/// <summary>
/// 意思決定
/// </summary>
public int DecideIntention()
{
NumYArray xData = new NumYArray();
xData.Add(situation);
//ニューラルネットワークによる結果表示
NumYArray resultArray = new NumYArray(nn.Predict(xData));
List <List <float> > resultList = resultArray.Get();
//ランダムによる意思決定
float n = Random.Range(0.0f, NumY.Sum(resultArray).Get()[0][0]);
float offset = 0.0f;
for (int i = 0; i < resultList[0].Count; i++)
{
offset += resultList[0][i];
if (n <= offset)
{
intention = i;
break;
}
}
return(this.intention);
}
private void Button_Click_Export(object sender, RoutedEventArgs e)
{//导出图表点击事件
// ExportFunction();
string[] sttime = new string[10];
int[] stnum = new int[10];
//sttime = Labels.ToArray();//将List<string> Labels存入string[] st 数组做流输出，存放横坐标的时间数据
sttime = NumX.ToArray();
stnum = NumY.ToArray();
//MessageBox.Show(st[9], "test");//测试消息框
ExportFunction(sttime, stnum);
}
public void linestart(object sender, EventArgs e)
{//折线图绘制函数
Labels.Add(DateTime.Now.ToString("HH:mm:s"));
Labels.RemoveAt(0);
NumX.Add(DateTime.Now.ToString("HH:mm:s"));//同步存放需要导出的x轴数据
NumX.RemoveAt(0);
SeriesCollection[0].Values.Add(MainBusiness.numberPeople());
SeriesCollection[0].Values.RemoveAt(0);
NumY.Add(MainBusiness.numberPeople());//同步存放需要导出的y轴数据
NumY.RemoveAt(0);
}
/// <summary>
/// データを元に結果を予測する
/// </summary>
/// <param name="xData">予測する状況</param>
/// <returns>予測結果</returns>
public NumYArray Predict(NumYArray xData)
{
//順伝播・フォワードプロパゲーション
NumYArray layerZ1 = new NumYArray(NumY.Dot(xData, this.w1) + this.b1);
NumYArray layerA1 = new NumYArray(NumY.Sigmoid(layerZ1));
NumYArray layerZ2 = new NumYArray(NumY.Dot(layerA1, this.w2) + this.b2);
NumYArray layerA2 = new NumYArray(NumY.Sigmoid(layerZ2));
NumYArray returnArray = new NumYArray(layerA2);
return(returnArray);
}
private void Button_Click_Import(object sender, RoutedEventArgs e)
{ //导入图表点击事件
OutputFunction(NumX, NumY); //调用流输入函数对动态图表的xy轴数据存储list进行重写
int[] numy = NumY.ToArray();
string[] numx = NumX.ToArray();
for (int i = 0; i < 10; i++)
{
Labels.Add(numx[i]);
Labels.RemoveAt(0);
MessageBox.Show(numx[i], "test");
SeriesCollection[0].Values.Add(numy[i]);//因为没找到y轴数据对应的Ilist中存放数据的list，所以自己建立一个同步存放的list进行重写
SeriesCollection[0].Values.RemoveAt(0);
}
}
public NeuralNetwork(int inputUnits, int hiddenUnits, int outpuUnits, string folderName)
{
//学習値のデータがあるならそこから読み込む
if (System.IO.File.Exists(@"Assets/Resources/AIData/" + folderName + "/LearningDataCSVw1.csv"))
{
w1 = NeuralReadCSV.readCSVData("AIData/" + folderName + "/LearningDataCSVw1");
b1 = NeuralReadCSV.readCSVData("AIData/" + folderName + "/LearningDataCSVb1");
w2 = NeuralReadCSV.readCSVData("AIData/" + folderName + "/LearningDataCSVw2");
b2 = NeuralReadCSV.readCSVData("AIData/" + folderName + "/LearningDataCSVb2");
}
else
{
w1 = NumY.RandomArray(inputUnits, hiddenUnits, 1.0f, -1.0f);
b1 = NumY.RandomArray(1, hiddenUnits, 1.0f, -1.0f);
w2 = NumY.RandomArray(hiddenUnits, outpuUnits, 1.0f, -1.0f);
b2 = NumY.RandomArray(1, outpuUnits, 1.0f, -1.0f);
}
}
/// <summary>
/// 入力されたデータを元にトレーニング
/// </summary>
/// <param name="epochs">トレーニング回数</param>
/// <param name="learningRate">学習率</param>
/// <returns></returns>
public bool Train(int epochs, float learningRate, bool isTrainNow)
{
if (!isTrainNow)
{
NumYArray layerZ1 = new NumYArray();
NumYArray layerA1 = new NumYArray();
NumYArray layerZ2 = new NumYArray();
NumYArray layerA2 = new NumYArray();
NumYArray dlayerZ1 = new NumYArray();
NumYArray dlayerZ2 = new NumYArray();
NumYArray dw1 = new NumYArray();
NumYArray db1 = new NumYArray();
NumYArray dw2 = new NumYArray();
NumYArray db2 = new NumYArray();
totalTrainNum = 0;
maxTrainNum = Mathf.RoundToInt(100 / xData.Get().Count);
if (maxTrainNum <= 0)
{
maxTrainNum = 1;
}
learnSpeed = 1;
}
nowTrainNum = 0;
//規定回数学習させ、他処理のために途中で抜ける
while (nowTrainNum < maxTrainNum * learnSpeed)
{
//許容値を超えているか判定する用出力結果
//Array a = Predict(xData);
int m = xData.Get()[0].Count;
//順伝播・フォワードプロパゲーション
layerZ1 = NumY.Dot(xData, this.w1) + this.b1;
layerA1 = NumY.Sigmoid(layerZ1);
layerZ2 = NumY.Dot(layerA1, this.w2) + this.b2;
layerA2 = NumY.Sigmoid(layerZ2);
//誤差逆伝播法・バックプロパゲーション
dlayerZ2 = (layerA2 - yData) / m;
dw2 = NumY.Dot(layerA1.T, dlayerZ2);
db2 = NumY.Sum(dlayerZ2, 0);
dlayerZ1 = NumY.Dot(dlayerZ2, w2.T) * NumY.SigmoidDerivative(layerZ1);
dw1 = NumY.Dot(xData.T, dlayerZ1);
db1 = NumY.Sum(dlayerZ1, 0);
//パラメータ更新
w2 -= learningRate * dw2;
b2 -= learningRate * db2;
w1 -= learningRate * dw1;
b1 -= learningRate * db1;
nowTrainNum++;
totalTrainNum++;
if (totalTrainNum >= epochs)
{
this.xData = new NumYArray();
this.yData = new NumYArray();
totalTrainNum = 0;
nowTrainNum = 0;
return(false);
}
}
nowTrainNum = 0;
return(true);
}
