Created
July 6, 2015 09:09
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linear regression with 1 variable in python
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testset = [ | |
[1, 4], | |
[2, 8], | |
[3, 12], | |
[4, 16] | |
] | |
theta0 = 0 | |
theta1 = 0 | |
learningrate = 0.01 | |
# h of theta(x) = theta0 + theta1 * x | |
def hypo(something): | |
global theta0 | |
global theta1 | |
return theta0 + (theta1 * something) | |
def ms1(testset): | |
s = 0 | |
for element in testset: | |
s = s + (hypo(element[0]) - element[1]) | |
return 1.0/len(testset) * s | |
def ms2(testset): | |
s = 0 | |
for element in testset: | |
s = s + ((hypo(element[0]) - element[1]) * element[0]) | |
return 1.0/len(testset) * s | |
def compute(testset): | |
global theta0 | |
global theta1 | |
print "Starting with theta0 = %s" % theta0 | |
print "Starting with theta1 = %s" % theta1 | |
prev = [0, 0] | |
while(True): | |
new_theta0 = theta0 - (learningrate * ms1(testset)) | |
new_theta1 = theta1 - (learningrate * ms2(testset)) | |
theta0 = new_theta0 | |
theta1 = new_theta1 | |
#print "%s - %s" % (theta0, theta1) | |
if theta0 == prev[0] and theta1 == prev[1]: | |
return prev | |
prev = [theta0, theta1] | |
compute(testset) | |
assert round(hypo(5)) == 20.0 | |
assert round(hypo(20)) == 80.0 |
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