Vladislavs Dovgalecs usptact

## Estimating coin bias from noisy observations
#
# Estimate coin bias given multiple observations from adversarial experts.
#

import sys

import pymc3 as pm
import numpy as np
import theano.tensor as tt
import matplotlib.pyplot as plt

## dawid-skene-pymc3
#
# Attempt to implement Dawid-Skene model in PyMC3 (broken)
#

import pymc3 as pm
import numpy as np
import matplotlib.pyplot as plt

import theano
import sys

## dawid-skene-pymc3_ver2
#
# Attempt to implement Dawid-Skene model in PyMC3 (probably working)
#

import pymc3 as pm
import numpy as np
import matplotlib.pyplot as plt

import theano
import sys

## ordinal_pymc3_DBDA23.2.2.py
import seaborn as sns
import pymc3 as pm
import numpy as np
from scipy.stats import norm
import pandas as pd

import theano.tensor as T
from theano.compile.ops import as_op

# Generate True data

## dataphilly-jul2016.ipynb

      
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                usptact
                / dataphilly-jul2016.ipynb
            
            
              Created
              January 26, 2017 06:35
                — forked from AustinRochford/dataphilly-jul2016.ipynb
            
              
                DataPhilly July 2016 Introduction to Probabilistic Programming
              
          
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## SegmentTiler
"""
Class to pack or tile segments.

Example data:
data = [
(0, 1), (0, 2), (0, 3), (1, 3), (2, 4), (0, 5)
]

NB: sorted segments are expected (cf. IntervalSort)!
"""

## IntervalSort
"""
Class to sort intervals.

A collection of intervals is sorted:
(1) in increasing order by start index
(2) in increasing order by end index

Sample data:
data = [(1, 2), (0, 1), (0, 3), (5, 9), (1, 4), (1, 2), (2, 5), (5, 8), (3, 6)]
"""

## monty.wppl
// Remove each element in array ys from array xs
var remove = function(xs, ys) {
  return _.without.apply(null, [xs].concat(ys));
};

var doors = [1, 2, 3];

// Monty chooses a door that is neither Alice's door
// nor the prize door
var monty = function(aliceDoor, prizeDoor) {

## four_dice.wppl
// 4 dice: 4, 6, 8 and 12 sides
// each dice is perfect
var dice = [
	Categorical(
		{
			ps: [1/4, 1/4, 1/4, 1/4],
			vs: [0, 1, 2, 3]
		}
	),
	Categorical(

## second_tallest
var person = function() {
        var is_female = sample(Bernoulli({p: 0.51}))
        if (is_female == false) {
                return {hgt: sample(Gaussian({mu: 178.0, sigma: 7.7})), is_female: is_female}
        } else {
                return {hgt: sample(Gaussian({mu: 163.0, sigma: 7.3})), is_female: is_female}
        }
}

var is_taller = function(h1, h2) {
	#
	# Estimate coin bias given multiple observations from adversarial experts.
	#

	import sys

	import pymc3 as pm
	import numpy as np
	import theano.tensor as tt
	import matplotlib.pyplot as plt
	#
	# Attempt to implement Dawid-Skene model in PyMC3 (broken)
	#

	import pymc3 as pm
	import numpy as np
	import matplotlib.pyplot as plt

	import theano
	import sys
	import seaborn as sns
	import pymc3 as pm
	import numpy as np
	from scipy.stats import norm
	import pandas as pd

	import theano.tensor as T
	from theano.compile.ops import as_op

	# Generate True data
	"""
	Class to pack or tile segments.

	Example data:
	data = [
	(0, 1), (0, 2), (0, 3), (1, 3), (2, 4), (0, 5)
	]

	NB: sorted segments are expected (cf. IntervalSort)!
	"""
	"""
	Class to sort intervals.

	A collection of intervals is sorted:
	(1) in increasing order by start index
	(2) in increasing order by end index

	Sample data:
	data = [(1, 2), (0, 1), (0, 3), (5, 9), (1, 4), (1, 2), (2, 5), (5, 8), (3, 6)]
	"""
	// Remove each element in array ys from array xs
	var remove = function(xs, ys) {
	return _.without.apply(null, [xs].concat(ys));
	};

	var doors = [1, 2, 3];

	// Monty chooses a door that is neither Alice's door
	// nor the prize door
	var monty = function(aliceDoor, prizeDoor) {
	// 4 dice: 4, 6, 8 and 12 sides
	// each dice is perfect
	var dice = [
	Categorical(
	{
	ps: [1/4, 1/4, 1/4, 1/4],
	vs: [0, 1, 2, 3]
	}
	),
	Categorical(
	var person = function() {
	var is_female = sample(Bernoulli({p: 0.51}))
	if (is_female == false) {
	return {hgt: sample(Gaussian({mu: 178.0, sigma: 7.7})), is_female: is_female}
	} else {
	return {hgt: sample(Gaussian({mu: 163.0, sigma: 7.3})), is_female: is_female}
	}
	}

	var is_taller = function(h1, h2) {