From 71852984c7e8c50dc2a6c647d39cc82965c37877 Mon Sep 17 00:00:00 2001 From: "Per.Andreas.Brodtkorb" Date: Wed, 29 Sep 2010 10:10:58 +0000 Subject: [PATCH] Added more kernels --- pywafo/src/wafo/kdetools.py | 1378 +++++++++++++++++++++++------------ 1 file changed, 905 insertions(+), 473 deletions(-) diff --git a/pywafo/src/wafo/kdetools.py b/pywafo/src/wafo/kdetools.py index fb9acd9..17ce1b5 100644 --- a/pywafo/src/wafo/kdetools.py +++ b/pywafo/src/wafo/kdetools.py @@ -9,10 +9,16 @@ # Licence: LGPL #------------------------------------------------------------------------------- #!/usr/bin/env python +from __future__ import division import numpy as np +from numpy import pi, sqrt, atleast_2d, exp, newaxis #@UnresolvedImport +import scipy +from scipy.linalg import sqrtm from scipy.special import gamma -from numpy import pi, atleast_2d #@UnresolvedImport from misc import tranproc, trangood +from itertools import product + + def sphere_volume(d, r=1.0): """ Returns volume of d-dimensional sphere with radius r @@ -23,6 +29,13 @@ def sphere_volume(d, r=1.0): dimension of sphere r : scalar or array_like radius of sphere (default 1) + + Example + ------- + >>> sphere_volume(2., r=2.) + 12.566370614359172 + >>> sphere_volume(2., r=1.) + 3.1415926535897931 Reference --------- @@ -30,7 +43,8 @@ def sphere_volume(d, r=1.0): 'Kernel smoothing' Chapman and Hall, pp 105 """ - return (r**d)* 2.*pi**(d/2.)/(d*gamma(d/2.)) + return (r ** d) * 2. * pi ** (d / 2.) / (d * gamma(d / 2.)) + class kde(object): @@ -72,11 +86,12 @@ class kde(object): own). The default is scotts_factor. """ - def __init__(self, dataset,**kwds): - self.kernel='gauss' + def __init__(self, dataset, **kwds): + self.kernel = 'gauss' self.hs = None - self.hsmethod=None + self.hsmethod = None self.L2 = None + self.alpha = 0 self.__dict__.update(kwds) self.dataset = atleast_2d(dataset) @@ -124,17 +139,17 @@ class kde(object): if m >= self.n: # there are more points than data, so loop over data for i in range(self.n): - diff = self.dataset[:,i, np.newaxis] - points + diff = self.dataset[:, i, np.newaxis] - points tdiff = np.dot(self.inv_cov, diff) - energy = np.sum(diff*tdiff,axis=0)/2.0 + energy = np.sum(diff * tdiff, axis=0) / 2.0 result += np.exp(-energy) else: # loop over points for i in range(m): - diff = self.dataset - points[:,i,np.newaxis] + diff = self.dataset - points[:, i, np.newaxis] tdiff = np.dot(self.inv_cov, diff) - energy = sum(diff*tdiff,axis=0)/2.0 - result[i] = np.sum(np.exp(-energy),axis=0) + energy = sum(diff * tdiff, axis=0) / 2.0 + result[i] = np.sum(np.exp(-energy), axis=0) result /= self._norm_factor @@ -142,468 +157,885 @@ class kde(object): __call__ = evaluate -##function [f, hs,lambda]= kdefun(A,options,varargin) -##%KDEFUN Kernel Density Estimator. -##% -##% CALL: [f, hs] = kdefun(data,options,x1,x2,...,xd) -##% -##% f = kernel density estimate evaluated at x1,x2,...,xd. -##% data = data matrix, size N x D (D = # dimensions) -##% options = kdeoptions-structure or cellvector of named parameters with -##% corresponding values, see kdeoptset for details. -##% x1,x2..= vectors/matrices defining the points to evaluate the density -##% -##% KDEFUN gives a slow, but exact kernel density estimate evaluated at x1,x2,...,xd. -##% Notice that densities close to normality appear to be the easiest for the kernel -##% estimator to estimate and that the degree of estimation difficulty increases with -##% skewness, kurtosis and multimodality. -##% -##% If D > 1 KDE calculates quantile levels by integration. An -##% alternative is to calculate them by ranking the kernel density -##% estimate obtained at the points DATA i.e. use the commands -##% -##% f = kde(data); -##% r = kdefun(data,[],num2cell(data,1)); -##% f.cl = qlevels2(r,f.PL); -##% -##% The first is probably best when estimating the pdf and the latter is the -##% easiest and most robust for multidimensional data when only a visualization -##% of the data is needed. -##% -##% For faster estimates try kdebin. -##% -##% Examples: -##% data = rndray(1,500,1); -##% x = linspace(sqrt(eps),5,55); -##% plotnorm((data).