Praxis3D/Dependencies/include/glm/gtx/pca.inl

/// @ref gtx_pca

#ifndef GLM_HAS_CXX11_STL
#include <algorithm>
#else
#include <utility>
#endif

namespace glm {


	template<length_t D, typename T, qualifier Q>
	GLM_INLINE mat<D, D, T, Q> computeCovarianceMatrix(vec<D, T, Q> const* v, size_t n)
	{
		return computeCovarianceMatrix<D, T, Q, vec<D, T, Q> const*>(v, v + n);
	}


	template<length_t D, typename T, qualifier Q>
	GLM_INLINE mat<D, D, T, Q> computeCovarianceMatrix(vec<D, T, Q> const* v, size_t n, vec<D, T, Q> const& c)
	{
		return computeCovarianceMatrix<D, T, Q, vec<D, T, Q> const*>(v, v + n, c);
	}


	template<length_t D, typename T, qualifier Q, typename I>
	GLM_FUNC_DECL mat<D, D, T, Q> computeCovarianceMatrix(I const& b, I const& e)
	{
		glm::mat<D, D, T, Q> m(0);

		size_t cnt = 0;
		for(I i = b; i != e; i++)
		{
			vec<D, T, Q> const& v = *i;
			for(length_t x = 0; x < D; ++x)
				for(length_t y = 0; y < D; ++y)
					m[x][y] += static_cast<T>(v[x] * v[y]);
			cnt++;
		}
		if(cnt > 0)
			m /= static_cast<T>(cnt);

		return m;
	}


	template<length_t D, typename T, qualifier Q, typename I>
	GLM_FUNC_DECL mat<D, D, T, Q> computeCovarianceMatrix(I const& b, I const& e, vec<D, T, Q> const& c)
	{
		glm::mat<D, D, T, Q> m(0);
		glm::vec<D, T, Q> v;

		size_t cnt = 0;
		for(I i = b; i != e; i++)
		{
			v = *i - c;
			for(length_t x = 0; x < D; ++x)
				for(length_t y = 0; y < D; ++y)
					m[x][y] += static_cast<T>(v[x] * v[y]);
			cnt++;
		}
		if(cnt > 0)
			m /= static_cast<T>(cnt);

		return m;
	}

	namespace _internal_
	{

		template<typename T>
		GLM_INLINE T transferSign(T const& v, T const& s)
		{
			return ((s) >= 0 ? glm::abs(v) : -glm::abs(v));
		}

		template<typename T>
		GLM_INLINE T pythag(T const& a, T const& b) {
			static const T epsilon = static_cast<T>(0.0000001);
			T absa = glm::abs(a);
			T absb = glm::abs(b);
			if(absa > absb) {
				absb /= absa;
				absb *= absb;
				return absa * glm::sqrt(static_cast<T>(1) + absb);
			}
			if(glm::equal<T>(absb, 0, epsilon)) return static_cast<T>(0);
			absa /= absb;
			absa *= absa;
			return absb * glm::sqrt(static_cast<T>(1) + absa);
		}

	}

	template<length_t D, typename T, qualifier Q>
	GLM_FUNC_DECL unsigned int findEigenvaluesSymReal
	(
		mat<D, D, T, Q> const& covarMat,
		vec<D, T, Q>& outEigenvalues,
		mat<D, D, T, Q>& outEigenvectors
	)
	{
		using _internal_::transferSign;
		using _internal_::pythag;

		T a[D * D]; // matrix -- input and workspace for algorithm (will be changed inplace)
		T d[D]; // diagonal elements
		T e[D]; // off-diagonal elements

		for(length_t r = 0; r < D; r++)
			for(length_t c = 0; c < D; c++)
				a[(r) * D + (c)] = covarMat[c][r];

