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with pm.Model() as model: | |
## Independent priors | |
alpha = pm.Normal('Person', mu = 0, sigma = 3, shape = (1, len(data))) | |
gamma = pm.Normal('Question', mu = 0, sigma = 3, shape = (data.shape[1], 1)) | |
## Log-Likelihood | |
def logp(d): | |
v1 = tt.transpose(d) * tt.log(tt.nnet.sigmoid(alpha - (gamma - gamma.mean(0)))) | |
v2 = tt.transpose((1-d)) * tt.log(1 - tt.nnet.sigmoid(alpha - (gamma - gamma.mean(0)))) |
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{ | |
"์์ธํน๋ณ์/๊ฐ๋จ๊ตฌ": { | |
"lat": "37.4951", | |
"long": "127.06278" | |
}, | |
"์์ธํน๋ณ์/๊ฐ๋๊ตฌ": { | |
"lat": "37.55274", | |
"long": "127.14546" | |
}, | |
"์์ธํน๋ณ์/๊ฐ๋ถ๊ตฌ": { |
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<!-- This loads of all of files that you call below from your 'static' folder --> | |
{% load staticfiles %} | |
<!--The static folder consists of your bootstrap files (.css, .js, fonts, etc)--> | |
<!--When you load the static folders is calls all those folders in the static files without having to code each one in individually--> | |
<html> | |
<head> | |
<!--This is the meta information which is good for SEO--> | |
{% block head-meta %} | |
<meta charset="utf-8"> |