Python scipy.special.kolmogorov() Examples

The following are 13 code examples of scipy.special.kolmogorov(). You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example. You may also want to check out all available functions/classes of the module scipy.special , or try the search function .
Example #1
Source File: _continuous_distns.py    From lambda-packs with MIT License 5 votes vote down vote up 
def _cdf(self, x):
        return 1.0 - sc.kolmogorov(x)
Example #2
Source File: _continuous_distns.py    From lambda-packs with MIT License 5 votes vote down vote up 
def _sf(self, x):
        return sc.kolmogorov(x)
Example #3
Source File: _continuous_distns.py    From GraphicDesignPatternByPython with MIT License 5 votes vote down vote up 
def _cdf(self, x):
        return 1.0 - sc.kolmogorov(x)
Example #4
Source File: _continuous_distns.py    From GraphicDesignPatternByPython with MIT License 5 votes vote down vote up 
def _sf(self, x):
        return sc.kolmogorov(x)
Example #5
Source File: test_kolmogorov.py    From GraphicDesignPatternByPython with MIT License 5 votes vote down vote up 
def test_nan(self):
        assert_(np.isnan(kolmogorov(np.nan)))
Example #6
Source File: test_kolmogorov.py    From GraphicDesignPatternByPython with MIT License 5 votes vote down vote up 
def test_basic(self):
        dataset = [(0, 1.0),
                   (0.5, 0.96394524366487511),
                   (1, 0.26999967167735456),
                   (2, 0.00067092525577969533)]

        dataset = np.asarray(dataset)
        FuncData(kolmogorov, dataset, (0,), 1, rtol=_rtol).check()
Example #7
Source File: test_kolmogorov.py    From GraphicDesignPatternByPython with MIT License 5 votes vote down vote up 
def test_round_trip(self):
        def _ki_k(_x):
            return kolmogi(kolmogorov(_x))

        x = np.linspace(0.0, 2.0, 21, endpoint=True)
        dataset = np.column_stack([x, x])
        FuncData(_ki_k, dataset, (0,), 1, rtol=_rtol).check()
Example #8
Source File: test_kolmogorov.py    From GraphicDesignPatternByPython with MIT License 5 votes vote down vote up 
def test_round_trip(self):
        def _k_ki(_p):
            return kolmogorov(kolmogi(_p))

        p = np.linspace(0.1, 1.0, 10, endpoint=True)
        dataset = np.column_stack([p, p])
        FuncData(_k_ki, dataset, (0,), 1, rtol=_rtol).check()
Example #9
Source File: _continuous_distns.py    From Splunking-Crime with GNU Affero General Public License v3.0 5 votes vote down vote up 
def _cdf(self, x):
        return 1.0 - sc.kolmogorov(x)
Example #10
Source File: _continuous_distns.py    From Splunking-Crime with GNU Affero General Public License v3.0 5 votes vote down vote up 
def _sf(self, x):
        return sc.kolmogorov(x)
Example #11
Source File: mstats_basic.py    From lambda-packs with MIT License 4 votes vote down vote up 
def ks_twosamp(data1, data2, alternative="two-sided"):
    """
    Computes the Kolmogorov-Smirnov test on two samples.

    Missing values are discarded.

    Parameters
    ----------
    data1 : array_like
        First data set
    data2 : array_like
        Second data set
    alternative : {'two-sided', 'less', 'greater'}, optional
        Indicates the alternative hypothesis.  Default is 'two-sided'.

    Returns
    -------
    d : float
        Value of the Kolmogorov Smirnov test
    p : float
        Corresponding p-value.

