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github | facundoq/courses-master | ComputeMarginal.m | .m | courses-master/pgm/p7/ComputeMarginal.m | 973 | utf_8 | b17f8235ad67cf5b95fb291d1d46eed1 | %ComputeMarginal Computes the marginal over a set of given variables
% M = ComputeMarginal(V, F, E) computes the marginal over variables V
% in the distribution induced by the set of factors F, given evidence E
%
% M is a factor containing the marginal over variables V
% V is a vector containing the variables i... |
github | facundoq/courses-master | AssignmentToIndex.m | .m | courses-master/pgm/p7/AssignmentToIndex.m | 609 | utf_8 | 3c17a18df90fc3a49aeebcb3e418e23f | % AssignmentToIndex Convert assignment to index.
%
% I = AssignmentToIndex(A, D) converts an assignment, A, over variables
% with cardinality D to an index into the .val vector for a factor.
% If A is a matrix then the function converts each row of A to an index.
%
% See also IndexToAssignment.m
%
% Copyright ... |
github | facundoq/courses-master | CreateCliqueTree.m | .m | courses-master/pgm/p7/CreateCliqueTree.m | 2,121 | utf_8 | a936108baf3997c53510b2efc9259f7b | %CREATECLIQUETREE Takes in a list of factors F, Evidence and returns a
%clique tree after calling ComputeInitialPotentials at the end.
%
% C = CREATECLIQUETREE(F) Takes a list of factors and creates a clique
% tree . The value of the cliques should be initialized to
% the initial potential.
% It returns a cl... |
github | facundoq/courses-master | submitWeb.m | .m | courses-master/pgm/p7/submitWeb.m | 580 | utf_8 | 5f4510147426716d140b1e22e95d36d7 | % submitWeb Creates files from your code and output for web submission.
%
% If the submit function does not work for you, use the web-submission mechanism.
% Call this function to produce a file for the part you wish to submit. Then,
% submit the file to the class servers using the "Web Submission" button on ... |
github | facundoq/courses-master | ComputeJointDistribution.m | .m | courses-master/pgm/p7/ComputeJointDistribution.m | 906 | utf_8 | 9a44d68defaa54e26172513735c2f85d | %ComputeJointDistribution Computes the joint distribution defined by a set
% of given factors
%
% Joint = ComputeJointDistribution(F) computes the joint distribution
% defined by a set of given factors
%
% Joint is a factor that encapsulates the joint distribution given by F
% F is a vector of factors (s... |
github | facundoq/courses-master | PrintFactor.m | .m | courses-master/pgm/p7/PrintFactor.m | 526 | utf_8 | 7ab925539a614f01ebbef8c4c001d3c2 | % Copyright (C) Daphne Koller, Stanford University, 2012
function [] = PrintFactor(F)
% Pretty print the factor F.
% The first row lists the variables and subsequent rows are
% the joint assignment and their associated factor value in
% the last column.
for i=1:length(F.var)
fprintf(1, '%d\t', F.var(i... |
github | facundoq/courses-master | InstanceNegLogLikelihood.m | .m | courses-master/pgm/p7/InstanceNegLogLikelihood.m | 5,486 | utf_8 | 1480ccb0976ff59c1d9df08e2dc5ffac | % function [nll, grad] = InstanceNegLogLikelihood(X, y, theta, modelParams)
% returns the negative log-likelihood and its gradient, given a CRF with parameters theta,
% on data (X, y).
%
% Inputs:
% X Data. (numCharacters x numImageFeatures matrix)
% X(:,1) is all ones... |
github | facundoq/courses-master | ObserveEvidence.m | .m | courses-master/pgm/p7/ObserveEvidence.m | 1,905 | utf_8 | be2baaabecfb90454113523ffef414c2 | % ObserveEvidence Modify a vector of factors given some evidence.
% F = ObserveEvidence(F, E) sets all entries in the vector of factors, F,
% that are not consistent with the evidence, E, to zero. F is a vector of
% factors, each a data structure with the following fields:
% .var Vector of variables in the... |
github | facundoq/courses-master | SetValueOfAssignment.m | .m | courses-master/pgm/p7/SetValueOfAssignment.m | 856 | utf_8 | b9b9de205d3d14c0abd0cc805bae2fc8 | %SETVALUEOFASSIGNMENT Sets the value of a variable assignment in a factor.
%
% F = SETVALUEOFASSIGNMENT(F, A, v) sets the value of a variable assignment,
% A, in factor F to v. The order of the variables in A are assumed to be the
% same as the order in F.var.
%
% F = SETVALUEOFASSIGNMENT(F, A, v, VO) set... |
github | facundoq/courses-master | IndexToAssignment.m | .m | courses-master/pgm/p2/IndexToAssignment.m | 598 | utf_8 | 91464eb3a4bee675ea43f291e36754f6 | % IndexToAssignment Convert index to variable assignment.
%
% A = IndexToAssignment(I, D) converts an index, I, into the .val vector
% into an assignment over variables with cardinality D. If I is a vector,
% then the function produces a matrix of assignments, one assignment
% per row.
%
% See also A... |
github | facundoq/courses-master | submit.m | .m | courses-master/pgm/p2/submit.m | 26,858 | utf_8 | 3d05a059f947090116edcf9659a11f24 | function submit(partId, webSubmit)
%SUBMIT Submit your code and output to the pgm-class servers
% SUBMIT() will connect to the pgm-class server and submit your solution
% There is no penalty for submitting, so go ahead and try this!
%
% If this function does not work for you, use the web-submission mechanism.
% ... |
github | facundoq/courses-master | GetValueOfAssignment.m | .m | courses-master/pgm/p2/GetValueOfAssignment.m | 805 | utf_8 | 0cdb098df0b0778650c51563f7710e12 | % GetValueOfAssignment Gets the value of a variable assignment in a factor.
%
% v = GetValueOfAssignment(F, A) returns the value of a variable assignment,
% A, in factor F. The order of the variables in A are assumed to be the
% same as the order in F.var.
%
% v = GetValueOfAssignment(F, A, VO) gets the v... |
github | facundoq/courses-master | AssignmentToIndex.m | .m | courses-master/pgm/p2/AssignmentToIndex.m | 600 | utf_8 | 429d0a6516a1cc5724d602ccf3df86e0 | % AssignmentToIndex Convert assignment to index.