		// 1. Householder reduction.
		length_t l, k, j, i;
		T scale, hh, h, g, f;
		static const T epsilon = static_cast<T>(0.0000001);

		for(i = D; i >= 2; i--)
		{
			l = i - 1;
			h = scale = 0;
			if(l > 1)
			{
				for(k = 1; k <= l; k++)
				{
					scale += glm::abs(a[(i - 1) * D + (k - 1)]);
				}
				if(glm::equal<T>(scale, 0, epsilon))
				{
					e[i - 1] = a[(i - 1) * D + (l - 1)];
				}
				else
				{
					for(k = 1; k <= l; k++)
					{
						a[(i - 1) * D + (k - 1)] /= scale;
						h += a[(i - 1) * D + (k - 1)] * a[(i - 1) * D + (k - 1)];
					}
					f = a[(i - 1) * D + (l - 1)];
					g = ((f >= 0) ? -glm::sqrt(h) : glm::sqrt(h));
					e[i - 1] = scale * g;
					h -= f * g;
					a[(i - 1) * D + (l - 1)] = f - g;
					f = 0;
					for(j = 1; j <= l; j++)
					{
						a[(j - 1) * D + (i - 1)] = a[(i - 1) * D + (j - 1)] / h;
						g = 0;
						for(k = 1; k <= j; k++)
						{
							g += a[(j - 1) * D + (k - 1)] * a[(i - 1) * D + (k - 1)];
						}
						for(k = j + 1; k <= l; k++)
						{
							g += a[(k - 1) * D + (j - 1)] * a[(i - 1) * D + (k - 1)];
						}
						e[j - 1] = g / h;
						f += e[j - 1] * a[(i - 1) * D + (j - 1)];
					}
					hh = f / (h + h);
					for(j = 1; j <= l; j++)
					{
						f = a[(i - 1) * D + (j - 1)];
						e[j - 1] = g = e[j - 1] - hh * f;
						for(k = 1; k <= j; k++)
						{
							a[(j - 1) * D + (k - 1)] -= (f * e[k - 1] + g * a[(i - 1) * D + (k - 1)]);
						}
					}
				}
			}
			else
			{
				e[i - 1] = a[(i - 1) * D + (l - 1)];
			}
			d[i - 1] = h;
		}
		d[0] = 0;
		e[0] = 0;
		for(i = 1; i <= D; i++)
		{
			l = i - 1;
			if(!glm::equal<T>(d[i - 1], 0, epsilon))
			{
				for(j = 1; j <= l; j++)
				{
					g = 0;
					for(k = 1; k <= l; k++)
					{
						g += a[(i - 1) * D + (k - 1)] * a[(k - 1) * D + (j - 1)];
					}
					for(k = 1; k <= l; k++)
					{
						a[(k - 1) * D + (j - 1)] -= g * a[(k - 1) * D + (i - 1)];
					}
				}
			}
			d[i - 1] = a[(i - 1) * D + (i - 1)];
			a[(i - 1) * D + (i - 1)] = 1;
			for(j = 1; j <= l; j++)
			{
				a[(j - 1) * D + (i - 1)] = a[(i - 1) * D + (j - 1)] = 0;
			}
		}

		// 2. Calculation of eigenvalues and eigenvectors (QL algorithm)
		length_t m, iter;
		T s, r, p, dd, c, b;
		const length_t MAX_ITER = 30;

		for(i = 2; i <= D; i++)
		{
			e[i - 2] = e[i - 1];
		}
		e[D - 1] = 0;

