本文整理汇总了C#中Accord.Math.Decompositions.SingularValueDecomposition.Reverse方法的典型用法代码示例。如果您正苦于以下问题：C# SingularValueDecomposition.Reverse方法的具体用法？C# SingularValueDecomposition.Reverse怎么用？C# SingularValueDecomposition.Reverse使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类Accord.Math.Decompositions.SingularValueDecomposition的用法示例。
在下文中一共展示了SingularValueDecomposition.Reverse方法的7个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的C#代码示例。
示例1: InverseTest
public void InverseTest()
{
double[,] value = new double[,]
{
{ 1.0, 1.0 },
{ 2.0, 2.0 }
};
var target = new SingularValueDecomposition(value);
double[,] expected = new double[,]
{
{ 0.1, 0.2 },
{ 0.1, 0.2 }
};
double[,] actual = target.Solve(Matrix.Identity(2));
Assert.IsTrue(Matrix.IsEqual(expected, actual, 1e-3));
Assert.IsTrue(Matrix.IsEqual(value, target.Reverse(), 1e-3));
actual = target.Inverse();
Assert.IsTrue(Matrix.IsEqual(expected, actual, 1e-3));
}示例2: InverseTest2
public void InverseTest2()
{
int n = 5;
var I = Matrix.Identity(n);
for (int i = 0; i < n; i++)
{
for (int j = 0; j < n; j++)
{
double[,] value = Matrix.Magic(n);
var target = new SingularValueDecomposition(value);
double[,] solution = target.Solve(I);
double[,] inverse = target.Inverse();
double[,] reverse = target.Reverse();
Assert.IsTrue(Matrix.IsEqual(solution, inverse, 1e-4));
Assert.IsTrue(Matrix.IsEqual(value, reverse, 1e-4));
}
}
}示例3: SingularValueDecompositionConstructorTest7
public void SingularValueDecompositionConstructorTest7()
{
int count = 100;
double[,] value = new double[count, 3];
double[] output = new double[count];
for (int i = 0; i < count; i++)
{
double x = i + 1;
double y = 2 * (i + 1) - 1;
value[i, 0] = x;
value[i, 1] = y;
value[i, 2] = 1;
output[i] = 4 * x - y + 3;
}
SingularValueDecomposition target = new SingularValueDecomposition(value,
computeLeftSingularVectors: true,
computeRightSingularVectors: true);
{
double[,] expected = value;
double[,] actual = Matrix.Multiply(Matrix.Multiply(target.LeftSingularVectors,
Matrix.Diagonal(target.Diagonal)), target.RightSingularVectors.Transpose());
// Checking the decomposition
Assert.IsTrue(Matrix.IsEqual(actual, expected, 1e-8));
Assert.IsTrue(Matrix.IsEqual(target.Reverse(), expected, 1e-8));
}
{
double[] solution = target.Solve(output);
double[] expected= output;
double[] actual = value.Multiply(solution);
Assert.IsTrue(Matrix.IsEqual(actual, expected, 1e-8));
}
}示例4: SingularValueDecompositionConstructorTest6
public void SingularValueDecompositionConstructorTest6()
{
// Test using SVD assumption auto-correction feature in place
double[,] value1 =
{
{ 2.5, 2.4 },
{ 0.5, 0.7 },
{ 2.2, 2.9 },
{ 1.9, 2.2 },
{ 3.1, 3.0 },
{ 2.3, 2.7 },
{ 2.0, 1.6 },
{ 1.0, 1.1 },
{ 1.5, 1.6 },
{ 1.1, 0.9 }
};
double[,] value2 = value1.Transpose();
var cvalue1 = value1.Copy();
var cvalue2 = value2.Copy();
var target1 = new SingularValueDecomposition(cvalue1, true, true, true, true);
var target2 = new SingularValueDecomposition(cvalue2, true, true, true, true);
Assert.IsFalse(cvalue1.IsEqual(value1, 1e-5));
// Assert.IsFalse(cvalue2.IsEqual(value2, 1e-5));
Assert.IsTrue(target1.LeftSingularVectors.IsEqual(target2.RightSingularVectors));
Assert.IsTrue(target1.RightSingularVectors.IsEqual(target2.LeftSingularVectors));
Assert.IsTrue(target1.DiagonalMatrix.IsEqual(target2.DiagonalMatrix));
Assert.IsTrue(Matrix.IsEqual(target1.Reverse(), value1, 1e-5));
Assert.IsTrue(Matrix.IsEqual(target2.Reverse(), value2, 1e-5));
}示例5: SingularValueDecompositionConstructorTest2
public void SingularValueDecompositionConstructorTest2()
{
// test for m-x-n matrices where m > n (4 > 2)
double[,] value = new double[,]
{
{ 1, 2 },
{ 3, 4 },
{ 5, 6 },
{ 7, 8 }
}; // value is 4x2, thus having more rows than columns
SingularValueDecomposition target = new SingularValueDecomposition(value, true, true, false);
double[,] actual = Matrix.Multiply(Matrix.Multiply(target.LeftSingularVectors,
Matrix.Diagonal(target.Diagonal)), target.RightSingularVectors.Transpose());
// Checking the decomposition
Assert.IsTrue(Matrix.IsEqual(actual, value, 0.01));
Assert.IsTrue(Matrix.IsEqual(target.Reverse(), value, 1e-2));
double[,] U = // economy svd
{
{ 0.152483233310201, 0.822647472225661, },
{ 0.349918371807964, 0.421375287684580, },
{ 0.547353510305727, 0.0201031031435023, },
{ 0.744788648803490, -0.381169081397574, },
};
// U should be equal except for some sign changes
Assert.IsTrue(Matrix.IsEqual(target.LeftSingularVectors, U, 0.001));
// Checking values
double[,] V =
{
{ 0.641423027995072, -0.767187395072177 },
{ 0.767187395072177, 0.641423027995072 },
};
// V should be equal except for some sign changes
Assert.IsTrue(Matrix.IsEqual(target.RightSingularVectors, V, 0.0001));
double[,] S =
{
{ 14.2690954992615, 0.000000000000000 },
{ 0.0000000000000, 0.626828232417543 },
};
// The diagonal values should be equal
Assert.IsTrue(Matrix.IsEqual(target.Diagonal, Matrix.Diagonal(S), 0.001));
}示例6: SingularValueDecompositionConstructorTest3
public void SingularValueDecompositionConstructorTest3()
{
// Test using SVD assumption auto-correction feature.