%
% I = AssignmentToIndex(A, D) converts an assignment, A, over variables
% with cardinality D to an index into the .val vector for a factor.
% If A is a matrix then the function converts each row of A to an index.
%
% See also IndexToAssignment.m and Samp... |
github | facundoq/courses-master | submitWeb.m | .m | courses-master/pgm/p2/submitWeb.m | 523 | utf_8 | 6bbecf7bb45a5031ca74002e71845d3e | % submitWeb Creates files from your code and output for web submission.
%
% If the submit function does not work for you, use the web-submission mechanism.
% Call this function to produce a file for the part you wish to submit. Then,
% submit the file to the class servers using the "Web Submission" button on ... |
github | facundoq/courses-master | SetValueOfAssignment.m | .m | courses-master/pgm/p2/SetValueOfAssignment.m | 1,154 | utf_8 | 33321dcc114579d30b93c1fc99b35e17 | % SetValueOfAssignment Sets the value of a variable assignment in a factor.
%
% F = SetValueOfAssignment(F, A, v) sets the value of a variable assignment,
% A, in factor F to v. The order of the variables in A are assumed to be the
% same as the order in F.var.
%
% F = SetValueOfAssignment(F, A, v, VO) se... |
github | facundoq/courses-master | RunInference.m | .m | courses-master/pgm/p3/RunInference.m | 1,864 | utf_8 | 949c039d2cf8422123f6642e76b152a8 | function pred = RunInference (factors)
% This function performs inference for a Markov network specified as a list
% of factors.
%
% Input:
% factors: An array of struct factors, each containing 'var', 'card', and
% 'val' fields.
%
% Output:
% pred: An array of predictions for every variable. In particular,
% ... |
github | facundoq/courses-master | IndexToAssignment.m | .m | courses-master/pgm/p3/IndexToAssignment.m | 585 | utf_8 | 5b59626ed9c81a59a158ac9e48e2c257 | % IndexToAssignment Convert index to variable assignment.
%
% A = IndexToAssignment(I, D) converts an index, I, into the .val vector
% into an assignment over variables with cardinality D. If I is a vector,
% then the function produces a matrix of assignments, one assignment
% per row.
%
% See also A... |
github | facundoq/courses-master | submit.m | .m | courses-master/pgm/p3/submit.m | 21,118 | utf_8 | b49fc354ceeadf746a58dfc4abae826f | function submit(partId, webSubmit)
%SUBMIT Submit your code and output to the pgm-class servers
% SUBMIT() will connect to the pgm-class server and submit your solution
% There is no penalty for submitting, so go ahead and try this!
%
% If this function does not work for you, use the web-submission mechanism.
% ... |
github | facundoq/courses-master | GetValueOfAssignment.m | .m | courses-master/pgm/p3/GetValueOfAssignment.m | 835 | utf_8 | e0d34fcad4369e061ca88650da3397c9 | %GETVALUEOFASSIGNMENT Gets the value of a variable assignment in a factor.
%
% v = GETVALUEOFASSIGNMENT(F, A) returns the value of a variable assignment,
% A, in factor F. The order of the variables in A are assumed to be the
% same as the order in F.var.
%
% v = GETVALUEOFASSIGNMENT(F, A, VO) gets the va... |
github | facundoq/courses-master | AssignmentToIndex.m | .m | courses-master/pgm/p3/AssignmentToIndex.m | 652 | utf_8 | 98d4cfb7615e1a71bb4c77d5982ddabf | % AssignmentToIndex Convert assignment to index.
%
% I = AssignmentToIndex(A, D) converts an assignment, A, over variables
% with cardinality D to an index into the .val vector for a factor.
% If A is a matrix then the function converts each row of A to an index.
%
% See also IndexToAssignment.m and Samp... |
github | facundoq/courses-master | submitWeb.m | .m | courses-master/pgm/p3/submitWeb.m | 581 | utf_8 | 40868cac2a1f7de8fbdc5855c255d44d | % submitWeb Creates files from your code and output for web submission.
%
% If the submit function does not work for you, use the web-submission mechanism.
% Call this function to produce a file for the part you wish to submit. Then,
% submit the file to the class servers using the "Web Submission" button on ... |
github | facundoq/courses-master | eq_factors.m | .m | courses-master/pgm/p3/eq_factors.m | 372 | utf_8 | 0425650440c2e53ea5fb0979fb30200d | function eq=eq_factors(F1,F2)
assert(length(F1)==length(F2),'Both factor lists must have the same number of elements');
eq=true;
for i=1:length(F1)
if ~eq_factor(F1(i),F2(i))
eq=false;
break;
end
end
end
function eq=eq_factor(f1,f2)
eq= eq_eps(f1.var,f2.var) && e... |
github | facundoq/courses-master | comparedata.m | .m | courses-master/pgm/p3/comparedata.m | 12,980 | utf_8 | a7242d9a91fcd2959131f5fce367f085 | function retval=comparedata(data1, data2, context, Params)
% function retval=comparedata(data1, data2, context, Params)
% compares to see if data1 and data2 are roughly recursively equal. "Rough" here is defined by
% the Params. Note that matlabs ISEQUAL function is a test for exact equality. comparedata
% ... |
github | facundoq/courses-master | SetValueOfAssignment.m | .m | courses-master/pgm/p3/SetValueOfAssignment.m | 856 | utf_8 | 2165785a0cb20136c69297b48edfd77e | %SETVALUEOFASSIGNMENT Sets the value of a variable assignment in a factor.
%
% F = SETVALUEOFASSIGNMENT(F, A, v) sets the value of a variable assignment,
% A, in factor F to v. The order of the variables in A are assumed to be the
% same as the order in F.var.
%
% F = SETVALUEOFASSIGNMENT(F, A, v, VO) set... |
github | facundoq/courses-master | ComputeExactMarginalsBP.m | .m | courses-master/pgm/old/p4/ComputeExactMarginalsBP.m | 1,027 | utf_8 | 7463656744c4aa90967fe0e0ac4c6c0f | %COMPUTEEXACTMARGINALSBP Runs exact inference and returns the marginals
%over all the variables (if isMax == 0) or the max-marginals (if isMax == 1).
