I want to be able to iterate over the matrix to apply a function to each row. How can I do it for a Numpy matrix ?
3 Answers
You can use numpy.apply_along_axis(). Assuming that your array is 2D, you can use it like:
import numpy as np
mymatrix = np.matrix([[11,12,13],
[21,22,23],
[31,32,33]])
def myfunction(x):
return sum(x)
print(np.apply_along_axis(myfunction, axis=1, arr=mymatrix))
#[36 66 96]
While you should certainly provide more information, if you are trying to go through each row, you can just iterate with a for loop:
import numpy
m = numpy.ones((3,5),dtype='int')
for row in m:
print str(row)
Here's my take if you want to try using multiprocesses to process each row of numpy array,
from multiprocessing import Pool
import numpy as np
def my_function(x):
pass # do something and return something
if __name__ == '__main__':
X = np.arange(6).reshape((3,2))
pool = Pool(processes = 4)
results = pool.map(my_function, map(lambda x: x, X))
pool.close()
pool.join()
pool.map take in a function and an iterable.
I used 'map' function to create an iterator over each rows of the array.
Maybe there's a better to create the iterable though.
