本文整理汇总了C#中MathNet.Numerics.LinearAlgebra.Double.DenseVector.Normalize方法的典型用法代码示例。如果您正苦于以下问题：C# DenseVector.Normalize方法的具体用法？C# DenseVector.Normalize怎么用？C# DenseVector.Normalize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MathNet.Numerics.LinearAlgebra.Double.DenseVector的用法示例。
在下文中一共展示了DenseVector.Normalize方法的3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: BhattacharyyaDistance
/// <summary>
/// The bhattacharyya distance.
/// </summary>
/// <param name="observedIsotopicPeakList">
/// The observed isotopic peak list.
/// </param>
/// <param name="expectedIsotopicPeakList">
/// The actual isotopic peak list.
/// </param>
/// <returns>
/// The <see cref="double"/>.
/// </returns>
private static double BhattacharyyaDistance(List<double> observedIsotopicPeakList, List<Peak> expectedIsotopicPeakList)
{
// calculate angle between two isotopic vectors in the isotopic space
double[] actualIsotopicPeakListArray = expectedIsotopicPeakList.Select(x => (double)x.Height).ToArray();
Vector<double> A = new DenseVector(observedIsotopicPeakList.ToArray());
Vector<double> B = new DenseVector(actualIsotopicPeakListArray);
A = A.Normalize(2);
B = B.Normalize(2);
Vector<double> C = A.PointwiseMultiply(B);
// Pointwise sqrt. Implements here because Math.Net.2.5 doesn't supports Pointwise exp getting Math.Net 3.5 introducces
// package compatibility issues with Informed Proteomics / Multidimensional peak finding, etc.
// TODO: Use PointwiseExponent after getting Math.net 3.5
double[] cArray = C.ToArray();
int size = cArray.Count();
double sum = 0;
for (int i = 0; i < size; i++)
{
cArray[i] = Math.Sqrt(cArray[i]);
sum += cArray[i];
}
return sum;
}示例2: EuclideanAlternative
/// <summary>
/// The bhattacharyya distance.
/// </summary>
/// <param name="observedIsotopicPeakList">
/// The observed isotopic peak list.
/// </param>
/// <param name="expectedIsotopicPeakList">
/// The actual isotopic peak list.
/// </param>
/// <returns>
/// The <see cref="double"/>.
/// </returns>
private static double EuclideanAlternative(List<double> observedIsotopicPeakList, List<Peak> expectedIsotopicPeakList)
{
// calculate angle between two isotopic vectors in the isotopic space
double[] expectedIsotopicPeakIntensityArray = expectedIsotopicPeakList.Select(x => (double)x.Height).ToArray();
Vector<double> A = new DenseVector(observedIsotopicPeakList.ToArray());
Vector<double> B = new DenseVector(expectedIsotopicPeakIntensityArray);
A = A.Normalize(2);
B = B.Normalize(2);
// calculate the euclidean distance between theoretical distribution and observed pattern
double isotopicScore = 0;
for (int i = 1; i < expectedIsotopicPeakList.Count; i++)
{
double diff = A[i] - B[i];
isotopicScore += diff * diff;
}
// Map the score to [0, 1]
return ScoreUtil.MapToZeroOneTrignometry(Math.Sqrt(isotopicScore), true, 0.03);
}示例3: computeTransform
private void computeTransform()
{
Func<SkeletonPoint, Vector<double>> conv = (sp) => new DenseVector(new double[] { sp.X, sp.Y, sp.Z });
p0 = conv(dBlue.Value);
var p1 = conv(dRed.Value);
var p2 = conv(dYellow.Value);
f2 = (p1 - p0).Normalize(1);
f1 = (p2 - p0).Normalize(1);
f1 = (f1 - (f1.DotProduct(f2) * f2)).Normalize(1);
f3 = new DenseVector(new double[] { f1[1] * f2[2] - f1[2] * f2[1], f1[2] * f2[0] - f1[0] * f2[2], f1[0] * f2[1] - f1[1] * f2[0] });
f3 = f3.Normalize(1);
IsValid = true;
}