%
% M = COMPUTEEXACTMARGINALSBP(F, E, isMax) takes a list of factors F,
% evidence E, and a flag isMax, runs exact inference and returns the
% final marginals for ... |
github | facundoq/courses-master | EliminateVar.m | .m | courses-master/pgm/old/p4/EliminateVar.m | 1,346 | utf_8 | d107c19a06837def63e9460f88420c58 | % Function used in production of clique trees
%
% Copyright (C) Daphne Koller, Stanford University, 2012
function [newF C E] = EliminateVar(F, C, E, Z)
useFactors = [];
scope = [];
for i=1:length(F)
if any(F(i).var == Z)
useFactors = [useFactors i];
scope = union(scope, F(i).var);
end
end
% ... |
github | facundoq/courses-master | IndexToAssignment.m | .m | courses-master/pgm/old/p4/IndexToAssignment.m | 641 | utf_8 | 4500d5b7500f21f11c89b80922de3c42 | % IndexToAssignment Convert index to variable assignment.
%
% A = IndexToAssignment(I, D) converts an index, I, into the .val vector
% into an assignment over variables with cardinality D. If I is a vector,
% then the function produces a matrix of assignments, one assignment
% per row.
%
% See also Assignme... |
github | facundoq/courses-master | CliqueTreeCalibrate.m | .m | courses-master/pgm/old/p4/CliqueTreeCalibrate.m | 1,848 | utf_8 | 41802a206ec191940f3cf20f266c6290 | %CLIQUETREECALIBRATE Performs sum-product or max-product algorithm for
%clique tree calibration.
% P = CLIQUETREECALIBRATE(P, isMax) calibrates a given clique tree, P
% according to the value of isMax flag. If isMax is 1, it uses max-sum
% message passing, otherwise uses sum-product. This function
% returns... |
github | facundoq/courses-master | FactorMarginalization.m | .m | courses-master/pgm/old/p4/FactorMarginalization.m | 1,691 | utf_8 | 1a6c278432109b94853fa27ff43fe933 | % FactorMarginalization Sums given variables out of a factor.
% B = FactorMarginalization(A,V) computes the factor with the variables
% in V summed out. The factor data structure has the following fields:
% .var Vector of variables in the factor, e.g. [1 2 3]
% .card Vector of cardinalities corresp... |
github | facundoq/courses-master | DecodedMarginalsToChars.m | .m | courses-master/pgm/old/p4/DecodedMarginalsToChars.m | 218 | utf_8 | 02261a4f2b86ec598a6e1c7e98525853 | % Copyright (C) Daphne Koller, Stanford University, 2012
function DecodedMarginalsToChars(decodedMarginals)
chars = 'abcdefghijklmnopqrstuvwxyz';
fprintf('%c', chars(decodedMarginals));
fprintf('\n');
end
|
github | facundoq/courses-master | MaxDecoding.m | .m | courses-master/pgm/old/p4/MaxDecoding.m | 825 | utf_8 | 77521fc67bc3fda7b4022e983878fcda | %MAXDECODING Finds the best assignment for each variable from the marginals M
%passed in. Returns A such that A(i) returns the index of the best
%instantiation for variable i.
%
% For instance: Let's say we have two variables 1 and 2.
% Marginals for 1 = [0.1, 0.3, 0.6]
% Marginals for 2 = [0.92, 0.08]
% A(1) ... |
github | facundoq/courses-master | FactorProduct.m | .m | courses-master/pgm/old/p4/FactorProduct.m | 2,357 | utf_8 | 06035e60b1296257aaf43c3e4e8aa15c | % FactorProduct Computes the product of two factors.
% C = FactorProduct(A,B) computes the product between two factors, A and B,
% where each factor is defined over a set of variables with given dimension.
% The factor data structure has the following fields:
% .var Vector of variables in the factor, e.g... |
github | facundoq/courses-master | PA4_Run_Tests.m | .m | courses-master/pgm/old/p4/PA4_Run_Tests.m | 2,900 | utf_8 | 9d8a8cc98992d397df6f86722b773415 | % A simple test suite for PA 4
%
% copy the comparedata.m file from last week's test suite or from
% http://www.mathworks.com/matlabcentral/fileexchange/1459-comparedata
% into the directory for this weeks assignment and save this file
% as PA4_Run_Tests.m
%
% If you call PA4_RUn_Tests, it will always run all tests in ... |
github | facundoq/courses-master | submit.m | .m | courses-master/pgm/old/p4/submit.m | 28,922 | utf_8 | b8b1d235d74470e02883bb719fbc8e8c | function submit(partId, webSubmit)
%SUBMIT Submit your code and output to the pgm-class servers
% SUBMIT() will connect to the pgm-class server and submit your solution
% There is no penalty for submitting, so go ahead and try this!
%
% If this function does not work for you, use the web-submission mechanism.
% ... |
github | facundoq/courses-master | StandardizeFactors.m | .m | courses-master/pgm/old/p4/StandardizeFactors.m | 621 | utf_8 | f622aaecac8d0b7e4493aacd074eda6c | % Function that sorts the variables in F and returns an equivalent
% factor G. Used only to standardize output for grading purposes.
function G = StandardizeFactors(F)
G = struct('var', [], 'card', [], 'val', []);
for i = 1:length(F)
G(i) = StandardizeFactor(F(i));
end
function G = StandardizeFactor(F);
G = stru... |
github | facundoq/courses-master | ComputeInitialPotentials.m | .m | courses-master/pgm/old/p4/ComputeInitialPotentials.m | 2,659 | utf_8 | b5526520826bacd5fc24dba1351ed2c5 | %COMPUTEINITIALPOTENTIALS Sets up the cliques in the clique tree that is
%passed in as a parameter.
%
% P = COMPUTEINITIALPOTENTIALS(C) Takes the clique tree skeleton C which is a
% struct with three fields:
% - nodes: cell array representing the cliques in the tree.
% - edges: represents the adjacency matrix o... |
github | facundoq/courses-master | GetValueOfAssignment.m | .m | courses-master/pgm/old/p4/GetValueOfAssignment.m | 838 | utf_8 | 44eefd1a7a3ae4efe72c80711a4ac67d | % GetValueOfAssignment Gets the value of a variable assignment in a factor.
%
% v = GetValueOfAssignment(F, A) returns the value of a variable assignment,
% A, in factor F. The order of the variables in A are assumed to be the
% same as the order in F.var.
