8000 Merge pull request #11383 from mattip/recreate-8717 · numpy/numpy@464f79e · GitHub
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eric-wiesereric-wieser
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Merge pull request #11383 from mattip/recreate-8717
ENH: Allow size=0 in numpy.random.choice
2 parents fca7a15 + 9013b0f commit 464f79e

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+30
-15
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‎doc/release/1.16.0-notes.rst

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@@ -34,6 +34,12 @@ New Features
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Improvements
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============
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``randint`` and ``choice`` now work on empty distributions
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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Even when no elements needed to be drawn, ``np.random.randint`` and
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``np.random.choice`` raised an error when the arguments described an empty
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distribution. This has been fixed so that e.g.
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``np.random.choice([], 0) == np.array([], dtype=float64)``.
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Changes
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=======

‎numpy/random/mtrand/mtrand.pyx

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@@ -22,8 +22,8 @@
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# SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
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include "Python.pxi"
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include "randint_helpers.pxi"
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include "numpy.pxd"
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include "randint_helpers.pxi"
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include "cpython/pycapsule.pxd"
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from libc cimport string
@@ -988,9 +988,9 @@ cdef class RandomState:
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raise ValueError("low is out of bounds for %s" % dtype)
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if ihigh > highbnd:
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raise ValueError("high is out of bounds for %s" % dtype)
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if ilow >= ihigh:
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raise ValueError("low >= high")
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if ilow >= ihigh and np.prod(size) != 0:
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raise ValueError("Range cannot be empty (low >= high) unless no samples are taken")
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with self.lock:
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ret = randfunc(ilow, ihigh - 1, size, self.state_address)
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@@ -1114,15 +1114,15 @@ cdef class RandomState:
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# __index__ must return an integer by python rules.
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pop_size = operator.index(a.item())
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except TypeError:
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raise ValueError("a must be 1-dimensional or an integer")
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if pop_size <= 0:
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raise ValueError("a must be greater than 0")
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raise ValueError("'a' must be 1-dimensional or an integer")
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if pop_size <= 0 and np.prod(size) != 0:
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raise ValueError("'a' must be greater than 0 unless no samples are taken")
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elif a.ndim != 1:
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raise ValueError("a must be 1-dimensional")
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raise ValueError("'a' must be 1-dimensional")
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else:
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pop_size = a.shape[0]
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if pop_size is 0:
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raise ValueError("a must be non-empty")
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if pop_size is 0 and np.prod(size) != 0:
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raise ValueError("'a' cannot be empty unless no samples are taken")
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if p is not None:
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d = len(p)
@@ -1136,9 +1136,9 @@ cdef class RandomState:
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pix = <double*>PyArray_DATA(p)
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if p.ndim != 1:
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raise ValueError("p must be 1-dimensional")
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raise ValueError("'p' must be 1-dimensional")
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if p.size != pop_size:
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raise ValueError("a and p must have same size")
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raise ValueError("'a' and 'p' must have same size")
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if np.logical_or.reduce(p < 0):
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raise ValueError("probabilities are not non-negative")
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if abs(kahan_sum(pix, d) - 1.) > atol:

‎numpy/random/mtrand/randint_helpers.pxi.in

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@@ -23,7 +23,7 @@ def get_dispatch(dtypes):
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{{for npy_dt, npy_udt, np_dt in get_dispatch(dtypes)}}
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def _rand_{{npy_dt}}(low, high, size, rngstate):
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def _rand_{{npy_dt}}(npy_{{npy_dt}} low, npy_{{npy_dt}} high, size, rngstate):
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"""
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_rand_{{npy_dt}}(low, high, size, rngstate)
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@@ -60,8 +60,8 @@ def _rand_{{npy_dt}}(low, high, size, rngstate):
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cdef npy_intp cnt
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cdef rk_state *state = <rk_state *>PyCapsule_GetPointer(rngstate, NULL)
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rng = <npy_{{npy_udt}}>(high - low)
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off = <npy_{{npy_udt}}>(<npy_{{npy_dt}}>low)
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off = <npy_{{npy_udt}}>(low)
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rng = <npy_{{npy_udt}}>(high) - <npy_{{npy_udt}}>(low)
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if size is None:
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rk_random_{{npy_udt}}(off, rng, 1, &buf, state)

‎numpy/random/tests/test_random.py

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Original file line numberDiff line numberDiff line change
@@ -440,6 +440,15 @@ def test_choice_return_shape(self):
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assert_equal(np.random.choice(6, s, replace=False, p=p).shape, s)
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assert_equal(np.random.choice(np.arange(6), s, replace=True).shape, s)
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# Check zero-size
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assert_equal(np.random.randint(0, 0, size=(3, 0, 4)).shape, (3, 0, 4))
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assert_equal(np.random.randint(0, -10, size=0).shape, (0,))
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assert_equal(np.random.randint(10, 10, size=0).shape, (0,))
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assert_equal(np.random.choice(0, size=0).shape, (0,))
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assert_equal(np.random.choice([], size=(0,)).shape, (0,))
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assert_equal(np.random.choice(['a', 'b'], size=(3, 0, 4)).shape, (3, 0, 4))
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assert_raises(ValueError, np.random.choice, [], 10)
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def test_bytes(self):
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np.random.seed(self.seed)
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actual = np.random.bytes(10)

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