From 19fcec84d8d7d21e796c7624e521b60d28ee21ed Mon Sep 17 00:00:00 2001 From: Daniel Baumann Date: Sun, 7 Apr 2024 20:45:59 +0200 Subject: Adding upstream version 16.2.11+ds. Signed-off-by: Daniel Baumann --- src/boost/libs/python/example/numpy/gaussian.cpp | 315 +++++++++++++++++++++++ 1 file changed, 315 insertions(+) create mode 100644 src/boost/libs/python/example/numpy/gaussian.cpp (limited to 'src/boost/libs/python/example/numpy/gaussian.cpp') diff --git a/src/boost/libs/python/example/numpy/gaussian.cpp b/src/boost/libs/python/example/numpy/gaussian.cpp new file mode 100644 index 000000000..5f138b397 --- /dev/null +++ b/src/boost/libs/python/example/numpy/gaussian.cpp @@ -0,0 +1,315 @@ +// Copyright Jim Bosch 2010-2012. +// Distributed under the Boost Software License, Version 1.0. +// (See accompanying file LICENSE_1_0.txt or copy at +// http://www.boost.org/LICENSE_1_0.txt) + +#include + +#include +#include + +#ifndef M_PI +#include +const double M_PI = boost::math::constants::pi(); +#endif + +namespace bp = boost::python; +namespace bn = boost::python::numpy; + +/** + * A 2x2 matrix class, purely for demonstration purposes. + * + * Instead of wrapping this class with Boost.Python, we'll convert it to/from numpy.ndarray. + */ +class matrix2 { +public: + + double & operator()(int i, int j) { + return _data[i*2 + j]; + } + + double const & operator()(int i, int j) const { + return _data[i*2 + j]; + } + + double const * data() const { return _data; } + +private: + double _data[4]; +}; + +/** + * A 2-element vector class, purely for demonstration purposes. + * + * Instead of wrapping this class with Boost.Python, we'll convert it to/from numpy.ndarray. + */ +class vector2 { +public: + + double & operator[](int i) { + return _data[i]; + } + + double const & operator[](int i) const { + return _data[i]; + } + + double const * data() const { return _data; } + + vector2 operator+(vector2 const & other) const { + vector2 r; + r[0] = _data[0] + other[0]; + r[1] = _data[1] + other[1]; + return r; + } + + vector2 operator-(vector2 const & other) const { + vector2 r; + r[0] = _data[0] - other[0]; + r[1] = _data[1] - other[1]; + return r; + } + +private: + double _data[2]; +}; + +/** + * Matrix-vector multiplication. + */ +vector2 operator*(matrix2 const & m, vector2 const & v) { + vector2 r; + r[0] = m(0, 0) * v[0] + m(0, 1) * v[1]; + r[1] = m(1, 0) * v[0] + m(1, 1) * v[1]; + return r; +} + +/** + * Vector inner product. + */ +double dot(vector2 const & v1, vector2 const & v2) { + return v1[0] * v2[0] + v1[1] * v2[1]; +} + +/** + * This class represents a simple 2-d Gaussian (Normal) distribution, defined by a + * mean vector 'mu' and a covariance matrix 'sigma'. + */ +class bivariate_gaussian { +public: + + vector2 const & get_mu() const { return _mu; } + + matrix2 const & get_sigma() const { return _sigma; } + + /** + * Evaluate the density of the distribution at a point defined by a two-element vector. + */ + double operator()(vector2 const & p) const { + vector2 u = _cholesky * (p - _mu); + return 0.5 * _cholesky(0, 0) * _cholesky(1, 1) * std::exp(-0.5 * dot(u, u)) / M_PI; + } + + /** + * Evaluate the density of the distribution at an (x, y) point. + */ + double operator()(double x, double y) const { + vector2 p; + p[0] = x; + p[1] = y; + return operator()(p); + } + + /** + * Construct from a mean vector and covariance matrix. + */ + bivariate_gaussian(vector2 const & mu, matrix2 const & sigma) + : _mu(mu), _sigma(sigma), _cholesky(compute_inverse_cholesky(sigma)) + {} + +private: + + /** + * This evaluates the inverse of the Cholesky factorization of a 2x2 matrix; + * it's just a shortcut in evaluating the density. + */ + static matrix2 compute_inverse_cholesky(matrix2 const & m) { + matrix2 l; + // First do cholesky factorization: l l^t = m + l(0, 0) = std::sqrt(m(0, 0)); + l(0, 1) = m(0, 1) / l(0, 0); + l(1, 1) = std::sqrt(m(1, 1) - l(0,1) * l(0,1)); + // Now do forward-substitution (in-place) to invert: + l(0, 0) = 1.0 / l(0, 0); + l(1, 0) = l(0, 1) = -l(0, 1) / l(1, 1); + l(1, 1) = 1.0 / l(1, 1); + return l; + } + + vector2 _mu; + matrix2 _sigma; + matrix2 _cholesky; + +}; + +/* + * We have a two options for wrapping get_mu and get_sigma into NumPy-returning Python methods: + * - we could deep-copy the data, making totally new NumPy arrays; + * - we could make NumPy arrays that point into the existing memory. + * The latter is often preferable, especially if the arrays are large, but it's dangerous unless + * the reference counting is correct: the returned NumPy array needs to hold a reference that + * keeps the memory it points to from being deallocated as long as it is alive. This is what the + * "owner" argument to from_data does - the NumPy array holds a reference to the owner, keeping it + * from being destroyed. + * + * Note that this mechanism isn't completely safe for data members that can have their internal + * storage reallocated. A std::vector, for instance, can be invalidated when it is resized, + * so holding a Python reference to a