// Copyright NVIDIA Corporation 2007 -- Ignacio Castano <icastano@nvidia.com>
// 
// 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.

// Math functions and operators to be used with vector types.

#ifndef CUDAMATH_H
#define CUDAMATH_H

#include <float.h>


inline __device__ __host__ float3 operator *(float3 a, float3 b)
{
    return make_float3(a.x*b.x, a.y*b.y, a.z*b.z);
}

inline __device__ __host__ float3 operator *(float f, float3 v)
{
    return make_float3(v.x*f, v.y*f, v.z*f);
}

inline __device__ __host__ float3 operator *(float3 v, float f)
{
    return make_float3(v.x*f, v.y*f, v.z*f);
}

inline __device__ __host__ float3 operator +(float3 a, float3 b)
{
    return make_float3(a.x+b.x, a.y+b.y, a.z+b.z);
}

inline __device__ __host__ void operator +=(float3 & b, float3 a)
{
    b.x += a.x;
    b.y += a.y;
    b.z += a.z;
}

inline __device__ __host__ float3 operator -(float3 a, float3 b)
{
    return make_float3(a.x-b.x, a.y-b.y, a.z-b.z);
}

inline __device__ __host__ void operator -=(float3 & b, float3 a)
{
    b.x -= a.x;
    b.y -= a.y;
    b.z -= a.z;
}

inline __device__ __host__ float3 operator /(float3 v, float f)
{
    float inv = 1.0f / f;
    return v * inv;
}

inline __device__ __host__ void operator /=(float3 & b, float f)
{
    float inv = 1.0f / f;
    b.x *= inv;
    b.y *= inv;
    b.z *= inv;
}

inline __device__ __host__ bool operator ==(float3 a, float3 b)
{
	return a.x == b.x && a.y == b.y && a.z == b.z;
}

inline __device__ __host__ float dot(float3 a, float3 b)
{
    return a.x * b.x + a.y * b.y + a.z * b.z;
}

inline __device__ __host__ float dot(float4 a, float4 b)
{
    return a.x * b.x + a.y * b.y + a.z * b.z + a.w * b.w;
}

inline __device__ __host__ float clamp(float f, float a, float b)
{
    return max(a, min(f, b));
}

inline __device__ __host__ float3 clamp(float3 v, float a, float b)
{
    return make_float3(clamp(v.x, a, b), clamp(v.y, a, b), clamp(v.z, a, b));
}

inline __device__ __host__ float3 clamp(float3 v, float3 a, float3 b)
{
    return make_float3(clamp(v.x, a.x, b.x), clamp(v.y, a.y, b.y), clamp(v.z, a.z, b.z));
}


inline __device__ __host__ float3 normalize(float3 v)
{
    float len = 1.0f / sqrtf(dot(v, v));
    return make_float3(v.x * len, v.y * len, v.z * len);
}




// Use power method to find the first eigenvector.
// http://www.miislita.com/information-retrieval-tutorial/matrix-tutorial-3-eigenvalues-eigenvectors.html
inline __device__ __host__ float3 firstEigenVector( float matrix[6] )
{
	// 8 iterations seems to be more than enough.

	float3 v = make_float3(1.0f, 1.0f, 1.0f);
	for(int i = 0; i < 8; i++) {
		float x = v.x * matrix[0] + v.y * matrix[1] + v.z * matrix[2];
		float y = v.x * matrix[1] + v.y * matrix[3] + v.z * matrix[4];
		float z = v.x * matrix[2] + v.y * matrix[4] + v.z * matrix[5];
		float m = max(max(x, y), z);        
		float iv = 1.0f / m;
		if (m == 0.0f) iv = 0.0f;
		v = make_float3(x*iv, y*iv, z*iv);
	}

	return v;
}

inline __device__ bool singleColor(const float3 * colors)
{
#if __DEVICE_EMULATION__
	bool sameColor = false;
	for (int i = 0; i < 16; i++)
	{
		sameColor &= (colors[idx] == colors[0]);
	}
	return sameColor;
#else
	__shared__ int sameColor[16];
	
	const int idx = threadIdx.x;
	
	sameColor[idx] = (colors[idx] == colors[0]);
	sameColor[idx] &= sameColor[idx^8];
	sameColor[idx] &= sameColor[idx^4];
	sameColor[idx] &= sameColor[idx^2];
	sameColor[idx] &= sameColor[idx^1];
	
	return sameColor[0];
#endif
}

inline __device__ void colorSums(const float3 * colors, float3 * sums)
{
#if __DEVICE_EMULATION__
	float3 color_sum = make_float3(0.0f, 0.0f, 0.0f);
	for (int i = 0; i < 16; i++)
	{
		color_sum += colors[i];
	}

	for (int i = 0; i < 16; i++)
	{
		sums[i] = color_sum;
	}
#else

	const int idx = threadIdx.x;

	sums[idx] = colors[idx];
	sums[idx] += sums[idx^8];
	sums[idx] += sums[idx^4];
	sums[idx] += sums[idx^2];
	sums[idx] += sums[idx^1];

#endif
}

inline __device__ float3 bestFitLine(const float3 * colors, float3 color_sum, float3 colorMetric)
{
	// Compute covariance matrix of the given colors.
#if __DEVICE_EMULATION__
	float covariance[6] = {0, 0, 0, 0, 0, 0};
	for (int i = 0; i < 16; i++)
	{
		float3 a = (colors[i] - color_sum * (1.0f / 16.0f)) * colorMetric;
		covariance[0] += a.x * a.x;
		covariance[1] += a.x * a.y;
		covariance[2] += a.x * a.z;
		covariance[3] += a.y * a.y;
		covariance[4] += a.y * a.z;
		covariance[5] += a.z * a.z;
	}
#else

	const int idx = threadIdx.x;

	float3 diff = (colors[idx] - color_sum * (1.0f / 16.0f)) * colorMetric;

	// @@ Eliminate two-way bank conflicts here.
	// @@ It seems that doing that and unrolling the reduction doesn't help...
	__shared__ float covariance[16*6];

	covariance[6 * idx + 0] = diff.x * diff.x;    // 0, 6, 12, 2, 8, 14, 4, 10, 0
	covariance[6 * idx + 1] = diff.x * diff.y;
	covariance[6 * idx + 2] = diff.x * diff.z;
	covariance[6 * idx + 3] = diff.y * diff.y;
	covariance[6 * idx + 4] = diff.y * diff.z;
	covariance[6 * idx + 5] = diff.z * diff.z;

	for(int d = 8; d > 0; d >>= 1)
	{
		if (idx < d)
		{
			covariance[6 * idx + 0] += covariance[6 * (idx+d) + 0];
			covariance[6 * idx + 1] += covariance[6 * (idx+d) + 1];
			covariance[6 * idx + 2] += covariance[6 * (idx+d) + 2];
			covariance[6 * idx + 3] += covariance[6 * (idx+d) + 3];
			covariance[6 * idx + 4] += covariance[6 * (idx+d) + 4];
			covariance[6 * idx + 5] += covariance[6 * (idx+d) + 5];
		}
	}

#endif

	// Compute first eigen vector.
	return firstEigenVector(covariance);
}


#endif // CUDAMATH_H