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diff --git a/thirdparty/squish/maths.cpp b/thirdparty/squish/maths.cpp
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+/* -----------------------------------------------------------------------------
+
+	Copyright (c) 2006 Simon Brown                          si@sjbrown.co.uk
+
+	Permission is hereby granted, free of charge, to any person obtaining
+	a copy of this software and associated documentation files (the 
+	"Software"), to	deal in the Software without restriction, including
+	without limitation the rights to use, copy, modify, merge, publish,
+	distribute, sublicense, and/or sell copies of the Software, and to 
+	permit persons to whom the Software is furnished to do so, subject to 
+	the following conditions:
+
+	The above copyright notice and this permission notice shall be included
+	in all copies or substantial portions of the Software.
+
+	THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS
+	OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF 
+	MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.
+	IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY 
+	CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, 
+	TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE 
+	SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
+	
+   -------------------------------------------------------------------------- */
+   
+/*! @file
+
+	The symmetric eigensystem solver algorithm is from 
+	http://www.geometrictools.com/Documentation/EigenSymmetric3x3.pdf
+*/
+
+#include "maths.h"
+#include <cfloat>
+
+namespace squish {
+
+Sym3x3 ComputeWeightedCovariance( int n, Vec3 const* points, float const* weights )
+{
+	// compute the centroid
+	float total = 0.0f;
+	Vec3 centroid( 0.0f );
+	for( int i = 0; i < n; ++i )
+	{
+		total += weights[i];
+		centroid += weights[i]*points[i];
+	}
+	centroid /= total;
+
+	// accumulate the covariance matrix
+	Sym3x3 covariance( 0.0f );
+	for( int i = 0; i < n; ++i )
+	{
+		Vec3 a = points[i] - centroid;
+		Vec3 b = weights[i]*a;
+		
+		covariance[0] += a.X()*b.X();
+		covariance[1] += a.X()*b.Y();
+		covariance[2] += a.X()*b.Z();
+		covariance[3] += a.Y()*b.Y();
+		covariance[4] += a.Y()*b.Z();
+		covariance[5] += a.Z()*b.Z();
+	}
+	
+	// return it
+	return covariance;
+}
+
+static Vec3 GetMultiplicity1Evector( Sym3x3 const& matrix, float evalue )
+{
+	// compute M
+	Sym3x3 m;
+	m[0] = matrix[0] - evalue;
+	m[1] = matrix[1];
+	m[2] = matrix[2];
+	m[3] = matrix[3] - evalue;
+	m[4] = matrix[4];
+	m[5] = matrix[5] - evalue;
+
+	// compute U
+	Sym3x3 u;
+	u[0] = m[3]*m[5] - m[4]*m[4];
+	u[1] = m[2]*m[4] - m[1]*m[5];
+	u[2] = m[1]*m[4] - m[2]*m[3];
+	u[3] = m[0]*m[5] - m[2]*m[2];
+	u[4] = m[1]*m[2] - m[4]*m[0];
+	u[5] = m[0]*m[3] - m[1]*m[1];
+
+	// find the largest component
+	float mc = std::fabs( u[0] );
+	int mi = 0;
+	for( int i = 1; i < 6; ++i )
+	{
+		float c = std::fabs( u[i] );
+		if( c > mc )
+		{
+			mc = c;
+			mi = i;
+		}
+	}
+