    """
    (data1, data2) = (ma.asarray(data1), ma.asarray(data2))
    (n1, n2) = (data1.count(), data2.count())
    n = (n1*n2/float(n1+n2))
    mix = ma.concatenate((data1.compressed(), data2.compressed()))
    mixsort = mix.argsort(kind='mergesort')
    csum = np.where(mixsort < n1, 1./n1, -1./n2).cumsum()
    # Check for ties
    if len(np.unique(mix)) < (n1+n2):
        csum = csum[np.r_[np.diff(mix[mixsort]).nonzero()[0],-1]]

    alternative = str(alternative).lower()[0]
    if alternative == 't':
        d = ma.abs(csum).max()
        prob = special.kolmogorov(np.sqrt(n)*d)
    elif alternative == 'l':
        d = -csum.min()
        prob = np.exp(-2*n*d**2)
    elif alternative == 'g':
        d = csum.max()
        prob = np.exp(-2*n*d**2)
    else:
        raise ValueError("Invalid value for the alternative hypothesis: "
                         "should be in 'two-sided', 'less' or 'greater'")

    return (d, prob)
Example #12
Source File: mstats_basic.py    From GraphicDesignPatternByPython with MIT License 4 votes vote down vote up 
def ks_twosamp(data1, data2, alternative="two-sided"):
    """
    Computes the Kolmogorov-Smirnov test on two samples.

    Missing values are discarded.

    Parameters
    ----------
    data1 : array_like
        First data set
    data2 : array_like
        Second data set
    alternative : {'two-sided', 'less', 'greater'}, optional
        Indicates the alternative hypothesis.  Default is 'two-sided'.

    Returns
    -------
    d : float
        Value of the Kolmogorov Smirnov test
    p : float
        Corresponding p-value.

    """
    (data1, data2) = (ma.asarray(data1), ma.asarray(data2))
    (n1, n2) = (data1.count(), data2.count())
    n = (n1*n2/float(n1+n2))
    mix = ma.concatenate((data1.compressed(), data2.compressed()))
    mixsort = mix.argsort(kind='mergesort')
    csum = np.where(mixsort < n1, 1./n1, -1./n2).cumsum()
    # Check for ties
    if len(np.unique(mix)) < (n1+n2):
        csum = csum[np.r_[np.diff(mix[mixsort]).nonzero()[0],-1]]

    alternative = str(alternative).lower()[0]
    if alternative == 't':
        d = ma.abs(csum).max()
        prob = special.kolmogorov(np.sqrt(n)*d)
    elif alternative == 'l':
        d = -csum.min()
        prob = np.exp(-2*n*d**2)
    elif alternative == 'g':
        d = csum.max()
        prob = np.exp(-2*n*d**2)
    else:
        raise ValueError("Invalid value for the alternative hypothesis: "
                         "should be in 'two-sided', 'less' or 'greater'")

    return (d, prob)
Example #13
Source File: mstats_basic.py    From Splunking-Crime with GNU Affero General Public License v3.0 4 votes vote down vote up 
def ks_twosamp(data1, data2, alternative="two-sided"):
    """
    Computes the Kolmogorov-Smirnov test on two samples.

    Missing values are discarded.

    Parameters
    ----------
    data1 : array_like
        First data set
    data2 : array_like
        Second data set
    alternative : {'two-sided', 'less', 'greater'}, optional
        Indicates the alternative hypothesis.  Default is 'two-sided'.

    Returns
    -------
    d : float
        Value of the Kolmogorov Smirnov test
    p : float
        Corresponding p-value.

    """
    (data1, data2) = (ma.asarray(data1), ma.asarray(data2))
    (n1, n2) = (data1.count(), data2.count())
    n = (n1*n2/float(n1+n2))
    mix = ma.concatenate((data1.compressed(), data2.compressed()))
    mixsort = mix.argsort(kind='mergesort')
    csum = np.where(mixsort < n1, 1./n1, -1./n2).cumsum()
    # Check for ties
    if len(np.unique(mix)) < (n1+n2):
        csum = csum[np.r_[np.diff(mix[mixsort]).nonzero()[0],-1]]

    alternative = str(alternative).lower()[0]
    if alternative == 't':
        d = ma.abs(csum).max()
        prob = special.kolmogorov(np.sqrt(n)*d)
    elif alternative == 'l':
        d = -csum.min()
        prob = np.exp(-2*n*d**2)
    elif alternative == 'g':
        d = csum.max()
        prob = np.exp(-2*n*d**2)
    else:
        raise ValueError("Invalid value for the alternative hypothesis: "
                         "should be in 'two-sided', 'less' or 'greater'")

    return (d, prob)