77 lines
2.2 KiB
Python
77 lines
2.2 KiB
Python
#!/usr/bin/python3
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# -*- coding: utf-8 -*-
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'''
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@Date : 2021.11.1
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@Author : Herry
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'''
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import numpy as np
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from vmdpy import VMD
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from PyEMD import EMD, EEMD
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class Decomposition:
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def __init__(self, data, length):
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self.data = data.reshape(-1)
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self.length = length
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def SSA(self):
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series = self.data
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# step1 嵌入
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windowLen = self.length # 嵌入窗口长度
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seriesLen = len(series) # 序列长度
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K = seriesLen - windowLen + 1
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X = np.zeros((windowLen, K))
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for i in range(K):
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X[:, i] = series[i:i + windowLen]
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# step2: svd分解， U和sigma已经按升序排序
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U, sigma, VT = np.linalg.svd(X, full_matrices=False)
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for i in range(VT.shape[0]):
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VT[i, :] *= sigma[i]
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A = VT
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# 重组
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rec = np.zeros((windowLen, seriesLen))
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for i in range(windowLen):
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for j in range(windowLen - 1):
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for m in range(j + 1):
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rec[i, j] += A[i, j - m] * U[m, i]
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rec[i, j] /= (j + 1)
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for j in range(windowLen - 1, seriesLen - windowLen + 1):
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for m in range(windowLen):
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rec[i, j] += A[i, j - m] * U[m, i]
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rec[i, j] /= windowLen
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for j in range(seriesLen - windowLen + 1, seriesLen):
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for m in range(j - seriesLen + windowLen, windowLen):
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rec[i, j] += A[i, j - m] * U[m, i]
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rec[i, j] /= (seriesLen - j)
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return rec.T
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def EMD(self):
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data = self.data
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decomp = EMD()
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decomp.emd(data)
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imfs, res = decomp.get_imfs_and_residue()
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IMFs = imfs.T
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IMFs = np.insert(IMFs, IMFs.shape[1], values=res, axis = 1)
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return IMFs
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def EEMD(self):
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data = self.data
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decomp = EEMD()
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decomp.eemd(data)
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imfs, res = decomp.get_imfs_and_residue()
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IMFs = imfs.T
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IMFs = np.insert(IMFs, IMFs.shape[1], values=res, axis = 1)
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return IMFs
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def VMD(self):
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data = self.data
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alpha, tau, length, DC, init, tol = 5000, 0, self.length, 0, 1, 1e-8
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u, u_hat, omega = VMD(data, alpha, tau, length, DC, init, tol)
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return u.T
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