+	// pick the column with this component
+	switch( mi )
+	{
+	case 0:
+		return Vec3( u[0], u[1], u[2] );
+
+	case 1:
+	case 3:
+		return Vec3( u[1], u[3], u[4] );
+
+	default:
+		return Vec3( u[2], u[4], u[5] );
+	}
+}
+
+static Vec3 GetMultiplicity2Evector( Sym3x3 const& matrix, float evalue )
+{
+	// compute M
+	Sym3x3 m;
+	m[0] = matrix[0] - evalue;
+	m[1] = matrix[1];
+	m[2] = matrix[2];
+	m[3] = matrix[3] - evalue;
+	m[4] = matrix[4];
+	m[5] = matrix[5] - evalue;
+
+	// find the largest component
+	float mc = std::fabs( m[0] );
+	int mi = 0;
+	for( int i = 1; i < 6; ++i )
+	{
+		float c = std::fabs( m[i] );
+		if( c > mc )
+		{
+			mc = c;
+			mi = i;
+		}
+	}
+
+	// pick the first eigenvector based on this index
+	switch( mi )
+	{
+	case 0:
+	case 1:
+		return Vec3( -m[1], m[0], 0.0f );
+
+	case 2:
+		return Vec3( m[2], 0.0f, -m[0] );
+
+	case 3:
+	case 4:
+		return Vec3( 0.0f, -m[4], m[3] );
+
+	default:
+		return Vec3( 0.0f, -m[5], m[4] );
+	}
+}
+
+Vec3 ComputePrincipleComponent( Sym3x3 const& matrix )
+{
+	// compute the cubic coefficients
+	float c0 = matrix[0]*matrix[3]*matrix[5] 
+		+ 2.0f*matrix[1]*matrix[2]*matrix[4] 
+		- matrix[0]*matrix[4]*matrix[4] 
+		- matrix[3]*matrix[2]*matrix[2] 
+		- matrix[5]*matrix[1]*matrix[1];
+	float c1 = matrix[0]*matrix[3] + matrix[0]*matrix[5] + matrix[3]*matrix[5]
+		- matrix[1]*matrix[1] - matrix[2]*matrix[2] - matrix[4]*matrix[4];
+	float c2 = matrix[0] + matrix[3] + matrix[5];
+
+	// compute the quadratic coefficients
+	float a = c1 - ( 1.0f/3.0f )*c2*c2;
+	float b = ( -2.0f/27.0f )*c2*c2*c2 + ( 1.0f/3.0f )*c1*c2 - c0;
+
+	// compute the root count check
+	float Q = 0.25f*b*b + ( 1.0f/27.0f )*a*a*a;
+
+	// test the multiplicity
+	if( FLT_EPSILON < Q )
+	{
+		// only one root, which implies we have a multiple of the identity
+        return Vec3( 1.0f );
+	}
+	else if( Q < -FLT_EPSILON )
+	{
+		// three distinct roots
+		float theta = std::atan2( std::sqrt( -Q ), -0.5f*b );
+		float rho = std::sqrt( 0.25f*b*b - Q );
+
+		float rt = std::pow( rho, 1.0f/3.0f );
+		float ct = std::cos( theta/3.0f );
+		float st = std::sin( theta/3.0f );
+
+		float l1 = ( 1.0f/3.0f )*c2 + 2.0f*rt*ct;
+		float l2 = ( 1.0f/3.0f )*c2 - rt*( ct + ( float )sqrt( 3.0f )*st );
+		float l3 = ( 1.0f/3.0f )*c2 - rt*( ct - ( float )sqrt( 3.0f )*st );
+
+		// pick the larger
+		if( std::fabs( l2 ) > std::fabs( l1 ) )
+			l1 = l2;
+		if( std::fabs( l3 ) > std::fabs( l1 ) )
+			l1 = l3;
+
+		// get the eigenvector
+		return GetMultiplicity1Evector( matrix, l1 );
+	}
+	else // if( -FLT_EPSILON <= Q && Q <= FLT_EPSILON )
+	{
+		// two roots
+		float rt;
+		if( b < 0.0f )
+			rt = -std::pow( -0.5f*b, 1.0f/3.0f );
+		else
+			rt = std::pow( 0.5f*b, 1.0f/3.0f );
+		
+		float l1 = ( 1.0f/3.0f )*c2 + rt;		// repeated
+		float l2 = ( 1.0f/3.0f )*c2 - 2.0f*rt;
+		
+		// get the eigenvector
+		if( std::fabs( l1 ) > std::fabs( l2 ) )
+			return GetMultiplicity2Evector( matrix, l1 );
+		else
+			return GetMultiplicity1Evector( matrix, l2 );
+	}
+}
+
+} // namespace squish