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thenoirlatte
thenoirlatte

Reputation: 349

How to find minimum sum of elements in a matrix python

How do I compute the minimum sum of elements in a matrix python? for example:

z = 
np.array([[1, 2, 3], 

 [3, 2, 5], 

 [2, 4, 4]])

The minimum sum of the element is : 1+2+2 = 5

I tried:

def calculate(a):
    sum = 0
    for i in range(len(a)):
        sum += np.sum(a[i])

    return sum

but it gives me 26 instead of 5

Upvotes: 0

Views: 1260

Answers (7)

kmario23
kmario23

Reputation: 61325

So, you've z as the following array. In NumPy, you've to understand axis of the array: (please see this answer for a better mental image: very-basic-numpy-array-dimension-visualization ). So, the array here is 2D.

#----> axis: 1      #| a
array([[1, 2, 3],   #| x
       [3, 2, 5],   #| i
       [2, 4, 4]])  #| s
                    #v 0

So, we find minimum along axis 1

In [70]: np.min(z, axis=1) 
Out[70]: array([1, 2, 2])

And since you also need sum, you call .sum() on the resulting 1D array from the above step. Below is the code in a single line:

In [71]: np.min(z, axis=1).sum()   
Out[71]: 5

A general rule of thumb would be to not use for loops with NumPy arrays since that would be awfully slow (in terms of runtime).

Upvotes: 0

Nicolas Gervais
Nicolas Gervais

Reputation: 36604

You shouldn't use for loops to perform operations with Numpy arrays. Numpy exists to provide optimized alternatives to loops, and using loops kind of defeats that purpose. You can take the minimum value of each row (collapsing the first axis), then sum up the resulting three values:

np.sum(np.min(z, axis=1))

Out[61]: 5

Upvotes: 3

pinegulf
pinegulf

Reputation: 1396

You could apply function to find minimum value for each row and sum after that. 

import numpy as np
z = np.array([[1, 2, 3], 
             [3, 2, 5], 
             [2, 4, 4]])

np.apply_along_axis(np.min,-1,z).sum()

How this works? First you define the function. Finding the minimum value, in this case. Second you define the axis you wish to find you values. See full description in documentation below. Lasly I run .sum() to sum up all the elements. For studying purposes I suggest running only 

np.apply_along_axis(np.min,-1,z)

see more on Numpy documentation

Upvotes: 1

Gabio
Gabio

Reputation: 9494

Try:

min_sum = sum(map(np.min,z)) 
print(min_sum) # output: 5

Upvotes: 1

Dhaval Taunk
Dhaval Taunk

Reputation: 1672

You can use np.min():-

z = np.array([[1,2,3],[3,2,5],[2,4,4]])
res = sum(np.min(z, axis=1))
print(res)

Output:

5

Upvotes: 1

Ch3steR
Ch3steR

Reputation: 20669

You find np.min over axis 1.

z = np.array([[1, 2, 3], [3, 2, 5], [2, 4, 4]])
z.min(1).sum()
# 5

Upvotes: 1

Mandera
Mandera

Reputation: 2992

You are adding the sum of the entire row to the sum, when you should be adding the minimum value. So just replace sum with min:

z = np.array([[1, 2, 3], [3, 2, 5], [2, 4, 4]])

def calculate(a):
    sum = 0
    for row in a:
        sum += np.min(row)
    return sum

print(calculate(z))

>>> 5

I also optimized your loop method to be clearer.

Upvotes: 1

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