^(.5)) % gives a straight line => L2 = 0.5 reasonable -##% f = kdefun(data,{'L2',.5},x); -##% plot(x,f,x,pdfray(x,1),'r') -##% -##% See also kde, mkernel, kdebin -## -##% Reference: -##% B. W. Silverman (1986) -##% 'Density estimation for statistics and data analysis' -##% Chapman and Hall , pp 100-110 -##% -##% Wand, M.P. and Jones, M.C. (1995) -##% 'Kernel smoothing' -##% Chapman and Hall, pp 43--45 -## -## -## -## -##%Tested on: matlab 5.2 -##% History: -##% revised pab Feb2004 -##% -options moved into a structure -##% revised pab Dec2003 -##% -removed some code -##% revised pab 27.04.2001 -##% - changed call from mkernel to mkernel2 (increased speed by 10%) -##% revised pab 01.01.2001 -##% - added the possibility that L2 is a cellarray of parametric -##% or non-parametric transformations (secret option) -##% revised pab 14.12.1999 -##% - fixed a small error in example in help header -##% revised pab 28.10.1999 -##% - added L2 -##% revised pab 21.10.99 -##% - added alpha to input arguments -##% - made it fully general for d dimensions -##% - HS may be a smoothing matrix -##% revised pab 21.09.99 -##% - adapted from kdetools by Christian Beardah -## -## defaultoptions = kdeoptset; -##% If just 'defaults' passed in, return the default options in g -##if ((nargin==1) && (nargout <= 1) && isequal(A,'defaults')), -## f = defaultoptions; -## return -##end -##error(nargchk(1,inf, nargin)) -## -##[n, d]=size(A); % Find dimensions of A, -## % n=number of data points, -## % d=dimension of the data. -##if (nargin<2 || isempty(options)) -## options = defaultoptions; -##else -## switch lower(class(options)) -## case {'char','struct'}, -## options = kdeoptset(defaultoptions,options); -## case {'cell'} -## -## options = kdeoptset(defaultoptions,options{:}); -## otherwise -## error('Invalid options') -## end -##end -##kernel = options.kernel; -##h = options.hs; -##alpha = options.alpha; -##L2 = options.L2; -##hsMethod = options.hsMethod; -## -##if isempty(h) -## h=zeros(1,d); -##end -## -##L22 = cell(1,d); -##k3=[]; -##if isempty(L2) -## L2=ones(1,d); % default no transformation -##elseif iscell(L2) % cellarray of non-parametric and parametric transformations -## Nl2 = length(L2); -## if ~(Nl2==1||Nl2==d), error('Wrong size of L2'), end -## [L22{1:d}] = deal(L2{1:min(Nl2,d)}); -## L2 = ones(1,d); % default no transformation -## for ix=1:d, -## if length(L22{ix})>1, -## k3=[k3 ix]; % Non-parametric transformation -## else -## L2(ix) = L22{ix}; % Parameter to the Box-Cox transformation -## end -## end -##elseif length(L2)==1 -## L2=L2(:,ones(1,d)); -##end -## -##amin=min(A); -##if any((amin(L2~=1)<=0)) , -## error('DATA cannot be negative or zero when L2~=1') -##end -## -## -##nv=length(varargin); -##if nv0 -## Xn = num2cell(lA,1); -## opt1 = kdeoptset('kernel',kernel,'hs',h,'alpha',0,'L2',1); -## f2 = kdefun(lA,opt1,Xn{:}); % get a pilot estimate by regular KDE (alpha=0) -## g = exp(sum(log(f2))/n); -## -## lambda=(f2(:)/g).^(-alpha); -##else -## lambda=ones(n,1); -##end -## -## -## -## -## -##f=zeros(Nx,1); -##if (min(hsiz)==1)||(d==1) -## for ix=1:n, % Sum over all data points -## Avec=lA(ix,:); -## Xnn=(lX-Avec(ones(Nx,1),:))./(h(ones(Nx,1),:) *lambda(ix)); -## f = f + mkernel2(Xnn,kernel)/lambda(ix)^d; -## end -##else % fully general -## h1=inv(h); -## for ix=1:n, % Sum over all data points -## Avec=lA(ix,:); -## Xnn=(lX-Avec(ones(Nx,1),:))*(h1/lambda(ix)); -## f = f + mkernel2(Xnn,kernel)/lambda(ix)^d; -## end -##end -##f=f/(n*deth); -## -##% transforming back -##if any(k1), % L2=0 i.e. logaritmic transformation -## for ix=k1 -## f=f./X(:,ix); -## end -## if any(max(abs(diff(f)))>10) -## disp('Warning: Numerical problems may have occured due to the logaritmic') -## disp('transformation. Check the KDE for spurious spikes') -## end -##end -##if any(k2) % L2~=0 i.e. power transformation -## for ix=k2 -## f=f.*(X(:,ix).^(L2(ix)-1))*L2(ix)*sign(L2(ix)); -## end -## if any(max(abs(diff(f)))>10) -## disp('Warning: Numerical problems may have occured due to the power') -## disp('transformation. Check the KDE for spurious spikes') -## end -##end -##if any(k3), % non-parametric transformation -## oneC = ones(Nx,1); -## for ix=k3 -## gn = L22{ix}; -## %Gn = fliplr(L22{ix}); -## %x0 = tranproc(lX(:,ix),Gn); -## if any(isnan(X(:,ix))), -## error('The transformation does not have a strictly positive derivative.') -## end -## hg1 = tranproc([X(:,ix) oneC],gn); -## der1 = abs(hg1(:,2)); % dg(X)/dX = 1/(dG(Y)/dY) -## % alternative 2 -## %pp = smooth(Gn(:,1),Gn(:,2),1,[],1); -## %dpp = diffpp(pp); -## %der1 = 1./abs(ppval(dpp,f.x{ix})); -## % Alternative 3 -## %pp = smooth(gn(:,1),gn(:,2),1,[],1); -## %dpp = diffpp(pp); -## %%plot(hg1(:,1),der1-abs(ppval(dpp,x0))) -## %der1 = abs(ppval(dpp,x0)); -## if any(der1<=0), -## error('The transformation must have a strictly positive derivative') -## end -## f = f.*der1; -## end -## if any(max(abs(diff(f)))>10) -## disp('Warning: Numerical problems may have occured due to the power') -## disp('transformation. Check the KDE for spurious spikes') -## end -##end -## -##f=reshape(f,xsiz); % restore original shape -##if nargout>1 -## hs=h; -##end -## -## -## -## -## -## -## -## -## -## -##function [z,c]=mkernel(varargin) -##%MKERNEL Multivariate Kernel Function. -##% -##% CALL: z = mkernel(x1,x2,...,xd,kernel); -##% z = mkernel(X,kernel); -##% -##% -##% z = kernel function values evaluated at x1,x2,...,xd -##% x1,x2..