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kernel_regression.m    118 lines (99 with data), 4.5 kB

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# Copyright (C) 2006 Michael Creel <michael.creel@uab.es>
#
# This program is free software; you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation; either version 2 of the License, or
# (at your option) any later version.
#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with this program; If not, see <http://www.gnu.org/licenses/>.
# kernel_regression: kernel regression estimator
#
# usage:
# fit = kernel_regression(eval_points, depvar, condvars, bandwidth)
#
# inputs:
# eval_points: PxK matrix of points at which to calculate the density
# depvar: Nx1 vector of observations of the dependent variable
# condvars: NxK matrix of data points
# bandwidth (optional): positive scalar, the smoothing parameter.
# Default is N ^ (-1/(4+K))
# kernel (optional): string. Name of the kernel function. Default is
# Gaussian kernel.
# prewhiten bool (optional): default true. If true, rotate data
# using Choleski decomposition of inverse of covariance,
# to approximate independence after the transformation, which
# makes a product kernel a reasonable choice.
# do_cv: bool (optional). default false. If true, calculate leave-1-out
# fit to calculate the cross validation score
# computenodes: int (optional, default 0).
# Number of compute nodes for parallel evaluation
# debug: bool (optional, default false). show results on compute nodes if doing
# a parallel run
# outputs:
# fit: Px1 vector: the fitted value at each of the P evaluation points.
#
function z = kernel_regression(eval_points, depvar, condvars, bandwidth, kernel, prewhiten, do_cv, computenodes, debug)
if nargin < 3; error("kernel_regression: at least 3 arguments are required"); endif
n = rows(condvars);
k = columns(condvars);
# set defaults for optional args
if (nargin < 4) bandwidth = (n ^ (-1/(4+k))); endif # bandwidth - see Li and Racine pg. 66
if (nargin < 5) kernel = "__kernel_normal"; endif # what kernel?
if (nargin < 6) prewhiten = true; endif # automatic prewhitening?
if (nargin < 7) do_cv = false; endif # ordinary or leave-1-out
if (nargin < 8) computenodes = 0; endif # parallel?
if (nargin < 9) debug = false; endif; # debug?
nn = rows(eval_points);
n = rows(depvar);
if prewhiten
H = bandwidth*chol(cov(condvars));
else
H = bandwidth;
endif
H_inv = inv(H);
# weight by inverse bandwidth matrix
eval_points = eval_points*H_inv;
condvars = condvars*H_inv;
data = [depvar condvars]; # put it all together for sending to nodes
# check if doing this parallel or serial
global PARALLEL NSLAVES NEWORLD NSLAVES TAG
PARALLEL = 0;
if computenodes > 0
PARALLEL = 1;
NSLAVES = computenodes;
LAM_Init(computenodes, debug);
endif
if !PARALLEL # ordinary serial version
points_per_node = nn; # do the all on this node
z = kernel_regression_nodes(eval_points, data, do_cv, kernel, points_per_node, computenodes, debug);
else # parallel version
z = zeros(nn,1);
points_per_node = floor(nn/(NSLAVES + 1)); # number of obsns per slave
# The command that the slave nodes will execute
cmd=['z_on_node = kernel_regression_nodes(eval_points, data, do_cv, kernel, points_per_node, computenodes, debug); ',...
'MPI_Send(z_on_node, 0, TAG, NEWORLD);'];
# send items to slaves
NumCmds_Send({"eval_points", "data", "do_cv", "kernel", "points_per_node", "computenodes", "debug","cmd"}, {eval_points, data, do_cv, kernel, points_per_node, computenodes, debug, cmd});
# evaluate last block on master while slaves are busy
z_on_node = kernel_regression_nodes(eval_points, data, do_cv, kernel, points_per_node, computenodes, debug);
startblock = NSLAVES*points_per_node + 1;
endblock = nn;
z(startblock:endblock,:) = z(startblock:endblock,:) + z_on_node;
# collect slaves' results
z_on_node = zeros(points_per_node,1); # size may differ between master and compute nodes - reset here
for i = 1:NSLAVES
MPI_Recv(z_on_node,i,TAG,NEWORLD);
startblock = i*points_per_node - points_per_node + 1;
endblock = i*points_per_node;
z(startblock:endblock,:) = z(startblock:endblock,:) + z_on_node;
endfor
# clean up after parallel
LAM_Finalize;
endif
endfunction