latin_hypercube_inline.h

/*
 * Copyright (c) 2010-2011, NVIDIA Corporation
 * All rights reserved.
 *
 * Redistribution and use in source and binary forms, with or without
 * modification, are permitted provided that the following conditions are met:
 *   * Redistributions of source code must retain the above copyright
 *     notice, this list of conditions and the following disclaimer.
 *   * Redistributions in binary form must reproduce the above copyright
 *     notice, this list of conditions and the following disclaimer in the
 *     documentation and/or other materials provided with the distribution.
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 *     names of its contributors may be used to endorse or promote products
 *     derived from this software without specific prior written permission.
 *
 * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
 * ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
 * WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
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 */

namespace cugar {

// get a set of stratified samples
template <typename T, uint32 DIM>
void LHSampler::sample(
    const uint32    num_samples,
    Vector<T, DIM>* samples)
{
    const T inv = T(1.0) / T(num_samples);

    for (uint32 i = 0; i < num_samples; ++i)
    {
        for (uint32 d = 0; d < DIM; ++d)
            samples[i][d] = (T(i) + m_random.next()) * inv;
    }

    for (uint32 d = 0; d < DIM; ++d)
    {
        for (uint32 i = 0; i < num_samples; i++)
        {
            const float r = m_random.next();
            const uint32 j = min(uint32(float(r) * (num_samples - i)) + i, num_samples - 1u);
            std::swap(samples[i][d], samples[j][d]);
        }
    }
}

// get a set of stratified samples
template <bool SOA, typename T>
void LHSampler::sample(
    const uint32    num_samples,
    const uint32    num_dims,
    T*              samples)
{
    const T inv = T(1.0) / T(num_samples);

    for (uint32 i = 0; i < num_samples; ++i)
    {
        for (uint32 d = 0; d < num_dims; ++d)
            samples[SOA ? i + d*num_samples : i * num_dims + d] = (T(i) + m_random.next()) * inv;
    }

    for (uint32 d = 0; d < num_dims; ++d)
    {
        for (uint32 i = 0; i < num_samples; i++)
        {
            const float r = m_random.next();
            const uint32 j = min(uint32(float(r) * (num_samples - i)) + i, num_samples - 1u);
            std::swap(samples[SOA ? i + d*num_samples : i * num_dims + d], samples[SOA ? j + d*num_samples : j * num_dims + d]);
        }
    }
}

} // namespace cugar