SciPy 0.6.0 API Documentation Generated by Endo, 2007-10-17

Class SciPy.​stats.​kde.​gaussian_kde

Representation of a kernel-density estimate using Gaussian kernels.

 Parameters
 ----------
 dataset : (# of dims, # of data)-array
     datapoints to estimate from

 Members
 -------
 d : int
     number of dimensions
 n : int
     number of datapoints

 Methods
 -------
 kde.evaluate(points) : array
     evaluate the estimated pdf on a provided set of points
 kde(points) : array
     same as kde.evaluate(points)
 kde.integrate_gaussian(mean, cov) : float
     multiply pdf with a specified Gaussian and integrate over the whole domain
 kde.integrate_box_1d(low, high) : float
     integrate pdf (1D only) between two bounds
 kde.integrate_box(low_bounds, high_bounds) : float
     integrate pdf over a rectangular space between low_bounds and high_bounds
 kde.integrate_kde(other_kde) : float
     integrate two kernel density estimates multiplied together

Internal Methods
----------------
 kde.covariance_factor() : float
     computes the coefficient that multiplies the data covariance matrix to
     obtain the kernel covariance matrix. Set this method to
     kde.scotts_factor or kde.silverman_factor (or subclass to provide your
     own). The default is scotts_factor.
 

Contents:
• Attributes
• Methods

Attributes

• covariance_factor

  This can be replaced with silverman_factor if one wants to use Silverman's rule for choosing the bandwidth of the kernels.

  covariance_factor = scotts_factor
  
  

Method summary

• __init__(self, dataset)
• evaluate(self, points)
• integrate_box(self, low_bounds, high_bounds, maxpts = None)
• integrate_box_1d(self, low, high)
• integrate_gaussian(self, mean, cov)
• integrate_kde(self, other)
• resample(self, size = None)
• scotts_factor(self)
• silverman_factor(self)

Methods

• __init__(self, dataset)
• evaluate(self, points)

  Evaluate the estimated pdf on a set of points.

  Parameters

  points : (# of dimensions, # of points)-array

  Alternatively, a (# of dimensions,) vector can be passed in and treated as a single point.

  Returns

  values : (# of points,)-array

  The values at each point.

  Raises

  ValueError if the dimensionality of the input points is different than the dimensionality of the KDE.

• integrate_box(self, low_bounds, high_bounds, maxpts = None)

  Computes the integral of a pdf over a rectangular interval.

  Parameters

  low_bounds : vector

  lower bounds of integration

  high_bounds : vector

  upper bounds of integration

  maxpts=None : int

  maximum number of points to use for integration

  Returns

  value : scalar

  the result of the integral

• integrate_box_1d(self, low, high)

  Computes the integral of a 1D pdf between two bounds.

  Parameters

  low : scalar

  lower bound of integration

  high : scalar

  upper bound of integration

  Returns

  value : scalar

  the result of the integral

  Raises

  ValueError if the KDE is over more than one dimension.

• integrate_gaussian(self, mean, cov)

  Multiply estimated density by a multivariate Gaussian and integrate over the wholespace.

  Parameters

  mean : vector

  the mean of the Gaussian

  cov : matrix

  the covariance matrix of the Gaussian

  Returns

  result : scalar

  the value of the integral

  Raises

  ValueError if the mean or covariance of the input Gaussian differs from the KDE's dimensionality.

• integrate_kde(self, other)

  Computes the integral of the product of this kernel density estimate with another.

  Parameters

  other : gaussian_kde instance

  the other kde

  Returns

  value : scalar

  the result of the integral

  Raises

  ValueError if the KDEs have different dimensionality.

• resample(self, size = None)

  Randomly sample a dataset from the estimated pdf.

  Parameters

  size : int, optional

  The number of samples to draw. If not provided, then the size is the same as the underlying dataset.

  Returns

  dataset : (self.d, size)-array

  sampled dataset

• scotts_factor(self)
• silverman_factor(self)