X-Git-Url: https://git.creatis.insa-lyon.fr/pubgit/?p=CreaPhase.git;a=blobdiff_plain;f=octave_packages%2Feconometrics-1.0.8%2Fkernel_regression.m;fp=octave_packages%2Feconometrics-1.0.8%2Fkernel_regression.m;h=ec0859bd2802a498a90df32e6f79f0ce4f145042;hp=0000000000000000000000000000000000000000;hb=f5f7a74bd8a4900f0b797da6783be80e11a68d86;hpb=1705066eceaaea976f010f669ce8e972f3734b05

diff --git a/octave_packages/econometrics-1.0.8/kernel_regression.m b/octave_packages/econometrics-1.0.8/kernel_regression.m
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+++ b/octave_packages/econometrics-1.0.8/kernel_regression.m
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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