特征匹配PCA降维

from sklearn.preprocessing import StandardScaler

scaler = StandardScaler().fit(des_query)
des_query = scaler.transform(des_query)
des_train = scaler.transform(des_train)

from sklearn.decomposition import PCA

pca = PCA(n_components=32, whiten=True).fit(des_query_new)
des_query_pca = pca.transform(des_query)
des_train_pca = pca.transform(des_train)

import torch

def desc_l2norm(desc):
    eps_l2_norm = 1e-10
    return (desc / torch.Tensor(desc).pow(2).sum(dim=1, keepdim=True).add(eps_l2_norm).pow(0.5)).numpy()

des_query_new = desc_l2norm(des_query_pca)
des_train_new = desc_l2norm(des_train_pca)