mvpa2.clfs.stats.AdaptiveNormal

class mvpa2.clfs.stats.AdaptiveNormal(dist, **kwargs)

Adaptive Normal Distribution: params are (0, sqrt(1/nfeatures))

Attributes

descr	Description of the object if any
tail

Methods

cdf(x)	Return value of the cumulative distribution function at x.
dists()	Implementations returns a sequence of the dist_class instances that were used to fit the distribution.
fit(measure, wdata[, vdata])	Cares about dimensionality of the feature space in measure
p(x[, return_tails])	Returns the p-value for values of x.
rcdf(x)	Implementations return the value of the reverse cumulative distribution function.
reset()

Parameters:

dist : distribution object

This can be any object the has a cdf() method to report the cumulative distribition function values.

enable_ca : None or list of str

Names of the conditional attributes which should be enabled in addition to the default ones

disable_ca : None or list of str

Names of the conditional attributes which should be disabled

tail : {‘left’, ‘right’, ‘any’, ‘both’}

Which tail of the distribution to report. For ‘any’ and ‘both’ it chooses the tail it belongs to based on the comparison to p=0.5. In the case of ‘any’ significance is taken like in a one-tailed test.

descr : str

Description of the instance

Attributes

descr	Description of the object if any
tail

Methods

cdf(x)	Return value of the cumulative distribution function at x.
dists()	Implementations returns a sequence of the dist_class instances that were used to fit the distribution.
fit(measure, wdata[, vdata])	Cares about dimensionality of the feature space in measure
p(x[, return_tails])	Returns the p-value for values of x.
rcdf(x)	Implementations return the value of the reverse cumulative distribution function.
reset()