#import "KRANN.h"

// Use singleton or [[KRANN alloc] init] _krMLP = [KRANN sharedNetwork]; // Learning rate _krMLP.learningRate = 0.8f; // Convergence error, 收斂誤差值 ( Normally is 10^-3 or 10^-6 ) _krMLP.convergenceError = 0.001f; // Limit iterations _krMLP.limitIteration = 1000; // Per iteration-training block [_krMLP setPerIteration:^(NSInteger times, NSDictionary *trainedInfo) { NSLog(@"Iteration times : %i", times); //NSLog(@"Iteration result : %f\n\n\n", [trainedInfo objectForKey:KRANNTrainedOutputResults]); }];

_krMLP.activationFunction = KRANNActivationFunctionSigmoid; // 各輸入向量陣列值 & 每一筆輸入向量的期望值( 輸出期望 )，因使用 S 形轉換函數，故 Input 值域須為 [0, 1]，輸出目標為 [0, 1] // Add the patterns, the weights connect with hidden layer, the output targets [_krMLP addPatterns:@[@1, @0.1, @0.5, @0.2] outputGoals:@[@0.7f, @0.8f]]; //Pattern 1, net 1, 2, 3, 4, the output layer is 2 nets [_krMLP addPatterns:@[@0, @1, @0.3, @0.9] outputGoals:@[@0.1f, @0.1f]]; //Pattern 2, same as pattern 1 [_krMLP addPatterns:@[@1, @0.3, @0.1, @0.4] outputGoals:@[@0.95f, @0.9f]]; //Pattern 3, same as pattern 1 // 輸入層各向量值到隱藏層神經元的權重 ( 連結同一個 Net 的就一組一組分開，有幾個 Hidden Net 就會有幾組 ) // Add pattern-weights in Input layer to first Hidden Layer [_krMLP addPatternWeights:@[@0.2, @-0.3]]; //W15, W16 [_krMLP addPatternWeights:@[@0.4, @0.1]]; //W25, W26 [_krMLP addPatternWeights:@[@-0.5, @0.2]]; //W35, W36 [_krMLP addPatternWeights:@[@-0.1, @0.3]]; //W45, W46 // 隱藏層神經元的偏權值 & 隱藏層神經元到輸出層神經元的權重值 // Add Hidden Layers the biases of nets and the output weights in connect with output layer. [_krMLP addHiddenLayerAtIndex:0 netBias:-0.4f netWeights:@[@-0.3f, @0.2f, @0.1f]]; //Net 5 [_krMLP addHiddenLayerAtIndex:0 netBias:0.2f netWeights:@[@-0.2f, @0.5f, @-0.1f]]; //Net 6 [_krMLP addHiddenLayerAtIndex:1 netBias:0.1f netWeights:@[@0.1f, @0.3f]]; //Net 7 [_krMLP addHiddenLayerAtIndex:1 netBias:0.25f netWeights:@[@0.2f, @0.1f]]; //Net 8 [_krMLP addHiddenLayerAtIndex:1 netBias:0.3f netWeights:@[@0.3f, @-0.4f]]; //Net 9 [_krMLP addHiddenLayerAtIndex:2 netBias:-0.25f netWeights:@[@0.4f, @0.3f]]; //Net 10 [_krMLP addHiddenLayerAtIndex:2 netBias:0.15f netWeights:@[@0.1f, @-0.2f]]; //Net 11 // 輸出層神經元偏權值, Net 12, Net 13 // Add the output layer biases [_krMLP addOutputBiases:@[@0.0f, @0.1f]]; __block typeof(_krMLP) _weakKRANN = _krMLP; // Training completed [_krMLP setTrainingCompletion:^(BOOL success, NSDictionary *trainedInfo, NSInteger totalTimes){ if( success ) { NSLog(@"Training done with total times : %i", totalTimes); NSLog(@"TrainedInfo 1 : %@", trainedInfo); //Start in checking the network is correctly trained. NSLog(@"======== Start in Verification ========"); [_weakKRANN setTrainingCompletion:^(BOOL success, NSDictionary *trainedInfo, NSInteger totalTimes){ NSLog(@"Training done with total times : %i", totalTimes); NSLog(@"TrainedInfo 2 : %@", trainedInfo); }]; [_weakKRANN recoverNetwork]; [_weakKRANN directOutputAtInputs:@[@1, @0.1, @0.5, @0.2]]; } }]; [_krMLP training];

// Use f(x)=tanh(x) _krMLP.activationFunction = KRANNActivationFunctionTanh; // How many hidden layers _krMLP.hiddenLayerCount = 0; //0 means let system decide the hidden layers number. [_krMLP addPatterns:@[@1, @0.1, @0.5, @0.2] outputGoals:@[@0.7f]]; //Pattern 1, net 1, 2, 3, 4, and 1 output [_krMLP addPatterns:@[@0, @-0.8, @0.3, @-0.9] outputGoals:@[@-0.1f]]; //Pattern 2, same as pattern 1 [_krMLP addPatterns:@[@1, @0.3, @0.1, @0.4] outputGoals:@[@0.9f]]; //Pattern 3, same as pattern 1 __block typeof(_krMLP) _weakKRANN = _krMLP; // Training completed [_krMLP setTrainingCompletion:^(BOOL success, NSDictionary *trainedInfo, NSInteger totalTimes){ if( success ) { NSLog(@"Training done with total times : %i", totalTimes); NSLog(@"TrainedInfo 1 : %@", trainedInfo); //Start in checking the network is correctly trained. NSLog(@"======== Start in Verification ========"); [_weakKRANN setTrainingCompletion:^(BOOL success, NSDictionary *trainedInfo, NSInteger totalTimes){ NSLog(@"Training done with total times : %i", totalTimes); NSLog(@"TrainedInfo 2 : %@", trainedInfo); }]; [_weakKRANN recoverNetwork]; [_weakKRANN directOutputAtInputs:@[@0, @-0.8, @0.3, @-0.9]]; } }]; [_krMLP trainingByRandomWithSave];

