Reputation: 139
Could you guys please tell me how I can make the following code more pythonic?
The code is correct. Full disclosure - it's problem 1b in Handout #4 of this machine learning course. I'm supposed to use newton's algorithm on the two data sets for fitting a logistic hypothesis. But they use matlab & I'm using scipy
Eg one question i have is the matrixes kept rounding to integers until I initialized one value to 0.0. Is there a better way?
Thanks
import os.path
import math
from numpy import matrix
from scipy.linalg import inv #, det, eig
x = matrix( '0.0;0;1' )
y = 11
grad = matrix( '0.0;0;0' )
hess = matrix('0.0,0,0;0,0,0;0,0,0')
theta = matrix( '0.0;0;0' )
# run until convergence=6or7
for i in range(1, 6):
#reset
grad = matrix( '0.0;0;0' )
hess = matrix('0.0,0,0;0,0,0;0,0,0')
xfile = open("q1x.dat", "r")
yfile = open("q1y.dat", "r")
#over whole set=99 items
for i in range(1, 100):
xline = xfile.readline()
s= xline.split(" ")
x[0] = float(s[1])
x[1] = float(s[2])
y = float(yfile.readline())
hypoth = 1/ (1+ math.exp(-(theta.transpose() * x)))
for j in range(0,3):
grad[j] = grad[j] + (y-hypoth)* x[j]
for k in range(0,3):
hess[j,k] = hess[j,k] - (hypoth *(1-hypoth)*x[j]*x[k])
theta = theta - inv(hess)*grad #update theta after construction
xfile.close()
yfile.close()
print "done"
print theta
Upvotes: 3
Views: 788
Reputation: 119221
One obvious change is to get rid of the "for i in range(1, 100):" and just iterate over the file lines. To iterate over both files (xfile and yfile), zip them. ie replace that block with something like:
import itertools
for xline, yline in itertools.izip(xfile, yfile):
s= xline.split(" ")
x[0] = float(s[1])
x[1] = float(s[2])
y = float(yline)
...
(This is assuming the file is 100 lines, (ie. you want the whole file). If you're deliberately restricting to the first 100 lines, you could use something like:
for i, xline, yline in itertools.izip(range(100), xfile, yfile):
However, its also inefficient to iterate over the same file 6 times - better to load it into memory in advance, and loop over it there, ie. outside your loop, have:
xfile = open("q1x.dat", "r")
yfile = open("q1y.dat", "r")
data = zip([line.split(" ")[1:3] for line in xfile], map(float, yfile))
And inside just:
for (x1,x2), y in data:
x[0] = x1
x[1] = x2
...
Upvotes: 9
Reputation: 24764
x = matrix([[0.],[0],[1]])
theta = matrix(zeros([3,1]))
for i in range(5):
grad = matrix(zeros([3,1]))
hess = matrix(zeros([3,3]))
[xfile, yfile] = [open('q1'+a+'.dat', 'r') for a in 'xy']
for xline, yline in zip(xfile, yfile):
x.transpose()[0,:2] = [map(float, xline.split(" ")[1:3])]
y = float(yline)
hypoth = 1 / (1 + math.exp(theta.transpose() * x))
grad += (y - hypoth) * x
hess -= hypoth * (1 - hypoth) * x * x.transpose()
theta += inv(hess) * grad
print "done"
print theta
Upvotes: 4
Reputation: 159
the matrixes kept rounding to integers until I initialized one value to 0.0. Is there a better way?
At the top of your code:
from __future__ import division
In Python 2.6 and earlier, integer division always returns an integer unless there is at least one floating point number within. In Python 3.0 (and in future division in 2.6), division works more how we humans might expect it to.
If you want integer division to return an integer, and you've imported from future, use a double //. That is
from __future__ import division
print 1//2 # prints 0
print 5//2 # prints 2
print 1/2 # prints 0.5
print 5/2 # prints 2.5
Upvotes: 3
Reputation: 6225
the code that reads the files into lists could be drastically simpler
for line in open("q1x.dat", "r"):
x = map(float,line.split(" ")[1:])
y = map(float, open("q1y.dat", "r").readlines())
Upvotes: 0