Issue in numpy when change numpy array value during vector normalization

Question

Say I have a numpy array a = np.array([[1,2,3],[2,3,4]])

I want make this two vectors into unit vectors. The working code is

for i in range(2):
    a[i] = a[i]/np.linalg.norm(a[i])

In the end, the result a become:

a =

array([[0, 0, 0],
       [0, 0, 0]])

while if I run the calculation and print out the results like

for i in range(2):
    print(a[i],i,np.linalg.norm(a[i]))

The code would print out correct results as unit vectors.

Hence my question are : 1. Why the numpy array become zero if I assign it to the unit vector results. 2. what is the correct way to change a list of vectors into unit vectors by numpy array?

Thank you all very much!

a is int dtype. values are truncated.

hpaulj
– hpaulj

2021-05-08 08:43:47 +00:00
Commented May 8, 2021 at 8:43 — hpaulj
– hpaulj, Commented May 8, 2021 at 8:43

Gusti Adli · Accepted Answer · 2021-05-08 08:49:28Z

1

Because numpy.array() requires you to declare the data type of objects contained in the array. If not declared it will be determined as the minimum data type. In your case it will be an int. To solve your problem you can either make a new array with dtype = float or declare a's dtype.

a = np.array([[1,2,3],[2,3,4]], dtype=np.float32)

answered May 8, 2021 at 8:49

Gusti Adli

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2 Comments

Roy Dai Over a year ago

Thank you very much for the clarification. Must numpy declare the data type at the start of the assignment? Can I change the dtype some where later?

Gusti Adli Over a year ago

no, you can change it later using .astype()

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