= input arguments, vectors or matrices with common size -##% or -##% X = cellarray of vector/matrices with common size -##% (i.e. X{1}=x1, X{2}=x2....) -##% -##% kernel = 'epanechnikov' - Epanechnikov kernel. -##% 'epa1' - product of 1D Epanechnikov kernel. -##% 'biweight' - Bi-weight kernel. -##% 'biw1' - product of 1D Bi-weight kernel. -##% 'triweight' - Tri-weight kernel. -##% 'triangular' - Triangular kernel. -##% 'gaussian' - Gaussian kernel -##% 'rectangular' - Rectangular kernel. -##% 'laplace' - Laplace kernel. -##% 'logistic' - Logistic kernel. -##% -##% Note that only the first 4 letters of the kernel name is needed. -##% -##% See also kde, kdefun, kdebin -## -##% Reference: -##% B. W. Silverman (1986) -##% 'Density estimation for statistics and data analysis' -##% Chapman and Hall, pp. 43, 76 -##% -##% Wand, M. P. and Jones, M. C. (1995) -##% 'Density estimation for statistics and data analysis' -##% Chapman and Hall, pp 31, 103, 175 -## -##%Tested on: matlab 5.3 -##% History: -##% Revised pab sep2005 -##% -replaced reference to kdefft with kdebin -##% revised pab aug2005 -##% -Fixed some bugs -##% revised pab Dec2003 -##% removed some old code -##% revised pab 27.04.2001 -##% - removed some old calls -##% revised pab 01.01.2001 -##% - speeded up tri3 -##% revised pab 01.12.1999 -##% - added four weight, sphere -##% - made comparison smarter => faster execution for d>1 -##% revised pab 26.10.1999 -##% fixed normalization fault in epan -##% by pab 21.09.99 -##% added multivariate epan, biweight and triweight -##% -##% collected all knorm,kepan ... into this file -##% adapted from kdetools CB -## -##d=length(varargin)-1; -##kstr=varargin{d+1}; % kernel string -##if iscell(varargin{1}) -## X=varargin{1}; -## d=numel(X); -##else -## X=varargin; -##end -## -##switch lower(kstr(1:4)) -## case {'sphe','epan','biwe','triw','four'} -## switch lower(kstr(1:4)) -## case 'sphe', r=0; %Sphere = rect for 1D -## case 'epan', r=1; %Multivariate Epanechnikov kernel. -## case 'biwe', r=2; %Multivariate Bi-weight Kernel -## case 'triw', r=3; %Multi variate Tri-weight Kernel -## case 'four', r=4; %Multi variate Four-weight Kernel -## % as r -> infty, b -> infty => kernel -> Gaussian distribution -## end -## b=1;% radius of the kernel -## b2=b^2; -## s=X{1}.^2; -## k=find(s<=b2); -## z=zeros(size(s)); -## ix=2; -## while (any(k) && (ix<=d)), -## s(k)=s(k)+X{ix}(k).^2; -## k1=(s(k)<=b2); -## k=k(k1); -## ix=ix+1; -## end; -## if any(k) -## c=2^r*prod(1:r)*vsph(d,b)/prod((d+2):2:(d+2*r)); % normalizing constant -## %c=beta(r+1,r+1)*vsph(d,b)*(2^(2*r)); % Wand and Jones pp 31 -## % the commented c above does note yield the right scaling -## % for d>1 -## z(k)=((1-s(k)/b2).^r)/c; -## end -## -## case 'rect', % 1D product Rectangular Kernel -## z=zeros(size(X{1})); -## k=find(abs(X{1})<=1); -## ix=2; -## while (any(k) && (ix<=d)), -## k1 =(abs(X{ix}(k))<=1); -## k=k(k1); -## ix=ix+1; -## end -## if any(k) -## z(k)=(0.5^d); -## end -## case {'epa1','biw1','triw1','fou1'} -## switch lower(kstr(1:4)) -## %case 'rect', r=0; %rectangular -## case 'epa1', r=1; %1D product Epanechnikov kernel. -## case 'biw1', r=2; %1D product Bi-weight Kernel -## case 'tri1', r=3; %1D product Tri-weight Kernel -## case 'fou1', r=4; %1D product Four-weight Kernel -## end -## b=1; -## b2=b^2; -## b21=1/b2; -## z=zeros(size(X{1})); -## k=find(abs(X{1})<=b); -## ix=2; -## while (any(k) && (ix<=d)), -## %for ix=2:d -## k1 =(abs(X{ix}(k))<=b); -## k = k(k1); -## ix=ix+1; -## end -## if any(k) -## c=2^r*prod(1:r)*vsph(1,b)/prod((1+2):2:(1+2*r)); % normalizing constant -## z(k) = (1-X{1}(k).^2*b21).^r; -## for ix=2:d -## z(k)=z(k).*(1-X{ix}(k).^2*b21).^r; -## end; -## z(k)=z(k)/c^d; -## end -## case 'tria',% 1D product Triangular Kernel -## z=zeros(size(X{1})); -## k=find(abs(X{1})<1); -## ix=2; -## while (any(k) && (ix<=d)), -## %for ix=2:d -## k1 =(abs(X{ix}(k))<1); -## k = k(k1); -## ix=ix+1; -## end -## if any(k) -## z(k) = (1-abs(X{1}(k))); -## for ix=2:d -## z(k)=z(k).*(1-abs(X{ix}(k))); -## end -## end -## case {'norm','gaus'},% multivariate gaussian Density Function. -## s=X{1}.^2; -## for ix=2:d -## s=s+X{ix}.^2; -## end; -## z=(2*pi)^(-d/2)*exp(-0.5*s); -## case 'lapl' % Laplace Kernel -## z=0.5*exp(-abs(X{1})); -## for ix=2:d -## z=z.*0.5*exp(-abs(X{ix})); -## end -## case 'logi', % Logistic Kernel -## z1=exp(X{1}); -## z=z1./(z1+1).^2; -## for ix=2:d -## z1=exp(X{ix}); -## z=z.*z1./(z1+1).