%
% v = GetValueOfAssignment(F, A, VO) gets the value o... |
github | facundoq/courses-master | GetNextCliques.m | .m | courses-master/pgm/old/p4/GetNextCliques.m | 1,380 | utf_8 | a4369fa83d781d4e7b3a9aeca267298a | %GETNEXTCLIQUES Find a pair of cliques ready for message passing
% [i, j] = GETNEXTCLIQUES(P, messages) finds ready cliques in a given
% clique tree, P, and a matrix of current messages. Returns indices i and j
% such that clique i is ready to transmit a message to clique j.
%
% We are doing clique tree message... |
github | facundoq/courses-master | FactorMaxMarginalization.m | .m | courses-master/pgm/old/p4/FactorMaxMarginalization.m | 1,731 | utf_8 | a40575c5f3f866ca4b304cfa3f370a6f | % FactorMaxMarginalization Max-marginalizes a factor
% by taking the max over a given set variables.
%
% B = FactorMaxMarginalization(A,V) computes the factor with the variables
% in V maxed out. The factor data structure has the following fields:
% .var Vector of variables in the factor, e.g. [1 2 3]
% ... |
github | facundoq/courses-master | ComputeMarginal.m | .m | courses-master/pgm/old/p4/ComputeMarginal.m | 1,227 | utf_8 | 67b61710048f402adfa7ea23875b4872 | %ComputeMarginal Computes the marginal over a set of given variables
% M = ComputeMarginal(V, F, E) computes the marginal over variables V
% in the distribution induced by the set of factors F, given evidence E
%
% M is a factor containing the marginal over variables V
% V is a vector containing the variables i... |
github | facundoq/courses-master | AssignmentToIndex.m | .m | courses-master/pgm/old/p4/AssignmentToIndex.m | 622 | utf_8 | 3da521179d034588af2bfe11ed13f8f7 | % AssignmentToIndex Convert assignment to index.
%
% I = AssignmentToIndex(A, D) converts an assignment, A, over variables
% with cardinality D to an index into the .val vector for a factor.
% If A is a matrix then the function converts each row of A to an index.
%
% See also IndexToAssignment.m
%
% Copyright ... |
github | facundoq/courses-master | CreateCliqueTree.m | .m | courses-master/pgm/old/p4/CreateCliqueTree.m | 2,238 | utf_8 | 0b309fb32a6aee40615f6221d60a710e | %CREATECLIQUETREE Takes in a list of factors F, Evidence and returns a
%clique tree after calling ComputeInitialPotentials at the end.
%
% P = CREATECLIQUETREE(F, Evidence) Takes a list of factors and creates a clique
% tree. The value of the cliques should be initialized to
% the initial potential.
% It ret... |
github | facundoq/courses-master | submitWeb.m | .m | courses-master/pgm/old/p4/submitWeb.m | 581 | utf_8 | 40868cac2a1f7de8fbdc5855c255d44d | % submitWeb Creates files from your code and output for web submission.
%
% If the submit function does not work for you, use the web-submission mechanism.
% Call this function to produce a file for the part you wish to submit. Then,
% submit the file to the class servers using the "Web Submission" button on ... |
github | facundoq/courses-master | ComputeJointDistribution.m | .m | courses-master/pgm/old/p4/ComputeJointDistribution.m | 1,270 | utf_8 | f5233de99da1f71ac79340c59698670d | %ComputeJointDistribution Computes the joint distribution defined by a set
% of given factors
%
% Joint = ComputeJointDistribution(F) computes the joint distribution
% defined by a set of given factors
%
% Joint is a factor that encapsulates the joint distribution given by F
% F is a vector of factors (s... |
github | facundoq/courses-master | comparedata.m | .m | courses-master/pgm/old/p4/comparedata.m | 12,980 | utf_8 | a7242d9a91fcd2959131f5fce367f085 | function retval=comparedata(data1, data2, context, Params)
% function retval=comparedata(data1, data2, context, Params)
% compares to see if data1 and data2 are roughly recursively equal. "Rough" here is defined by
% the Params. Note that matlabs ISEQUAL function is a test for exact equality. comparedata
% ... |
github | facundoq/courses-master | ObserveEvidence.m | .m | courses-master/pgm/old/p4/ObserveEvidence.m | 2,193 | utf_8 | 4af4a267a25aaff99190410099accb86 | % ObserveEvidence Modify a vector of factors given some evidence.
% F = ObserveEvidence(F, E) sets all entries in the vector of factors, F,
% that are not consistent with the evidence, E, to zero. F is a vector of
% factors, each a data structure with the following fields:
% .var Vector of variables in the... |
github | facundoq/courses-master | SetValueOfAssignment.m | .m | courses-master/pgm/old/p4/SetValueOfAssignment.m | 1,181 | utf_8 | 6a83e76d1be90e51b6e7eba649f9f209 | % SetValueOfAssignment Sets the value of a variable assignment in a factor.
%
% F = SetValueOfAssignment(F, A, v) sets the value of a variable assignment,
% A, in factor F to v. The order of the variables in A are assumed to be the
% same as the order in F.var.
%
% F = SetValueOfAssignment(F, A, v, VO) sets the... |
github | facundoq/courses-master | IndexToAssignment.m | .m | courses-master/pgm/old/p2/IndexToAssignment.m | 598 | utf_8 | 91464eb3a4bee675ea43f291e36754f6 | % IndexToAssignment Convert index to variable assignment.
%
% A = IndexToAssignment(I, D) converts an index, I, into the .val vector
% into an assignment over variables with cardinality D. If I is a vector,
% then the function produces a matrix of assignments, one assignment
% per row.
%
% See also A... |
github | facundoq/courses-master | submit.m | .m | courses-master/pgm/old/p2/submit.m | 26,858 | utf_8 | 3d05a059f947090116edcf9659a11f24 | function submit(partId, webSubmit)
%SUBMIT Submit your code and output to the pgm-class servers
% SUBMIT() will connect to the pgm-class server and submit your solution
% There is no penalty for submitting, so go ahead and try this!
%
% If this function does not work for you, use the web-submission mechanism.
% ... |
github | facundoq/courses-master | GetValueOfAssignment.m | .m | courses-master/pgm/old/p2/GetValueOfAssignment.m | 805 | utf_8 | 0cdb098df0b0778650c51563f7710e12 | % GetValueOfAssignment Gets the value of a variable assignment in a factor.
%
% v = GetValueOfAssignment(F, A) returns the value of a variable assignment,
% A, in factor F. The order of the variables in A are assumed to be the
% same as the order in F.var.
