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 | source code

__repr__(self) repr(x) | source code

cdf(self, x) Returns the cdf value at `x`. | source code

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)

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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)

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repr(x)

Overrides: object.__repr__
  (inherited documentation)