public void TestXOR()
{
double[][] _inputPattern =
{
new double[2] {
0.0, 0.0
},
new double[2] {
1.0, 0.0
},
new double[2] {
0.0, 1.0
},
new double[2] {
1.0, 1.0
}
};
double[][] _targetOutputPattern =
{
new double[1] {
0.0
},
new double[1] {
1.0
},
new double[1] {
1.0
},
new double[1] {
0.0
}
};
MLPNetworkWithBias network = new MLPNetworkWithBias(2, 5, 1, 2.8);
network.InitLog("XOR-");
Random rnd = new Random();
for (int i = 0; i < 50000; i++)
{
//network.BatchTrain(_inputPattern, _targetOutputPattern);
int index = rnd.Next(0, 4);
//Console.WriteLine(index);
network.OnlineTrain(_inputPattern[index], _targetOutputPattern[index]);
}
for (int i = 0; i < _inputPattern.Length; i++)
{
double[] outst = network.Run(_inputPattern[i]);
Console.WriteLine("XOR({0}, {1}) = {2:0.000}", _inputPattern[i][0], _inputPattern[i][1], outst[0]);
}
}
public void InitMLP(int nrHidden, double learnRate)
{
int nrInput = _dataMgr.count;
int nrOutput;
if (_normalizeOuput)
{
nrOutput = 1;
}
else
{
nrOutput = 10;
}
_network = new MLPNetworkWithBias(nrInput, nrHidden, nrOutput, learnRate);
}
public void TestDigit()
{
double[] pattern0 =
{
0, 1, 1, 1, 0,
0, 1, 0, 1, 0,
0, 1, 0, 1, 0,
0, 1, 0, 1, 0,
0, 1, 1, 1, 0
};
double[] pattern1 =
{
0, 0, 1, 0, 0,
0, 1, 1, 0, 0,
0, 0, 1, 0, 0,
0, 0, 1, 0, 0,
0, 0, 1, 0, 0
};
double[] pattern2 =
{
0, 1, 1, 1, 0,
0, 0, 0, 1, 0,
0, 1, 1, 1, 0,
0, 1, 0, 0, 0,
0, 1, 1, 1, 0
};
double[] pattern3 =
{
0, 1, 1, 1, 0,
0, 0, 0, 1, 0,
0, 1, 1, 1, 0,
0, 0, 0, 1, 0,
0, 1, 1, 1, 0
};
double[] pattern4 =
{
0, 1, 0, 1, 0,
0, 1, 0, 1, 0,
0, 1, 1, 1, 0,
0, 0, 0, 1, 0,
0, 0, 0, 1, 0
};
double[] pattern5 =
{
0, 1, 1, 1, 0,
0, 1, 0, 0, 0,
0, 1, 1, 1, 0,
0, 0, 0, 1, 0,
0, 1, 1, 1, 0
};
double[] pattern6 =
{
0, 1, 1, 1, 0,
0, 1, 0, 0, 0,
0, 1, 1, 1, 0,
0, 1, 0, 1, 0,
0, 1, 1, 1, 0
};
double[] pattern7 =
{
0, 1, 1, 1, 0,
0, 0, 0, 1, 0,
0, 0, 0, 1, 0,
0, 0, 1, 0, 0,
0, 1, 0, 0, 0
};
double[] pattern8 =
{
0, 1, 1, 1, 0,
0, 1, 0, 1, 0,
0, 1, 1, 1, 0,
0, 1, 0, 1, 0,
0, 1, 1, 1, 0
};
double[] pattern9 =
{
0, 1, 1, 1, 0,
0, 1, 0, 1, 0,
0, 1, 1, 1, 0,
0, 0, 0, 1, 0,
0, 1, 1, 1, 0
};
double[][] _inputPattern =
{
pattern0, pattern1, pattern2, pattern3, pattern4,
pattern5, pattern6, pattern7, pattern8, pattern9
};
double[][] _targetOutputPattern =
{
new double[] { 0.0 },
new double[] { 0.1 },
new double[] { 0.2 },
new double[] { 0.3 },
new double[] { 0.4 },
new double[] { 0.5 },
new double[] { 0.6 },
new double[] { 0.7 },
new double[] { 0.8 },
new double[] { 0.9 }
};
MLPNetworkWithBias network = new MLPNetworkWithBias(25, 25, 1, 0.8);
network.InitLog("Digit-");
Random rnd = new Random();
for (int i = 0; i < 50000; i++)
{
//network.BatchTrain(_inputPattern, _targetOutputPattern);
int index = rnd.Next(0, 10);
Console.WriteLine("index :" + i);
network.OnlineTrain(_inputPattern[index], _targetOutputPattern[index]);
}
for (int i = 0; i < _inputPattern.Length; i++)
{
double[] output = network.Run(_inputPattern[i]);
Console.WriteLine("{0} pattern = {1:0.000} which is digit {2}", i,
output[0], (int)Math.Round(output[0] * 10));
}
}
