python - Numpy assignment behaviour -


b 

array([-0.06106568, -0.10843541, -0.0694688 ,  0.02464023, -0.03686665,        -0.0582096 , -0.13476669, -0.08505708,  0.00391955, -0.12300518,        -0.01183732, -0.05374973, -0.12300518, -0.05312849,  0.01963862,         0.00155719, -0.10843541, -0.08490177, -0.08505708, -0.02026149,        -0.01777489,  0.01183732, -0.11575136,  0.04278603, -0.0694688 ,        -0.06106568, -0.08755022, -0.01660802, -0.06087603, -0.06582411])  = b a[a <= 0] = 0 a[a > 0] = 1

with code above replace elements in a, b changes... please explain me mistake?

by using expression a = b copy reference b. if want copy value, should rather walk through b's items , copy values a.

in numpy should use copy function.

>>> import numpy >>> b = numpy.array([-0.06106568, -0.10843541, -0.0694688 ,  0.02464023, -0.03686665, ...        -0.0582096 , -0.13476669, -0.08505708,  0.00391955, -0.12300518, ...        -0.01183732, -0.05374973, -0.12300518, -0.05312849,  0.01963862, ...         0.00155719, -0.10843541, -0.08490177, -0.08505708, -0.02026149, ...        -0.01777489,  0.01183732, -0.11575136,  0.04278603, -0.0694688 , ...        -0.06106568, -0.08755022, -0.01660802, -0.06087603, -0.06582411]) >>> = numpy.copy(b) >>> array([-0.06106568, -0.10843541, -0.0694688 ,  0.02464023, -0.03686665,        -0.0582096 , -0.13476669, -0.08505708,  0.00391955, -0.12300518,        -0.01183732, -0.05374973, -0.12300518, -0.05312849,  0.01963862,         0.00155719, -0.10843541, -0.08490177, -0.08505708, -0.02026149,        -0.01777489,  0.01183732, -0.11575136,  0.04278603, -0.0694688 ,        -0.06106568, -0.08755022, -0.01660802, -0.06087603, -0.06582411]) >>> a[a<= 0] = 0 >>> a[a> 0] = 1 >>> array([ 0.,  0.,  0.,  1.,  0.,  0.,  0.,  0.,  1.,  0.,  0.,  0.,  0.,         0.,  1.,  1.,  0.,  0.,  0.,  0.,  0.,  1.,  0.,  1.,  0.,  0.,         0.,  0.,  0.,  0.]) >>> b array([-0.06106568, -0.10843541, -0.0694688 ,  0.02464023, -0.03686665,        -0.0582096 , -0.13476669, -0.08505708,  0.00391955, -0.12300518,        -0.01183732, -0.05374973, -0.12300518, -0.05312849,  0.01963862,         0.00155719, -0.10843541, -0.08490177, -0.08505708, -0.02026149,        -0.01777489,  0.01183732, -0.11575136,  0.04278603, -0.0694688 ,        -0.06106568, -0.08755022, -0.01660802, -0.06087603, -0.06582411])

there native ways it, if work mathematics, using numpy recommended.

update

i don't have in mind non-numpy way cause entire compatibility case.


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