_krMLP.activationFunction = KRANNActivationFunctionSigmoid; // How many hidden layers, if you set 0 that means let system decide the hidden layers number. _krMLP.hiddenLayerCount = 1; // 1 [_krMLP addPatterns:@[@0, @0, @0, @0, @0, @0, @0, @0, @0, @0, @0, @0, @0, @0, @0, @0, @0, @0, @0, @0, @0, @0, @0, @0, @0, @0, @0, @1, @1, @1, @1, @1, @1, @1, @1, @1] outputGoals:@[@1, @0, @0, @0, @0, @0, @0, @0, @0, @0]]; // 2 [_krMLP addPatterns:@[@1, @0, @0, @0, @1, @1, @1, @1, @1, @1, @0, @0, @0, @1, @0, @0, @0, @1, @1, @0, @0, @0, @1, @0, @0, @0, @1, @1, @1, @1, @1, @1, @0, @0, @0, @1] outputGoals:@[@0, @1, @0, @0, @0, @0, @0, @0, @0, @0]]; // 3 [_krMLP addPatterns:@[@1, @0, @0, @0, @1, @0, @0, @0, @1, @1, @0, @0, @0, @1, @0, @0, @0, @1, @1, @0, @0, @0, @1, @0, @0, @0, @1, @1, @1, @1, @1, @1, @1, @1, @1, @1] outputGoals:@[@0, @0, @1, @0, @0, @0, @0, @0, @0, @0]]; // 4 [_krMLP addPatterns:@[@1, @1, @1, @1, @1, @0, @0, @0, @0, @0, @0, @0, @0, @1, @0, @0, @0, @0, @0, @0, @0, @0, @1, @0, @0, @0, @0, @1, @1, @1, @1, @1, @1, @1, @1, @1] outputGoals:@[@0, @0, @0, @1, @0, @0, @0, @0, @0, @0]]; // 5 [_krMLP addPatterns:@[@1, @1, @1, @1, @1, @0, @0, @0, @1, @1, @0, @0, @0, @1, @0, @0, @0, @1, @1, @0, @0, @0, @1, @0, @0, @0, @1, @1, @0, @0, @0, @1, @1, @1, @1, @1] outputGoals:@[@0, @0, @0, @0, @1, @0, @0, @0, @0, @0]]; // 6 [_krMLP addPatterns:@[@1, @1, @1, @1, @1, @1, @1, @1, @1, @1, @0, @0, @0, @1, @0, @0, @0, @1, @1, @0, @0, @0, @1, @0, @0, @0, @1, @1, @0, @0, @0, @1, @1, @1, @1, @1] outputGoals:@[@0, @0, @0, @0, @0, @1, @0, @0, @0, @0]]; // 7 [_krMLP addPatterns:@[@1, @0, @0, @0, @0, @0, @0, @0, @0, @1, @0, @0, @0, @0, @0, @0, @0, @0, @1, @0, @0, @0, @0, @0, @0, @0, @0, @1, @1, @1, @1, @1, @1, @1, @1, @1] outputGoals:@[@0, @0, @0, @0, @0, @0, @1, @0, @0, @0]]; //8 [_krMLP addPatterns:@[@1, @1, @1, @1, @1, @1, @1, @1, @1, @1, @0, @0, @0, @1, @0, @0, @0, @1, @1, @0, @0, @0, @1, @0, @0, @0, @1, @1, @1, @1, @1, @1, @1, @1, @1, @1] outputGoals:@[@0, @0, @0, @0, @0, @0, @0, @1, @0, @0]]; // 9 [_krMLP addPatterns:@[@1, @1, @1, @1, @1, @0, @0, @0, @0, @1, @0, @0, @0, @1, @0, @0, @0, @0, @1, @0, @0, @0, @1, @0, @0, @0, @0, @1, @1, @1, @1, @1, @1, @1, @1, @1] outputGoals:@[@0, @0, @0, @0, @0, @0, @0, @0, @1, @0]]; // 0 [_krMLP addPatterns:@[@1, @1, @1, @1, @1, @1, @1, @1, @1, @1, @0, @0, @0, @0, @0, @0, @0, @1, @1, @0, @0, @0, @0, @0, @0, @0, @1, @1, @1, @1, @1, @1, @1, @1, @1, @1] outputGoals:@[@0, @0, @0, @0, @0, @0, @0, @0, @0, @1]]; __block typeof(_krMLP) _weakKRANN = _krMLP; // Traning completed [_krMLP setTrainingCompletion:^(BOOL success, NSDictionary *trainedInfo, NSInteger totalTimes){ if( success ) { NSLog(@"Training done with total times : %i", totalTimes); NSLog(@"TrainedInfo 1 : %@", trainedInfo); // Start in checking the network is correctly trained. NSLog(@"======== Start in Verification ========"); [_weakKRANN setTrainingCompletion:^(BOOL success, NSDictionary *trainedInfo, NSInteger totalTimes){ NSLog(@"Training done with total times : %i", totalTimes); NSLog(@"TrainedInfo 2 : %@", trainedInfo); }]; [_weakKRANN recoverNetwork]; // Verified number " 7 ", and it has some defects. [_weakKRANN directOutputAtInputs:@[@1, @1, @1, @0, @0, @0, @0, @0, @0, @1, @0, @0, @0, @0, @0, @0, @0, @0, @1, @0, @0, @0, @0, @0, @0, @0, @0, @1, @1, @1, @1, @1, @1, @1, @1, @1]]; } }]; [_krMLP trainingByRandomSettings];