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]