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[CreaPhase.git] / octave_packages / econometrics-1.0.8 / kernel_regression.m

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