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 ?
23 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.
