我们从Python开源项目中，提取了以下50个代码示例，用于说明如何使用numpy.broadcast()。
def _broadcast_to(array, shape, subok, readonly): shape = tuple(shape) if np.iterable(shape) else (shape,) array = np.array(array, copy=False, subok=subok) if not shape and array.shape: raise ValueError('cannot broadcast a non-scalar to a scalar array') if any(size < 0 for size in shape): raise ValueError('all elements of broadcast shape must be non-' 'negative') needs_writeable = not readonly and array.flags.writeable extras = ['reduce_ok'] if needs_writeable else [] op_flag = 'readwrite' if needs_writeable else 'readonly' broadcast = np.nditer( (array,), flags=['multi_index', 'refs_ok', 'zerosize_ok'] + extras, op_flags=[op_flag], itershape=shape, order='C').itviews[0] result = _maybe_view_as_subclass(array, broadcast) if needs_writeable and not result.flags.writeable: result.flags.writeable = True return result
def _broadcast_shape(*args): """Returns the shape of the ararys that would result from broadcasting the supplied arrays against each other. """ if not args: raise ValueError('must provide at least one argument') # use the old-iterator because np.nditer does not handle size 0 arrays # consistently b = np.broadcast(*args[:32]) # unfortunately, it cannot handle 32 or more arguments directly for pos in range(32, len(args), 31): # ironically, np.broadcast does not properly handle np.broadcast # objects (it treats them as scalars) # use broadcasting to avoid allocating the full array b = broadcast_to(0, b.shape) b = np.broadcast(b, *args[pos:(pos + 31)]) return b.shape
def _broadcast_shape(*args): """Returns the shape of the ararys that would result from broadcasting the supplied arrays against each other. """ if not args: raise ValueError('must provide at least one argument') if len(args) == 1: # a single argument does not work with np.broadcast return np.asarray(args[0]).shape # use the old-iterator because np.nditer does not handle size 0 arrays # consistently b = np.broadcast(*args[:32]) # unfortunately, it cannot handle 32 or more arguments directly for pos in range(32, len(args), 31): # ironically, np.broadcast does not properly handle np.broadcast # objects (it treats them as scalars) # use broadcasting to avoid allocating the full array b = broadcast_to(0, b.shape) b = np.broadcast(b, *args[pos:(pos + 31)]) return b.shape
def broadcast_shape(shp1, shp2): """Broadcast the shape of those arrays Parameters ---------- shp1 : tuple shape of array 1 shp2 : tuple shape of array 2 Returns ------- tuple shape resulting from broadcasting two arrays using numpy rules Raises ------ ValueError Arrays cannot be broadcasted """ try: return np.broadcast(np.empty(shp1), np.empty(shp2)).shape except ValueError: raise ValueError("Arrays cannot be broadcasted - %s and %s " % (str(shp1), str(shp2)))
def _broadcast_shape(*args): """Returns the shape of the arrays that would result from broadcasting the supplied arrays against each other. """ if not args: raise ValueError('must provide at least one argument') # use the old-iterator because np.nditer does not handle size 0 arrays # consistently b = np.broadcast(*args[:32]) # unfortunately, it cannot handle 32 or more arguments directly for pos in range(32, len(args), 31): # ironically, np.broadcast does not properly handle np.broadcast # objects (it treats them as scalars) # use broadcasting to avoid allocating the full array b = broadcast_to(0, b.shape) b = np.broadcast(b, *args[pos:(pos + 31)]) return b.shape
def test_broadcast_in_args(self): # gh-5881 arrs = [np.empty((6, 7)), np.empty((5, 6, 1)), np.empty((7,)), np.empty((5, 1, 7))] mits = [np.broadcast(*arrs), np.broadcast(np.broadcast(*arrs[:2]), np.broadcast(*arrs[2:])), np.broadcast(arrs[0], np.broadcast(*arrs[1:-1]), arrs[-1])] for mit in mits: assert_equal(mit.shape, (5, 6, 7)) assert_equal(mit.nd, 3) assert_equal(mit.numiter, 4) for a, ia in zip(arrs, mit.iters): assert_(a is ia.base)
def test_broadcast_single_arg(self): # gh-6899 arrs = [np.empty((5, 6, 7))] mit = np.broadcast(*arrs) assert_equal(mit.shape, (5, 6, 7)) assert_equal(mit.nd, 3) assert_equal(mit.numiter, 1) assert_(arrs[0] is mit.iters[0].base)
def test_number_of_arguments(self): arr = np.empty((5,)) for j in range(35): arrs = [arr] * j if j < 1 or j > 32: assert_raises(ValueError, np.broadcast, *arrs) else: mit = np.broadcast(*arrs) assert_equal(mit.numiter, j)
def test_boolean_assignment_value_mismatch(self): # A boolean assignment should fail when the shape of the values # cannot be broadcast to the subscription. (see also gh-3458) a = np.arange(4) def f(a, v): a[a > -1] = v assert_raises(ValueError, f, a, []) assert_raises(ValueError, f, a, [1, 2, 3]) assert_raises(ValueError, f, a[:1], [1, 2, 3])
