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# | |
# 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 |
# | |
# 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 |
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. |