D], (pt:Point[_3D]) => Vector(0,0,0))
	val scalarValuedKernel = GaussianKernel[_3D](30) * 10 

	case class XmirroredKernel(ker : PDKernel[_3D]) extends PDKernel[_3D] {
	  override def domain = RealSpace[_3D]
	  override def k(x: Point[_3D], y: Point[_3D]) = ker(Point(x(0) * -1f ,x(1), x(2)), y)
	}

	def SymmetrizeKernel(ker : PDKernel[_3D]) : MatrixValuedPDKernel[_3D,_3D] = {
	   val xmirrored = XmirroredKernel(ker)
	   val k1 = DiagonalKernel(ker) 
	   val k2 = DiagonalKernel(xmirrored * -1f, xmirrored, xmirrored)  
	   k1 + k2
	}

	val sim = SymmetrizeKernel(scalarValuedKernel)

	val gp = GaussianProcess(zeroMean, sim)

	val model = StatisticalMeshModel.augmentModel(littleModel, gp, 50)
	
  	show(model, "model")

	remove("littleModel")

	val lms = LandmarkIO.readLandmarksJson[_3D](new File("datasets/noseFittingLandmarks.json")).get

	addLandmarksTo(lms, "noseless")

	// execute this only if you were unable to click the landmarks
	val lm = LandmarkIO.readLandmarksJson[_3D](new File("datasets/noseFittingModelLms.json")).get

	addLandmarksTo(lm,"model")

	val modelPts : Seq[Point[_3D]] = getLandmarksOf("model").get.map{lm => lm.point}

	val noselessPts = getLandmarksOf("noseless").get.map{lm => lm.point}

	val littleNoise = NDimensionalNormalDistribution(Vector(0,0,0), SquareMatrix((0.5f,0,0), (0,0.5f,0), (0,0,0.5f)))

	val trainingData = (modelPts zip noselessPts).map{ case (mPt, nPt) => 
	  (model.mean.findClosestPoint(mPt).id, nPt, littleNoise)
	}

	val posterior = model.posterior(trainingData.toIndexedSeq)
	show(posterior, "posterior")

	val modelLMs = LandmarkIO.readLandmarksJson[_3D](new File("datasets/modelLandmarks.json")).get
	addLandmarksTo( modelLMs, "model")

	val noselessLMs = LandmarkIO.readLandmarksJson[_3D](new File("datasets/noselessLandmarks.json")).get
	addLandmarksTo( noselessLMs, "noseless")

	val modelLandmarks = getLandmarksOf("model").get
	val noselessLandmarks = getLandmarksOf("noseless").get

	val trainingData = (modelLandmarks zip noselessLandmarks).map{ case (mLm, nLm) => 
	  (model.mean.findClosestPoint(mLm.point).id, nLm.point, littleNoise)
	}

	val betterPosterior = model.posterior(trainingData.toIndexedSeq)
	show(betterPosterior, "betterPosterior")

	val nosePtIDs = model.referenceMesh.pointIds.filter { id => 
	  (model.referenceMesh.point(id) - model.referenceMesh.point(PointId(8152))).norm <= 42
	}

	val posteriorNoseModel = betterPosterior.marginal(nosePtIDs.toIndexedSeq)

	show(posteriorNoseModel, "posteriorNoseModel")