规范化范围为0和1的矩阵行

I am trying to normalize all rows of my matrix data at once within range 0 and 1. But I don't know how to do it.. For example, I want to normalize each "obs1", "obs2", "obs3". Thus, minimum, maximum, and sum of each "obs1", "obs2", "obs3" will be used. My data format is,

我试图在0和1范围内一次归一化我的矩阵数据的所有行。但我不知道该怎么做..例如，我想规范化每个“obs1”，“obs2”，“obs3” 。因此，将使用每个“obs1”，“obs2”，“obs3”的最小值，最大值和总和。我的数据格式是，

`mydata

`MYDATA

             a        b        c         d         e
obs1   8.15609  11.5379  11.1401   8.95186   7.95722
obs2 339.89800 856.3470 691.3490 590.28600 543.67200
obs3   2.12776  46.4561 136.8860 118.09100 119.86400

Also, When I searched to perform this, people used "function()". When/for what does this used?

此外，当我搜索执行此操作时，人们使用“function（）”。何时使用？

Thank you very much for your help in advance! :)

非常感谢您的帮助！ :)

3 个解决方案

#1

To normalize for each row, you can use apply and then subtract the minimum from each column and divide by the difference between maximum and minimum:

要标准化每一行，您可以使用apply然后从每列中减去最小值并除以最大值和最小值之间的差值：

t(apply(mydata, 1, function(x)(x-min(x))/(max(x)-min(x))))

gives you

给你

              a         b         c         d         e
obs1 0.05553973 1.0000000 0.8889038 0.2777796 0.0000000
obs2 0.00000000 1.0000000 0.6805144 0.4848262 0.3945675
obs3 0.00000000 0.3289472 1.0000000 0.8605280 0.8736849

What happens is that you apply the function

会发生什么是您应用该功能

function(x){
   (x-min(x))/(max(x)-min(x))
}

to each row of your data frame.

到数据框的每一行。

#2

for(i in 2:length(mydata[1,])){

    mydata[,i] <- prop.table(mydata[,i])

}

Normalized matrix will be updated in mydata

归一化矩阵将在mydata中更新

#3

You could use the apply with rescale as the following:

您可以使用apply with rescale，如下所示：

apply(mydata, 1, rescale)

申请（mydata，1，rescale）

where the second argument 1 tells apply to work with rows.

其中第二个参数1告诉应用于行。

The default range is [0, 1] but a custom range can be specified with the to argument that will be forwarded to the rescale function:

默认范围是[0,1]，但可以使用将转发到rescale函数的to参数指定自定义范围：

apply(mydata, 1, rescale, to=c(1,2))

apply（mydata，1，rescale，to = c（1,2））

Dependecy:

Dependecy：

if(!require(scales)){ install.packages("scales", dependencies=TRUE) library(scales) }

if（！require（scales））{install.packages（“scales”，dependencies = TRUE）library（scales）}

#1