def setUp(self): self.a = np.arange(np.prod([3, 1, 5, 6])).reshape(3, 1, 5, 6) self.b = np.empty((3, 0, 5, 6)) self.complex_indices = ['skip', Ellipsis, 0, # Boolean indices, up to 3-d for some special cases of eating up # dimensions, also need to test all False np.array(False), np.array([True, False, False]), np.array([[True, False], [False, True]]), np.array([[[False, False], [False, False]]]), # Some slices: slice(-5, 5, 2), slice(1, 1, 100), slice(4, -1, -2), slice(None, None, -3), # Some Fancy indexes: np.empty((0, 1, 1), dtype=np.intp), # empty and can be broadcast np.array([0, 1, -2]), np.array([[2], [0], [1]]), np.array([[0, -1], [0, 1]], dtype=np.dtype('intp').newbyteorder()), np.array([2, -1], dtype=np.int8), np.zeros([1]*31, dtype=int), # trigger too large array. np.array([0., 1.])] # invalid datatype # Some simpler indices that still cover a bit more self.simple_indices = [Ellipsis, None, -1, [1], np.array([True]), 'skip'] # Very simple ones to fill the rest: self.fill_indices = [slice(None, None), 0]
def broadcast_to(array, shape, subok=False): """Broadcast an array to a new shape. Parameters ---------- array : array_like The array to broadcast. shape : tuple The shape of the desired array. subok : bool, optional If True, then sub-classes will be passed-through, otherwise the returned array will be forced to be a base-class array (default). Returns ------- broadcast : array A readonly view on the original array with the given shape. It is typically not contiguous. Furthermore, more than one element of a broadcasted array may refer to a single memory location. Raises ------ ValueError If the array is not compatible with the new shape according to NumPy's broadcasting rules. Notes ----- .. versionadded:: 1.10.0 Examples -------- >>> x = np.array([1, 2, 3]) >>> np.broadcast_to(x, (3, 3)) array([[1, 2, 3], [1, 2, 3], [1, 2, 3]]) """ return _broadcast_to(array, shape, subok=subok, readonly=True)
def iterkeys(self,index): #import pdb; pdb.set_trace() if not isinstance(index,tuple) and self.shape[0] == 1: index = (1,index) if isinstance(index, int): key = np.unravel_index(index-1, self.shape, order='F') yield tuple(k+1 for k in key) elif isinstance(index,slice): index = range((index.start or 1)-1, index.stop or np.prod(self.shape), index.step or 1) for key in np.transpose(np.unravel_index(index, self.shape, order='F')): # 0-based yield tuple(k+1 for k in key) elif isinstance(index,(list,np.ndarray)): index = np.asarray(index)-1 for key in np.transpose(np.unravel_index(index, self.shape, order='F')): yield tuple(k+1 for k in key) else: assert isinstance(index,tuple),index.__class__ indices = [] # 1-based for i,ix in enumerate(index): if isinstance(ix,slice): indices.append(np.arange((ix.start or 1), (ix.stop or self.shape[i]) + 1, ix.step or 1, dtype=int)) else: indices.append(np.asarray(ix)) assert len(index) == 2 indices[0].shape = (-1,1) for key in np.broadcast(*indices): yield tuple(map(int,key))
def test_number_of_arguments(self): arr = np.empty((5,)) for j in range(35): arrs = [arr] * j if j < 2 or j > 32: assert_raises(ValueError, np.broadcast, *arrs) else: mit = np.broadcast(*arrs) assert_equal(mit.numiter, j)
def _compare_index_result(self, arr, index, mimic_get, no_copy): """Compare mimicked result to indexing result. """ arr = arr.copy() indexed_arr = arr[index] assert_array_equal(indexed_arr, mimic_get) # Check if we got a view, unless its a 0-sized or 0-d array. # (then its not a view, and that does not matter) if indexed_arr.size != 0 and indexed_arr.ndim != 0: assert_(np.may_share_memory(indexed_arr, arr) == no_copy) # Check reference count of the original array if no_copy: # refcount increases by one: assert_equal(sys.getrefcount(arr), 3) else: assert_equal(sys.getrefcount(arr), 2) # Test non-broadcast setitem: b = arr.copy() b[index] = mimic_get + 1000 if b.size == 0: return # nothing to compare here... if no_copy and indexed_arr.ndim != 0: # change indexed_arr in-place to manipulate original: indexed_arr += 1000 assert_array_equal(arr, b) return # Use the fact that the array is originally an arange: arr.flat[indexed_arr.ravel()] += 1000 assert_array_equal(arr, b)
def shape_from_elems(*elems): if len(elems) == 0: return 1, return np.broadcast(*[np.ones(elem.shape) for elem in elems]).shape
def Softmax(x): # TODO make this numerically stable by shifting by max? exp = Exp(x) if len(x.shape) == 1: # workaround because numpy einsum can't broadcast? https://github.com/numpy/numpy/issues/9984 return exp / Einsum("i->", exp) elif len(x.shape) == 2: return exp / Einsum("bi,o->bo", exp, np.array([1])) elif len(x.shape) == 3: return exp / Einsum("abi,o->abo", exp, np.array([1])) elif len(x.shape) == 4: return exp / Einsum("abci,o->abco", exp, np.array([1])) else: raise ValueError("5D tensors not yet supported")
def test_broadcast_in_args(self): # gh-5881 arrs = [np.empty((6, 7)), np.empty((5, 6, 1)), np.empty((7,)), np.empty((5, 1, 7))] mits = [np.broadcast(*arrs), np.broadcast(np.broadcast(*arrs[:2]), np.broadcast(*arrs[2:])), np.broadcast(arrs[0], np.broadcast(*arrs[1:-1]), arrs[-1])] for mit in mits: assert_equal(mit.shape, (5, 6, 7)) assert_equal(mit.ndim, 3) assert_equal(mit.nd, 3) assert_equal(mit.numiter, 4) for a, ia in zip(arrs, mit.iters): assert_(a is ia.base)
def test_broadcast_single_arg(self): # gh-6899 arrs = [np.empty((5, 6, 7))] mit = np.broadcast(*arrs) assert_equal(mit.shape, (5, 6, 7)) assert_equal(mit.ndim, 3) assert_equal(mit.nd, 3) assert_equal(mit.numiter, 1) assert_(arrs[0] is mit.iters[0].base)
