/// <summary>
/// 分类问题中产生的如果是概率，需要把概率转换为编码结果
/// 后面再解码得到最终的分类
/// </summary>
/// <param name="probability">概率</param>
/// <param name="code">编码</param>
public static void ProbabilityToCode(TensorOld probability, TensorOld code)
{
if (probability.Rank == 1)
{
probability = probability.Reshape(1, probability.ElementCount);
}
if (probability.Rank != 2)
{
throw new Exception("to do codec, Rank must be 2");
}
if (probability.shape[1] == 1)
{
TensorOld.Apply(probability, code, a => a > 0.5 ? 1 : 0);
}
else
{
var buff = new double[probability.shape[1]];
for (int i = 0; i < probability.shape[0]; i++)
{
probability.GetByDim1(i, buff);
ComputeCode(buff);
Array.Copy(buff, 0, code.GetRawValues(), i * buff.Length, buff.Length);
}
}
}
public List <double> PredictValue(TensorOld X)
{
X = Normalize(X);
LastRawResult = Model.Predict(X);
LastCodecResult = Utilities.ProbabilityToCode(LastRawResult);
return(LastCodecResult.GetRawValues().ToList());
}
private static bool RowEqual(TensorOld t1, TensorOld t2, int start, int len)
{
for (int i = 0; i < len; i++)
{
if (t1.GetRawValues()[start + i] != t2.GetRawValues()[start + i])
{
return(false);
}
}
return(true);
}
private void SetTrainingData(TensorOld trainX, TensorOld trainY, TensorOld testX, TensorOld testY)
{
if (Normalizer != null)
{
TrainX = Normalizer.Normalize(trainX);
if (testX != null)
{
TestX = Normalizer.Normalize(testX);
}
}
else
{
TrainX = trainX;
TestX = testX;
}
if (LabelCodec != null)
{
TrainY = LabelCodec.Encode(trainY.GetRawValues());
if (testY != null)
{
TestY = LabelCodec.Encode(testY.GetRawValues());
}
}
else
{
TrainY = trainY;
TestY = testY;
}
var shape = TrainX.Shape;
shape[0] = BatchSize;
xBuff = new TensorOld(shape);
yBuff = new TensorOld(BatchSize, TrainY.shape[1]);
sampleCount = TrainX.shape[0];
xWidth = TrainX.ElementCount / TrainX.shape[0];
yWidth = TrainY.ElementCount / TrainY.shape[0];
batchPerEpoch = trainX.shape[0] / BatchSize;
if (trainX.shape[0] % BatchSize != 0)
{
batchPerEpoch++;
}
}
private void CopyToBatch(int trainIndex, int batchIndex)
{
Array.Copy(TrainX.GetRawValues(), trainIndex * xWidth, xBuff.GetRawValues(), batchIndex * xWidth, xWidth);
Array.Copy(TrainY.GetRawValues(), trainIndex * yWidth, yBuff.GetRawValues(), batchIndex * yWidth, yWidth);
}