^2; -## end -## -## otherwise, error('unknown kernel') -## end -## -## +#function [f, hs,lambda]= kdefun(A,options,varargin) +#%KDEFUN Kernel Density Estimator. +#% +#% CALL: [f, hs] = kdefun(data,options,x1,x2,...,xd) +#% +#% f = kernel density estimate evaluated at x1,x2,...,xd. +#% data = data matrix, size N x D (D = # dimensions) +#% options = kdeoptions-structure or cellvector of named parameters with +#% corresponding values, see kdeoptset for details. +#% x1,x2..= vectors/matrices defining the points to evaluate the density +#% +#% KDEFUN gives a slow, but exact kernel density estimate evaluated at x1,x2,...,xd. +#% Notice that densities close to normality appear to be the easiest for the kernel +#% estimator to estimate and that the degree of estimation difficulty increases with +#% skewness, kurtosis and multimodality. +#% +#% If D > 1 KDE calculates quantile levels by integration. An +#% alternative is to calculate them by ranking the kernel density +#% estimate obtained at the points DATA i.e. use the commands +#% +#% f = kde(data); +#% r = kdefun(data,[],num2cell(data,1)); +#% f.cl = qlevels2(r,f.PL); +#% +#% The first is probably best when estimating the pdf and the latter is the +#% easiest and most robust for multidimensional data when only a visualization +#% of the data is needed. +#% +#% For faster estimates try kdebin. +#% +#% Examples: +#% data = rndray(1,500,1); +#% x = linspace(sqrt(eps),5,55); +#% plotnorm((data).^(.5)) % gives a straight line => L2 = 0.5 reasonable +#% f = kdefun(data,{'L2',.5},x); +#% plot(x,f,x,pdfray(x,1),'r') +#% +#% See also kde, mkernel, kdebin +# +#% Reference: +#% B. W. Silverman (1986) +#% 'Density estimation for statistics and data analysis' +#% Chapman and Hall , pp 100-110 +#% +#% Wand, M.P. and Jones, M.C. (1995) +#% 'Kernel smoothing' +#% Chapman and Hall, pp 43--45 +# +# +# +# +#%Tested on: matlab 5.2 +#% History: +#% revised pab Feb2004 +#% -options moved into a structure +#% revised pab Dec2003 +#% -removed some code +#% revised pab 27.04.2001 +#% - changed call from mkernel to mkernel2 (increased speed by 10%) +#% revised pab 01.01.2001 +#% - added the possibility that L2 is a cellarray of parametric +#% or non-parametric transformations (secret option) +#% revised pab 14.12.1999 +#% - fixed a small error in example in help header +#% revised pab 28.10.1999 +#% - added L2 +#% revised pab 21.10.99 +#% - added alpha to input arguments +#% - made it fully general for d dimensions +#% - HS may be a smoothing matrix +#% revised pab 21.09.99 +#% - adapted from kdetools by Christian Beardah +# +# defaultoptions = kdeoptset; +#% If just 'defaults' passed in, return the default options in g +#if ((nargin==1) && (nargout <= 1) && isequal(A,'defaults')), +# f = defaultoptions; +# return +#end +#error(nargchk(1,inf, nargin)) +# +#[n, d]=size(A); % Find dimensions of A, +# % n=number of data points, +# % d=dimension of the data. +#if (nargin<2 || isempty(options)) +# options = defaultoptions; +#else +# switch lower(class(options)) +# case {'char','struct'}, +# options = kdeoptset(defaultoptions,options); +# case {'cell'} +# +# options = kdeoptset(defaultoptions,options{:}); +# otherwise +# error('Invalid options') +# end +#end +#kernel = options.kernel; +#h = options.hs; +#alpha = options.alpha; +#L2 = options.L2; +#hsMethod = options.hsMethod; +# +#if isempty(h) +# h=zeros(1,d); +#end +# +#L22 = cell(1,d); +#k3=[]; +#if isempty(L2) +# L2=ones(1,d); % default no transformation +#elseif iscell(L2) % cellarray of non-parametric and parametric transformations +# Nl2 = length(L2); +# if ~(Nl2==1||Nl2==d), error('Wrong size of L2'), end +# [L22{1:d}] = deal(L2{1:min(Nl2,d)}); +# L2 = ones(1,d); % default no transformation +# for ix=1:d, +# if length(L22{ix})>1, +# k3=[k3 ix]; % Non-parametric transformation +# else +# L2(ix) = L22{ix}; % Parameter to the Box-Cox transformation +# end +# end +#elseif length(L2)==1 +# L2=L2(:,ones(1,d)); +#end +# +#amin=min(A); +#if any((amin(L2~=1)<=0)) , +# error('DATA cannot be negative or zero when L2~=1') +#end +# +# +#nv=length(varargin); +#if nv0 +# Xn = num2cell(lA,1); +# opt1 = kdeoptset('kernel',kernel,'hs',h,'alpha',0,'L2',1); +# f2 = kdefun(lA,opt1,Xn{:}); % get a pilot estimate by regular KDE (alpha=0) +# g = exp(sum(log(f2))/n); +# +# lambda=(f2(:)/g).^(-alpha); +#else +# lambda=ones(n,1); +#end +# +# +# +# +# +#f=zeros(Nx,1); +#if (min(hsiz)==1)||(d==1) +# for ix=1:n, % Sum over all data points +# Avec=lA(ix,:); +# Xnn=(lX-Avec(ones(Nx,1),:))./(h(ones(Nx,1),:) *lambda(ix)); +# f = f + mkernel2(Xnn,kernel)/lambda(ix)^d; +# end +#else % fully general +# h1=inv(h); +# for ix=1:n, % Sum over all data points +# Avec=lA(ix,:); +# Xnn=(lX-Avec(ones(Nx,1),:))*(h1/lambda(ix)); +# f = f + mkernel2(Xnn,kernel)/lambda(ix)^d; +# end +#end +#f=f/(n*deth); +# +#% transforming back +#if any(k1), % L2=0 i.e. logaritmic transformation +# for ix=k1 +# f=f./X(:,ix); +# end +# if any(max(abs(diff(f)))>10) +# disp('Warning: Numerical problems may have occured due to the logaritmic') +# disp('transformation. Check the KDE for spurious spikes') +# end +#end +#if any(k2) % L2~=0 i.e. power transformation +# for ix=k2 +# f=f.