GAPS is an affine invariant ensemble sampler by Goodman & Weare (DOI: 10.2140/camcos.2010.5.65). The implementation is largely based on emcee by Dan Foreman-Mackey.
Since the code is still in early stages the you may want to install this module as 'editable' via:
git clone https://github.com/sgaebel/GAPS /path/to/GAPS/
pip install -e /path/to/GAPS/
The main dependencies are numpy, matplotlib, and pyopencl. Installation instructions for pyopencl are available in its documentation and may vary strongly based on the operating system.
Under Windows a good method is to install the AMD APP SDK, and the binaries for cffi and pyopencl.
The core component of this module is the gaps.run_sampler() function, which requires the user to define a logP_fn function. This function signature is required to be of the form cfloat logP_fn(const cfloat parameters USER_DATA), where USER_DATA enables the user the pass one or more global arrays through the sampler to logP_fn. Currently USER_DATA arrays may only be of type __global const cdouble. To get started quickly, check out examples/fitting_2d_gaussian.py.
cfloat logP_fn(const cfloat parameters)cfloat logP_fn(const cfloat parameters, __global const cdouble some_data[DIM1])cfloat logP_fn(const cfloat parameters, __global const cdouble some_data[DIM1][1200], __global const cdouble other_data[DIM2])
In addition to the sampler itself, GAPS also provides a number of mathematical constants, functions, and distributions:
M_1_SQRTPI : 1 / sqrt(pi)M_1_SQRT2PI : 1 / sqrt(2 * pi)M_LOG_1_SQRTPI : log(1 / sqrt(pi))M_LOG_1_SQRT2PI : log(1 / sqrt(2 * pi))M_SQRT_2_PI : sqrt(2 * pi)M_LOG_SQRT_2_PI : log(sqrt(2 * pi))M_1_SQRT2 : 1 / sqrt(2)cdouble sum(__global const cdouble * iterable, const size_t length)cdouble product(__global const cdouble * iterable, const size_t length)cdouble logsumexp(__global const cdouble * log_values, size_t length)cdouble logaddexp(const cdouble x, const cdouble y)cdouble mean(__global const cdouble * iterable, const size_t length)cdouble stddev(__global const cdouble * iterable, const size_t length)cdouble iter_min(__global const cdouble * iterable, const size_t length)cdouble iter_max(__global const cdouble * iterable, const size_t length)cdouble gaussian(const cdouble value, const cdouble mean, const cdouble stddev)cdouble log_gaussian(const cdouble value, const cdouble mean, const cdouble stddev)cdouble trunc_gaussian(const cdouble value, const cdouble mean, const cdouble stddev, const cdouble low, const cdouble high)cdouble log_trunc_gaussian(const cdouble value, const cdouble mean, const cdouble stddev, const cdouble low, const cdouble high)cdouble power_law(const cdouble value, const cdouble slope, const cdouble low, const cdouble high)cdouble power_law_falling(const cdouble value, const cdouble slope, const cdouble cutoff)cdouble log_power_law(const cdouble value, const cdouble slope, const cdouble low, const cdouble high)cdouble log_power_law_falling(const cdouble value, const cdouble slope, const cdouble cutoff)
Currently there is no templating available, so all function only accept __global arrays. These functions are defined in gaps/auxiliary_sources.py and can be copied and/or modified if needed.
Please refer to the OpenCL documentations for more basic functions and constants:
Some examples are available in examples/ and illustrate basic use cases.