%
% v = GetValueOfAssignment(F, A, VO) gets the v... |
github | facundoq/courses-master | AssignmentToIndex.m | .m | courses-master/pgm/old/p2/AssignmentToIndex.m | 600 | utf_8 | 429d0a6516a1cc5724d602ccf3df86e0 | % AssignmentToIndex Convert assignment to index.
%
% I = AssignmentToIndex(A, D) converts an assignment, A, over variables
% with cardinality D to an index into the .val vector for a factor.
% If A is a matrix then the function converts each row of A to an index.
%
% See also IndexToAssignment.m and Samp... |
github | facundoq/courses-master | submitWeb.m | .m | courses-master/pgm/old/p2/submitWeb.m | 523 | utf_8 | 6bbecf7bb45a5031ca74002e71845d3e | % submitWeb Creates files from your code and output for web submission.
%
% If the submit function does not work for you, use the web-submission mechanism.
% Call this function to produce a file for the part you wish to submit. Then,
% submit the file to the class servers using the "Web Submission" button on ... |
github | facundoq/courses-master | SetValueOfAssignment.m | .m | courses-master/pgm/old/p2/SetValueOfAssignment.m | 1,154 | utf_8 | 33321dcc114579d30b93c1fc99b35e17 | % SetValueOfAssignment Sets the value of a variable assignment in a factor.
%
% F = SetValueOfAssignment(F, A, v) sets the value of a variable assignment,
% A, in factor F to v. The order of the variables in A are assumed to be the
% same as the order in F.var.
%
% F = SetValueOfAssignment(F, A, v, VO) se... |
github | facundoq/courses-master | PS2_RunTests.m | .m | courses-master/pgm/old/p2/PS2_RunTests/PS2_RunTests.m | 5,637 | utf_8 | 6b7a92686737a22e5e43bf7da194732b | function PS2_RunTests()
clear;
constTOL = 1e-6;
load('PA2_TestInput');
load('PA2_TestOutput');
partNames = validParts();
partId = promptPart();
len = length(partNames);
partNamesAlligned = char( partNames );
resultAll = 1;
for i = 1:len
... |
github | facundoq/courses-master | comparedata.m | .m | courses-master/pgm/old/p2/PS2_RunTests/comparedata.m | 12,980 | utf_8 | a7242d9a91fcd2959131f5fce367f085 | function retval=comparedata(data1, data2, context, Params)
% function retval=comparedata(data1, data2, context, Params)
% compares to see if data1 and data2 are roughly recursively equal. "Rough" here is defined by
% the Params. Note that matlabs ISEQUAL function is a test for exact equality. comparedata
% ... |
github | facundoq/courses-master | RunInference.m | .m | courses-master/pgm/old/p3/RunInference.m | 1,769 | utf_8 | 1ba1656e4edc7d51078478fedd09878a | function pred = RunInference (factors)
% This function performs inference for a Markov network specified as a list
% of factors.
%
% Input:
% factors: An array of struct factors, each containing 'var', 'card', and
% 'val' fields.
%
% Output:
% pred: An array of predictions for every variable. In particular,
% ... |
github | facundoq/courses-master | IndexToAssignment.m | .m | courses-master/pgm/old/p3/IndexToAssignment.m | 585 | utf_8 | 5b59626ed9c81a59a158ac9e48e2c257 | % IndexToAssignment Convert index to variable assignment.
%
% A = IndexToAssignment(I, D) converts an index, I, into the .val vector
% into an assignment over variables with cardinality D. If I is a vector,
% then the function produces a matrix of assignments, one assignment
% per row.
%
% See also A... |
github | facundoq/courses-master | PA3Test.m | .m | courses-master/pgm/old/p3/PA3Test.m | 3,670 | utf_8 | 5b202bcf62d9afa7d2d59c42b1484ab3 | % A simple test suite for PA 3
%
% copy the comparedata.m file from last week's test suite or from
% http://www.mathworks.com/matlabcentral/fileexchange/1459-comparedata
% into the directory for this weeks assignment and save this file
% as PA3Test.m
%
% A test can have three different results:
% - If the test suite s... |
github | facundoq/courses-master | submit.m | .m | courses-master/pgm/old/p3/submit.m | 21,118 | utf_8 | b49fc354ceeadf746a58dfc4abae826f | function submit(partId, webSubmit)
%SUBMIT Submit your code and output to the pgm-class servers
% SUBMIT() will connect to the pgm-class server and submit your solution
% There is no penalty for submitting, so go ahead and try this!
%
% If this function does not work for you, use the web-submission mechanism.
% ... |
github | facundoq/courses-master | GetValueOfAssignment.m | .m | courses-master/pgm/old/p3/GetValueOfAssignment.m | 835 | utf_8 | e0d34fcad4369e061ca88650da3397c9 | %GETVALUEOFASSIGNMENT Gets the value of a variable assignment in a factor.
%
% v = GETVALUEOFASSIGNMENT(F, A) returns the value of a variable assignment,
% A, in factor F. The order of the variables in A are assumed to be the
% same as the order in F.var.
%
% v = GETVALUEOFASSIGNMENT(F, A, VO) gets the va... |
github | facundoq/courses-master | AssignmentToIndex.m | .m | courses-master/pgm/old/p3/AssignmentToIndex.m | 652 | utf_8 | 98d4cfb7615e1a71bb4c77d5982ddabf | % AssignmentToIndex Convert assignment to index.
%
% I = AssignmentToIndex(A, D) converts an assignment, A, over variables
% with cardinality D to an index into the .val vector for a factor.
% If A is a matrix then the function converts each row of A to an index.
%
% See also IndexToAssignment.m and Samp... |
github | facundoq/courses-master | submitWeb.m | .m | courses-master/pgm/old/p3/submitWeb.m | 581 | utf_8 | 40868cac2a1f7de8fbdc5855c255d44d | % submitWeb Creates files from your code and output for web submission.
%
% If the submit function does not work for you, use the web-submission mechanism.