// Test for m-x-n matrices where m < n. The available SVD
// routine was not meant to be used in this case.
double[,] value = new double[,]
{
{ 1, 2 },
{ 3, 4 },
{ 5, 6 },
{ 7, 8 }
}.Transpose(); // value is 2x4, having less rows than columns.
var target = new SingularValueDecomposition(value, true, true, true);
double[,] actual = Matrix.Multiply(Matrix.Multiply(target.LeftSingularVectors,
Matrix.Diagonal(target.Diagonal)), target.RightSingularVectors.Transpose());
// Checking the decomposition
Assert.IsTrue(Matrix.IsEqual(actual, value, 0.01));
Assert.IsTrue(Matrix.IsEqual(target.Reverse(), value, 1e-2));
// Checking values
double[,] U =
{
{ 0.641423027995072, -0.767187395072177 },
{ 0.767187395072177, 0.641423027995072 },
};
// U should be equal despite some sign changes
Assert.IsTrue(Matrix.IsEqual(target.LeftSingularVectors, U, 0.001));
double[,] V = // economy svd
{
{ 0.152483233310201, 0.822647472225661, },
{ 0.349918371807964, 0.421375287684580, },
{ 0.547353510305727, 0.0201031031435023, },
{ 0.744788648803490, -0.381169081397574, },
};
// V can be different, but for the economy SVD it is often equal
Assert.IsTrue(Matrix.IsEqual(target.RightSingularVectors, V, 0.0001));
double[,] S =
{
{ 14.2690954992615, 0.000000000000000 },
{ 0.0000000000000, 0.626828232417543 },
};
// The diagonal values should be equal
Assert.IsTrue(Matrix.IsEqual(target.Diagonal, Matrix.Diagonal(S), 0.001));
}示例7: SingularValueDecompositionConstructorTest1
public void SingularValueDecompositionConstructorTest1()
{
// This test catches the bug in SingularValueDecomposition in the line
// for (int j = k + 1; j < nu; j++)
// where it should be
// for (int j = k + 1; j < n; j++)
// Test for m-x-n matrices where m < n. The available SVD
// routine was not meant to be used in this case.
double[,] value = new double[,]
{
{ 1, 2 },
{ 3, 4 },
{ 5, 6 },
{ 7, 8 }
}.Transpose(); // value is 2x4, having less rows than columns.
var target = new SingularValueDecomposition(value, true, true, false);
double[,] actual = target.LeftSingularVectors.Multiply(
target.DiagonalMatrix).Multiply(target.RightSingularVectors.Transpose());
// Checking the decomposition
Assert.IsTrue(Matrix.IsEqual(actual, value, 1e-2));
Assert.IsTrue(Matrix.IsEqual(target.Reverse(), value, 1e-2));
// Checking values
double[,] U =
{
{ -0.641423027995072, -0.767187395072177 },
{ -0.767187395072177, 0.641423027995072 },
};
// U should be equal
Assert.IsTrue(Matrix.IsEqual(target.LeftSingularVectors, U, 0.001));
double[,] V = // economy svd
{
{ -0.152483233310201, 0.822647472225661, },
{ -0.349918371807964, 0.421375287684580, },
{ -0.547353510305727, 0.0201031031435023, },
{ -0.744788648803490, -0.381169081397574, },
};
// V can be different, but for the economy SVD it is often equal
Assert.IsTrue(Matrix.IsEqual(target.RightSingularVectors.Submatrix(0, 3, 0, 1), V, 0.0001));
double[,] S =
{
{ 14.2690954992615, 0.000000000000000 },
{ 0.0000000000000, 0.626828232417543 },
};
// The diagonal values should be equal
Assert.IsTrue(Matrix.IsEqual(target.Diagonal.First(2),
Matrix.Diagonal(S), 0.001));
}