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A first general restructuration of the doc according to the pattern [tour|tutorial|reference]. In the reference part, objects are documented per topic. In each topic, [definition|c++|python|hdf5] (not yet implemented)
97 lines
2.9 KiB
ReStructuredText
97 lines
2.9 KiB
ReStructuredText
.. highlight:: c
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.. _random:
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Random number generator
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-----------------------
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Generic use
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***********
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TRIQS comes with a set of random number generators implemented in the class
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``random_generator``. Here's an example illustrating the
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use of the generator::
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#include <iostream>
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#include <triqs/mc_tools/random_generator.hpp>
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int main() {
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// Construct a random number generator
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triqs::mc_tools::random_generator RNG("mt19937", 23432);
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// Generate double numbers in [0,1[
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for (size_t i = 0; i < 100; i++) std::cout << RNG() << " ";
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std::cout << std::endl << std::endl;
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// Generate double numbers in [0,5[
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for (size_t i = 0; i < 100; i++) std::cout << RNG(5.0) << " ";
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std::cout << std::endl << std::endl;
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// Generate int numbers in [0,5[
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for (size_t i = 0; i < 100; i++) std::cout << RNG(5) << " ";
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std::cout << std::endl << std::endl;
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return 0;
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}
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After having included the header
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:file:`<triqs/mc_tools/random_generator.hpp>` one constructs a
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random generator with::
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triqs::mc_tools::random_generator RNG("mt19937", 23432);
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The first argument is a string which allows you to select the generator. Here
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mt19937 stands for a version of a Mersenne Twister. The second argument is
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the random seed.
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Now that you have an instance of a generator ``RNG``, you can generate
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either ``double`` numbers or ``int`` numbers. If ``RNG()`` is just called
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with parenthesis, it produces numbers uniformly distributed on the
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interval :math:`[0,1[`. If there is a ``double`` argument ``D``, then
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numbers are produced uniformly on :math:`[0,D]`. Finally, if there is
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a ``int`` argument ``I``, integer numbers are generated on :math:`[0,I[`.
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Getting a list of random number generators
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******************************************
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In order to have a list of all the strings describing the available random
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generators, you can call the static method ``random_generator_names``. For
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example::
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#include <iostream>
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#include <triqs/mc_tools/random_generator.hpp>
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int main() {
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std::cout << "A list of all random generators" << std::endl << std::endl;
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std::cout << triqs::mc_tools::random_generator::random_generator_names() << std::endl;
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return 0;
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}
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Accessing the random number generator from the Monte Carlo class
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****************************************************************
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When you construct an instance of a Monte Carlo class ``mc_generic``, this
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instance automatically has an access to a random number generator.
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Imagine you constructed an instance::
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triqs::mc_tools::mc_generic<double> SpinMC(n_cycles, length_cycle, n_warmup_cycles,
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random_name, random_seed, verbosity);
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Now, you can use ``SpinMC`` to have a random number generator::
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triqs::mc_tools::random_generator RNG = SpinMC.rng();
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Typically, this generator will be passed as a argument to your
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implementation of a move::
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SpinMC.add_move(mymove(config, SpinMC.rng(), ...), "my move")
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