% Call this function to produce a file for the part you wish to submit. Then,
% submit the file to the class servers using the "Web Submission" button on ... |
github | facundoq/courses-master | comparedata.m | .m | courses-master/pgm/old/p3/comparedata.m | 12,980 | utf_8 | a7242d9a91fcd2959131f5fce367f085 | function retval=comparedata(data1, data2, context, Params)
% function retval=comparedata(data1, data2, context, Params)
% compares to see if data1 and data2 are roughly recursively equal. "Rough" here is defined by
% the Params. Note that matlabs ISEQUAL function is a test for exact equality. comparedata
% ... |
github | facundoq/courses-master | SetValueOfAssignment.m | .m | courses-master/pgm/old/p3/SetValueOfAssignment.m | 856 | utf_8 | 2165785a0cb20136c69297b48edfd77e | %SETVALUEOFASSIGNMENT Sets the value of a variable assignment in a factor.
%
% F = SETVALUEOFASSIGNMENT(F, A, v) sets the value of a variable assignment,
% A, in factor F to v. The order of the variables in A are assumed to be the
% same as the order in F.var.
%
% F = SETVALUEOFASSIGNMENT(F, A, v, VO) set... |
github | facundoq/courses-master | IndexToAssignment.m | .m | courses-master/pgm/old/p1/IndexToAssignment.m | 599 | utf_8 | ae992bdc926d3d43576329d3737682a2 | % IndexToAssignment Convert index to variable assignment.
%
% A = IndexToAssignment(I, D) converts an index, I, into the .val vector
% into an assignment over variables with cardinality D. If I is a vector,
% then the function produces a matrix of assignments, one assignment
% per row.
%
% See also A... |
github | facundoq/courses-master | FactorMarginalization.m | .m | courses-master/pgm/old/p1/FactorMarginalization.m | 1,670 | utf_8 | 33b5e1d1854ef38351d0e2f83985c6c7 | % FactorMarginalization Sums given variables out of a factor.
% B = FactorMarginalization(A,V) computes the factor with the variables
% in V summed out. The factor data structure has the following fields:
% .var Vector of variables in the factor, e.g. [1 2 3]
% .card Vector of cardinalities cor... |
github | facundoq/courses-master | FactorProduct.m | .m | courses-master/pgm/old/p1/FactorProduct.m | 2,447 | utf_8 | 171395bdd14a08975b7bb77172cefcd5 | % FactorProduct Computes the product of two factors.
% C = FactorProduct(A,B) computes the product between two factors, A and B,
% where each factor is defined over a set of variables with given dimension.
% The factor data structure has the following fields:
% .var Vector of variables in the factor,... |
github | facundoq/courses-master | submit.m | .m | courses-master/pgm/old/p1/submit.m | 22,940 | utf_8 | a27c89f0871722dbca10d1936da76cae | function submit(partId, webSubmit)
%SUBMIT Submit your code and output to the pgm-class servers
% SUBMIT() will connect to the pgm-class server and submit your solution
% There is no penalty for submitting, so go ahead and try this!
% Call this function without arguments (i.e. submit). Make sure your working
% ... |
github | facundoq/courses-master | StandardizeFactors.m | .m | courses-master/pgm/old/p1/StandardizeFactors.m | 581 | utf_8 | 372bcf4422698c44d7cbbec1b0135d85 | % Function that sorts the variables in F and returns an equivalent
% factor G. Used only to standardize output for grading purposes.
function G = StandardizeFactors(F)
G = struct('var', [], 'card', [], 'val', []);
for i = 1:length(F)
G(i) = StandardizeFactor(F(i));
end
function G = StandardizeFactor(F);
G = stru... |
github | facundoq/courses-master | GetValueOfAssignment.m | .m | courses-master/pgm/old/p1/GetValueOfAssignment.m | 806 | utf_8 | a63908225b529b3a7d605707c809a0a2 | % GetValueOfAssignment Gets the value of a variable assignment in a factor.
%
% v = GetValueOfAssignment(F, A) returns the value of a variable assignment,
% A, in factor F. The order of the variables in A are assumed to be the
% same as the order in F.var.
%
% v = GetValueOfAssignment(F, A, VO) gets the v... |
github | facundoq/courses-master | ComputeMarginal.m | .m | courses-master/pgm/old/p1/ComputeMarginal.m | 1,409 | utf_8 | aa24323bce17b3c2026804439ecf64d1 | %ComputeMarginal Computes the marginal over a set of given variables
% M = ComputeMarginal(V, F, E) computes the marginal over variables V
% in the distribution induced by the set of factors F, given evidence E
%
% M is a factor containing the marginal over variables V
% V is a vector containing the variab... |
github | facundoq/courses-master | AssignmentToIndex.m | .m | courses-master/pgm/old/p1/AssignmentToIndex.m | 601 | utf_8 | 2c6715c575574bb93ebc1dd4a52002ac | % AssignmentToIndex Convert assignment to index.
%
% I = AssignmentToIndex(A, D) converts an assignment, A, over variables
% with cardinality D to an index into the .val vector for a factor.
% If A is a matrix then the function converts each row of A to an index.
%
% See also IndexToAssignment.m and Fact... |
github | facundoq/courses-master | submitWeb.m | .m | courses-master/pgm/old/p1/submitWeb.m | 829 | utf_8 | f9a016a2c73dbb464925709b4f7c2f51 | % submitWeb Creates files from your code and output for web submission.
%
% If the submit function does not work for you, use the web-submission mechanism.
% Call this function to produce a file for the part you wish to submit. Then,
% submit the file to the class servers using the "Web Submission" button on ... |
github | facundoq/courses-master | ComputeJointDistribution.m | .m | courses-master/pgm/old/p1/ComputeJointDistribution.m | 1,193 | utf_8 | fcd1712d5a333fbdaf4194dfc5c6bf35 | %ComputeJointDistribution Computes the joint distribution defined by a set
% of given factors
%
% Joint = ComputeJointDistribution(F) computes the joint distribution
% defined by a set of given factors
%
% Joint is a factor that encapsulates the joint distribution given by F
% F is a vector of factors (s... |
github | facundoq/courses-master | ObserveEvidence.m | .m | courses-master/pgm/old/p1/ObserveEvidence.m | 2,205 | utf_8 | 739504ed5aebf8614c3449db44e59e07 | % ObserveEvidence Modify a vector of factors given some evidence.
% F = ObserveEvidence(F, E) sets all entries in the vector of factors, F,
% that are not consistent with the evidence, E, to zero. F is a vector of
% factors, each a data structure with the following fields:
% .var Vector of variables in... |
github | facundoq/courses-master | SetValueOfAssignment.m | .m | courses-master/pgm/old/p1/SetValueOfAssignment.m | 1,155 | utf_8 | 8c432b1246dea3f405d525c595d2a82f | % SetValueOfAssignment Sets the value of a variable assignment in a factor.
