Package mvpa :: Package clfs :: Module stats :: Class Nonparametric
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Class Nonparametric

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Non-parametric 1d distribution -- derives cdf based on stored values.

Introduced to complement parametric distributions present in scipy.stats.

Instance Methods [hide private]
 
__init__(self, dist_samples, correction='clip')
x.__init__(...) initializes x; see help(type(x)) for signature
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__repr__(self)
repr(x)
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cdf(self, x)
Returns the cdf value at x.
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Inherited from object: __delattr__, __format__, __getattribute__, __hash__, __new__, __reduce__, __reduce_ex__, __setattr__, __sizeof__, __str__, __subclasshook__

Static Methods [hide private]
 
fit(dist_samples) source code
Properties [hide private]

Inherited from object: __class__

Method Details [hide private]

__init__(self, dist_samples, correction='clip')
(Constructor)

source code 
x.__init__(...) initializes x; see help(type(x)) for signature
Parameters:
  • dist_samples (ndarray) - Samples to be used to assess the distribution.
  • correction ({'clip'} or None, optional) - Determines the behavior when .cdf is queried. If None -- no correction is made. If 'clip' -- values are clipped to lie in the range [1/(N+2), (N+1)/(N+2)] (simply because non-parametric assessment lacks the power to resolve with higher precision in the tails, so 'imagery' samples are placed in each of the two tails).
Overrides: object.__init__

__repr__(self)
(Representation operator)

source code 
repr(x)
Overrides: object.__repr__
(inherited documentation)