Vladislavs Dovgalecs usptact

## keybase.md

      
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                usptact
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                keybase.io manifest
              
          
    Keybase proof

I hereby claim:

I am usptact on github.
I am xeon (https://keybase.io/xeon) on keybase.
I have a public key whose fingerprint is 20E1 A134 927A DF11 C161  F498 9F70 1928 BDBA A247

To claim this, I am signing this object:

  
## 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)]
"""

## letor_metrics.py
# (C) Mathieu Blondel, November 2013
# License: BSD 3 clause

import numpy as np


def ranking_precision_score(y_true, y_score, k=10):
    """Precision at rank k

    Parameters

## softrank_distribution
"""
Demo script to compute rank distributions given pairwise preference probabilities.

Data:
- pairwise preference probabilities (not including self)

Output:
- distribution over ranks

Michael Taylor, John Guiver, Stephen Robertson and Tom Minka, "SoftRank: Optimising Non-Smooth Rank Metrics", WSDM 2008.
	#
	# 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)]
	"""
	# (C) Mathieu Blondel, November 2013
	# License: BSD 3 clause

	import numpy as np


	def ranking_precision_score(y_true, y_score, k=10):
	"""Precision at rank k

	Parameters
	"""
	Demo script to compute rank distributions given pairwise preference probabilities.

	Data:
	- pairwise preference probabilities (not including self)

	Output:
	- distribution over ranks

	Michael Taylor, John Guiver, Stephen Robertson and Tom Minka, "SoftRank: Optimising Non-Smooth Rank Metrics", WSDM 2008.