             "Gesture 3", "Gesture 3", "Gesture 3", "Gesture 3", "Gesture 3", "Gesture 3", "Gesture 3", "Gesture 3", "Gesture 3", "Gesture 3", "Gesture 3", "Gesture 3", "Gesture 3", "Gesture 3", "Gesture 3", "Gesture 3", "Gesture 3", "Gesture 3", "Gesture 3", "Gesture 3", "Gesture 3", "Gesture 3", "Gesture 3", "Gesture 3", "Gesture 3", "Gesture 3", "Gesture 3", "Gesture 3", "Gesture 3", "Gesture 3", "Gesture 3", "Gesture 3", "Gesture 3", "Gesture 3", "Gesture 3", "Gesture 3", "Gesture 3", "Gesture 3", "Gesture 3"]
 
 
-
+        //TEST FEATURES AND LABELS
 //        let labels = ["Gesture 1", "Gesture 2", "Gesture 3"]
 //        let features = [[0.03490489,-0.02601342, 0.936972],[0.2710942, -2.89515, 1.256121],[-0.09389891, -3.128638, 1.631254]]
 

    var ringBuffer = RingBuffer()
    var orientationBuffer = RingBuffer()
    let magValue = 1.5
    var numDataPoints = 0
    var recording = false


        }
    }
    
    func motionEventOccurred(mag:Double) {
        let data = self.ringBuffer.getDataAsVector() as NSArray
        if data[0] as! Double == 0.0 {
            print("not full full")
        } else {
//            self.getPredictionData(data: (self.orientationBuffer.getDataAsVector()) as NSArray ) // OLD PREDICTION
            self.predictUsingModel(data: (self.orientationBuffer.getDataAsVector()) as! [Double],mag:mag)
            
        }
    }
    
    func predictUsingModel(data:[Double],mag:Double) {
        
        let pred = svm.classifyOne(data)
        var prediction = ""
