Numpy modify array in place?
- by User
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 the number of training examples and n is the number of features).
However, when I inspect the array in the interpreter after the script runs, I see that the values are not normalized; that is, they still have the original values. I guess this is because the assignment to the array variable inside the function is only seen within the function.
How can I do this normalization in place? Or do I have to return a new array from the normalize function?
import numpy
def normalize(array, imin = -1, imax = 1):
"""I = Imin + (Imax-Imin)*(D-Dmin)/(Dmax-Dmin)"""
dmin = array.min()
dmax = array.max()
array = imin + (imax - imin)*(array - dmin)/(dmax - dmin)
print array[0]
def main():
array = numpy.loadtxt('test.csv', delimiter=',', skiprows=1)
for column in array.T:
normalize(column)
return array
if __name__ == "__main__":
a = main()