*(X(:,ix).^(L2(ix)-1))*L2(ix)*sign(L2(ix)); +# end +# if any(max(abs(diff(f)))>10) +# disp('Warning: Numerical problems may have occured due to the power') +# disp('transformation. Check the KDE for spurious spikes') +# end +#end +#if any(k3), % non-parametric transformation +# oneC = ones(Nx,1); +# for ix=k3 +# gn = L22{ix}; +# %Gn = fliplr(L22{ix}); +# %x0 = tranproc(lX(:,ix),Gn); +# if any(isnan(X(:,ix))), +# error('The transformation does not have a strictly positive derivative.') +# end +# hg1 = tranproc([X(:,ix) oneC],gn); +# der1 = abs(hg1(:,2)); % dg(X)/dX = 1/(dG(Y)/dY) +# % alternative 2 +# %pp = smooth(Gn(:,1),Gn(:,2),1,[],1); +# %dpp = diffpp(pp); +# %der1 = 1./abs(ppval(dpp,f.x{ix})); +# % Alternative 3 +# %pp = smooth(gn(:,1),gn(:,2),1,[],1); +# %dpp = diffpp(pp); +# %%plot(hg1(:,1),der1-abs(ppval(dpp,x0))) +# %der1 = abs(ppval(dpp,x0)); +# if any(der1<=0), +# error('The transformation must have a strictly positive derivative') +# end +# f = f.*der1; +# end +# if any(max(abs(diff(f)))>10) +# disp('Warning: Numerical problems may have occured due to the power') +# disp('transformation. Check the KDE for spurious spikes') +# end +#end +# +#f=reshape(f,xsiz); % restore original shape +#if nargout>1 +# hs=h; +#end + + +def mkernel_multi(X, p=None): + # p=0; %Sphere = rect for 1D + # p=1; %Multivariate Epanechnikov kernel. + # p=2; %Multivariate Bi-weight Kernel + # p=3; %Multi variate Tri-weight Kernel + # p=4; %Multi variate Four-weight Kernel + d, n = X.shape + r = 1 # radius of the kernel + c = 2 ** p * np.prod(np.r_[1:p + 1]) * sphere_volume(d, r) / np.prod(np.r_[(d + 2):(2 * p + d + 1):2])# normalizing constant + # c = beta(r+1,r+1)*vsph(d,b)*(2^(2*r)); % Wand and Jones pp 31 + # the commented c above does note yield the right scaling for d>1 + x2 = X ** 2 + return ((1.0 - x2.sum(axis=0) / r ** 2).clip(min=0.0)) ** p / c + +def mkernel_epanechnikov(X): + return mkernel_multi(X, p=1) +def mkernel_biweight(X): + return mkernel_multi(X, p=2) +def mkernel_triweight(X): + return mkernel_multi(X, p=3) + +def mkernel_product(X, p): + # p=0; %rectangular + # p=1; %1D product Epanechnikov kernel. + # p=2; %1D product Bi-weight Kernel + # p=3; %1D product Tri-weight Kernel + # p=4; %1D product Four-weight Kernel + d, n = X.shape + r = 1.0 # radius + pdf = (1 - (X / r) ** 2).clip(min=0.0) + c = 2 ** p * np.prod(np.r_[1:p + 1]) * sphere_volume(1, r) / np.prod(np.r_[(1 + 2):(2 * p + 2):2])# normalizing constant + return np.prod(pdf, axis=0) / c ** d + +def mkernel_p1epanechnikov(X): + return mkernel_product(X, p=1) +def mkernel_p1biweight(X): + return mkernel_product(X, p=2) +def mkernel_p1triweight(X): + return mkernel_product(X, p=3) + +def mkernel_rectangular(X): + d, n = X.shape + return np.where(np.all(np.abs(X) <= 1, axis=0), 0.5 ** d, 0.0) + +def mkernel_triangular(X): + pdf = (1 - np.abs(X)).clip(min=0.0) + return np.prod(pdf, axis=0) + +def mkernel_gaussian(X): + x2 = X ** 2 + d, n = X.shape + return (2 * pi) ** (-d / 2) * exp(-0.5 * x2.sum(axis=0)) + +def mkernel_laplace(X): + ''' Return multivariate laplace kernel''' + d, n = X.shape + absX = np.abs(X) + return 0.5 ** d * exp(-absX.sum(axis=0)) + +def mkernel_logistic(X): + ''' Return multivariate logistic kernel''' + s = exp(X) + return np.prod(s / (s + 1) ** 2, axis=0) + +_MKERNEL_DICT = dict( + epan=mkernel_epanechnikov, + biwe=mkernel_biweight, + triw=mkernel_triweight, + p1ep=mkernel_p1epanechnikov, + p1bi=mkernel_p1biweight, + p1tr=mkernel_p1triweight, + rect=mkernel_rectangular, + tria=mkernel_triangular, + lapl=mkernel_laplace, + logi=mkernel_logistic, + gaus=mkernel_gaussian + ) +_KERNEL_EXPONENT_DICT = dict(re=0, sp=0, ep=1, bi=2, tr=3, fo=4, fi=5, si=6, se=7) +_KERNEL_STATS_DICT = dict(epan=(1. / 5, 3. / 5, np.inf), + biwe=(1. / 7, 5. / 7, 45. / 2), + triw=(1. / 9, 350. / 429, np.inf), + rect=(1. / 3, 1. / 2, np.inf), + tria=(1. / 6, 2. / 3, np.inf), + lapl=(2, 1. / 4, np.inf), + logi=(pi ** 2 / 3, 1. / 6, 1 / 42), + gaus=(1, 1. / (2 * sqrt(pi)), 3. / (8 * sqrt(pi))) + ) + + + +class Kernel(object): + ''' + Multivariate kernel + + Parameters + ---------- + name : string + defining the kernel. Valid options are: + 'epanechnikov' - Epanechnikov kernel. + 'biweight' - Bi-weight kernel. + 'triweight' - Tri-weight kernel. + 'p1epanechnikov' - product of 1D Epanechnikov kernel. + 'p1biweight' - product of 1D Bi-weight kernel. + 'p1triweight' - product of 1D Tri-weight kernel. + 