本项目实现了三个层次（输入层、隐藏层和输出层）神经网络（ニューラルネットワーク），并实现了反向传播神经网络（BPN），QuickProp 理论和 Kecman 的理论（EDBD）。KRBPN 可以用于产品推荐（おすすめの商品）、用户行为分析（ユーザーの行動分析）、数据挖掘（データマイニング）和数据分析（データ分析）。
本项目旨在快速为移动设备进行基本数据分析。如果您需要了解如何使用这个网络，请通过电子邮件向我咨询。
platform :ios, '7.0'
pod "KRBPN", "~> 2.0.6"#import "KRBPN.h"// Use singleton or [[KRBPN alloc] init]
_krBPN = [KRBPN sharedNetwork];
// Learning rate
_krBPN.learningRate = 0.8f;
// Convergence error, 收斂誤差值 ( Normally is 10^-3 or 10^-6 )
_krBPN.convergenceError = 0.001f;
// Limit iterations
_krBPN.limitIteration = 1000;
// If you wanna up to your wishing to decide that initital hidden-net counts, you could use this :
_krBPN.hiddenNets = 5;
// If you wanna use enhanced theory, and to setup that customized learning rate, you could use this as below :
_krBPN.quickPropFixedRate = 0.5f;
// If you wanna directly use QuickProp method to enhance that network training :
_krBPN.learningMode = KRBPNLearningModeByQuickPropDynamic;
// If you wanna use Kecman's theory to enhance that network training, please use this :
_krBPN.learningMode = KRBPNLearningModeByQuickPropFixed;
// If you wanna use Fahlman's QuickProp combined Kecman's Fixed Rate. Yes, in my experiences this usage method is better than others.
_krBPN.learningMode = KRBPNLearningModeByQuickPropSmartHybrid;
// Per iteration-training block
[_krBPN setEachIteration:^(NSInteger times, NSDictionary *trainedInfo){
NSLog(@"Iterations : %i", times);
NSLog(@"Result per Iteration : %@", [trainedInfo objectForKey:KRBPNTrainedOutputResults]);
}];使用 Sigmoid 激活函数并自定义所有权重与偏差。
// Sigmoid is that values range in [0.0, 1.0] and the outputs too.
_krBPN.activeFunction = KRBPNActivationBySigmoid;
// Add the 3 patterns, the weights connect with hidden layer, the output targets
[_krBPN addPatterns:@[@1, @0.1, @0.5, @0.2] outputGoals:@[@0.7f, @0.8f]];
[_krBPN addPatterns:@[@0, @1, @0.3, @0.9] outputGoals:@[@0.1f, @0.1f]];
[_krBPN addPatterns:@[@1, @0.3, @0.1, @0.4] outputGoals:@[@0.95f, @0.9f]];
// Add input to hidden layer that pattern-weights
// Here is 2 hidden nets
[_krBPN addPatternWeights:@[@0.2, @-0.3]];
[_krBPN addPatternWeights:@[@0.4, @0.1]];
[_krBPN addPatternWeights:@[@-0.5, @0.2]];
[_krBPN addPatternWeights:@[@-0.1, @0.3]];
// Add hidden layer biases of nets and the output weights
[_krBPN addHiddenLayerNetBias:-0.4f outputWeights:@[@-0.3f, @0.2f]];
[_krBPN addHiddenLayerNetBias:0.2f outputWeights:@[@-0.2f, @0.5f]];
// Add 2 output nets biases
[_krBPN addOutputBiases:@[@0.0f, @0.1f]];
__block typeof(_krBPN) _weakKrBPN = _krBPN;
[_krBPN setTrainingCompletion:^(BOOL success, NSDictionary *trainedInfo, NSInteger totalTimes){
if( success )
{
NSLog(@"Training done with total times : %i", totalTimes);
NSLog(@"TrainedInfo : %@", trainedInfo);
//Start in checking the network is correctly trained.
NSLog(@"======== Start in Verification ========");
[_weakKrBPN setTrainingCompletion:^(BOOL success, NSDictionary *trainedInfo, NSInteger totalTimes){
NSLog(@"Training done with total times : %i", totalTimes);
NSLog(@"Verified TrainedInfo : %@", trainedInfo);
}];
[_weakKrBPN recoverNetwork];
[_weakKrBPN directOutputAtInputs:@[@1, @0.1, @0.5, @0.2]];
}
}];
[_krBPN training];此示例使用有界于 [-1.0, 1.0] 的 tanh() 作为激活函数，并直接随机所有设置（权重、偏差、网络计算）。
_krBPN.activeFunction = KRBPNActivationByTanh;
[_krBPN addPatterns:@[@1, @0.1, @0.5, @0.2] outputGoals:@[@0.7f]];
[_krBPN addPatterns:@[@-0.5, @0.8, @-0.3, @0.9] outputGoals:@[@-0.1f]];
[_krBPN addPatterns:@[@1, @0.3, @0.1, @0.4] outputGoals:@[@0.9f]];
__block typeof(_krBPN) _weakKrBPN = _krBPN;
[_krBPN setTrainingCompletion:^(BOOL success, NSDictionary *trainedInfo, NSInteger totalTimes){
if( success )
{
NSLog(@"Training done with total times : %i", totalTimes);
NSLog(@"TrainedInfo : %@", trainedInfo);
//Start in checking the network is correctly trained.
