usptact

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usptact

#
# 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

usptact

#
# 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

usptact

#
# 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

usptact

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

usptact

usptact

"""
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)!
"""

usptact

"""
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)]
"""

usptact

# (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

usptact

"""
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.