# Path: distributions/util.py
# def multinomial_goodness_of_fit(
#         probs,
#         counts,
#         total_count,
#         truncated=False,
#         plot=False):
#     """
#     Pearson's chi^2 test, on possibly truncated data.
#     http://en.wikipedia.org/wiki/Pearson%27s_chi-squared_test
# 
#     Returns:
#         p-value of truncated multinomial sample.
#     """
#     assert len(probs) == len(counts)
#     assert truncated or total_count == sum(counts)
#     chi_squared = 0
#     dof = 0
#     if plot:
#         print_histogram(probs, counts)
#     for p, c in zip(probs, counts):
#         if p == 1:
#             return 1 if c == total_count else 0
#         assert p < 1, 'bad probability: %g' % p
#         if p > 0:
#             mean = total_count * p
#             variance = total_count * p * (1 - p)
#             assert variance > 1,\
#                 'WARNING goodness of fit is inaccurate; use more samples'
#             chi_squared += (c - mean) ** 2 / variance
#             dof += 1
#         else:
#             print 'WARNING zero probability in goodness-of-fit test'
#             if c > 0:
#                 return float('inf')
# 
#     if not truncated:
#         dof -= 1
# 
#     survival = scipy.stats.chi2.sf(chi_squared, dof)
#     return survival


import os
import glob
import math
import numpy
import importlib
import distributions
    import distributions.dbg.random
        import distributions.hp.random
            import traceback
from collections import defaultdict
from itertools import izip
from numpy.testing import assert_array_almost_equal
from nose import SkipTest
from nose.tools import assert_true, assert_less, assert_equal
from distributions.util import multinomial_goodness_of_fit
        counts[sample] += 1
        probs[sample] += prob

    for key, count in counts.iteritems():
        probs[key] /= count
    total_prob = sum(probs.itervalues())
    assert_close(total_prob, 1.0, tol=1e-2, err_msg='total_prob is biased')

    return counts, probs


def assert_counts_match_probs(counts, probs, tol=1e-3):
    '''
    Check goodness of fit of observed counts to predicted probabilities
    using Pearson's chi-squared test.

    Inputs:
        - counts : key -> int
        - probs : key -> float
    '''
    keys = counts.keys()
    probs = [probs[key] for key in keys]
    counts = [counts[key] for key in keys]
    total_count = sum(counts)

    print 'EXPECT\tACTUAL\tVALUE'
    for prob, count, key in sorted(izip(probs, counts, keys), reverse=True):
        expect = prob * total_count
        print '{:0.1f}\t{}\t{}'.format(expect, count, key)


# Path: distributions/util.py
# def multinomial_goodness_of_fit(
#         probs,
#         counts,
#         total_count,
#         truncated=False,
#         plot=False):
#     """
#     Pearson's chi^2 test, on possibly truncated data.
#     http://en.wikipedia.org/wiki/Pearson%27s_chi-squared_test
# 
#     Returns:
#         p-value of truncated multinomial sample.
#     """
#     assert len(probs) == len(counts)
#     assert truncated or total_count == sum(counts)
#     chi_squared = 0
#     dof = 0
#     if plot:
#         print_histogram(probs, counts)
#     for p, c in zip(probs, counts):
#         if p == 1:
#             return 1 if c == total_count else 0
#         assert p < 1, 'bad probability: %g' % p
#         if p > 0:
#             mean = total_count * p
#             variance = total_count * p * (1 - p)
#             assert variance > 1,\
#                 'WARNING goodness of fit is inaccurate; use more samples'
#             chi_squared += (c - mean) ** 2 / variance
#             dof += 1
#         else:
#             print 'WARNING zero probability in goodness-of-fit test'
#             if c > 0:
#                 return float('inf')
# 
#     if not truncated:
#         dof -= 1
# 
#     survival = scipy.stats.chi2.sf(chi_squared, dof)
#     return survival


# Path: distributions/tests/util.py
import os
import glob
import math
import numpy
import importlib
import distributions
    import distributions.dbg.random
        import distributions.hp.random
            import traceback
from collections import defaultdict
from itertools import izip
from numpy.testing import assert_array_almost_equal
from nose import SkipTest
from nose.tools import assert_true, assert_less, assert_equal
from distributions.util import multinomial_goodness_of_fit

        counts[sample] += 1
        probs[sample] += prob

    for key, count in counts.iteritems():
        probs[key] /= count
    total_prob = sum(probs.itervalues())
    assert_close(total_prob, 1.0, tol=1e-2, err_msg='total_prob is biased')

    return counts, probs


def assert_counts_match_probs(counts, probs, tol=1e-3):
    '''
    Check goodness of fit of observed counts to predicted probabilities
    using Pearson's chi-squared test.

    Inputs:
        - counts : key -> int
        - probs : key -> float
    '''
    keys = counts.keys()
    probs = [probs[key] for key in keys]
    counts = [counts[key] for key in keys]
    total_count = sum(counts)

    print 'EXPECT\tACTUAL\tVALUE'
    for prob, count, key in sorted(izip(probs, counts, keys), reverse=True):
        expect = prob * total_count
        print '{:0.1f}\t{}\t{}'.format(expect, count, key)


    gof = multinomial_goodness_of_fit(probs, counts, total_count)