mean

import pandas as pd import numpy as np df = pd.DataFrame({'a':['one','two','one','two'], 'b':['key1', 'key1','key2','key2'], 'c':[2,3,4,5], 'd':[2,1,2,3], 'e':[3,3,4,4]}) df 1 2 3 4 5 6 7 8 9 # 对a进行分组，使用mean聚合函数，方法1 df.groupby('a').agg(['mean']) 1 2 # 对a进行分组，使用mean聚合函数，方法2 df[['c','d','e']].groupby(df['a']).agg(['mean']) 1 2 # 对a,b进行分组，使用mean，sum聚合函数(http://www.my516.com) df.groupby(['a','b']).agg(['mean','sum']) 1 2 --------------------- 来源： https://www.cnblogs.com/hyhy904/p/11357288.html

import pandas as pd import numpy as np df = pd.DataFrame({'a':['one','two','one','two'], 'b':['key1', 'key1','key2','key2'], 'c':[2,3,4,5], 'd':[2,1,2,3], 'e':[3,3,4,4]}) df 1 2 3 4 5 6 7 8 9 # 对a进行分组，使用mean聚合函数，方法1 df.groupby('a').agg(['mean']) 1 2 # 对a进行分组，使用mean聚合函数，方法2 df[['c','d','e']].groupby(df['a']).agg(['mean']) 1 2 # 对a,b进行分组，使用mean，sum聚合函数(http://www.my516.com) df.groupby(['a','b']).agg(['mean','sum']) 1 2 --------------------- 来源： https://www.cnblogs.com/hyhy904/p/11357288.html

I have a 352x11 matrix, indexed by column 1 with 10 data points. Some of the index values are repeated. I'd like to find the repeated indices and calculate the mean data points for the repeated trials (avoiding loops, if possible). For example, x = 26 77.5700 17.9735 32.7200 27 40.5887 16.6100 31.5800 28 60.4734 18.5397 33.6200 28 35.6484 27.2000 54.8000 29 95.3448 19.0000 37.7300 30 82.7273 30.4394 39.1400 to end up with: ans = 26 77.5700 17.9735 32.7200 27 40.5887 16.6100 31.5800 28 48.0609 22.8699 44.2150 29 95.3448 19.0000 37.7300 30 82.7273 30.4394 39.1400 I was thinking if I used J =

I'm a newbie of R and I don't know how to get R calculate the means of a subgroups of means which are the means of a subgroup themselves. I'll explain clearer. I have a data frame like this: GROUP WORD WLN 1 1 4 1 1 3 1 1 3 1 2 2 1 2 2 1 2 3 2 3 1 2 3 1 2 3 2 2 4 1 2 4 1 2 4 1 ... ... ... but the real one has a total of 5 groups and 25 words (5 words each group; every word has being assigned a number from 1 to 4 by 5 subjects...). I need to get the means of WLN for every word and I can do that easily with a loop and save the results in a vector; but then I need a vector with the means of these

I need to get the mean of all columns of a large data set using R, grouped by 2 variables. Lets try it with mtcars: library(dplyr) g_mtcars <- group_by(mtcars, cyl, gear) summarise(g_mtcars, mean (hp)) # Source: local data frame [8 x 3] # Groups: cyl [?] # # cyl gear `mean(hp)` # <dbl> <dbl> <dbl> # 1 4 3 97.0000 # 2 4 4 76.0000 # 3 4 5 102.0000 # 4 6 3 107.5000 # 5 6 4 116.5000 # 6 6 5 175.0000 # 7 8 3 194.1667 # 8 8 5 299.5000 It works for "hp", but I need to get the mean for every other columns of mtcars (except "cyl" and "gear" that make a group). The data set is large, with several

When generating random numbers in R using rnorm (or runif etc.), they seldom have the exact mean and SD as the distribution they are sampled from. Is there any simple one-or-two-liner that does this for me? As a preliminary solution, I've created this function but it seems like something that should be native to R or some package. # Draw sample from normal distribution with guaranteed fixed mean and sd rnorm_fixed = function(n, mu=0, sigma=1) { x = rnorm(n) # from standard normal distribution x = sigma * x / sd(x) # scale to desired SD x = x - mean(x) + mu # center around desired mean return(x

Is there a way to calculate mean and standard deviation for a vector containing samples using Boost ? Or do I have to create an accumulator and feed the vector into it? David Nehme Using accumulators is the way to compute means and standard deviations in Boost . accumulator_set<double, stats<tag::variance> > acc; for_each(a_vec.begin(), a_vec.end(), bind<void>(ref(acc), _1)); cout << mean(acc) << endl; cout << sqrt(variance(acc)) << endl; musiphil I don't know if Boost has more specific functions, but you can do it with the standard library. Given std::vector<double> v , this is the naive way:

零记忆非线性变换法 零记忆非线性变换法(ZMNL：Zero Memory Nonlinearity)的思路清晰，是目前使用最多的经典算法。ZMNL法的基本思路是：首先产生相关的高斯随机序列，然后经某种非线性变换得到需要的相关非高斯随机序列。其过程如下图所示： 图中，先产生不相关的高斯白噪声序列，经过线性滤波器，使其满足谱特性,即经过后得到的杂波序列,其功率谱函数为系统幅频函数的平方，其幅度分布仍然服从高斯分布。杂波序列经过非线性滤波器后得到随机序列，即为所需要的杂波序列。其中滤波器使序列满足特定的幅度分布特性。 该法最关键也是比较困难的地方就是由给定的非高斯序列的相关函数推导得出变换之前的高斯序列的相关函数，而且非线性关系会随杂波幅度分布的不同而不同。 ZMNL方法可以实现对对数正态分布、韦布尔分布、K分布等相关非高斯分布杂波的模拟仿真，且比较容易实现、运算速度快，是杂仿真中常用的方法。 使用的是MATLAB R2016b版本，关于各种分布的理论知识在这里我就不多说了，直接上程序。程序运行出现三种图，分别为：杂波波形图、杂波幅度分布图和杂波功率谱。 瑞利分布 clear all；close all; azi_num=2000; %取2000个点 fr=1000; %雷达重复频率 lamda0=0.05; %杂波波长 sigmav=1.0; %杂波方差 sigmaf=2*sigmav