public double[] feedResult(double[] pattern)
{
lock (locker)
{
return(nn.feedResult(pattern));
}
}
void set_test(string setfile)
{
learn.open(setfile, out var stream, out int len, out nn, out var nnfile, out var dir, out var file);
string line = "";
uint skip = 0;
uint pos = 0;
do
{
pos += skip;
for (int i = 0; i < skip; i++)
{
learn.GetNextGame(stream, len, out var a, out var b);
}
pos++;
learn.GetNextGame(stream, len, out var inputs, out var result);
double ourOutput = nn.feedResult(inputs)[0];
Console.WriteLine();
Console.WriteLine("#" + pos);
Console.WriteLine($"Inputs : {string.Join(",", inputs)}");
Console.WriteLine($"Correct : {result[0]}");
Console.WriteLine($"OurOutput : {ourOutput}");
line = Console.ReadLine();
uint.TryParse(line, out skip);
}while (line != "stop");
}
public void nn_test(string nnfile, string serializerName = nameof(StdSerializers.AllOneHotSerial))
{
string path = File.Exists(nnfile) ? nnfile : root.StdDir + nnfile;
nn = new vnn(File.ReadAllBytes(path));
getPlayers(out Player A, out Player B, out var data);
ObviousWin(A, B);
//double[] inputs = LearningGround.serializer.ConvertPosition(A);
double[] inputs = StdSerializers.Util.CreateFromName(serializerName, data).ConvertPosition(A);
double output = nn.feedResult(inputs)[0];
double formatted = Math.Round(output, 2) * 100;
Console.WriteLine($"A will win = {formatted}%");
}
public static void TestLoop(this vnn nn, Func <string, double[]> input_translator, Func <double[], string> output_translator)
{
try
{
while (true)
{
double[] inp = new double[nn.NInput];
Console.Write("in = ");
inp = input_translator(Console.ReadLine());
Console.WriteLine();
double[] ans = nn.feedResult(inp);
Console.WriteLine("out = " + output_translator(ans));
Console.WriteLine("".PadRight(10, '='));
}
} catch { }
}
unsafe void testWeights(double[] inputs, Action <ISimpleMLP> randFunc, string name)
{
var nn = new vnn(NINPUTS, NHIDDEN, NOUTPUT, randFunc);
//var nn = new vnnCpp(NINPUTS, NHIDDEN, NOUTPUT);
var re = nn.feedResult(inputs);
//Histogram.PrintHist(nn.hiddenNeurons);
Histogram.PrintHist(re);
Histogram.ShowHist(re, name: name);
//PrintHist(re);
//Write("Inputs\t: ");
//PrintHist(nn.inputNeurons.Take(NINPUTS).ToArray());
//Write("Hidden\t: ");
//PrintHist(nn.hiddenNeurons.Take(NHIDDEN).ToArray(), lower: 0, upper: 1);
//Write("Output\t: ");
//PrintHist(nn.outputNeurons, lower: 0, upper: 1);
}
static void GetError(FileStream logStream, int len, vnn nn, out double err, out double corr, out double avg_out)
{
const int n = 3000;
err = 0.0; corr = 0.0; avg_out = 0.0;
for (int i = 0; i < n; i++)
{
if (learn.GetNextGame(logStream, len, out var inp, out var res))
{
double output = nn.feedResult(inp)[0];
double diff = Math.Abs(res[0] - output);
err = (err * i + diff) / (double)(i + 1);
avg_out = (avg_out * i + output) / (double)(i + 1);
if (diff < 0.5)
{
corr++;
}
}
public static void TestLoop(this vnn nn, Func <double[], double[]> answerkey, Func <string, double[]> translator)
{
try
{
while (true)
{
double[] inp = new double[nn.NInput];
Console.Write("in = ");
inp = translator(Console.ReadLine());
Console.WriteLine();
double[] ans = nn.feedResult(inp);
double[] corr = answerkey(ans);
for (int i = 0; i < nn.NOutput; i++)
{
Console.WriteLine("ou[{0}] = {1:0.000}; co[{0}] = {2:0.000}; dx[{0}] = {3:0.000};", i, ans[i], corr[i], Abs(corr[i] - ans[i]));
}
Console.WriteLine("".PadRight(10, '='));
}
} catch { }
}
public static void TestLoop(this vnn nn)
{
try
{
while (true)
{
double[] inp = new double[nn.NInput];
for (int i = 0; i < nn.NInput; i++)
{
System.Console.Write("in[" + i + "] = ");
inp[i] = double.Parse(System.Console.ReadLine());
}
System.Console.WriteLine();
double[] ans = nn.feedResult(inp);
for (int i = 0; i < nn.NOutput; i++)
{
Console.WriteLine("ou[" + i + "] = " + Round(ans[i], 5));
}
Console.WriteLine("".PadRight(10, '='));
}
} catch { }
}
void fulladopt_test(string datafile)
{
learn.open(datafile, out var stream, out int len, out nn, out var nnfile, out var dir, out var file);
ReportAsync();
WaitStopAsync();
err = 0.0; corr = 0.0; i = 0;
while (learn.GetNextGame(stream, len, out var inp, out var res))
{
double output = nn.feedResult(inp)[0];
double diff = Math.Abs(res[0] - output);
err = (err * i + diff) / (double)(i + 1);
corr = (corr * i + (diff < 0.5 ? 1 : 0)) / (double)(i + 1);
i++;
if (stop)
{
return;
}
}
}