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authorYanzhenXiangRobotics <xyz000327@gmail.com>2023-05-10 23:18:37 +0200
committerYanzhenXiangRobotics <xyz000327@gmail.com>2023-05-10 23:18:37 +0200
commit241e6d754e71e9784c49fc70f218269b07290b64 (patch)
treef8a2c2d5e57fbc6369d1fa9500ba81a8547d3465 /templates
parentUpdate progress.md (diff)
downloadmpc_pe-241e6d754e71e9784c49fc70f218269b07290b64.tar.gz
mpc_pe-241e6d754e71e9784c49fc70f218269b07290b64.zip
Revert mistakenly pushed to master
This reverts commit c5cdd03fc0588f7d34d8b97713caa6d19e5becea.
Diffstat (limited to '')
-rwxr-xr-xtemplates/chebycenter.m20
-rwxr-xr-xtemplates/cprnd.m296
-rw-r--r--templates/generate_disturbances.m6
-rw-r--r--templates/generate_params_z.m1
-rw-r--r--templates/license.txt27
5 files changed, 0 insertions, 350 deletions
diff --git a/templates/chebycenter.m b/templates/chebycenter.m
deleted file mode 100755
index cbdce1c..0000000
--- a/templates/chebycenter.m
+++ /dev/null
@@ -1,20 +0,0 @@
-function [c,r] = chebycenter(A,b)
-%CHEBYCENTER Compute Chebyshev center of polytope Ax <= b.
-% The Chebyshev center of a polytope is the center of the largest
-% hypersphere enclosed by the polytope.
-% Requires optimization toolbox.
-
-[n,p] = size(A);
-an = sqrt(sum(A.^2,2));
-A1 = zeros(n,p+1);
-A1(:,1:p) = A;
-A1(:,p+1) = an;
-f = zeros(p+1,1);
-f(p+1) = -1;
-
-options = optimset;
-options = optimset(options,'Display', 'off');
-c = linprog(f,A1,b,[],[],[],[],[],options);
-r = c(p+1);
-c = c(1:p);
-end
diff --git a/templates/cprnd.m b/templates/cprnd.m
deleted file mode 100755
index 4183ac3..0000000
--- a/templates/cprnd.m
+++ /dev/null
@@ -1,296 +0,0 @@
-function [X S] = cprnd(N,A,b,options)
-%CPRND Draw from the uniform distribution over a convex polytope.
-% X = cprnd(N,A,b) Returns an N-by-P matrix of random vectors drawn
-% from the uniform distribution over the interior of the polytope
-% defined by Ax <= b. A is a M-by-P matrix of constraint equation
-% coefficients. b is a M-by-1 vector of constraint equation
-% constants.
-%
-% cprnd(N,A,b,options) allows various options to be specified.
-
-% 'method' Specifies the algorithm. One of the strings 'gibbs',
-% 'hitandrun' (the default), and 'achr'. The default
-% algorithm 'hitandrun' is a vanilla hit-and-run sampler
-% [1]. 'gibbs' specifies a Gibbs sampler, 'achr' is the
-% Adaptive Centering Hit-and-Run algorithm of [2].
-%
-% 'x0' Vector x0 is a starting point which should be interior
-% to the polytope. If x0 is not supplied CPRND uses the
-% Chebyshev center as the initial point.
-%
-% 'isotropic' Perform an adaptive istropic transformation of the
-% polytope. The values 0, 1 and 2 respectively turn
-% off the transformation, construct it during a runup
-% period only, or continuously update the
-% tranformation throughout sample production. The
-% transformation makes sense only for the Gibbs and
-% Hit-and-run samplers (ACHR is invariant under
-% linear transformations).
-%
-% 'discard' Number of initial samples (post-runup) to discard.
-%
-% 'runup' When the method is gibbs or hitandrun and the
-% isotropic transformation is used, this is the
-% number of initial iterations of the algorithm in
-% the untransformed space. That is a sample of size
-% runup is generated and its covariance used as the
-% basis of a transformation.
-% When the method is achr runup is the number of
-% conventional hitandrun iterations. See [2].
-%
-% 'ortho' Zero/one flag. If turned on direction vectors for
-% the hitandrun algorithm are generated in random
-% orthonormal blocks rather than one by
-% one. Experimental and of dubious utility.
-%
-% 'quasi' Allows the user to specify a quasirandom number generator
-% (such as 'halton' or 'sobol'). Experimental and of
-% dubious utility.
