NumPy参考 >例行程序 >Random sampling (numpy.random) > Examples of using Numba, Cython, CFFI
BitGenerators被设计为可以使用用于高性能Python的标准工具numba和Cython进行扩展。该Generator对象还可以与用户提供的BitGenerators一起使用，只要它们导出一小组所需的功能即可。
Numba可以与CType或CFFI一起使用。BitGenerators的当前迭代都通过这两个接口导出了一小组函数。
本示例说明了如何使用numba使用纯Python实现来生成高斯样本，然后将其编译。随机数由提供ctypes.next_double。
import numpy as np
import numba as nb
from numpy.random import PCG64
from timeit import timeit
bit_gen = PCG64()
next_d = bit_gen.cffi.next_double
state_addr = bit_gen.cffi.state_address
def normals(n, state):
out = np.empty(n)
for i in range((n + 1) // 2):
x1 = 2.0 * next_d(state) - 1.0
x2 = 2.0 * next_d(state) - 1.0
r2 = x1 * x1 + x2 * x2
while r2 >= 1.0 or r2 == 0.0:
x1 = 2.0 * next_d(state) - 1.0
x2 = 2.0 * next_d(state) - 1.0
r2 = x1 * x1 + x2 * x2
f = np.sqrt(-2.0 * np.log(r2) / r2)
out[2 * i] = f * x1
if 2 * i + 1 < n:
out[2 * i + 1] = f * x2
return out
# Compile using Numba
normalsj = nb.jit(normals, nopython=True)
# Must use state address not state with numba
n = 10000
def numbacall():
return normalsj(n, state_addr)
rg = np.random.Generator(PCG64())
def numpycall():
return rg.normal(size=n)
# Check that the functions work
r1 = numbacall()
r2 = numpycall()
assert r1.shape == (n,)
assert r1.shape == r2.shape
t1 = timeit(numbacall, number=1000)
print('{:.2f} secs for {} PCG64 (Numba/PCG64) gaussian randoms'.format(t1, n))
t2 = timeit(numpycall, number=1000)
print('{:.2f} secs for {} PCG64 (NumPy/PCG64) gaussian randoms'.format(t2, n))
将文件distributions.c编译为a DLL或
之后，CType和CFFI都允许在Numba中直接使用更复杂的发行版so。下面的示例部分中显示了一个使用更复杂发行版的示例。
Cython可用于解压缩PyCapsuleBitGenerator提供的工具。该示例使用PCG64和上面的示例。使用Cython编写高性能代码的通常警告-删除边界检查并环绕，提供数组对齐信息-仍然适用。
#!/usr/bin/env python
#cython: language_level=3
"""
This file shows how the to use a BitGenerator to create a distribution.
"""
import numpy as np
cimport numpy as np
cimport cython
from cpython.pycapsule cimport PyCapsule_IsValid, PyCapsule_GetPointer
from libc.stdint cimport uint16_t, uint64_t
from numpy.random cimport bitgen_t
from numpy.random import PCG64
@cython.boundscheck(False)
@cython.wraparound(False)
def uniforms(Py_ssize_t n):
"""
Create an array of `n` uniformly distributed doubles.
A 'real' distribution would want to process the values into
some non-uniform distribution
"""
cdef Py_ssize_t i
cdef bitgen_t *rng
cdef const char *capsule_name = "BitGenerator"
cdef double[::1] random_values
x = PCG64()
capsule = x.capsule
# Optional check that the capsule if from a BitGenerator
if not PyCapsule_IsValid(capsule, capsule_name):
raise ValueError("Invalid pointer to anon_func_state")
# Cast the pointer
rng = <bitgen_t *> PyCapsule_GetPointer(capsule, capsule_name)
random_values = np.empty(n, dtype='float64')
with x.lock, nogil:
for i in range(n):
# Call the function
random_values[i] = rng.next_double(rng.state)
randoms = np.asarray(random_values)
return randoms
也可以使用基本RNG结构的成员直接访问BitGenerator。
@cython.boundscheck(False)
@cython.wraparound(False)
def uint10_uniforms(Py_ssize_t n):
"""Uniform 10 bit integers stored as 16-bit unsigned integers"""
cdef Py_ssize_t i
cdef bitgen_t *rng
cdef const char *capsule_name = "BitGenerator"
cdef uint16_t[::1] random_values
cdef int bits_remaining
cdef int width = 10
cdef uint64_t buff, mask = 0x3FF
x = PCG64()
capsule = x.capsule
if not PyCapsule_IsValid(capsule, capsule_name):
raise ValueError("Invalid pointer to anon_func_state")
rng = <bitgen_t *> PyCapsule_GetPointer(capsule, capsule_name)
random_values = np.empty(n, dtype='uint16')
# Best practice is to release GIL and acquire the lock
bits_remaining = 0
with x.lock, nogil:
for i in range(n):
if bits_remaining < width:
buff = rng.next_uint64(rng.state)
random_values[i] = buff & mask
buff >>= width
randoms = np.asarray(random_values)
return randoms
请参阅通过Cython扩展numpy.random以获取完整的工作示例，其中包括最小的安装程序和cython文件。
CFFI可用于直接访问中的功能
include/numpy/random/distributions.h。头文件的某些“按摩”是必需的：
"""
Use cffi to access any of the underlying C functions from distributions.h
"""
import os
import numpy as np
import cffi
from .parse import parse_distributions_h
ffi = cffi.FFI()
inc_dir = os.path.join(np.get_include(), 'numpy')
# Basic numpy types
ffi.cdef('''
typedef intptr_t npy_intp;
typedef unsigned char npy_bool;
''')
parse_distributions_h(ffi, inc_dir)
一旦标头由解析ffi.cdef，就可以_generator使用该BitGenerator.cffi接口直接从共享库访问函数。
# Compare the distributions.h random_standard_normal_fill to
# Generator.standard_random
bit_gen = np.random.PCG64()
rng = np.random.Generator(bit_gen)
state = bit_gen.state
interface = rng.bit_generator.cffi
n = 100
vals_cffi = ffi.new('double[%d]' % n)
lib.random_standard_normal_fill(interface.bit_generator, n, vals_cffi)
# reset the state
bit_gen.state = state
vals = rng.standard_normal(n)
for i in range(n):
assert vals[i] == vals_cffi[i]
Generator可以与其他用户提供的BitGenerators一起使用。编写新的BitGenerator的最简单方法是检查现有BitGenerator之一的pyx文件。必须提供的键结构是，
capsule其中包含PyCapsule指向类型的struct指针的
bitgen_t，
typedef struct bitgen {
void *state;
uint64_t (*next_uint64)(void *st);
uint32_t (*next_uint32)(void *st);
double (*next_double)(void *st);
uint64_t (*next_raw)(void *st);
} bitgen_t;
提供5个指针。第一个是BitGenerators使用的数据结构的不透明指针。接下来的三个是函数指针，它们返回下一个64位和32位无符号整数，下一个随机double和下一个原始值。该最终函数用于测试，因此如果不需要，可以将其设置为下一个64位无符号整数函数。内部函数
Generator使用此结构，如下所示
bitgen_state->next_uint64(bitgen_state->state)