%
% F = SetValueOfAssignment(F, A, v) sets the value of a variable assignment,
% A, in factor F to v. The order of the variables in A are assumed to be the
% same as the order in F.var.
%
% F = SetValueOfAssignment(F, A, v, VO) se... |
github | facundoq/courses-master | FactorSum.m | .m | courses-master/pgm/p6/FactorSum.m | 2,340 | utf_8 | d2e83cf81bd5e92fe61deaa53e6f4643 | % FactorSum Computes the sum of two factors.
% C = FactorProduct(A,B) computes the sum between two factors, A and B,
% where each factor is defined over a set of variables with given dimension.
% The factor data structure has the following fields:
% .var Vector of variables in the factor, e.g. [1 2 3]
% ... |
github | facundoq/courses-master | EliminateVar.m | .m | courses-master/pgm/p6/EliminateVar.m | 1,446 | utf_8 | 63d8d8f317c1282c1feff173a9c8caef | % Function used in production of clique trees
% F = list of factors
% E = adjacency matrix for variables
% Z = variable to eliminate
%
% Copyright (C) Daphne Koller, Stanford University, 2012
function [newF E] = EliminateVar(F, E, Z)
% Index of factors to multiply (b/c they contain Z)
useFactors = [];
% Union ... |
github | facundoq/courses-master | IndexToAssignment.m | .m | courses-master/pgm/p6/IndexToAssignment.m | 641 | utf_8 | 4500d5b7500f21f11c89b80922de3c42 | % IndexToAssignment Convert index to variable assignment.
%
% A = IndexToAssignment(I, D) converts an index, I, into the .val vector
% into an assignment over variables with cardinality D. If I is a vector,
% then the function produces a matrix of assignments, one assignment
% per row.
%
% See also Assignme... |
github | facundoq/courses-master | OptimizeMEU.m | .m | courses-master/pgm/p6/OptimizeMEU.m | 2,343 | utf_8 | e693eb879b674aea24c37cf401a27b2b | % Copyright (C) Daphne Koller, Stanford University, 2012
function [MEU OptimalDecisionRule] = OptimizeMEU( I,euf )
% Inputs: An influence diagram I with a single decision node and a single utility node.
% I.RandomFactors = list of factors for each random variable. These are CPDs, with
% th... |
github | facundoq/courses-master | SimpleCalcExpectedUtility.m | .m | courses-master/pgm/p6/SimpleCalcExpectedUtility.m | 1,451 | utf_8 | 70b3e3d62760d8ea01aa659bef8d5310 | % Copyright (C) Daphne Koller, Stanford University, 2012
function EU = SimpleCalcExpectedUtility(I)
% Inputs: An influence diagram, I (as described in the writeup).
% I.RandomFactors = list of factors for each random variable. These are CPDs, with
% the child variable = D.var(1)
% ... |
github | facundoq/courses-master | CPDFromFactor.m | .m | courses-master/pgm/p6/CPDFromFactor.m | 780 | utf_8 | 376ed3dcc66b1fb5b12b7816829e56cd | % Copyright (C) Daphne Koller, Stanford University, 2012
function [CPD] = CPDFromFactor(F, Y)
nvars = length(F.var);
% Reorder the var, card and val fields of Fnew so that the first var is the
% child variable.
Fnew = F;
YIndexInF = find(F.var == Y);
this.card = F.card( YIndexInF );
% Parents is a d... |
github | facundoq/courses-master | FactorMarginalization.m | .m | courses-master/pgm/p6/FactorMarginalization.m | 1,750 | utf_8 | c5e05c8d4059cb32dcd028428a787bb2 | % FactorMarginalization Sums given variables out of a factor.
% B = FactorMarginalization(A,V) computes the factor with the variables
% in V summed out. The factor data structure has the following fields:
% .var Vector of variables in the factor, e.g. [1 2 3]
% .card Vector of cardinalities corresp... |
github | facundoq/courses-master | NormalizeFactorValues.m | .m | courses-master/pgm/p6/NormalizeFactorValues.m | 234 | utf_8 | 8f1abae0e673dcb23d8168c1cff3aae2 | % Copyright (C) Daphne Koller, Stanford University, 2012
function F = NormalizeFactorValues( F )
for i=1:length(F)
ThisFactor = F(i);
ThisFactor.val = ThisFactor.val / sum(ThisFactor.val);
F(i) = ThisFactor;
end
|
github | facundoq/courses-master | OptimizeWithJointUtility.m | .m | courses-master/pgm/p6/OptimizeWithJointUtility.m | 1,162 | utf_8 | 07ac72d15add3aea32d608afbc98ab93 | % Copyright (C) Daphne Koller, Stanford University, 2012
function [meu, odr] = OptimizeWithJointUtility( I )
% Inputs: An influence diagram I with a single decision node and one or more utility nodes.
% I.RandomFactors = list of factors for each random variable. These are CPDs, with
% the c... |
github | facundoq/courses-master | FactorProduct.m | .m | courses-master/pgm/p6/FactorProduct.m | 2,357 | utf_8 | 06035e60b1296257aaf43c3e4e8aa15c | % FactorProduct Computes the product of two factors.
% C = FactorProduct(A,B) computes the product between two factors, A and B,
% where each factor is defined over a set of variables with given dimension.
% The factor data structure has the following fields:
% .var Vector of variables in the factor, e.g... |
github | facundoq/courses-master | CalculateExpectedUtilityFactor.m | .m | courses-master/pgm/p6/CalculateExpectedUtilityFactor.m | 1,453 | utf_8 | 2596595287f074dfe245294f7f49262b | % Copyright (C) Daphne Koller, Stanford University, 2012
function EUF = CalculateExpectedUtilityFactor( I )
% Inputs: An influence diagram I with a single decision node and a single utility node.
% I.RandomFactors = list of factors for each random variable. These are CPDs, with
% the child... |
github | facundoq/courses-master | submit.m | .m | courses-master/pgm/p6/submit.m | 29,000 | utf_8 | 9740d8a11ffbe15f4d78728c9ce198ea | function submit(partId, webSubmit)
%SUBMIT Submit your code and output to the pgm-class servers
% SUBMIT() will connect to the pgm-class server and submit your solution
% There is no penalty for submitting, so go ahead and try this!