'triangular' - Triangular kernel. + 'gaussian' - Gaussian kernel + 'rectangular' - Rectangular kernel. + 'laplace' - Laplace kernel. + 'logistic' - Logistic kernel. + Note that only the first 4 letters of the kernel name is needed. + + Examples + -------- + >>> Kernel('gaussian').stats() + (1, 0.28209479177387814, 0.21157109383040862) + >>> Kernel('laplace').stats() + (2, 0.25, inf) + + >>> triweight = Kernel('triweight'); triweight.stats() + (0.1111111111111111, 0.81585081585081587, inf) + + >>> triweight(np.linspace(-1,1,11)) + array([ 0. , 0.05103, 0.28672, 0.64827, 0.96768, 1.09375, + 0.96768, 0.64827, 0.28672, 0.05103, 0. ]) + >>> triweight.hns(np.random.normal(size=100)) + + See also + -------- + mkernel + References + ---------- + B. W. Silverman (1986) + 'Density estimation for statistics and data analysis' + Chapman and Hall, pp. 43, 76 + + Wand, M. P. and Jones, M. C. (1995) + 'Density estimation for statistics and data analysis' + Chapman and Hall, pp 31, 103, 175 + ''' + def __init__(self, name): + self.kernel = _MKERNEL_DICT[name[:4]] + self.name = self.kernel.__name__.replace('mkernel_','').title() + + def stats(self): + ''' Return some 1D statistics of the kernel. + + Returns + ------- + mu2 : real scalar + 2'nd order moment, i.e.,int(x^2*kernel(x)) + R : real scalar + integral of squared kernel, i.e., int(kernel(x)^2) + Rdd : real scalar + integral of squared double derivative of kernel, i.e., int( (kernel''(x))^2 ). + + Reference + --------- + Wand,M.P. and Jones, M.C. (1995) + 'Kernel smoothing' + Chapman and Hall, pp 176. + ''' + name = self.name[2:6] if self.name[:2].lower()=='p1' else self.name[:4] + return _KERNEL_STATS_DICT[name.lower()] + + def hns(self, data): + ''' + HNS Normal Scale Estimate of Smoothing Parameter. + + CALL: h = hns(data,kernel) + + h = one dimensional optimal value for smoothing parameter + given the data and kernel. size 1 x D + data = data matrix, size N x D (D = # dimensions ) + + HNS only gives an optimal value with respect to mean integrated + square error, when the true underlying distribution + is Gaussian. This works reasonably well if the data resembles a + Gaussian distribution. However if the distribution is asymmetric, + multimodal or have long tails then HNS may return a to large + smoothing parameter, i.e., the KDE may be oversmoothed and mask + important features of the data. (=> large bias). + One way to remedy this is to reduce H by multiplying with a constant + factor, e.g., 0.85. Another is to try different values for H and make a + visual check by eye. + + Example: + data = rndnorm(0, 1,20,1) + h = hns(data,'epan'); + + See also hste, hbcv, hboot, hos, hldpi, hlscv, hscv, hstt, kde + + Reference: + --------- + B. W. Silverman (1986) + 'Density estimation for statistics and data analysis' + Chapman and Hall, pp 43-48 + Wand,M.P. and Jones, M.C. (1995) + 'Kernel smoothing' + Chapman and Hall, pp 60--63 + ''' + + A = np.atleast_2d(data) + d,n = A.shape + + # R= int(mkernel(x)^2), mu2= int(x^2*mkernel(x)) + mu2, R, Rdd = self.stats() + AMISEconstant = (8*sqrt(pi)*R/(3*mu2**2*n))**(1./5) + iqr = np.abs(np.percentile(A, 75, axis=1)-np.percentile(A, 25, axis=1))# interquartile range + stdA = np.std(A, axis=1) + # % use of interquartile range guards against outliers. + # % the use of interquartile range is better if + # % the distribution is skew or have heavy tails + # % This lessen the chance of oversmoothing. + return np.where(iqr>0, np.minimum(stdA, iqr/1.349), stdA)*AMISEconstant + def hos(self, data): + ''' Return Oversmoothing Parameter. + + + + h = one dimensional maximum smoothing value for smoothing parameter + given the data and kernel. size 1 x D + data = data matrix, size N x D (D = # dimensions ) + + The oversmoothing or maximal smoothing principle relies on the fact + that there is a simple upper bound for the AMISE-optimal bandwidth for + estimation of densities with a fixed value of a particular scale + measure. While HOS will give too large bandwidth for optimal estimation + of a general density it provides an excellent starting point for + subjective choice of bandwidth. A sensible strategy is to plot an + estimate with bandwidth HOS and then sucessively look at plots based on + convenient fractions of HOS to see what features are present in the + data for various amount of smoothing. The relation to HNS is given by: + + HOS = HNS/0.93 + + Example: + data = rndnorm(0, 1,20,1) + h = hos(data,'epan'); + + See also hste, hbcv, hboot, hldpi, hlscv, hscv, hstt, kde, kdefun + + Reference: + B. W. Silverman (1986) + 'Density estimation for statistics and data analysis' + Chapman and Hall, pp 43-48 + + Wand,M.P. and Jones, M.C. (1986) + 'Kernel smoothing' + Chapman and