                 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 {

        }
        return accel1D
    }
    // MARK: NeuralNetwork Config
    //      Initialize a specific untrained NeuralNet
    //      that can handle more than 2 classifications.

    }
    
    //  train Neural Network for the misc action dataset
    func trainNNMisc(nnet: NeuralNet, structure: NeuralNet.Structure, dataSet: [[[Float]]], actLog: [String], errorThreshold: Float, nnInputNum: Int, bootTrainDataNum: Int, bootTestDataNum: Int, testStartPt: Int, bootStepSize: Int, ptsPerData: Int, rowNum: Int){
        
        //Create the output train and test answers
        var tmpTrainAns = [[Float]]()
