KSVD字典学习_ksvd tdata

import numpy as np
import pandas as pd
from scipy.io import loadmat
 
train_data_mat = loadmat("../data/train_data2.mat")
 
train_data = train_data_mat["Data"]
train_label = train_data_mat["Label"]
 
print(train_data.shape, train_label.shape)

# 初始化字典
u, s, v = np.linalg.svd(train_data)
n_comp = 50
dict_data = u[:, :n_comp]

#字典更新
def dict_update(y, d, x, n_components):
    """
    使用KSVD更新字典的过程
    """
    for i in range(n_components):
        index = np.nonzero(x[i, :])[0]
        if len(index) == 0:
            continue
        # 更新第i列
        d[:, i] = 0
        # 计算误差矩阵
        r = (y - np.dot(d, x))[:, index]
        # 利用svd的方法，来求解更新字典和稀疏系数矩阵
        u, s, v = np.linalg.svd(r, full_matrices=False)
        # 使用左奇异矩阵的第0列更新字典
        d[:, i] = u[:, 0]
        # 使用第0个奇异值和右奇异矩阵的第0行的乘积更新稀疏系数矩阵
        for j,k in enumerate(index):
            x[i, k] = s[0] * v[0, j]
    return d, x

'''注： 上面代码的16~17需要注意python的numpy中的普通索引和花式索引的区别，花式索引会产生一个原数组的副本，所以对花式索引的操作并不会改变原数据，因此不能像第10行一样，需利用直接索引更新x。'''

# 迭代更新求解
from sklearn import linear_model
 
max_iter = 10
dictionary = dict_data
 
y = train_data
tolerance = 1e-6
 
for i in range(max_iter):
    # 稀疏编码
    x = linear_model.orthogonal_mp(dictionary, y)
    e = np.linalg.norm(y - np.dot(dictionary, x))
    if e < tolerance:
        break
    dict_update(y, dictionary, x, n_comp)
 
sparsecode = linear_model.orthogonal_mp(dictionary, y)
 
train_restruct = dictionary.dot(sparsecode)

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