What’s new or different — NumPy v2.3.dev0 Manual (original) (raw)

NumPy 1.17.0 introduced Generator as an improved replacement for the legacy RandomState. Here is a quick comparison of the two implementations.

• The normal, exponential and gamma generators use 256-step Ziggurat methods which are 2-10 times faster than NumPy’s default implementation instandard_normal, standard_exponential orstandard_gamma. Because of the change in algorithms, it is not possible to reproduce the exact random values using Generator for these distributions or any distribution method that relies on them.

In [1]: import numpy.random

In [2]: rng = np.random.default_rng()

In [3]: %timeit -n 1 rng.standard_normal(100000) ...: %timeit -n 1 numpy.random.standard_normal(100000) ...: 1.43 ms +- 123 us per loop (mean +- std. dev. of 7 runs, 1 loop each) 3.16 ms +- 600 us per loop (mean +- std. dev. of 7 runs, 1 loop each)

In [4]: %timeit -n 1 rng.standard_exponential(100000) ...: %timeit -n 1 numpy.random.standard_exponential(100000) ...: 605 us +- 17.8 us per loop (mean +- std. dev. of 7 runs, 1 loop each) 2 ms +- 95 us per loop (mean +- std. dev. of 7 runs, 1 loop each)

In [5]: %timeit -n 1 rng.standard_gamma(3.0, 100000) ...: %timeit -n 1 numpy.random.standard_gamma(3.0, 100000) ...: 2.68 ms +- 277 us per loop (mean +- std. dev. of 7 runs, 1 loop each) 5.01 ms +- 132 us per loop (mean +- std. dev. of 7 runs, 1 loop each)

• integers is now the canonical way to generate integer random numbers from a discrete uniform distribution. This replaces bothrandint and the deprecated random_integers.
• The rand and randn methods are only available through the legacyRandomState.
• Generator.random is now the canonical way to generate floating-point random numbers, which replaces RandomState.random_sample,sample, and ranf, all of which were aliases. This is consistent with Python’s random.random.
• All bit generators can produce doubles, uint64s and uint32s via CTypes (ctypes) and CFFI (cffi). This allows these bit generators to be used in numba.
• The bit generators can be used in downstream projects via Cython.
• All bit generators use SeedSequence to convert seed integers to initialized states.
• Optional dtype argument that accepts np.float32 or np.float64to produce either single or double precision uniform random variables for select distributions. integers accepts a dtype argument with any signed or unsigned integer dtype.
  • Uniforms (random and integers)
  • Normals (standard_normal)
  • Standard Gammas (standard_gamma)
  • Standard Exponentials (standard_exponential)

In [6]: rng = np.random.default_rng()

In [7]: rng.random(3, dtype=np.float64) Out[7]: array([0.92307296, 0.51447856, 0.38346102])

In [8]: rng.random(3, dtype=np.float32) Out[8]: array([0.42287135, 0.5827375 , 0.5434114 ], dtype=float32)

In [9]: rng.integers(0, 256, size=3, dtype=np.uint8) Out[9]: array([ 59, 35, 193], dtype=uint8)

• Optional out argument that allows existing arrays to be filled for select distributions
  • Uniforms (random)
  • Normals (standard_normal)
  • Standard Gammas (standard_gamma)
  • Standard Exponentials (standard_exponential)
    This allows multithreading to fill large arrays in chunks using suitable BitGenerators in parallel.

In [10]: rng = np.random.default_rng()

In [11]: existing = np.zeros(4)

In [12]: rng.random(out=existing[:2]) Out[12]: array([0.1922003 , 0.78958125])

In [13]: print(existing) [0.1922003 0.78958125 0. 0. ]

• Optional axis argument for methods like choice,permutation and shuffle that controls which axis an operation is performed over for multi-dimensional arrays.

In [14]: rng = np.random.default_rng()

In [15]: a = np.arange(12).reshape((3, 4))

In [16]: a Out[16]: array([[ 0, 1, 2, 3], [ 4, 5, 6, 7], [ 8, 9, 10, 11]])

In [17]: rng.choice(a, axis=1, size=5) Out[17]: array([[ 1, 0, 0, 3, 3], [ 5, 4, 4, 7, 7], [ 9, 8, 8, 11, 11]])

In [18]: rng.shuffle(a, axis=1) # Shuffle in-place

In [19]: a Out[19]: array([[ 3, 0, 2, 1], [ 7, 4, 6, 5], [11, 8, 10, 9]])

• Added a method to sample from the complex normal distribution (complex_normal)