/// <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);
}
/// <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);
}