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src/c/o/cogent-1.5.3/cogent/maths/stats/test.py   cogent(Download)
from __future__ import division
import warnings
from cogent.maths.stats.distribution import chi_high, z_low, z_high, zprob, \
    t_high, t_low, tprob, f_high, f_low, fprob, binomial_high, binomial_low, \
    ndtri
        G /= q
 
    p = chi_high(max(G,0), 1)
 
    #find which tail we were in if the test was directional
            (6*tot*df))
        G = G/q
    return G, chi_high(max(G,0), df)
 
 
        G /= q
 
    return G, chi_high(G, k - 1)
 
def G_fit_from_Dict2D(data):
        df = (len(data) - 1) * (len([col for col in data.Cols]) - 1)
 
    return test, chi_high(test, df)
 
 

src/p/y/pycogent-HEAD/cogent/maths/stats/test.py   pycogent(Download)
from __future__ import division
import warnings
from cogent.maths.stats.distribution import chi_high, z_low, z_high, zprob, \
    t_high, t_low, tprob, f_high, f_low, fprob, binomial_high, binomial_low, \
    ndtri
        G /= q
 
    p = chi_high(max(G,0), 1)
 
    #find which tail we were in if the test was directional
            (6*tot*df))
        G = G/q
    return G, chi_high(max(G,0), df)
 
 
        G /= q
 
    return G, chi_high(G, k - 1)
 
def G_fit_from_Dict2D(data):
        df = (len(data) - 1) * (len([col for col in data.Cols]) - 1)
 
    return test, chi_high(test, df)
 
 

src/q/i/qiime-1.8.0/qiime/pycogent_backports/test.py   qiime(Download)
from __future__ import division
import warnings
from cogent.maths.stats.distribution import (chi_high, z_low, z_high, zprob,
    t_high, t_low, tprob, f_high, f_low, fprob, binomial_high, binomial_low,
    ndtri)
        G /= q
 
    p = chi_high(max(G,0), 1)
 
    #find which tail we were in if the test was directional
            (6*tot*df))
        G = G/q
    return G, chi_high(max(G,0), df)
 
## Start functions for G goodness of fit 
        n = sum([arr.mean() for arr in data]) #total observations
        G = williams_correction(n, a, G)
    return G, chi_high(G, a-1) #a-1 degrees of freedom because of sum constraint
## End functions for G goodness of fit test
 
    else: 
        try:
            return chi_high(stat, 2 * len(probs))
        except OverflowError, e:
            return nan 

src/q/i/qiime-HEAD/qiime/pycogent_backports/test.py   qiime(Download)
from __future__ import division
import warnings
from cogent.maths.stats.distribution import (chi_high, z_low, z_high, zprob,
                                             t_high, t_low, tprob, f_high, f_low, fprob, binomial_high, binomial_low,
                                             ndtri)
        G /= q
 
    p = chi_high(max(G, 0), 1)
 
    # find which tail we were in if the test was directional
                 (6 * tot * df))
        G = G / q
    return G, chi_high(max(G, 0), df)
 
# Start functions for G goodness of fit
    return (
        # a-1 degrees of freedom because of sum constraint
        G, chi_high(G, a - 1)
    )
# End functions for G goodness of fit test
    else:
        try:
            return chi_high(stat, 2 * len(probs))
        except OverflowError as e:
            return nan

src/c/o/cogent-1.5.3/tests/test_maths/test_stats/test_distribution.py   cogent(Download)
 
from cogent.util.unit_test import TestCase, main
from cogent.maths.stats.distribution import z_low, z_high, zprob, chi_low, \
    chi_high, t_low, t_high, tprob, poisson_high, poisson_low, poisson_exact, \
    binomial_high, binomial_low, binomial_exact, f_low, f_high, fprob, \

src/p/y/pycogent-HEAD/tests/test_maths/test_stats/test_distribution.py   pycogent(Download)
 
from cogent.util.unit_test import TestCase, main
from cogent.maths.stats.distribution import z_low, z_high, zprob, chi_low, \
    chi_high, t_low, t_high, tprob, poisson_high, poisson_low, poisson_exact, \
    binomial_high, binomial_low, binomial_exact, f_low, f_high, fprob, \