         
     }
     
+    //  train Neural Network for the misc action dataset
+    func trainNNMisc(nnet: NeuralNet, structure: NeuralNet.Structure, mTrain: [[Float]], mTest: [[Float]], actLog: [String]){
+        
+        //Create the output train and test answers
+        var tmpTrainAns = [[Float]]()
+        var tmpTestAns = [[Float]]()
+        let actNum = actLog.count
+        var tmpAns: [Float] = Array(repeating: 0.0, count: actNum)
+        for action in actLog {
+            let ind = actLog.index(of: action)!
+            tmpAns[ind] = 1.0
+            for i in 0..<bootTrainDataNum {
+                tmpTrainAns.append(tmpAns)
+            }
+            for i in 0..<bootTestDataNum {
+                tmpTestAns.append(tmpAns)
+            }
+            
+        }
+        
+        
+        
+    }
+    
     // MARK: NeuralNetwork Inference
     //      This is the action "guessing" portion.
     

        catch {
            print(error)
        }
    }
    
    //Get number of outputs based on number of different actions
    func getOuputNums(outputs: [String]) -> Int {
        return 2 //
    }
    
    //Checks the log of actions and appends the log if it's a new action

    var trainAnswers = [[Float]]()
    var testAnswers = [[Float]]()
    var guessMatrix = [[Float]]()
    var miscActMatrix = [[[Float]]]()
    var miscActTrain = [[Float]]()
    var miscActTest = [[Float]]()
    var actionsLog = [String]()
    
    // MARK: init
    
