def categorical(prob, shape):
out = empty([1] + list(shape))
n = len(shape)
it0, it1 = nested_iters([prob, out], [range(1, n + 1), [0]],
op_flags=[['readonly'], ['readwrite']],
flags=['reduce_ok'])
for i in it0:
p, o = it1.itviews
p = cumsum(exp(p - max(p, axis=0)))
r = uniform() * p[-1]
o[0] = searchsorted(p, r)
return out[0, ...]
def categorical(prob, shape):
out = empty([1] + list(shape))
n = len(shape)
it0, it1 = nested_iters([prob, out], [list(range(1, n + 1)), [0]],
op_flags=[['readonly'], ['readwrite']],
flags=['reduce_ok'])
for i in it0:
p, o = it1.itviews
p = cumsum(exp(p - max(p, axis=0)))
r = uniform() * p[-1]
o[0] = searchsorted(p, r)
return out[0, ...]
axes : list of list of int
Each item is used as an “op_axes” argument to an nditer
flags, op_flags, op_dtypes, order, casting, buffersize (optional)
See nditer parameters of the same name
Returns
iters : tuple of `nditer`
An `nditer` for each item in axes, outermost first
See also
nditer()
"""
# Basic usage.
a = np.arange(12).reshape(2, 3, 2)
a
i, j = np.nested_iters(a, [[1], [0, 2]], flags=["multi_index"])
for x in i:
print(i.multi_index)
for y in j:
print('', j.multi_index, y)
#(0,)
# (0, 0) 0
# (0, 1) 1
# (1, 0) 6
# (1, 1) 7
#(1,)
# (0, 0) 2
# (0, 1) 3
# (1, 0) 8
# (1, 1) 9
["2011-01"], "2011-02",
roll="forward")) # E: numpy.ndarray[Any, numpy.dtype[numpy.datetime64]]
reveal_type(np.is_busday("2012")) # E: numpy.bool_
reveal_type(np.is_busday(
["2012"])) # E: numpy.ndarray[Any, numpy.dtype[numpy.bool_]]
reveal_type(np.datetime_as_string(M)) # E: numpy.str_
reveal_type(np.datetime_as_string(
AR_M)) # E: numpy.ndarray[Any, numpy.dtype[numpy.str_]]
reveal_type(np.compare_chararrays(
"a", "b", "!=",
rstrip=False)) # E: numpy.ndarray[Any, numpy.dtype[numpy.bool_]]
reveal_type(np.compare_chararrays(
b"a", b"a", "==", True)) # E: numpy.ndarray[Any, numpy.dtype[numpy.bool_]]
reveal_type(np.add_docstring(func, "test")) # E: None
reveal_type(np.nested_iters([AR_i8, AR_i8], [[0], [1]],
flags=["c_index"])) # E: tuple[numpy.nditer]
reveal_type(
np.nested_iters([AR_i8, AR_i8], [[0], [1]],
op_flags=[["readonly",
"readonly"]])) # E: tuple[numpy.nditer]
reveal_type(np.nested_iters([AR_i8, AR_i8], [[0], [1]],
op_dtypes=np.int_)) # E: tuple[numpy.nditer]
reveal_type(
np.nested_iters([AR_i8, AR_i8], [[0], [1]], order="C",
casting="no")) # E: tuple[numpy.nditer]
