/// <summary>
/// 测试
/// </summary>
void FormulaTest()//
{
List <Matrix <double> > C0 = new List <Matrix <double> >(), C1 = new List <Matrix <double> >();
List <Matrix <double> > Test0 = new List <Matrix <double> >(), Test1 = new List <Matrix <double> >();
var m2 = new DenseMatrix(new[, ] {
{ 1.0 }, { 0.5 }
});
var k2 = new DenseMatrix(new[, ] {
{ 1.0 }
});
var v2 = new DenseMatrix(new[, ] {
{ 3.0, 1.0 }, { 1.0, 2.0 }
});
var m1 = new DenseMatrix(new[, ] {
{ 0.0 }, { 0.0 }
});
var k1 = new DenseMatrix(new[, ] {
{ 1.0 }
});
var v1 = new DenseMatrix(new[, ] {
{ 1.0, 0.0 }, { 0.0, 1.0 }
});
ClassifiedData.Clear();
for (int i = 0; i < 50; i++)
{
C0.Add(MatrixNormal.Sample(new Random(), m2, v2, k2));
ClassifiedData.Add(new data(C0[0][0, 0], C0[0][1, 0], 0));
C1.Add(MatrixNormal.Sample(new Random(), m1, v1, k1));
ClassifiedData.Add(new data(C1[0][0, 0], C1[0][1, 0], 1));
}
for (int i = 0; i < 20; i++)
{
Test0.Add(MatrixNormal.Sample(new Random(), m2, v2, k2));
Test1.Add(MatrixNormal.Sample(new Random(), m1, v1, k1));
TestData.Add(new data(Test0[0][0, 0], Test0[0][1, 0], 0));
C1.Add(MatrixNormal.Sample(new Random(), m1, v1, k1));
TestData.Add(new data(Test1[0][0, 0], Test1[0][1, 0], 1));
}
int indicator = 0;
int endNetworks = 50;
int startNetworks = 1;
//int seed = 0;
do
{
indicator = 0;
for (int ii = startNetworks; ii <= endNetworks; ii++)
{
Jobs ajob = new Jobs(ii, 10);
/// 新建一个神经网络
NeuronNetworks nn = new NeuronNetworks(ajob.m);//, new Random(DateTime.Now.Millisecond));
/// 当前的迭代次数
nn.currentLearnRepeats = 0;
DateTime _start = DateTime.Now;
while (true)
//(++nn.currentLearnRepeats < nn.maxLearnRepeats)
{
++nn.currentLearnRepeats;
foreach (data d in ClassifiedData)
{
nn.Train(d.input1, d.input2, d.output);
}
//if (nn.currentLearnRepeats % ajob.testpoint == 0) // recheck whether it is converge.
{
double errorsum = 0;
for (int i = 0; i < ClassifiedData.Count; i++)
{
double estimateOut = nn.Display(ClassifiedData[i].input1, ClassifiedData[i].input2) - ClassifiedData[i].output;
errorsum += (estimateOut * estimateOut) / 2;
}
if (errorsum < nn.ε)
{
break;
}
if ((DateTime.Now - _start).Seconds > ajob.timeout)//timeout
{
Console.WriteLine(DateTime.Now - _start);
Console.WriteLine("Request timeout.");
break;
}
}
}
double sum = 0;
foreach (data d in TestData)
{
double t = (nn.Display(d.input1, d.input2) - d.output);
//Console.WriteLine("{0},{1}->{2}", d.input1, d.input2, nn.Display(d.input1, d.input2));
sum += t * t;
}
//Console.WriteLine(DateTime.Now - _start);未知样本的总差值=
if (sum > 10)
{
indicator++;
}
//Console.WriteLine("Task: m={0},testpoint={1};", ajob.m, ajob.testpoint);
//Console.WriteLine("开始计算差值");
//Console.WriteLine(nn.currentLearnRepeats);
sum = 0;
foreach (data d in ClassifiedData)
{
double t = (nn.Display(d.input1, d.input2) - d.output);
//Console.WriteLine("{0},{1}->{2}", d.input1, d.input2, nn.Display(d.input1, d.input2));
sum += t * t;
}
//Console.WriteLine(DateTime.Now - _start);样本训练后的总差值=
Console.Write("{0}\t", sum);
sum = 0;
foreach (data d in TestData)
{
double t = (nn.Display(d.input1, d.input2) - d.output);
//Console.WriteLine("{0},{1}->{2}", d.input1, d.input2, nn.Display(d.input1, d.input2));
//if (t > 0.7) t = 1;
//else t = 0;
sum += t * t;
}
//Console.WriteLine(DateTime.Now - _start);未知样本的总差值=
Console.WriteLine("{0}", sum);
//if (sum < 10) Console.ReadKey();
if (indicator >= (endNetworks - startNetworks) * 0.9)
{
Console.Clear();
}
}
} while (indicator > 5);
Console.WriteLine("Press any key to exit.");
if (indicator < 5)
{
Console.ReadKey();
}
}
/// <summary>
/// 线程执行的任务
/// </summary>
void dojob()
{
Jobs ajob = new Jobs(0, 0);
/// 当前已经没有待执行的任务，退出
while (AllJobs.Count > 0)
{
/// 否则选取一个任务
ajob = AllJobs.Pop();
/// 输出当前任务信息
Console.WriteLine("Running for m={0},testpoint={1};", ajob.m, ajob.testpoint);
/// 新建一个神经网络
NeuronNetworks nn = new NeuronNetworks(ajob.m);
/// 设定学习率
nn.ε = 0.01;
/// 当前的迭代次数
nn.currentLearnRepeats = 0;
DateTime _start = DateTime.Now;
while (true)
//(++nn.currentLearnRepeats < nn.maxLearnRepeats)
{
++nn.currentLearnRepeats;
foreach (data d in testdata)
{
nn.Train(d.input1, d.input2, d.output);
}
if (nn.currentLearnRepeats % ajob.testpoint == 0) // recheck whether it is converge.
{
double errorsum = 0;
for (int i = 0; i < testdata.Count; i++)
{
double estimateOut = nn.Display(testdata[i].input1, testdata[i].input2) - testdata[i].output;
errorsum += estimateOut * estimateOut;
}
if (errorsum < 2 * nn.ε)
{
break;
}
if ((DateTime.Now - _start).Seconds > ajob.timeout)//timeout
{
Console.WriteLine(DateTime.Now - _start);
Console.WriteLine("Request timeout.");
break;
}
}
}
Console.WriteLine("Task: m={0},testpoint={1};", ajob.m, ajob.testpoint);
Console.WriteLine("开始计算差值");
Console.WriteLine(nn.currentLearnRepeats);
double sum = 0;
foreach (data d in testdata)
{
double t = (nn.Display(d.input1, d.input2) - d.output);
//Console.WriteLine("{0},{1}->{2}", d.input1, d.input2, nn.Display(d.input1, d.input2));
sum += t * t;
}
Console.WriteLine(DateTime.Now - _start);
Console.WriteLine("样本训练后的总差值={0}", sum);
sum = 0;
for (int i = 0; i < generated.Length; i++)
{
if (!generated[i])
{
double a = i - i % 100;
a = a / 10000;
double b = i % 100;
b = b / 100;
double t = (nn.Display(a, b) - 1 / Math.Sqrt(1 + a * a + b * b));
sum += t * t;
}
}
Console.WriteLine(DateTime.Now - _start);
Console.WriteLine("未知样本的总差值={0}", sum);
}
}