     
     @IBAction func guessActBPressed(_ sender: Any) {
         actionType = "Guess"
-        self.startLoggingData()
-        delay(14){
+        delay(2){
+            AudioServicesPlaySystemSound(self.systemSoundID)
+            self.startLoggingData()
+        }
+        delay(15){
+            AudioServicesPlaySystemSound(self.systemSoundID)
             self.exportToText(currMatrix: self.dataMatrix, action: self.actionType)
-            self.guessMatrix = self.bootStrapDataM(arr2D: self.dataMatrix, setsNum: self.bootTestDataNum, startPt: self.testStartPt, stride: self.bootStepSize, windowSize: self.ptsPerData, rowNums: self.rowNum, xyz: 1)
+            
+            //self.guessMatrix = self.bootStrapDataM(arr2D: self.dataMatrix, setsNum: self.bootTestDataNum, startPt: self.testStartPt, stride: self.bootStepSize, windowSize: self.ptsPerData, rowNums: self.rowNum, xyz: 1)
+            
+            self.guessMatrix = self.triAxisBootWrapper(arr2D: self.dataMatrix, setsNum: self.bootTestDataNum, startPt: self.testStartPt, stride: self.bootStepSize, windowSize: self.ptsPerData, rowNums: self.rowNum)
             
             //Take 1 sample from the guessMatrix
             

        rowNum = Int(dataLogTime/updateInterval)
        testStartPt = bootTrainDataNum * bootStepSize
        nnInputNum = ptsPerData * 3 //100 pts per data * 3 axis
        hiddenLayerNum = Int(round(Double(nnInputNum * (2/3))))
        //Initialize a preliminary untrained neural network for binary classification
        do {
            structure = try NeuralNet.Structure(inputs: nnInputNum, hidden: hiddenLayerNum, outputs: 2)

                } else {
                    action = "NeuralNet's confused\n\(roundedInfer)"
                }
                */
                
                self.currentAppStatus.text = action
            } catch {print(error)}
        }