-%
-%
-
-% By default CPRND employs the hit-and-run sampler which may
-% exhibit marked serial correlation and very long convergence times.
-%
-% References
-% [1] Kroese, D.P. and Taimre, T. and Botev, Z.I., "Handbook of Monte
-% Carlo Methods" (2011), pp. 240-244.
-% [2] Kaufman, David E. and Smith, Robert L., "Direction Choice for
-% Accelerated Convergence in Hit-and-Run Sampling", Op. Res. 46,
-% pp. 84-95.
-%
-% Copyright (c) 2011-2012 Tim J. Benham, School of Mathematics and Physics,
-% University of Queensland.
-
- function y = stdize(z)
- y = z/norm(z);
- end
-
- p = size(A,2); % dimension
- m = size(A,1); % num constraint ineqs
- x0 = [];
- runup = []; % runup necessary to method
- discard = []; % num initial pts discarded
- quasi = 0;
- method = 'achr';
- orthogonal = 0;
- isotropic = [];
-
- % gendir generates a random unit (direction) vector.
- gendir = @() stdize(randn(p,1));
-
- % Alternative function ogendir forces directions to come in
- % orthogonal bunches.
- Ucache = {};
- function u = ogendir()
- if length(Ucache) == 0
- u = stdize(randn(p,1));
- m = null(u'); % orthonormal basis for nullspace
- Ucache = mat2cell(m',ones(1,p-1));
- else
- u = Ucache{end}';
- Ucache(end) = [];
- end
- end
-
- % Check input arguments
-
- if m < p+1
- % obv a prob here
- error('cprnd:obvprob',['At least ',num2str(p+1),' inequalities ' ...
- 'required']);
- end
-
- if nargin == 4
- if isstruct(options)
-
- if isfield(options,'method')
- method = lower(options.method);
- switch method
- case 'gibbs'
- case 'hitandrun'
- case 'achr'
- otherwise
- error('cprnd:badopt',...
- ['The method option takes only the ' ...
- 'values "gibbs", "hitandrun", and "ACHR".']);
- end
- end
-
- if isfield(options,'isotropic')
- % Controls application of isotropic transformation,
- % which seems to help a lot.
- isotropic = options.isotropic;
- end
-
- if isfield(options,'discard')
- % num. of samples to discard
- discard = options.discard;
- end
-
- if isfield(options,'quasi')
- % Use quasi random numbers, which doesn't seem to
- % help much.
- quasi = options.quasi;
- if quasi && ~ischar(quasi), quasi='halton'; end
- if ~strcmp(quasi,'none')
- qstr = qrandstream(quasi,p,'Skip',1);
- gendir = @() stdize(norminv(qrand(qstr,1),0,1)');
- end
- end
-
- if isfield(options,'x0')
- % initial interior point
- x0 = options.x0;
- end
-
- if isfield(options,'runup')
- % number of points to generate before first output point
- runup = options.runup;
- end
-
- if isfield(options,'ortho')
- % Generate direction vectors in orthogonal
- % groups. Seems to help a little.
- orthogonal = options.ortho;
- end
-
- else
- x0 = options; % legacy support
- end
- end
-
- % Default and apply options
-
- if isempty(isotropic)
- if ~strcmp(method,'achr')
- isotropic = 2;
- else
- isotropic = 0;
- end
- end
-
- if orthogonal
- gendir = @() ogendir();
- end
-
- % Choose a starting point x0 if user did not provide one.
- if isempty(x0)
- x0 = chebycenter(A,b); % prob. if p==1?
- end
-
- % Default the runup to something arbitrary.
- if isempty(runup)
- if strcmp(method,'achr')
- runup = 10*(p+1);
- elseif isotropic > 0
- runup = 10*p*(p+1);
- else
- runup = 0;
- end
- end
-
- % Default the discard to something arbitrary
- if isempty(discard)
- if strcmp(method,'achr')
- discard = 25*(p+1);
- else
- discard = runup;
- end
- end
-
- X = zeros(N+runup+discard,p);
-
- n = 0; % num generated so far
- x = x0;
-
- % Initialize variables for keeping track of sample mean, covariance
- % and isotropic transform.
- M = zeros(p,1); % Incremental mean.
- S2 = zeros(p,p); % Incremental sum of
- % outer prodcts.
