public void FeedForwardNeuralNetwork_LoadModelDenseMatrix()
{
var target = new FeedForwardNeuralNetwork <double>(
new[] { 2L, 3L, 2L });
var parser = new DoubleParser <string>();
var matrix = TestsHelper.ReadMatrix <double>(
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 ArrayMathMatrix <double>(i, j),
parser,
true);
var vector = TestsHelper.ReadVector(
5,
"[-1.0, 0.0, 1.0, -0.5, 0.5]",
new ArrayVectorFactory <double>(),
parser,
true);
var model = new NeuralNetworkModel <double, ILongMathMatrix <double>, IMathVector <double> >(
matrix,
vector);
target.LoadModel(
model);
this.AssertTargetFromMatrix(
model,
target);
}
public void FeedForwardNeuralNetwork_LoadModelComplexDenseMatrix()
{
var schema = new[] { 5L, 3L, 4L, 2L, 5L };
var target = new FeedForwardNeuralNetwork <double>(
schema);
var model = this.GetComplexTestModel();
target.LoadModel <CoordinateSparseMathMatrix <double>, ArrayMathVector <double> >(
model);
// Verificação do carregamento
this.AssertTargetFromMatrix(
model,
target);
}
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]);
}
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);
}
