# Source code for brian2.input.binomial

"""
Implementation of BinomialFunction
"""

import numpy as np

from brian2.core.base import Nameable
from brian2.core.functions import DEFAULT_FUNCTIONS, Function
from brian2.units.fundamentalunits import check_units
from brian2.utils.stringtools import replace

__all__ = ["BinomialFunction"]

def _pre_calc_constants_approximated(n, p):
loc = n * p
scale = np.sqrt(n * p * (1 - p))
return loc, scale

def _pre_calc_constants(n, p):
reverse = p > 0.5
if reverse:
P = 1.0 - p
else:
P = p
q = 1.0 - P
qn = np.exp(n * np.log(q))
bound = min(n, n * P + 10.0 * np.sqrt(n * P * q + 1))
return reverse, q, P, qn, bound

def _generate_cython_code(n, p, use_normal, name):
# Cython implementation
# Inversion transform sampling
if use_normal:
loc, scale = _pre_calc_constants_approximated(n, p)
cython_code = """
cdef float %NAME%(const int vectorisation_idx):
return _randn(vectorisation_idx) * %SCALE% + %LOC%
"""
cython_code = replace(
cython_code,
{"%SCALE%": f"{scale:.15f}", "%LOC%": f"{loc:.15f}", "%NAME%": name},
)
dependencies = {"_randn": DEFAULT_FUNCTIONS["randn"]}
else:
reverse, q, P, qn, bound = _pre_calc_constants(n, p)
# The following code is an almost exact copy of numpy's
# rk_binomial_inversion function
# (numpy/random/mtrand/distributions.c)
cython_code = """
cdef long %NAME%(const int vectorisation_idx):
cdef long X = 0
cdef double px = %QN%
cdef double U = _rand(vectorisation_idx)
while U > px:
X += 1
if X > %BOUND%:
X = 0
px = %QN%
U = _rand(vectorisation_idx)
else:
U -= px
px = ((%N%-X+1) * %P% * px)/(X*%Q%)
return %RETURN_VALUE%
"""
cython_code = replace(
cython_code,
{
"%N%": f"{int(n)}",
"%P%": f"{p:.15f}",
"%Q%": f"{q:.15f}",
"%QN%": f"{qn:.15f}",
"%BOUND%": f"{bound:.15f}",
"%RETURN_VALUE%": f"{int(n)}-X" if reverse else "X",
"%NAME%": name,
},
)
dependencies = {"_rand": DEFAULT_FUNCTIONS["rand"]}

return cython_code, dependencies

def _generate_cpp_code(n, p, use_normal, name):
# C++ implementation
# Inversion transform sampling
if use_normal:
loc, scale = _pre_calc_constants_approximated(n, p)
cpp_code = """
float %NAME%(const int vectorisation_idx)
{
return _randn(vectorisation_idx) * %SCALE% + %LOC%;
}
"""
cpp_code = replace(
cpp_code,
{"%SCALE%": f"{scale:.15f}", "%LOC%": f"{loc:.15f}", "%NAME%": name},
)
dependencies = {"_randn": DEFAULT_FUNCTIONS["randn"]}
else:
reverse, q, P, qn, bound = _pre_calc_constants(n, p)
# The following code is an almost exact copy of numpy's
# rk_binomial_inversion function
# (numpy/random/mtrand/distributions.c)
cpp_code = """
long %NAME%(const int vectorisation_idx)
{
long X = 0;
double px = %QN%;
double U = _rand(vectorisation_idx);
while (U > px)
{
X++;
if (X > %BOUND%)
{
X = 0;
px = %QN%;
U = _rand(vectorisation_idx);
} else
{
U -= px;
px = ((%N%-X+1) * %P% * px)/(X*%Q%);
}
}
return %RETURN_VALUE%;
}
"""
cpp_code = replace(
cpp_code,
{
"%N%": f"{int(n)}",
"%P%": f"{P:.15f}",
"%Q%": f"{q:.15f}",
"%QN%": f"{qn:.15f}",
"%BOUND%": f"{bound:.15f}",
"%RETURN_VALUE%": f"{int(n)}-X" if reverse else "X",
"%NAME%": name,
},
)
dependencies = {"_rand": DEFAULT_FUNCTIONS["rand"]}

return {"support_code": cpp_code}, dependencies

[docs]class BinomialFunction(Function, Nameable):
r"""
BinomialFunction(n, p, approximate=True, name='_binomial*')

A function that generates samples from a binomial distribution.

Parameters
----------
n : int
Number of samples
p : float
Probablility
approximate : bool, optional
Whether to approximate the binomial with a normal distribution if
:math:n p > 5 \wedge n (1 - p) > 5. Defaults to True.
"""

#: Container for implementing functions for different targets
#: This container can be extended by other codegeneration targets/devices
#: The key has to be the name of the target, the value a function
#: that takes three parameters (n, p, use_normal) and returns a tuple of
#: (code, dependencies)
implementations = {"cpp": _generate_cpp_code, "cython": _generate_cython_code}

@check_units(n=1, p=1)
def __init__(self, n, p, approximate=True, name="_binomial*"):
Nameable.__init__(self, name)

# Python implementation
use_normal = approximate and (n * p > 5) and n * (1 - p) > 5
if use_normal:
loc = n * p
scale = np.sqrt(n * p * (1 - p))

def sample_function(vectorisation_idx):
try:
N = len(vectorisation_idx)
except TypeError:
N = int(vectorisation_idx)
return np.random.normal(loc, scale, size=N)

else:

def sample_function(vectorisation_idx):
try:
N = len(vectorisation_idx)
except TypeError:
N = int(vectorisation_idx)
return np.random.binomial(n, p, size=N)

Function.__init__(
self,
pyfunc=lambda: sample_function(1),
arg_units=[],
return_unit=1,
stateless=False,
auto_vectorise=True,
)