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POMersenne twister warm up vs. reproducibility
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copied!<p>In my current C++11 project I need to perform M simulations. For each simulation <code>m = 1, ..., M</code>, I randomly generate a data set by using a <code>std::mt19937</code> object, constructed as follows:</p> <pre><code>std::mt19937 generator(m); DatasetFactory dsf(generator); </code></pre> <p>According to <a href="https://stackoverflow.com/a/15509942/1849221">https://stackoverflow.com/a/15509942/1849221</a> and <a href="https://stackoverflow.com/a/14924350/1849221">https://stackoverflow.com/a/14924350/1849221</a>, the Mersenne Twister PRNG benefits from a warm up phase, which is currently absent in my code. I report for convenience the proposed snippet of code:</p> <pre><code>#include <random> std::mt19937 get_prng() { std::uint_least32_t seed_data[std::mt19937::state_size]; std::random_device r; std::generate_n(seed_data, std::mt19937::state_size, std::ref(r)); std::seed_seq q(std::begin(seed_data), std::end(seed_data)); return std::mt19937{q}; } </code></pre> <p>The problem in my case is that I need reproducibility of results, i.e., among different executions, for each simulation, the data set has to be the same. That's the reason why in my current solution I use the current simulation to seed the Mersenne Twister PRNG. It seems to me that the usage of <code>std::random_device</code> prevents data from being the same (AFAIK, this is the exact purpose of <code>std::random_device</code>). </p> <p><strong>EDIT:</strong> by <em>different executions</em> I mean re-launching the executable.</p> <p>How can I introduce the afore-mentioned warm up phase in my code without affecting reproducibility? Thanks.</p> <h2>Possible solution #1</h2> <p>Here's a tentative implementation based on the second proposal by @SteveJessop</p> <pre><code>#include <random> std::mt19937 get_generator(unsigned int seed) { std::minstd_rand0 lc_generator(seed); std::uint_least32_t seed_data[std::mt19937::state_size]; std::generate_n(seed_data, std::mt19937::state_size, std::ref(lc_generator)); std::seed_seq q(std::begin(seed_data), std::end(seed_data)); return std::mt19937{q}; } </code></pre> <h2>Possible solution #2</h2> <p>Here's a tentative implementation based on the joint contribution by @SteveJassop and @AndréNeve. The <code>sha256</code> function is adapted from <a href="https://stackoverflow.com/a/10632725/1849221">https://stackoverflow.com/a/10632725/1849221</a></p> <pre><code>#include <openssl/sha.h> #include <sstream> #include <iomanip> #include <random> std::string sha256(const std::string str) { unsigned char hash[SHA256_DIGEST_LENGTH]; SHA256_CTX sha256; SHA256_Init(&sha256); SHA256_Update(&sha256, str.c_str(), str.size()); SHA256_Final(hash, &sha256); std::stringstream ss; for(int i = 0; i < SHA256_DIGEST_LENGTH; i++) ss << std::hex << std::setw(2) << std::setfill('0') << (int)hash[i]; return ss.str(); } std::mt19937 get_generator(unsigned int seed) { std::string seed_str = sha256(std::to_string(seed)); std::seed_seq q(seed_str.begin(), seed_str.end()); return std::mt19937{q}; } </code></pre> <p>Compile with: <code>-I/opt/ssl/include/ -L/opt/ssl/lib/ -lcrypto</code> </p>

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