Hall, pp 60--63 + ''' + return self.hns(data)/0.93 + def hmns(self, data): + ''' + Returns Multivariate Normal Scale Estimate of Smoothing Parameter. + + CALL: h = hmns(data,kernel) + + h = M dimensional optimal value for smoothing parameter + given the data and kernel. size D x D + data = data matrix, size N x D (D = # dimensions ) + kernel = 'epanechnikov' - Epanechnikov kernel. + 'biweight' - Bi-weight kernel. + 'triweight' - Tri-weight kernel. + 'gaussian' - Gaussian kernel + + Note that only the first 4 letters of the kernel name is needed. + + HMNS only gives a optimal value with respect to mean integrated + square error, when the true underlying distribution is + Multivariate Gaussian. This works reasonably well if the data resembles a + Multivariate Gaussian distribution. However if the distribution is + asymmetric, multimodal or have long tails then HNS is maybe more + appropriate. + + Example: + data = rndnorm(0, 1,20,2) + h = hmns(data,'epan'); + + See also hns, hste, hbcv, hboot, hos, hldpi, hlscv, hscv, hstt + Reference: + B. W. Silverman (1986) + 'Density estimation for statistics and data analysis' + Chapman and Hall, pp 43-48, 87 + + Wand,M.P. and Jones, M.C. (1995) + 'Kernel smoothing' + Chapman and Hall, pp 60--63, 86--88 + ''' + # TODO implement more kernels + + A = np.atleast_2d(data) + d,n = A.shape + + if d==1: + return self.hns(data) + name = self.name[:4].lower() + if name=='epan': # Epanechnikov kernel + a=(8*(d+4)*(2*sqrt(pi))**d/sphere_volume(d))**(1./(4+d)) + elif name=='biwe': # Bi-weight kernel + a=2.7779; + if d>2: + raise ValueError('not implemented for d>2') + elif name=='triw': # Triweight + a=3.12; + if d>2: + raise ValueError('not implemented for d>2') + elif name=='gaus': # Gaussian kernel + a = (4/(d+2))**(1./(d+4)) + else: + raise ValueError('Unknown kernel.') + + covA = scipy.cov(A) + + return a*sqrtm(covA)*n*(-1./(d+4)) + + + + + + def evaluate(self, X): + return self.kernel(np.atleast_2d(X)) + __call__ = evaluate + +def mkernel(X, kernel): + ''' + MKERNEL Multivariate Kernel Function. + + Paramaters + --------- + X : array-like + matrix size d x n (d = # dimensions, n = # evaluation points) + kernel : string + defining kernel + 'epanechnikov' - Epanechnikov kernel. + 'biweight' - Bi-weight kernel. + 'triweight' - Tri-weight kernel. + 'p1epanechnikov' - product of 1D Epanechnikov kernel. + 'p1biweight' - product of 1D Bi-weight kernel. + 'p1triweight' - product of 1D Tri-weight kernel. + 'triangular' - Triangular kernel. + 'gaussian' - Gaussian kernel + 'rectangular' - Rectangular kernel. + 'laplace' - Laplace kernel. + 'logistic' - Logistic kernel. + Note that only the first 4 letters of the kernel name is needed. + Returns + ------- + z : ndarray + kernel function values evaluated at X + + + See also + -------- + kde, kdefun, kdebin + + References + ---------- + B. W. Silverman (1986) + 'Density estimation for statistics and data analysis' + Chapman and Hall, pp. 43, 76 + + Wand, M. P. and Jones, M. C. (1995) + 'Density estimation for statistics and data analysis' + Chapman and Hall, pp 31, 103, 175 + ''' + fun = _MKERNEL_DICT[kernel[:4]] + return fun(np.atleast_2d(X)) + + +def accum(accmap, a, func=None, size=None, fill_value=0, dtype=None): + """ + An accumulation function similar to Matlab's `accumarray` function. + + Parameters + ---------- + accmap : ndarray + This is the "accumulation map". It maps input (i.e. indices into + `a`) to their destination in the output array. The first `a.ndim` + dimensions of `accmap` must be the same as `a.shape`. That is, + `accmap.shape[:a.ndim]` must equal `a.shape`. For example, if `a` + has shape (15,4), then `accmap.shape[:2]` must equal (15,4). In this + case `accmap[i,j]` gives the index into the output array where + element (i,j) of `a` is to be accumulated. If the output is, say, + a 2D, then `accmap` must have shape (15,4,2). The value in the + last dimension give indices into the output array. If the output is + 1D, then the shape of `accmap` can be either (15,4) or (15,4,1) + a : ndarray + The input data to be accumulated. + func : callable or None + The accumulation function. The function will be passed a list + of values from `a` to be accumulated. + If None, numpy.sum is assumed. + size : ndarray or None + The size of the output array. If None, the size will be determined + from `accmap`. + fill_value : scalar + The default value for elements of the output array. + dtype : numpy data type, or None + The data type of the output array. If None, the data type of + `a` is used. + + Returns + ------- + out : ndarray + The accumulated results. + + The shape of `out` is `size` if `size` is given. Otherwise the + shape is determined by the (lexicographically) largest indices of + the output found in `accmap`. + + + Examples + -------- + >>> from numpy import array, prod + >>> a = array([[1,2,3],[4,-1,6],[-1,8,9]]) + >>> a + array([[ 1, 2, 3], + [ 4, -1, 6], + [-1, 8, 9]]) + >>> # Sum the diagonals. + >>> accmap = array([[0,1,2],[2,0,1],[1,2,0]]) + >>> s = accum(accmap, a) + array([9, 7, 15]) + >>> # A 2D output, from sub-arrays with shapes and positions like this: + >>> # [ (2,2) (2,1)] + >>> # [ (1,2) (1,1)] + >>> accmap = array([ + [[0,0],[0,0],[0,1]], + [[0,0],[0,0],[0,1]], + [[1,0],[1,0],[1,1]], + ]) + >>> # Accumulate using a product. + >>> accum(accmap, a, func=prod, dtype=float) + array([[ -8., 18.], + [ -8., 9.]]) + >>> # Same accmap, but create an array of lists of values. + >>> accum(accmap, a, func=lambda x: x, dtype='O') + array([[[1, 2, 4, -1], [3, 6]], + [[-1, 8], [9]]], dtype=object) + """ + + # Check for bad arguments and handle the defaults. + if accmap.shape[:a.ndim] != a.shape: + raise ValueError("The initial dimensions of accmap must be the same as a.shape") + if func is None: + func = np.sum + if dtype is None: + dtype = a.dtype + if accmap.shape == a.shape: + accmap = np.expand_dims(accmap, -1) + adims = tuple(range(a.ndim)) + if size is None: + size = 1 + np.squeeze(np.apply_over_axes(np.max, accmap, axes=adims)) + size = np.atleast_1d(size) + + # Create an array of python lists of values. + vals = np.empty(size, dtype='O') + for s in product(*[range(k) for k in size]): + vals[s] = [] + for s in product(*[range(k) for k in a.shape]): + indx = tuple(accmap[s]) + val = a[s] + vals[indx].append(val) + + # Create the output array. + out = np.empty(size, dtype=dtype) + for s in product(*[range(k) for k in size]): + if vals[s] == []: + out[s] = fill_value + else: + out[s] = func(vals[s]) + + return out +def gridcount(data,X): + ''' + GRIDCOUNT D-dimensional histogram using linear binning. + + Parameters + ---------- + data = column vectors with D-dimensional data, size D x Nd + X = row vectors defining discretization, size D x N + Must include the range of the data. + + Returns + ------- + c = gridcount, size N x N x ... x N + + GRIDCOUNT obtains the grid counts using linear binning. + There are 2 strategies: simple- or linear- binning. + Suppose that an observation occurs at x and that the nearest point + below and above is y and z, respectively. Then simple binning strategy + assigns a unit weight to either y or z, whichever is closer. Linear + binning, on the other hand, assigns the grid point at y with the weight + of (z-x)/(z-y) and the gridpoint at z a weight of (y-x)/(z-y). + + In terms of approximation error of using gridcounts as pdf-estimate, + linear binning is significantly more accurate than simple binning. + + NOTE: The interval [min(X);max(X)] must include the range of the data. + The order of C is permuted in the same order as + meshgrid for D==2 or D==3. + + Example + ------- + N = 500; + data = rndray(1,N,1); + x = linspace(0,max(data)+1,50).'; + dx = x(2)-x(1); + c = gridcount(data,x); + plot(x,c,'.') 1D histogram + plot(x,c/dx/N) 1D probability density plot + trapz(x,c/dx/N) + + See also bincount, kdebin + + Reference + ---------- + Wand,M.P. and Jones, M.C. (1995) + 'Kernel smoothing' + Chapman and Hall, pp 182-192 + ''' + dat = np.atleast_2d(data) + x = np.atleast_2d(X) + d,n = dat.shape + d1, inc = x.shape + + if d!=d1: + raise ValueError('Dimension 0 of data and X do not match.') + + + dx = np.diff(x[:,:2],axis=1) + xlo = x[:,0] + xup = x[:,-1] + + datlo = dat.min(axis=1) + datup = dat.max(axis=1) + if ((datlo2 + useSparse = 0; + Nc = prod(csiz); + c = zeros(Nc,1); + + fact2 = [0 inc*(1:d-1)]; + fact1 = [1 cumprod(csiz(1:d-1))]; + fact1 = fact1(ones(n,1),:); + for ir=0:2^(d-1)-1, + bt0(:,:,1) = bitget(ir,1:d); + bt0(:,:,2) = ~bt0(:,:,1); + + for ix = 0:1 + one = mod(ix,2)+1; + two = mod(ix+1,2)+1; + % Convert to linear index (faster than sub2ind) + b1 = sum((binx + bt0(ones(n,1),:,one)-1).*fact1,2)+1; %linear index to c + bt2 = bt0(:,:,two) + fact2; + b2 = binx + bt2(ones(n,1),:); % linear index to X + try + if useSparse + % Fast gridding using sparse + c = c + sparse(b1,1,abs(prod(X(b2)-data,2)),Nc,1); + else + c = c + accum(b1,abs(prod(X(b2)-data,2)),[Nc,1]); + %c = c + accum([b1,ones(n,1)],abs(prod(X(b2)-data,2)),[Nc,1]); + %[len,bin,val] = bincount(b1,abs(prod(X(b2)-data,2))); + %c(bin) = c(bin)+val; + end + catch + c = c + sparse(b1,1,abs(prod(X(b2)-data,2)),Nc,1); + end + end + end + c = reshape(c/w,csiz); + end + switch d % make sure c is stored in the same way as meshgrid + case 2, c = c.'; + case 3, c = permute(c,[2 1 3]); + end + return +def main(): + import doctest + doctest.testmod() + +if __name__ == '__main__': + main()