feifeizhuge / blog Goto Github PK
View Code? Open in Web Editor NEWusing issue as the form to write private technical blog
using issue as the form to write private technical blog
Anfänger: http://python.jobbole.com/82023/#article-comment
Erweiter: http://python.jobbole.com/83747/
solver=’sgd’ or ‘adam’, 什么是sgd,什么是adam
读写excel文件的函数xlsfinfo检查文件是否包含excel表格;xlread读写excel文件;xlswrite写excel文件。xlsread的调用格式为:
def spines_invisible(ax_obj):
ax_obj.spines['right'].set_visible(False)
ax_obj.spines['top'].set_visible(False)
ax_obj.spines['bottom'].set_visible(False)
plt.xticks([])
# Creates two subplots and unpacks the output array immediately
f, ax_list = plt.subplots(10, 1, sharey=True, sharex=True)
# Remove the spine and trick from the axis
[spines_invisible(ax_obj) for ax_obj in ax_list]
转载于 http://www.ilovematlab.cn/thread-558129-1-1.html 作者: 科西嘉特卫
目前正在从事陀螺仪数据处理方面的研究工作,对数据预处理后,需要对陀螺仪随机漂移数据进行建模,一般选用AR或ARMA时间序列模型,因此需进行模型参数估计。
选用AR模型时,使用aryule(x,p)函数,输入x为模型输入数据,p为模型阶数,函数返回模型参数;
选用ARMA模型时,使用armax函数,该函数实际为armax模型的参数估计,原型为sys = armax(data,[na nb nc nk]),可以看出有4个阶次输入参数,但在其帮助文档有这么一句话:“If data is a time series that has no input channels and one output channel, then armax calculates an ARMA model for the time series : A(q)y(t)=C(q)e(t)
In this case, orders = [na nc]”。所以,当只有两个阶次输入时,就可以进行ARMA模型的参数估计。
实例如下:(因数据较大,仅展示运行结果)
AR模型:
ssy=aryule(GyroX,2)
ssy =
1.0000 -0.9545 0.0455
ARMA模型:
sy=armax(GyroX,[2,1])
sy =
Discrete-time ARMA model: A(z)y(t) = C(z)e(t)
A(z) = 1 - 1.412 z^-1 + 0.4651 z^-2
C(z) = 1 - 0.4473 z^-1
Sample time: 1 seconds
Parameterization:
Polynomial orders: na=2 nc=1
Number of free coefficients: 3
Use "polydata", "getpvec", "getcov" for parameters and their uncertainties.
Status:
Estimated using ARMAX on time domain data.
Fit to estimation data: 59.34% (prediction focus)
FPE: 0.0447, MSE: 0.04468
**大学 李老师 线性代数
in statistic we have many ways to estimate standard deviation. median absolute deviation or mean absolute deviation is one kind of them.
mad(median absolute deviation) = median(|x_i-median(x)|)
mean absolute deviation = mean(abs(x_i-mean(x)))
get details in R function for median absolute deviation
在使用dataframe处理数据的时候碰到了按照条件选取行的问题,单个条件时可以使用:
df[df['one'] > 5]
如果多个条件的话需要这么写:
import numpy as np
df[np.logical_and(df['one']> 5,df['two']>5)]
也可以这么写
df[(df['one']> 5) & (df['two']>5)]
作者:u100k
来源:CSDN
原文:https://blog.csdn.net/u100k/article/details/51394486
版权声明:本文为博主原创文章,转载请附上博文链接!
在做练习的时候,发现了一些函数的用法和数学原理特此记录,完整的代码。
根据文档numpy.eig,会返回w,v两个矩阵,w是eigenvalue, v是eigenvector, 值得注意的是,w和v没有根据大小排序,不符合pca的要求。
因此我们使用 numpy.argsort ,他帮助我们返回排序下标index,再次在原数组中索引这些下标就能得到排序后的数组。
最后注意的是,排序结果是从小到大,pca希望的是从大到小,所有最后还有一个,如何数组颠倒顺序
when I try to install this package in Rstudio(1.1.463) on window7 computer, it occur two big problems.
Error in curl::curl_fetch_memory(url, handle = h) : Could not resolve host: api.github.com
BinSeg_MultiDim.h:21:26: fatal error: gsl/gsl_math.h: No such file or directory
After one day struggle I have solved all problems.
About question one this is because you use company's computer and all access to Internet is through proxy. So your access to github is blocked and need use install_local('path')
to install this package locally.
download the github file as zip form
use install_local('path')
. Here path is the path where you put your zip file
at the same time you are highly probable required to install Rtools, just do it
you miss a gsl library. This is the GNU Scientific Library. To solve this you need to download Gsl for Windows, here is the link gsl library
install this gsl on your computer
find C:\Users\yourname\GnuWin32\include\gsl
and copy the whole file of gsl and put it to your R path C:\Users\yourname\Documents\R\R-3.5.3\include
Fertig! 👍
入门学习可以通过这个博客链接
设置不同的字体和颜色转载于cnds
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