Numpy modify array in place?
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Published on 2012-04-13T23:10:30Z
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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()
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