NSLog(@"======== Start in Verification ========");
[_weakKrBPN setTrainingCompletion:^(BOOL success, NSDictionary *trainedInfo, NSInteger totalTimes){
NSLog(@"Training done with total times : %i", totalTimes);
NSLog(@"Verified TrainedInfo : %@", trainedInfo);
}];
[_weakKrBPN recoverNetwork];
[_weakKrBPN directOutputAtInputs:@[@-0.5, @0.8, @-0.3, @0.9]];
}
}];
[_krBPN trainingByRandomSettings];识别数字 0 到 9，此训练有 10 个输出（与 10 个分类组相同）。数字 1 为 [1, 0, 0, 0, ..., 0]，数字 3 为 [0, 0, 1, 0, ..., 0]。
_krBPN.activeFunction = KRBPNActivationBySigmoid;
// Number 1
[_krBPN 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]];
// Number 2
[_krBPN 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]];
// Number 3
[_krBPN 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]];
// Number 4
[_krBPN 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]];
// Number 5
[_krBPN 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]];
// Number 6
[_krBPN 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]];
// Number 7
[_krBPN 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]];
// Number 8
[_krBPN 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]];
// Number 9
[_krBPN 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]];
// Number 0
[_krBPN 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(_krBPN) _weakKrBPN = _krBPN;
[_krBPN setTrainingCompletion:^(BOOL success, NSDictionary *trainedInfo, NSInteger totalTimes){
if( success )
{
NSLog(@"Training done with total times : %i", totalTimes);
NSLog(@"TrainedInfo : %@", trainedInfo);
//Start in checking the network is correctly trained.
NSLog(@"======== Start in Verification ========");
[_weakKrBPN setTrainingCompletion:^(BOOL success, NSDictionary *trainedInfo, NSInteger totalTimes){
NSLog(@"Training done with total times : %i", totalTimes);
NSLog(@"Verified TrainedInfo : %@", trainedInfo);
}];
[_weakKrBPN recoverNetwork];
//Verified number " 7 ", and it has some defects.
[_weakKrBPN 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]];
}
}];
[_krBPN trainingByRandomSettings];预测癌症医疗的存活率。
_krBPN.activeFunction = KRBPNActivationBySigmoid;
// ER, PR, Hev-2, K, 治療與否, 每 10 年為 0.1 單位 => 存活率
[_krBPN addPatterns:@[@0.9, @0.9, @0.0, @0.15, @0, @0.1f] outputGoals:@[@0.12f]];
[_krBPN addPatterns:@[@0.9, @0.9, @0.0, @0.15, @1, @0.1f] outputGoals:@[@0.12f]];
[_krBPN addPatterns:@[@0.5, @0.4, @0.0, @0.25, @0, @0.1f] outputGoals:@[@0.23f]];
[_krBPN addPatterns:@[@0.5, @0.4, @0.0, @0.25, @1, @0.1f] outputGoals:@[@0.14f]];
[_krBPN addPatterns:@[@0.0, @0.0, @0.0, @0.3, @0, @0.1f] outputGoals:@[@0.35f]];
[_krBPN addPatterns:@[@0.0, @0.0, @0.0, @0.3, @1, @0.1f] outputGoals:@[@0.22f]];
[_krBPN addPatterns:@[@0.0, @0.0, @1.0, @0.3, @0, @0.1f] outputGoals:@[@0.3f]];
[_krBPN addPatterns:@[@0.0, @0.0, @1.0, @0.3, @1, @0.1f] outputGoals:@[@0.12f]];
__block typeof(_krBPN) _weakKrBPN = _krBPN
[_krBPN setTrainingCompletion:^(BOOL success, NSDictionary *trainedInfo, NSInteger totalTimes){
if( success )
{
NSLog(@"Training done with total times : %i", totalTimes);
NSLog(@"TrainedInfo : %@", trainedInfo);
//Start in checking the network is correctly trained.
NSLog(@"======== Start in Verification ========");
[_weakKrBPN setTrainingCompletion:^(BOOL success, NSDictionary *trainedInfo, NSInteger totalTimes){
NSLog(@"Training done with total times : %i", totalTimes);
NSLog(@"Verified TrainedInfo : %@", trainedInfo);
}];
[_weakKrBPN recoverNetwork];
// To predict how many years could be alive with the patients.
[_weakKrBPN directOutputAtInputs:@[@0.6, @0.6, @0.0, @0.25, @0.0, @0.1f]];
[_weakKrBPN directOutputAtInputs:@[@0.6, @0.6, @0.0, @0.25, @1.0, @0.1f]];
}
}];
[_krBPN trainingByRandomSettings];V2.0.6
MIT。