		for(l = 1; l <= D; l++)
		{
			iter = 0;
			do
			{
				for(m = l; m <= D - 1; m++)
				{
					dd = glm::abs(d[m - 1]) + glm::abs(d[m - 1 + 1]);
					if(glm::equal<T>(glm::abs(e[m - 1]) + dd, dd, epsilon))
						break;
				}
				if(m != l)
				{
					if(iter++ == MAX_ITER)
					{
						return 0; // Too many iterations in FindEigenvalues
					}
					g = (d[l - 1 + 1] - d[l - 1]) / (2 * e[l - 1]);
					r = pythag<T>(g, 1);
					g = d[m - 1] - d[l - 1] + e[l - 1] / (g + transferSign(r, g));
					s = c = 1;
					p = 0;
					for(i = m - 1; i >= l; i--)
					{
						f = s * e[i - 1];
						b = c * e[i - 1];
						e[i - 1 + 1] = r = pythag(f, g);
						if(glm::equal<T>(r, 0, epsilon))
						{
							d[i - 1 + 1] -= p;
							e[m - 1] = 0;
							break;
						}
						s = f / r;
						c = g / r;
						g = d[i - 1 + 1] - p;
						r = (d[i - 1] - g) * s + 2 * c * b;
						d[i - 1 + 1] = g + (p = s * r);
						g = c * r - b;
						for(k = 1; k <= D; k++)
						{
							f = a[(k - 1) * D + (i - 1 + 1)];
							a[(k - 1) * D + (i - 1 + 1)] = s * a[(k - 1) * D + (i - 1)] + c * f;
							a[(k - 1) * D + (i - 1)] = c * a[(k - 1) * D + (i - 1)] - s * f;
						}
					}
					if(glm::equal<T>(r, 0, epsilon) && (i >= l))
						continue;
					d[l - 1] -= p;
					e[l - 1] = g;
					e[m - 1] = 0;
				}
			} while(m != l);
		}

		// 3. output
		for(i = 0; i < D; i++)
			outEigenvalues[i] = d[i];
		for(i = 0; i < D; i++)
			for(j = 0; j < D; j++)
				outEigenvectors[i][j] = a[(j) * D + (i)];

		return D;
	}

	template<typename T, qualifier Q>
	GLM_INLINE void sortEigenvalues(vec<2, T, Q>& eigenvalues, mat<2, 2, T, Q>& eigenvectors)
	{
		if (eigenvalues[0] < eigenvalues[1])
		{
			std::swap(eigenvalues[0], eigenvalues[1]);
			std::swap(eigenvectors[0], eigenvectors[1]);
		}
	}

	template<typename T, qualifier Q>
	GLM_INLINE void sortEigenvalues(vec<3, T, Q>& eigenvalues, mat<3, 3, T, Q>& eigenvectors)
	{
		if (eigenvalues[0] < eigenvalues[1])
		{
			std::swap(eigenvalues[0], eigenvalues[1]);
			std::swap(eigenvectors[0], eigenvectors[1]);
		}
		if (eigenvalues[0] < eigenvalues[2])
		{
			std::swap(eigenvalues[0], eigenvalues[2]);
			std::swap(eigenvectors[0], eigenvectors[2]);
		}
		if (eigenvalues[1] < eigenvalues[2])
		{
			std::swap(eigenvalues[1], eigenvalues[2]);
			std::swap(eigenvectors[1], eigenvectors[2]);
		}
	}

	template<typename T, qualifier Q>
	GLM_INLINE void sortEigenvalues(vec<4, T, Q>& eigenvalues, mat<4, 4, T, Q>& eigenvectors)
	{
		if (eigenvalues[0] < eigenvalues[2])
		{
			std::swap(eigenvalues[0], eigenvalues[2]);
			std::swap(eigenvectors[0], eigenvectors[2]);
		}
		if (eigenvalues[1] < eigenvalues[3])
		{
			std::swap(eigenvalues[1], eigenvalues[3]);
			std::swap(eigenvectors[1], eigenvectors[3]);
		}
		if (eigenvalues[0] < eigenvalues[1])
		{
			std::swap(eigenvalues[0], eigenvalues[1]);
			std::swap(eigenvectors[0], eigenvectors[1]);
		}
		if (eigenvalues[2] < eigenvalues[3])
		{
			std::swap(eigenvalues[2], eigenvalues[3]);
			std::swap(eigenvectors[2], eigenvectors[3]);
		}
		if (eigenvalues[1] < eigenvalues[2])
		{
			std::swap(eigenvalues[1], eigenvalues[2]);
			std::swap(eigenvectors[1], eigenvectors[2]);
		}
	}

}//namespace glm