vmd 矩阵分解实战

from matplotlib import pyplot as plt
import numpy as np
from scipy.signal import hilbert
import pandas as pd
from sklearn.preprocessing import MinMaxScaler

#vmd分解类
class VMD:
    def __init__(self, K, alpha, tau, tol=1e-7, maxIters=200, eps=1e-9):
        """
        :param K: 模态数
        :param alpha: 每个模态初始中心约束强度
        :param tau: 对偶项的梯度下降学习率
        :param tol: 终止阈值
        :param maxIters: 最大迭代次数
        :param eps: eps
        """
        self.K =K
        self.alpha = alpha
        self.tau = tau
        self.tol = tol
        self.maxIters = maxIters
        self.eps = eps

    def __call__(self, f):
        N = f.shape[0]
        # 对称拼接
        f = np.concatenate((f[:N // 2][::-1], f, f[N // 2:][::-1]))
        T = f.shape[0]
        t = np.linspace(1, T, T) / T
        omega = t - 1. / T
        # 转换为解析信号
        f = hilbert(f)
        f_hat = np.fft.fft(f)