/// <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 static void SquareError(TensorOld y, TensorOld yHat, TensorOld result)
{
TensorOld.Minus(y, yHat, result);
result.Apply(a => a * a);
}
/// <summary>
/// 根据gradient优化target
/// </summary>
/// <param name="target">优化的目标</param>
/// <param name="graident">目标的梯度</param>
public void Optimize(TensorOld target, TensorOld graident)
{
TensorOld.Apply(target, graident, target, (a, b) => a - LearningRate * b);
}
