public void FeedFrowardNeuralNetwork_RunSimpleMatrixTest()
{
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 actual = target.Run(
new[] { 1.0, 0.0 },
(u, v, l) =>
{
var result = 0.0;
for (var i = 0L; i < l; ++i)
{
result += u[i] * v[i];
}
return(result);
},
(d1, d2) =>
{
if (d2 > d1)
{
return(1.0);
}
else
{
return(0.0);
}
});
var expected = new[] { 0.0, 0.0 };
CollectionAssert.AreEqual(expected, actual);
actual = target.Run(
new[] { 0.0, 1.0 },
(u, v, l) =>
{
var result = 0.0;
for (var i = 0L; i < l; ++i)
{
result += u[i] * v[i];
}
return(result);
},
(d1, d2) =>
{
if (d2 > d1)
{
return(1.0);
}
else
{
return(0.0);
}
});
expected = new[] { 0.0, 1.0 };
CollectionAssert.AreEqual(expected, actual);
actual = target.Run(
new[] { 1.0, -1.0 },
(u, v, l) =>
{
var result = 0.0;
for (var i = 0L; i < l; ++i)
{
result += u[i] * v[i];
}
return(result);
},
(d1, d2) =>
{
if (d2 > d1)
{
return(1.0);
}
else
{
return(0.0);
}
});
expected = new[] { 0.0, 0.0 };
CollectionAssert.AreEqual(expected, actual);
}
