Sklearn中的Normalize函数可以通过设置参数来选择使用L1范数归一化还是L2范数归一化。具体使用方法如下: 代码语言:txt 复制 from sklearn.preprocessing import normalize # 创建一个2D数组 data = [[1, 2], [3, 4], [5, 6]] # 使用L1范数归一化 normalized_data_l1 = normalize(data, norm='l1') # 使...
norm_embed: out = F.normalize(out, p=2, dim=-1) return out Example #4Source File: so3.py From c3dpo_nrsfm with MIT License 6 votes def rand_rot(N, dtype=None, max_rot_angle=float(math.pi), axes=(1, 1, 1), get_ss=False): rand_axis = torch.zeros((N, 3)).type(...
Also included is the helper functionNORM2COORDwhich performs the reverse operation, mapping coordinates normalized to the parent container ofaxishandleto the data space ofaxishandle. Example: myaxes=axes(); x=-10:10; y=x.^2; plot(x,y); normx=[0.5,0.55]; normy=[0.5,0.55]; annotation(...
In this method, we will compute the vector norm of an array using the mathematical formula. When we divide the array with this norm vector, we get the normalized vector. The following code implements this. importnumpyasnp v=np.random.rand(10)normalized_v=v/np.sqrt(np.sum(v**2))print...