I'm self learning python and have found a problem which requires down sampling a feature vector. I need some help understanding how down-sampling a array. in the array each row represents an image by being number from 0 to 255. I was wonder how you apply down-sampling to the array? I don't want to scikit-learn because I want to understand how to apply down-sampling.
If you could explain down-sampling too that would be amazing thanks.
the feature vector is 400x250
If with downsampling you mean something like this, you can simply slice the array. For a 1D example:
import numpy as np
a = np.arange(1,11,1)
print(a)
print(a[::3])
The last line is equivalent to:
print(a[0:a.size:3])
with the slicing notation as start:stop:step
Result:
[ 1 2 3 4 5 6 7 8 9 10]
[ 1 4 7 10]
For a 2D array the idea is the same:
b = np.arange(0,100)
c = b.reshape([10,10])
print(c[::3,::3])
This gives you, in both dimensions, every third item from the original array.
Or, if you only want to down sample a single dimension:
d = np.zeros((400,250))
print(d.shape)
e = d[::10,:]
print(e.shape)
(400, 250)
(40, 250)
The are lots of other examples in the Numpy manual
[[0,1,..,9],[10,11,..,19], the down sampled array should contain elements [[1,3,..],[11,13,..]])? Which items should be kept? Or you mention that you don't want to use scikit-learn, but which routine should it reproduce?from skimage.measure import block_reduce
new_matrix=block_reduce(Matrix_for_downsample,block_size=(m,n),func=np.mean/np.max/..)
scikit-learn.If you want to downsample along certain dimensions, you can use mean, which not only decimates, but also downsamples. Below example: Downsamples an ndarray of size (h,w,3) along axes 0,1, but not along dim 3:
def downsample_2x(arrayn3d):
"""
Downsamples an ndarray of size `(h,w,3)` along axes 0,1 (along w,h)
Input can be non-float, e.g. uint8
"""
dtype1 = array3d.dtype
a = array3d.astype(float)
(h,w,_) = a.shape
assert w % 2 == 0
assert h % 2 == 0
w2 = int(w/2)
h2 = int(h/2)
a = a.reshape((h,w2,2,3))
a = np.mean(a, axis=2)
assert a.shape == (h,w2,3)
a = a.reshape((h2,2,w2,3))
a = np.mean(a, axis=1)
assert a.shape == (h2,w2,3)
a = np.floor(a).astype(dtype1)
return a
Which gives a matrix of size (w/2,h/2,3). If w and h are not even numbers, it will be slightly more complicated.
This is not the most efficient way to do it, but the steps and ideas should be clear.
