Unity 机器学习代理工具包 (ML-Agents) 是一个开源项目,它使游戏和模拟能够作为训练智能代理的环境。
您最多选择25个主题 主题必须以中文或者字母或数字开头,可以包含连字符 (-),并且长度不得超过35个字符
 
 
 
 
 

91 行
2.9 KiB

using System;
using NUnit.Framework;
using MLAgents.InferenceBrain.Utils;
namespace MLAgents.Tests
{
public class RandomNormalTest
{
const float k_FirstValue = -1.19580f;
const float k_SecondValue = -0.97345f;
const double k_Epsilon = 0.0001;
[Test]
public void RandomNormalTestTwoDouble()
{
var rn = new RandomNormal(2018);
Assert.AreEqual(k_FirstValue, rn.NextDouble(), k_Epsilon);
Assert.AreEqual(k_SecondValue, rn.NextDouble(), k_Epsilon);
}
[Test]
public void RandomNormalTestWithMean()
{
var rn = new RandomNormal(2018, 5.0f);
Assert.AreEqual(k_FirstValue + 5.0, rn.NextDouble(), k_Epsilon);
Assert.AreEqual(k_SecondValue + 5.0, rn.NextDouble(), k_Epsilon);
}
[Test]
public void RandomNormalTestWithStddev()
{
var rn = new RandomNormal(2018, 0.0f, 4.2f);
Assert.AreEqual(k_FirstValue * 4.2, rn.NextDouble(), k_Epsilon);
Assert.AreEqual(k_SecondValue * 4.2, rn.NextDouble(), k_Epsilon);
}
[Test]
public void RandomNormalTestWithMeanStddev()
{
const float mean = -3.2f;
const float stddev = 2.2f;
var rn = new RandomNormal(2018, mean, stddev);
Assert.AreEqual(k_FirstValue * stddev + mean, rn.NextDouble(), k_Epsilon);
Assert.AreEqual(k_SecondValue * stddev + mean, rn.NextDouble(), k_Epsilon);
}
[Test]
public void RandomNormalTestDistribution()
{
const float mean = -3.2f;
const float stddev = 2.2f;
var rn = new RandomNormal(2018, mean, stddev);
const int numSamples = 100000;
// Adapted from https://www.johndcook.com/blog/standard_deviation/
// Computes stddev and mean without losing precision
double oldM = 0.0, newM = 0.0, oldS = 0.0, newS = 0.0;
for (var i = 0; i < numSamples; i++)
{
var x = rn.NextDouble();
if (i == 0)
{
oldM = newM = x;
oldS = 0.0;
}
else
{
newM = oldM + (x - oldM) / i;
newS = oldS + (x - oldM) * (x - newM);
// set up for next iteration
oldM = newM;
oldS = newS;
}
}
var sampleMean = newM;
var sampleVariance = newS / (numSamples - 1);
var sampleStddev = Math.Sqrt(sampleVariance);
// Note a larger epsilon here. We could get closer to the true values with more samples.
Assert.AreEqual(mean, sampleMean, 0.01);
Assert.AreEqual(stddev, sampleStddev, 0.01);
}
}
}