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Is this as good as pastebin?

Seems good enough! 

And with history!

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...paste contents here and save...

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<div class='et-tabs-container' id='et-tabs-container98799'>
    <ul class='et-tabs-control'>
        <li><a href='#'>Sample Video 1</a></li>
        <li><a href='#'>Sample Video 2</a></li>
        <li><a href='#'>Basic Whiteboard Video</a></li>
        <li><a href='#'>Advanced Whiteboard Video</a></li>
    </ul> <!-- .et-tabs-control -->
    <div class='et-tabs-content'>
        <div class='et_slidecontent' style="padding: 20px;">
          Test 1

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# Masah Ka Bayan 

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Masah ka bayan

• It is allowed to do masah (wipe with wet hand) on leather socks and better to wash feet fully.
• Here, sock does not refer to modern socks but thick leather socks.

Necessary conditions for Masah

1. Socks should have four qualities
2. Should be thick enough to support themselves without tying
3. It should be possible to walk 3 mile (shar'i) in socks

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468634551 [com.codeglue.beerponghd] Beer Pong HD () 
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599577426 [com.MiracleStudios.TrollMaze] Scary Troll Maze Prank Free- Harlem Shake () 
644824809 [

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model = Model()

# Y = [1, 0, 0, 1, 1, ... 1, 0, 1]  # binary computed from n_predictors features

with model:
    # Priors for unknown model parameters
    # alpha = Beta('alpha', alpha=0.5, beta=0.5) 
    beta = Normal('beta', mu=0, tau=2. ** -2, shape=(1, n_predictors))
    
    # expected probability 

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model = Model()
with model:
  beta = Uniform('beta', lower=-10, upper=10, shape=(1, n_predictors))
  
  p = tinvlogit(sum(beta * predictors, 1))
  Y_obs = Bernoulli('Y_obs', p=p, observed=Y)
  
  start = find_MAP()
  step = NUTS(scaling=start)
  trace = sample(mcmc_training_sample_size, step, start)

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# multiAgents.py
# --------------
# Licensing Information:  You are free to use or extend these projects for
# educational purposes provided that (1) you do not distribute or publish
# solutions, (2) you retain this notice, and (3) you provide clear
# attribution to UC Berkeley, including a link to http://ai.berkeley.edu.
# 
# Attribution Information: The Pacman AI projects were developed at UC Berkeley.
# The core projects and autograders were primarily created by John DeNero
# ([email protected]) and Dan Klein ([email protected]).

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# multiAgents.py
# --------------
# Licensing Information:  You are free to use or extend these projects for
# educational purposes provided that (1) you do not distribute or publish
# solutions, (2) you retain this notice, and (3) you provide clear
# attribution to UC Berkeley, including a link to http://ai.berkeley.edu.
# 
# Attribution Information: The Pacman AI projects were developed at UC Berkeley.
# The core projects and autograders were primarily created by John DeNero
# ([email protected]) and Dan Klein ([email protected]).