BRDFLafortuneFitting.Program.ComputeSummedDifferences C# (CSharp) Method

        private static double ComputeSummedDifferences( BRDFSample[] _Samples, double _Normalizer, double[] _GoalBDRF, CosineLobe[] _LobeEstimates )
        {
            // Sum differences between current ZH estimates and current SH goal estimates
            double	SumSquareDifference = 0.0;
            double	GoalValue, CurrentValue, TempLobeDot;

            int		SamplesCount = _Samples.Length;
            int		LobesCount = _LobeEstimates.Length;

            for ( int SampleIndex=0; SampleIndex < SamplesCount; SampleIndex++ )
            {
                BRDFSample	Sample = _Samples[SampleIndex];

                // Get BRDF value for that sample
                GoalValue = _GoalBDRF[Sample.m_BRDFIndex];
            // 				if ( GoalValue < 0.0 )
            // 					throw new Exception( "Unexpected negative value!" );
                if ( double.IsNaN( GoalValue ) )
                    throw new Exception( "Unexpected NaN!" );

                // DEBUG => Multiply by cos(ThetaIn) to test if it gets us a better conditioning
             				GoalValue *= Sample.m_CosThetaIn;

                // Estimate cosine lobe value in that direction
                CurrentValue = 0.0;
                for ( int LobeIndex=0; LobeIndex < LobesCount; LobeIndex++ )
                {
                    CosineLobe	Lobe = _LobeEstimates[LobeIndex];
                    TempLobeDot = Lobe.C.x * Sample.m_DotProduct.x + Lobe.C.y * Sample.m_DotProduct.y + Lobe.C.z * Sample.m_DotProduct.z;
                    TempLobeDot = Math.Max( 0.0, TempLobeDot );
                    TempLobeDot = Math.Pow( TempLobeDot, Lobe.N );
                    CurrentValue += TempLobeDot;
                }

                // Sum difference between estimate and goal
                SumSquareDifference += (CurrentValue - GoalValue) * (CurrentValue - GoalValue);
            //				SumSquareDifference += Math.Abs( CurrentValue - GoalValue );	// Just to avoid super large numbers!
            }

            // Normalize
            SumSquareDifference *= _Normalizer;

            return	SumSquareDifference;
        }