def calculate():
# 计算每个数据点到每个中心点的欧氏距离
# 这里是将数据点都放入矩阵，直接按矩阵进行运算
reshape = ops.Reshape()
tile = ops.Tile()
reduce_sum = ops.ReduceSum(keep_dims=False)
square = ops.Square()
argmin = ops.Argmin()
centroid_matrix = reshape(tile(centroids, (num_pts, 1)),
(num_pts, k, num_feats))
point_matrix = reshape(tile(data_points, (1, k)), (num_pts, k, num_feats))
distances = reduce_sum(square(point_matrix - centroid_matrix), 2)
centroid_group = argmin(distances)
return centroid_group
def test_argmin_invalid_output_type():
P.Argmin(-1, mstype.int64)
P.Argmin(-1, mstype.int32)
with pytest.raises(TypeError):
P.Argmin(-1, mstype.float32)
with pytest.raises(TypeError):
P.Argmin(-1, mstype.float64)
with pytest.raises(TypeError):
P.Argmin(-1, mstype.uint8)
with pytest.raises(TypeError):
P.Argmin(-1, mstype.bool_)
def __init__(self):
super(ArgminNet, self).__init__()
self.argmin = P.Argmin(axis=1)
'desc_bprop': [[1, 12, 1, 1]]}),
('DepthToSpace', {
'block': P.DepthToSpace(2),
'desc_inputs': [[1, 12, 1, 1]],
'desc_bprop': [[1, 3, 2, 2]]}),
('Split', {
'block': P.Split(1, 2),
'desc_inputs': [Tensor(np.array([[1, 1, 1, 1], [2, 2, 2, 2]]))],
'skip': ['backward']}),
('Argmax', {
'block': P.Argmax(),
'desc_inputs': [[128, 32, 32, 64]],
'desc_bprop': [0],
'skip': ['backward']}),
('Argmin', {
'block': P.Argmin(),
'desc_inputs': [[128, 32, 32, 64]],
'desc_bprop': [1],
'skip': ['backward']}),
('ArgMaxWithValue', {
'block': P.ArgMaxWithValue(),
'desc_inputs': [[128, 32, 32, 64]],
'desc_bprop': [[1], [1]],
'skip': ['backward']}),
('ArgMinWithValue', {
'block': P.ArgMinWithValue(),
'desc_inputs': [[128, 32, 32, 64]],
'desc_bprop': [[1], [1]],
'skip': ['backward']}),
('Transpose_dim3', {
'block': P.Transpose(),
