Numpy modify array in place?

Question

I have the following code which is attempting to normalize the values of an m x n array (It will be used as input to a neural network, where m is t

Answer 1

There is a nice way to do in-place normalization when using numpy. np.vectorize is is very usefull when combined with a lambda function when applied to an array. See the example below:

import numpy as np

def normalizeMe(value,vmin,vmax):

    vnorm = float(value-vmin)/float(vmax-vmin)

    return vnorm

imin = 0
imax = 10
feature = np.random.randint(10, size=10)

# Vectorize your function (only need to do it once)
temp = np.vectorize(lambda val: normalizeMe(val,imin,imax)) 
normfeature = temp(np.asarray(feature))

print feature
print normfeature

One can compare the performance with a generator expression, however there are likely many other ways to do this.

%%timeit
temp = np.vectorize(lambda val: normalizeMe(val,imin,imax)) 
normfeature1 = temp(np.asarray(feature))
10000 loops, best of 3: 25.1 µs per loop


%%timeit
normfeature2 = [i for i in (normalizeMe(val,imin,imax) for val in feature)]
100000 loops, best of 3: 9.69 µs per loop

%%timeit
normalize(np.asarray(feature))
100000 loops, best of 3: 12.7 µs per loop

So vectorize is definitely not the fastest, but can be conveient in cases where performance is not as important.