%
% If this function does not work for you, use the web-submission mechanism.
% ... |
github | facundoq/courses-master | NormalizeCPDFactors.m | .m | courses-master/pgm/p6/NormalizeCPDFactors.m | 845 | utf_8 | eea30946b1d108ecb184666d4a5980b3 | % Copyright (C) Daphne Koller, Stanford University, 2012
function [F] = NormalizeCPDFactors(F)
NumFactors = length(F);
for i=1:NumFactors
f = F(i);
dummy.var = f.var(2:end);
dummy.card = f.card(2:end);
dummy.val = zeros(1,prod(dummy.card));
% Now for each joint assignment to parents, r... |
github | facundoq/courses-master | GetValueOfAssignment.m | .m | courses-master/pgm/p6/GetValueOfAssignment.m | 838 | utf_8 | 44eefd1a7a3ae4efe72c80711a4ac67d | % GetValueOfAssignment Gets the value of a variable assignment in a factor.
%
% v = GetValueOfAssignment(F, A) returns the value of a variable assignment,
% A, in factor F. The order of the variables in A are assumed to be the
% same as the order in F.var.
%
% v = GetValueOfAssignment(F, A, VO) gets the value o... |
github | facundoq/courses-master | test.m | .m | courses-master/pgm/p6/test.m | 5,425 | utf_8 | bf9d0e1af9af6ba2d656f27fe1ebc618 | % A simple test suite for PA 6
%
% Based on the code by Mihaly Barasz posted on the forum.
%
% copy the CompareData.m file from last weeks test suite
% into the directory for this weeks assignment and save this file
% as PA6_RunTests.m
%
function result = test(anyway)
if ~exist('CompareData', 'file')
fprintf('please... |
github | facundoq/courses-master | AssignmentToIndex.m | .m | courses-master/pgm/p6/AssignmentToIndex.m | 621 | utf_8 | 8595f64ecc3b8cd1684ddd5753e83de3 | % AssignmentToIndex Convert assignment to index.
%
% I = AssignmentToIndex(A, D) converts an assignment, A, over variables
% with cardinality D to an index into the .val vector for a factor.
% If A is a matrix then the function converts each row of A to an index.
%
% See also IndexToAssignment.m
%
% Copyright ... |
github | facundoq/courses-master | submitWeb.m | .m | courses-master/pgm/p6/submitWeb.m | 581 | utf_8 | 40868cac2a1f7de8fbdc5855c255d44d | % submitWeb Creates files from your code and output for web submission.
%
% If the submit function does not work for you, use the web-submission mechanism.
% Call this function to produce a file for the part you wish to submit. Then,
% submit the file to the class servers using the "Web Submission" button on ... |
github | facundoq/courses-master | PrintFactor.m | .m | courses-master/pgm/p6/PrintFactor.m | 526 | utf_8 | 7ab925539a614f01ebbef8c4c001d3c2 | % Copyright (C) Daphne Koller, Stanford University, 2012
function [] = PrintFactor(F)
% Pretty print the factor F.
% The first row lists the variables and subsequent rows are
% the joint assignment and their associated factor value in
% the last column.
for i=1:length(F.var)
fprintf(1, '%d\t', F.var(i... |
github | facundoq/courses-master | VariableElimination.m | .m | courses-master/pgm/p6/VariableElimination.m | 1,379 | utf_8 | fbe2c579afa50401ca8cf8aea00e68fa | % VariableElimination takes in a list of factors F and a list of variables to eliminate
% and returns the resulting factors after running sum-product to eliminate
% the given variables. Note that it may return more than one
% factor.
%
% Fnew = VariableElimination(F, Z)
% F = list of factors
% Z = list of var... |
github | facundoq/courses-master | exchange_variable_order.m | .m | courses-master/pgm/p6/exchange_variable_order.m | 727 | utf_8 | a4a3750de5d5379914f27955c1866cba | function f=exchange_variable_order(f,v1,v2)
if (v1~=v2)
v1_index=find(f.var==v1);
v2_index=find(f.var==v2);
f=exchange_variable_order_indices(f,v1_index,v2_index);
end
end
function g=exchange_variable_order_indices(f,v1_index,v2_index)
%[sortedVars, order] = sort(F.var);
%G.var = sorte... |
github | facundoq/courses-master | CompareData.m | .m | courses-master/pgm/p6/CompareData.m | 13,243 | utf_8 | 902e19647aeaa160c7022598ee7216c1 | function retval = CompareData(data1, data2, context, Params)
% function retval = comparedata(data1, data2, context, Params)
% compares to see if data1 and data2 are roughly recursively equal. "Rough" here is defined by
% the Params. Note that matlabs ISEQUAL function is a test for exact equality. comparedata
% ... |
github | facundoq/courses-master | OptimizeLinearExpectations.m | .m | courses-master/pgm/p6/OptimizeLinearExpectations.m | 1,773 | utf_8 | 7702e69bf798c297ff7c82db5052eaf3 | % Copyright (C) Daphne Koller, Stanford University, 2012
function [meu, odr] = OptimizeLinearExpectations( I )
% Inputs: An influence diagram I with a single decision node and one or more utility nodes.
% I.RandomFactors = list of factors for each random variable. These are CPDs, with
% the... |
github | facundoq/courses-master | ObserveEvidence.m | .m | courses-master/pgm/p6/ObserveEvidence.m | 2,321 | utf_8 | 7ccbe679d2443d219a227f6f29d5785b | % ObserveEvidence Modify a vector of factors given some evidence.
% F = ObserveEvidence(F, E) sets all entries in the vector of factors, F,
% that are not consistent with the evidence, E, to zero. F is a vector of
% factors, each a data structure with the following fields:
% .var Vector of variables in the... |
github | facundoq/courses-master | SetValueOfAssignment.m | .m | courses-master/pgm/p6/SetValueOfAssignment.m | 1,180 | utf_8 | 6fbb116264c5e21b678618dbe6f44a9d | % SetValueOfAssignment Sets the value of a variable assignment in a factor.
%
% F = SetValueOfAssignment(F, A, v) sets the value of a variable assignment,
% A, in factor F to v. The order of the variables in A are assumed to be the
% same as the order in F.var.
%
% F = SetValueOfAssignment(F, A, v, VO) sets the... |
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