C++ class that holds a std::vector may not be a guarantee + * that the memory in the std::vector will remain valid. + */ + +/** + * These two functions are custom wrappers for get_mu and get_sigma, providing the shallow-copy + * conversion with reference counting described above. + * + * It's also worth noting that these return NumPy arrays that cannot be modified in Python; + * the const overloads of vector::data() and matrix::data() return const references, + * and passing a const pointer to from_data causes NumPy's 'writeable' flag to be set to false. + */ +static bn::ndarray py_get_mu(bp::object const & self) { + vector2 const & mu = bp::extract(self)().get_mu(); + return bn::from_data( + mu.data(), + bn::dtype::get_builtin(), + bp::make_tuple(2), + bp::make_tuple(sizeof(double)), + self + ); +} +static bn::ndarray py_get_sigma(bp::object const & self) { + matrix2 const & sigma = bp::extract(self)().get_sigma(); + return bn::from_data( + sigma.data(), + bn::dtype::get_builtin(), + bp::make_tuple(2, 2), + bp::make_tuple(2 * sizeof(double), sizeof(double)), + self + ); +} + +/** + * To allow the constructor to work, we need to define some from-Python converters from NumPy arrays + * to the matrix/vector types. The rvalue-from-python functionality is not well-documented in Boost.Python + * itself; you can learn more from boost/python/converter/rvalue_from_python_data.hpp. + */ + +/** + * We start with two functions that just copy a NumPy array into matrix/vector objects. These will be used + * in the templated converted below. The first just uses the operator[] overloads provided by + * bp::object. + */ +static void copy_ndarray_to_mv2(bn::ndarray const & array, vector2 & vec) { + vec[0] = bp::extract(array[0]); + vec[1] = bp::extract(array[1]); +} + +/** + * Here, we'll take the alternate approach of using the strides to access the array's memory directly. + * This can be much faster for large arrays. + */ +static void copy_ndarray_to_mv2(bn::ndarray const & array, matrix2 & mat) { + // Unfortunately, get_strides() can't be inlined, so it's best to call it once up-front. + Py_intptr_t const * strides = array.get_strides(); + for (int i = 0; i < 2; ++i) { + for (int j = 0; j < 2; ++j) { + mat(i, j) = *reinterpret_cast(array.get_data() + i * strides[0] + j * strides[1]); + } + } +} + +/** + * Here's the actual converter. Because we've separated the differences into the above functions, + * we can write a single template class that works for both matrix2 and vector2. + */ +template +struct mv2_from_python { + + /** + * Register the converter. + */ + mv2_from_python() { + bp::converter::registry::push_back( + &convertible, + &construct, + bp::type_id< T >() + ); + } + + /** + * Test to see if we can convert this to the desired type; if not return zero. + * If we can convert, returned pointer can be used by construct(). + */ + static void * convertible(PyObject * p) { + try { + bp::object obj(bp::handle<>(bp::borrowed(p))); + std::auto_ptr array( + new bn::ndarray( + bn::from_object(obj, bn::dtype::get_builtin(), N, N, bn::ndarray::V_CONTIGUOUS) + ) + ); + if (array->shape(0) != 2) return 0; + if (N == 2 && array->shape(1) != 2) return 0; + return array.release(); + } catch (bp::error_already_set & err) { + bp::handle_exception(); + return 0; + } + } + + /** + * Finish the conversion by initializing the C++ object into memory prepared by Boost.Python. + */ + static void construct(PyObject * obj, bp::converter::rvalue_from_python_stage1_data * data) { + // Extract the array we passed out of the convertible() member function. + std::auto_ptr array(reinterpret_cast(data->convertible)); + // Find the memory block Boost.Python has prepared for the result. + typedef bp::converter::rvalue_from_python_storage storage_t; + storage_t * storage = reinterpret_cast(data); + // Use placement new to initialize the result. + T * m_or_v = new (storage->storage.bytes) T(); + // Fill the result with the values from the NumPy array. + copy_ndarray_to_mv2(*array, *m_or_v); + // Finish up. + data->convertible = storage->storage.bytes; + } + +}; + + +BOOST_PYTHON_MODULE(gaussian) { + bn::initialize(); + + // Register the from-python converters + mv2_from_python< vector2, 1 >(); + mv2_from_python< matrix2, 2 >(); + + typedef double (bivariate_gaussian::*call_vector)(vector2 const &) const; + + bp::class_("bivariate_gaussian", bp::init()) + + // Declare the constructor (wouldn't work without the from-python converters). + .def(bp::init< vector2 const &, matrix2 const & >()) + + // Use our custom reference-counting getters + .add_property("mu", &py_get_mu) + .add_property("sigma", &py_get_sigma) + + // First overload accepts a two-element array argument + .def("__call__", (call_vector)&bivariate_gaussian::operator()) + + // This overload works like a binary NumPy universal function: you can pass + // in scalars or arrays, and the C++ function will automatically be called + // on each element of an array argument. + .def("__call__", bn::binary_ufunc::make()) + ; +} -- cgit v1.2.3