- S = eye(p); T = eye(p); W = A;
-
- while n < N+runup+discard
- y = x;
-
- % compute approximate stochastic transformation
- if isotropic>0
- if n == runup || (isotropic > 1 && n > runup)
- T = chol(S,'lower');
- W = A*T;
- end
- y = T\y;
- end
-
- switch method
-
- case 'gibbs'
- % choose p new components
- for i = 1:p
- % Find points where the line with the (p-1) components x_i
- % fixed intersects the bounding polytope.
- e = circshift(eye(p,1),i-1);
- z = W*e;
- c = (b - W*y)./z;
- tmin = max(c(z<0)); tmax = min(c(z>0));
- % choose a point on that line segment
- y = y + (tmin+(tmax-tmin)*rand)*e;
- end
-
- case 'hitandrun'
- % choose a direction
- u = gendir();
- % determine intersections of x + ut with the polytope
- z = W*u;
- c = (b - W*y)./z;
- tmin = max(c(z<0)); tmax = min(c(z>0));
- % choose a point on that line segment
- y = y + (tmin+(tmax-tmin)*rand)*u;
-
- case 'achr'
- % test whether in runup or not
- if n < runup
- % same as hitandrun
- u = gendir();
- else
- % choose a previous point at random
- v = X(randi(n),:)';
- % line sampling direction is from v to sample mean
- u = (v-M)/norm(v-M);
- end
- % proceed as in hit and run
- z = A*u;
- c = (b - A*y)./z;
- tmin = max(c(z<0)); tmax = min(c(z>0));
- % Choose a random point on that line segment
- y = y + (tmin+(tmax-tmin)*rand)*u;
- end
-
- if isotropic>0
- x = T*y;
- else
- x = y;
- end
-
- X(n+1,:)=x';
- n = n + 1;
-
- % Incremental mean and covariance updates
- delta0 = x - M; % delta new point wrt old mean
- M = M + delta0/n; % sample mean
- delta1 = x - M; % delta new point wrt new mean
- if n > 1
- S2 = S2 + (n-1)/(n*n)*delta0*delta0'...
- + delta1*delta1';
- S0 = S;
- S = S2/(n-1); % sample covariance
- else
- S = eye(p);
- end
-
- end
-
- X = X((discard+runup+1):(N+discard+runup),:);
-
-end
diff --git a/templates/generate_disturbances.m b/templates/generate_disturbances.m
index f08cdb5..570a4c9 100644
--- a/templates/generate_disturbances.m
+++ b/templates/generate_disturbances.m
@@ -8,10 +8,4 @@
function Wt = generate_disturbances(params)
% YOUR CODE HERE
- % params_z = generate_params_z(params);
- Hw = params.constraints.DisturbanceMatrix;
- hw = params.constraints.DisturbanceRHS;
- % Pw = Polyhedron('A', Hw, 'b', hw);
- N = params.model.HorizonLength;
- Wt = cprnd(N,Hw,hw)';
end \ No newline at end of file
diff --git a/templates/generate_params_z.m b/templates/generate_params_z.m
index 6d25f63..a7cfaa9 100644
--- a/templates/generate_params_z.m
+++ b/templates/generate_params_z.m
@@ -9,7 +9,6 @@
function [params_z] = generate_params_z(params)
% initialize params_z
params_z = params;
-% display(params.model);
% add initial condition of z-subsystem
params_z.model = rmfield(params_z.model,{'InitialConditionA', ...
diff --git a/templates/license.txt b/templates/license.txt
deleted file mode 100644
index 4d6117c..0000000
--- a/templates/license.txt
+++ /dev/null
@@ -1,27 +0,0 @@
-Copyright (c) 2011, Tim Benham
-All rights reserved.
-
-Redistribution and use in source and binary forms, with or without
-modification, are permitted provided that the following conditions are
-met:
-
- * Redistributions of source code must retain the above copyright
- notice, this list of conditions and the following disclaimer.
- * Redistributions in binary form must reproduce the above copyright
- notice, this list of conditions and the following disclaimer in
- the documentation and/or other materials provided with the distribution
- * Neither the name of the University of Queensland nor the names
- of its contributors may be used to endorse or promote products derived
- from this software without specific prior written permission.
-
-THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
-AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
-IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
-ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE
-LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
-CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
-SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
-INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
-CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
-ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
-POSSIBILITY OF SUCH DAMAGE.