85 lines
1.9 KiB
Python
85 lines
1.9 KiB
Python
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import numpy as np
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import numba as nb
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from numpy.random import PCG64
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from timeit import timeit
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bit_gen = PCG64()
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next_d = bit_gen.cffi.next_double
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state_addr = bit_gen.cffi.state_address
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def normals(n, state):
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out = np.empty(n)
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for i in range((n + 1) // 2):
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x1 = 2.0 * next_d(state) - 1.0
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x2 = 2.0 * next_d(state) - 1.0
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r2 = x1 * x1 + x2 * x2
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while r2 >= 1.0 or r2 == 0.0:
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x1 = 2.0 * next_d(state) - 1.0
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x2 = 2.0 * next_d(state) - 1.0
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r2 = x1 * x1 + x2 * x2
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f = np.sqrt(-2.0 * np.log(r2) / r2)
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out[2 * i] = f * x1
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if 2 * i + 1 < n:
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out[2 * i + 1] = f * x2
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return out
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# Compile using Numba
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normalsj = nb.jit(normals, nopython=True)
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# Must use state address not state with numba
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n = 10000
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def numbacall():
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return normalsj(n, state_addr)
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rg = np.random.Generator(PCG64())
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def numpycall():
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return rg.normal(size=n)
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# Check that the functions work
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r1 = numbacall()
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r2 = numpycall()
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assert r1.shape == (n,)
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assert r1.shape == r2.shape
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t1 = timeit(numbacall, number=1000)
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print('{:.2f} secs for {} PCG64 (Numba/PCG64) gaussian randoms'.format(t1, n))
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t2 = timeit(numpycall, number=1000)
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print('{:.2f} secs for {} PCG64 (NumPy/PCG64) gaussian randoms'.format(t2, n))
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# example 2
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next_u32 = bit_gen.ctypes.next_uint32
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ctypes_state = bit_gen.ctypes.state
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@nb.jit(nopython=True)
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def bounded_uint(lb, ub, state):
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mask = delta = ub - lb
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mask |= mask >> 1
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mask |= mask >> 2
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mask |= mask >> 4
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mask |= mask >> 8
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mask |= mask >> 16
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val = next_u32(state) & mask
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while val > delta:
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val = next_u32(state) & mask
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return lb + val
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print(bounded_uint(323, 2394691, ctypes_state.value))
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@nb.jit(nopython=True)
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def bounded_uints(lb, ub, n, state):
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out = np.empty(n, dtype=np.uint32)
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for i in range(n):
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out[i] = bounded_uint(lb, ub, state)
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bounded_uints(323, 2394691, 10000000, ctypes_state.value)
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