[英]converting a 3d matrix into feature vectors
So I have data in shape (100,100,5000). 所以我的数据形状为(100,100,5000)。
Basically, it is a 100 by 100 pixel image which each (x,y) pixel having some spectrum vector.. So, the data is format 基本上,它是一个100 x 100像素的图像,每个(x,y)像素都具有一些光谱矢量。因此,数据为格式
[ [ [ 0, 0.2.....],[0.1,0.3.....].. and so on]]
When we do d[0][0][0] corresponds to (0,0) pixel in image and the spectrum value is 0 当我们做d [0] [0] [0]对应于图像中的(0,0)像素,光谱值为0
Now, i want to run kmeans algorithm in it.. Thats the background. 现在,我想在其中运行kmeans算法。
To run kmeans, I want to convert it into feature vector.. as 要运行kmeans,我想将其转换为特征向量。
[0,0,0]
[0,0,0.2]
and so on..
Is there a "python" way to do this conversion. 有没有一种“ Python”的方式来进行这种转换。 I am guessing there should be some numpy reshape method which can easily achieve this, rather than writing the three for loops which I am trying to avoid.
我猜应该有一些可以轻松实现此目的的numpy重塑方法,而不是编写我试图避免的三个for循环。
Use numpy reshape http://docs.scipy.org/doc/numpy/reference/generated/numpy.reshape.html eg 使用numpy重塑http://docs.scipy.org/doc/numpy/reference/generated/numpy.reshape.html例如
a.reshape((m,n))
where m
and n
are the desired coordinates of your array a
其中
m
和n
是数组a
的所需坐标
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