<?xml version="1.0" encoding="utf-8" ?><rss version="2.0"><channel><title><![CDATA[Emanon666的博客]]></title><description><![CDATA[]]></description><link>https://blog.youkuaiyun.com/Emanon666</link><language>zh-cn</language><generator>https://blog.youkuaiyun.com/</generator><copyright><![CDATA[Copyright &copy; Emanon666]]></copyright><item><title><![CDATA[谱宽参数--程序备份·]]></title><link>https://blog.youkuaiyun.com/Emanon666/article/details/121146947</link><guid>https://blog.youkuaiyun.com/Emanon666/article/details/121146947</guid><author>Emanon666</author><pubDate>Thu, 04 Nov 2021 17:17:06 +0800</pubDate><description><![CDATA[import numpy as np
import statsmodels.tsa.api as smt
import math
from sympy import *
from scipy.fft import fft
import matplotlib.pyplot as plt

def specwid(data, H):
    """
    计算谱宽参数
    :param data:数据
    :param H:上限频率
    :param fs:采样频率
    :return:e谱.]]></description><category></category></item><item><title><![CDATA[python 汉明码]]></title><link>https://blog.youkuaiyun.com/Emanon666/article/details/121070340</link><guid>https://blog.youkuaiyun.com/Emanon666/article/details/121070340</guid><author>Emanon666</author><pubDate>Sun, 31 Oct 2021 21:29:20 +0800</pubDate><description><![CDATA[import numpy as np

def func(X):
    '''
    返回G * H_T
    '''

    H = np.array(
        [[0, 0, 0, 1, 1, 1, 1],
         [0, 1, 1, 0, 0, 1, 1],
         [1, 0, 1, 0, 1, 0, 1]])

    G1 = np.eye(4)

    G2 = [[X[0], X[1], X[2]]
        , [X[3], X[4], X[5.]]></description><category></category></item><item><title><![CDATA[代解决的代码4]]></title><link>https://blog.youkuaiyun.com/Emanon666/article/details/121020916</link><guid>https://blog.youkuaiyun.com/Emanon666/article/details/121020916</guid><author>Emanon666</author><pubDate>Thu, 28 Oct 2021 19:03:32 +0800</pubDate><description><![CDATA[import numpy as np
import sympy
from scipy.optimize import fsolve
from sympy import *
import math

H = np.array(
            [[0, 0, 0, 1, 1, 1, 1],
             [0, 1, 1, 0, 0, 1, 1],
             [1, 0, 1, 0, 1, 0, 1]])

def func(X):
    '''
    求校验矩阵H
.]]></description><category></category></item><item><title><![CDATA[待解决的代码2]]></title><link>https://blog.youkuaiyun.com/Emanon666/article/details/120999310</link><guid>https://blog.youkuaiyun.com/Emanon666/article/details/120999310</guid><author>Emanon666</author><pubDate>Wed, 27 Oct 2021 18:52:08 +0800</pubDate><description><![CDATA[import numpy as np
import sympy
from scipy.optimize import fsolve
from sympy import *

H = np.array(
            [[0, 0, 0, 1, 1, 1, 1],
             [0, 1, 1, 0, 0, 1, 1],
             [1, 0, 1, 0, 1, 0, 1]])

def func(X):
    '''

    '''

    H = np.ar.]]></description><category></category></item><item><title><![CDATA[待解决错误代码]]></title><link>https://blog.youkuaiyun.com/Emanon666/article/details/120996421</link><guid>https://blog.youkuaiyun.com/Emanon666/article/details/120996421</guid><author>Emanon666</author><pubDate>Wed, 27 Oct 2021 16:41:29 +0800</pubDate><description><![CDATA[import numpy as np
import sympy
from scipy.optimize import fsolve
from sympy import *

H = np.array(
            [[0, 0, 0, 1, 1, 1, 1],
             [0, 1, 1, 0, 0, 1, 1],
             [1, 0, 1, 0, 1, 0, 1]])

G1 = np.eye(4)
g_15,g_16,g_17 = symbols('g_1.]]></description><category></category></item><item><title><![CDATA[python fastICA]]></title><link>https://blog.youkuaiyun.com/Emanon666/article/details/120940774</link><guid>https://blog.youkuaiyun.com/Emanon666/article/details/120940774</guid><author>Emanon666</author><pubDate>Sun, 24 Oct 2021 20:57:09 +0800</pubDate><description><![CDATA[import numpy as np
import math
import random
import matplotlib.pyplot as plt
import scipy.io as scio
from scipy.fft import fft

'''
函数名称：center_data
函数功能：对数据中心化：对输入矩阵的每个元素，都减去该元素所在行(每一行一共有m个元素)的均值
输入参数：X          要处理的矩阵，大小为(n,m)
返回参数：X_center   进行中心化处理之后的.]]></description><category></category></item><item><title><![CDATA[Python 对一维信号做小波分解]]></title><link>https://blog.youkuaiyun.com/Emanon666/article/details/120938343</link><guid>https://blog.youkuaiyun.com/Emanon666/article/details/120938343</guid><author>Emanon666</author><pubDate>Sun, 24 Oct 2021 18:48:12 +0800</pubDate><description><![CDATA[import pywt
import scipy.io as scio
import numpy as np
from scipy.fft import fft
import matplotlib.pyplot as plt

def waveletdec(s,wname='db6',level=4, mode='symmetric'):
        N = len(s)
        w = pywt.Wavelet(wname)
        a = s
        ca = []
   .]]></description><category></category></item><item><title><![CDATA[【Matlab】使用LMS算法估计时间延迟]]></title><link>https://blog.youkuaiyun.com/Emanon666/article/details/120469290</link><guid>https://blog.youkuaiyun.com/Emanon666/article/details/120469290</guid><author>Emanon666</author><pubDate>Sat, 25 Sep 2021 12:23:25 +0800</pubDate><description><![CDATA[LMS算法程序：

function [yn,W,en]=LMS(xn,dn,M,mu,itr)
% LMS(Least Mean Squre)算法
% 输入参数:
% xn 输入的信号序列 (列向量)
% dn 所期望的响应序列 (列向量)
% M 滤波器的阶数 (标量)
% mu 收敛因子(步长) (标量) 要求大于0,小于xn的相关矩阵最大特征值的倒数 
% itr 迭代次数...]]></description><category></category></item></channel></rss>