public void FeedForwardNeuralNetwork_ThreeLayerTrainTest()
{
var target = new FeedForwardNeuralNetwork <double>(
new[] { 2L, 2L, 1L });
var pattern = new NeuralNetworkTrainingPattern <double, ArrayMathVector <double>, ArrayMathVector <double> >[] {
new NeuralNetworkTrainingPattern <double, ArrayMathVector <double>, ArrayMathVector <double> >(
new ArrayMathVector <double>(new[] { 0.0, 0.0 }),
new ArrayMathVector <double>(new[] { 0.0 })),
new NeuralNetworkTrainingPattern <double, ArrayMathVector <double>, ArrayMathVector <double> >(
new ArrayMathVector <double>(new[] { 0.1, 0.0 }),
new ArrayMathVector <double>(new[] { 1.0 })),
new NeuralNetworkTrainingPattern <double, ArrayMathVector <double>, ArrayMathVector <double> >(
new ArrayMathVector <double>(new[] { 0.0, 1.0 }),
new ArrayMathVector <double>(new[] { 1.0 })),
new NeuralNetworkTrainingPattern <double, ArrayMathVector <double>, ArrayMathVector <double> >(
new ArrayMathVector <double>(new[] { 1.0, 1.0 }),
new ArrayMathVector <double>(new[] { 0.0 }))
};
var rand = new Random();
var field = new DoubleField();
target.Train(
pattern,
100,
field,
(d1, d2) =>
{
return(1.0 / (1.0 + Math.Exp(-d2 + d1)));
},
(u, v, l) =>
{
var result = 0.0;
for (var i = 0L; i < l; ++i)
{
result += u[i] * v[i];
}
return(result);
},
(y) => y * (1 - y),
(w, y, i) => w[i],
(c, w) =>
{
//var len = c.LongLength;
//for(var i = 0L;i< len; ++i)
//{
// c[i] = rand.NextDouble();
//}
//len = w.LongLength;
//for(var i = 0L; i < len; ++i)
//{
// var curr = w[i];
// var innerLen = curr.LongLength;
// for (var j = 0L; j < innerLen; ++j)
// {
// w[i][j] = rand.NextDouble();
// }
//}
c[0] = 0.6;
c[1] = 0.6;
c[2] = 0.6;
w[0][0] = 1.0;
w[0][1] = -1.0;
w[1][0] = -1.0;
w[1][1] = 1.0;
w[2][0] = 1.0;
w[2][1] = 1.0;
});
var outputMatrix = target.InternalReserveOutput();
Func <double, double, double> activationFunction = (d1, d2) =>
{
if (d2 > d1)
{
return(1.0);
}
else
{
return(0.0);
}
};
Func <double[], double[], long, double> propFunc = (u, v, l) =>
{
var result = 0.0;
for (var i = 0L; i < l; ++i)
{
result += u[i] * v[i];
}
return(result);
};
target.InternalComputeLayerOutputs(
new ArrayMathVector <double>(new[] { 0.0, 1.0 }),
outputMatrix,
activationFunction,
propFunc);
Assert.Inconclusive("Test not yet completed.");
}
public void FeedFrowardNeuralNetwork_InternalComputeOutputs()
{
var target = new FeedForwardNeuralNetwork <double>(
new[] { 2L, 3L, 2L });
var parser = new DoubleParser <string>();
var matrix = TestsHelper.ReadMatrix(
5,
5,
"[[-1.0, 1.0, 0.5, 0, 0], [1.0, -1.0, 0.5, 0, 0], [0, 0, 0, -1.0, 2.0], [0, 0, 0, 0.5, -1.5], [0, 0, 0, 1.0, -0.5]]",
(i, j) => new SparseDictionaryMatrix <double>(i, j, 0),
parser,
true);
var vector = TestsHelper.ReadVector(
5,
"[0.5, 0.5, 0.5, 0.5, 0.5]",
new SparseDictionaryMathVectorFactory <double>(),
parser,
true);
var model = new NeuralNetworkModel <double, SparseDictionaryMatrix <double>, IMathVector <double> >(
matrix,
vector);
target.LoadModel(model);
var outputMatrix = target.InternalReserveOutput();
target.InternalComputeLayerOutputs(
new ArrayMathVector <double>(new[] { 1.0, -1.0 }),
outputMatrix,
(d1, d2) =>
{
if (d2 > d1)
{
return(1.0);
}
else
{
return(0.0);
}
},
(u, v, l) =>
{
var result = 0.0;
for (var i = 0L; i < l; ++i)
{
result += u[i] * v[i];
}
return(result);
});
Assert.AreEqual(target.Schema.LongCount() - 1L, outputMatrix.LongLength);
var currOut = outputMatrix[0];
Assert.AreEqual(0.0, currOut[0]);
Assert.AreEqual(1.0, currOut[1]);
Assert.AreEqual(0.0, currOut[2]);
currOut = outputMatrix[1];
Assert.AreEqual(0.0, currOut[0]);
Assert.AreEqual(0.0, currOut[1]);
}
