Estimating slope and intercept using log-likelihood for linear regression
I have y as (1,10) and y-predicted as (1,10) and also I have slope and intercept values that were used to get y-predicted. I am having confusion in implementing linear regression with log-likelihood. I have seen a lot of blog posts for doing this but none of them are working(surely I am doing something wrong). Here is the loglikelihood function that I made which tells logL of y-predicted with some given slope(a) and intercept (b):-
import math
def loglik(a,b):
"prob of y, given values of a and b"
loglike=
for i in range(len(x)): #x has features
predicted=a*x[i]+b #doing prediction using given a and b
real=y[i] #actual label
l=math.log(predicted)/math.log(real) #not sure that it is right
loglike.append(l)
loglike=np.array(loglike)
result = np.prod(loglike)
return result
I then have to call this function, make arrays of results and make a color mesh and that's how I am doing that:-
# a has slope and b has intercept of the fitted model
arange =[a-0.5,a-0.4,a-0.3,a-0.2,a-0.1,a,a+0.1,a+0.2,a+0.3,a+0.4] #(a-0.5,a+0.4)
brange = [b,b-4,b-3,b-2,b-1,b,b+1,b+2,b+3,b+4] #(b-5,b+4)
ll=
for i in range(10):
sublist=
for j in range(10):
sublist.append(loglik(arange[i],brange[j]))
ll.append(np.array(sublist))
arange=np.array(arange)
brange=np.array(brange)
ll=np.array(ll)
Kindly tell me what I am doing wrong, I have tried implementing formula for log-likelihood and the results are quite weird that's how I know that I am doing wrong. After this I have to select a and b values by maximizing loglike and do gradient ascent to implement the regression model. Any help will be much appreciated.
python-3.x linear-regression log-likelihood
asked Nov 27 '18 at 15:21
AsimAsim
596416
add a comment |
I have y as (1,10) and y-predicted as (1,10) and also I have slope and intercept values that were used to get y-predicted. I am having confusion in implementing linear regression with log-likelihood. I have seen a lot of blog posts for doing this but none of them are working(surely I am doing something wrong). Here is the loglikelihood function that I made which tells logL of y-predicted with some given slope(a) and intercept (b):-
import math
def loglik(a,b):
"prob of y, given values of a and b"
loglike=
for i in range(len(x)): #x has features
predicted=a*x[i]+b #doing prediction using given a and b
real=y[i] #actual label
l=math.log(predicted)/math.log(real) #not sure that it is right
loglike.append(l)
loglike=np.array(loglike)
result = np.prod(loglike)
return result
I then have to call this function, make arrays of results and make a color mesh and that's how I am doing that:-
# a has slope and b has intercept of the fitted model
arange =[a-0.5,a-0.4,a-0.3,a-0.2,a-0.1,a,a+0.1,a+0.2,a+0.3,a+0.4] #(a-0.5,a+0.4)
brange = [b,b-4,b-3,b-2,b-1,b,b+1,b+2,b+3,b+4] #(b-5,b+4)
ll=
for i in range(10):
sublist=
for j in range(10):
sublist.append(loglik(arange[i],brange[j]))
ll.append(np.array(sublist))
arange=np.array(arange)
brange=np.array(brange)
ll=np.array(ll)
Kindly tell me what I am doing wrong, I have tried implementing formula for log-likelihood and the results are quite weird that's how I know that I am doing wrong. After this I have to select a and b values by maximizing loglike and do gradient ascent to implement the regression model. Any help will be much appreciated.
python-3.x linear-regression log-likelihood
asked Nov 27 '18 at 15:21
AsimAsim
596416
add a comment |
I have y as (1,10) and y-predicted as (1,10) and also I have slope and intercept values that were used to get y-predicted. I am having confusion in implementing linear regression with log-likelihood. I have seen a lot of blog posts for doing this but none of them are working(surely I am doing something wrong). Here is the loglikelihood function that I made which tells logL of y-predicted with some given slope(a) and intercept (b):-
import math
def loglik(a,b):
"prob of y, given values of a and b"
loglike=
for i in range(len(x)): #x has features
predicted=a*x[i]+b #doing prediction using given a and b
real=y[i] #actual label
l=math.log(predicted)/math.log(real) #not sure that it is right
loglike.append(l)
loglike=np.array(loglike)
result = np.prod(loglike)
return result
I then have to call this function, make arrays of results and make a color mesh and that's how I am doing that:-
# a has slope and b has intercept of the fitted model
arange =[a-0.5,a-0.4,a-0.3,a-0.2,a-0.1,a,a+0.1,a+0.2,a+0.3,a+0.4] #(a-0.5,a+0.4)
brange = [b,b-4,b-3,b-2,b-1,b,b+1,b+2,b+3,b+4] #(b-5,b+4)
ll=
for i in range(10):
sublist=
for j in range(10):
sublist.append(loglik(arange[i],brange[j]))
ll.append(np.array(sublist))
arange=np.array(arange)
brange=np.array(brange)
ll=np.array(ll)
Kindly tell me what I am doing wrong, I have tried implementing formula for log-likelihood and the results are quite weird that's how I know that I am doing wrong. After this I have to select a and b values by maximizing loglike and do gradient ascent to implement the regression model. Any help will be much appreciated.
python-3.x linear-regression log-likelihood
asked Nov 27 '18 at 15:21
AsimAsim
596416
I have y as (1,10) and y-predicted as (1,10) and also I have slope and intercept values that were used to get y-predicted. I am having confusion in implementing linear regression with log-likelihood. I have seen a lot of blog posts for doing this but none of them are working(surely I am doing something wrong). Here is the loglikelihood function that I made which tells logL of y-predicted with some given slope(a) and intercept (b):-
import math
def loglik(a,b):
"prob of y, given values of a and b"
loglike=
for i in range(len(x)): #x has features
predicted=a*x[i]+b #doing prediction using given a and b
real=y[i] #actual label
l=math.log(predicted)/math.log(real) #not sure that it is right
loglike.append(l)
loglike=np.array(loglike)
result = np.prod(loglike)
return result
I then have to call this function, make arrays of results and make a color mesh and that's how I am doing that:-
# a has slope and b has intercept of the fitted model
arange =[a-0.5,a-0.4,a-0.3,a-0.2,a-0.1,a,a+0.1,a+0.2,a+0.3,a+0.4] #(a-0.5,a+0.4)
brange = [b,b-4,b-3,b-2,b-1,b,b+1,b+2,b+3,b+4] #(b-5,b+4)
ll=
for i in range(10):
sublist=
for j in range(10):
sublist.append(loglik(arange[i],brange[j]))
ll.append(np.array(sublist))
arange=np.array(arange)
brange=np.array(brange)
ll=np.array(ll)
Kindly tell me what I am doing wrong, I have tried implementing formula for log-likelihood and the results are quite weird that's how I know that I am doing wrong. After this I have to select a and b values by maximizing loglike and do gradient ascent to implement the regression model. Any help will be much appreciated.
python-3.x linear-regression log-likelihood
python-3.x linear-regression log-likelihood
asked Nov 27 '18 at 15:21
AsimAsim
596416
asked Nov 27 '18 at 15:21
AsimAsim
596416
asked Nov 27 '18 at 15:21
AsimAsim
596416
asked Nov 27 '18 at 15:21
AsimAsim
596416
asked Nov 27 '18 at 15:21
AsimAsim
596416
596416
add a comment |
add a comment |
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