     @IBAction func trainNNBPressed(_ sender: Any) {
         currentAppStatus.text = "Training NeuralNet"
         
+        self.nnInit(inputNums: self.nnInputNum, hiddenNums: self.hiddenLayerNum, actLog: self.actionsLog)
+        
+        // Comment this out if training for only standing and walking
+        self.trainNNMisc(nnet: nn, structure: structure, dataSet: miscActMatrix, actLog: actionsLog, errorThreshold: errorThreshold, nnInputNum: self.nnInputNum, bootTrainDataNum: self.bootTrainDataNum, bootTestDataNum: self.bootTestDataNum, testStartPt: self.testStartPt, bootStepSize: self.bootStepSize, ptsPerData: self.ptsPerData, rowNum: self.rowNum)
+        
+        //Uncomment this if only training for standing and walking
+        /*
         self.trainNN(nnet: nn, structure: structure, wTrainMatrix: walkTrainMatrix, wTestMatrix: walkTestMatrix, sTrainMatrix: standTrainMatrix, sTestMatrix: standTestMatrix)
+        */
     }
     
     @IBAction func guessActBPressed(_ sender: Any) {

    }
    
    //  train Neural Network for the misc action dataset
    func trainNNMisc(nnet: NeuralNet, structure: NeuralNet.Structure, mTrain: [[Float]], mTest: [[Float]], actLog: [String], errorThreshold: Float){
        
        //Create the output train and test answers
        var tmpTrainAns = [[Float]]()

                tmpTestAns.append(tmpAns)
            }
            
        }
        
        //Create train and test datasets
        var mTrain = [[Float]]()
        var mTest = [[Float]]()
        for action in actLog {
            let ind = actLog.index(of: action)!
            mTrain += (self.triAxisBootWrapper(arr2D: dataSet[ind], setsNum: bootTrainDataNum, startPt: 0, stride: bootStepSize, windowSize: ptsPerData, rowNums: rowNum))
            mTest += (self.triAxisBootWrapper(arr2D: dataSet[ind], setsNum: bootTestDataNum, startPt: testStartPt, stride: bootStepSize, windowSize: ptsPerData, rowNums: rowNum))
        }
        
        do {
