/// <summary>
/// 随机构建训练样本
/// </summary>
/// <param name="oneDimensionCount">样本 features count</param>
/// <param name="batchSize">样本数量</param>
/// <returns></returns>
public static List <float> CreateInputs(int oneDimensionCount = 64, int batchSize = 15)
{
var inputs = new List <List <float> >();
//构建指定feature数目的多样本集合
for (int i = 0; i < batchSize; i++)
{
var input = new List <float>();
for (int j = 0; j < oneDimensionCount; j++)
{
var num = NP.Random(10);
input.Add(num);
}
inputs.Add(input);
}
//转换成一纬数组
var outputs = new List <float>();
inputs.ForEach(p =>
{
outputs.AddRange(p);
});
//返回一维数组，备用
return(outputs);
}
/// <summary>
/// create ont hot array stochastic
/// </summary>
/// <param name="length"></param>
/// <returns></returns>
public static double[] StochasticOnehot(int length)
{
double[] action = new double[length];
for (int i = 0; i < length; i++)
{
action[i] = NP.Random(2);
}
return(action);
}
/// <summary>
/// 构建10个长度的oneHot编码结果样本集
/// </summary>
/// <param name="batchSzie">样本数量</param>
/// <returns></returns>
public static List <double> CreateLabels(int batchSzie = 15, int oneHot = 10)
{
var inputs = new List <double[]>();
//构建多样本的输出label
for (int i = 0; i < batchSzie; i++)
{
var label = NP.Random(10);
inputs.Add(ToOneHot(label, oneHot));
}
var outputs = new List <double>();
inputs.ForEach(p =>
{
outputs.AddRange(p);
});
//返回一维数组，备用
return(outputs);
}
