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github | RoardFruit/pgm-master | ComputeJointDistribution.m | .m | pgm-master/PGM_Programming_Assignment_7/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 | RoardFruit/pgm-master | InstanceNegLogLikelihood.m | .m | pgm-master/PGM_Programming_Assignment_7/InstanceNegLogLikelihood.m | 3,401 | utf_8 | 8736eaff12b944528990223073ed2a91 | % 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 | RoardFruit/pgm-master | ObserveEvidence.m | .m | pgm-master/PGM_Programming_Assignment_7/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 | RoardFruit/pgm-master | SetValueOfAssignment.m | .m | pgm-master/PGM_Programming_Assignment_7/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 | RoardFruit/pgm-master | IndexToAssignment.m | .m | pgm-master/PGM_Programming_Assignment_2/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 | RoardFruit/pgm-master | submit.m | .m | pgm-master/PGM_Programming_Assignment_2/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 | RoardFruit/pgm-master | GetValueOfAssignment.m | .m | pgm-master/PGM_Programming_Assignment_2/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 | RoardFruit/pgm-master | AssignmentToIndex.m | .m | pgm-master/PGM_Programming_Assignment_2/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 | RoardFruit/pgm-master | submitWeb.m | .m | pgm-master/PGM_Programming_Assignment_2/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 | RoardFruit/pgm-master | SetValueOfAssignment.m | .m | pgm-master/PGM_Programming_Assignment_2/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 | RoardFruit/pgm-master | RunInference.m | .m | pgm-master/PGM_Programming_Assignment_3/RunInference.m | 1,768 | utf_8 | 3081367cc94df2ef86e1a944d0b2a35d | 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 | RoardFruit/pgm-master | IndexToAssignment.m | .m | pgm-master/PGM_Programming_Assignment_3/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 | RoardFruit/pgm-master | submit.m | .m | pgm-master/PGM_Programming_Assignment_3/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 | RoardFruit/pgm-master | GetValueOfAssignment.m | .m | pgm-master/PGM_Programming_Assignment_3/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 | RoardFruit/pgm-master | AssignmentToIndex.m | .m | pgm-master/PGM_Programming_Assignment_3/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 | RoardFruit/pgm-master | submitWeb.m | .m | pgm-master/PGM_Programming_Assignment_3/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 | RoardFruit/pgm-master | SetValueOfAssignment.m | .m | pgm-master/PGM_Programming_Assignment_3/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 | RoardFruit/pgm-master | EM_HMM.m | .m | pgm-master/PGM_Programming_Assignment_9/EM_HMM.m | 8,386 | utf_8 | 4aa14051881ce16e6900f34ee47c3979 | % File: EM_HMM.m
%
% Copyright (C) Daphne Koller, Stanford Univerity, 2012
function [P loglikelihood ClassProb PairProb] = EM_HMM(actionData, poseData, G, InitialClassProb, InitialPairProb, maxIter)
% INPUTS
% actionData: structure holding the actions as described in the PA
% poseData: N x 10 x 3 matrix, where N is n... |
github | RoardFruit/pgm-master | ShowPose.m | .m | pgm-master/PGM_Programming_Assignment_9/ShowPose.m | 1,415 | utf_8 | d88d1852aab394ef850174ee2185f4bd | % File: ShowPose.m
%
% Copyright (C) Daphne Koller, Stanford Univerity, 2012
% visualize a configuration the body parts
function img = ShowPose( pose )
% pose 10 x 3. 10 : body parts,
% 3 : y, x, alpha
pose(:,1) = pose(:,1) + 100;
pose(:,2) = pose(:,2) + 150;
pose = reshape(pose, [10 3]);
part_leng... |
github | RoardFruit/pgm-master | lognormpdf.m | .m | pgm-master/PGM_Programming_Assignment_9/lognormpdf.m | 556 | utf_8 | 93179aac759911fb170081a38b7025a8 | % File: lognormpdf.m
%
% Copyright (C) Daphne Koller, Stanford Univerity, 2012
function [log_prob] = lognormpdf(x,mu,sigma)
% LOGNORMPDF Natural logarithm of the normal probability density function (pdf)
% Y = lognormpdf(X,MU,SIGMA) returns the log of the pdf of the normal
% distribution parameterized by mean MU and ... |
github | RoardFruit/pgm-master | RecognizeUnknownActions.m | .m | pgm-master/PGM_Programming_Assignment_9/RecognizeUnknownActions.m | 1,550 | utf_8 | 5c0912c4f852469b8cb3f0455ede53e8 | % You should put all your code for recognizing unknown actions in this file.
% Describe the method you used in YourMethod.txt.
% Don't forget to call SavePrediction() at the end with your predicted labels to save them for submission, then submit using submit.m
% File: RecognizeActions.m
%
% Copyright (C) Daphne Koller... |
github | RoardFruit/pgm-master | IndexToAssignment.m | .m | pgm-master/PGM_Programming_Assignment_9/IndexToAssignment.m | 587 | utf_8 | 16baab12b20308058dd13e7565a96e60 | % 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 | RoardFruit/pgm-master | CliqueTreeCalibrate.m | .m | pgm-master/PGM_Programming_Assignment_9/CliqueTreeCalibrate.m | 3,196 | utf_8 | b2d6a4c7d27c772e31acc30dbf52518b | %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-product
% message passing, otherwise uses sum-product. This function
% ret... |
github | RoardFruit/pgm-master | CreateCliqueTreeHMM.m | .m | pgm-master/PGM_Programming_Assignment_9/CreateCliqueTreeHMM.m | 2,423 | utf_8 | 89c8f4dc2a761a58cf501fad96aa5b9a | % CreateCliqueTreeHMM Takes in a list of factors F and returns a clique
% tree. Should only be called when F meets the following conditions:
%
% 1) Factors are over 1 or 2 variables
% 2) All 2-variable factors are over variables (i,i+1)
% 3) All variables have the same cardinality
%
% Roughly, these conditions mean tha... |
github | RoardFruit/pgm-master | FitG.m | .m | pgm-master/PGM_Programming_Assignment_9/FitG.m | 352 | utf_8 | 1c8b5ca906775262ec10c160aa613b83 | % File: FitG.m
%
% Copyright (C) Daphne Koller, Stanford Univerity, 2012
function [mu sigma] = FitG(X, W)
% X: (N x 1): N examples (1 dimensional)
% W: (N x 1): Weights over examples (W(i) is the weight for X(i))
% Fit N(mu, sigma^2) to the empirical distribution
mu = 0;
sigma = 1;
mu = W'*X/sum(W);
v = W'*(X.*X)/... |
github | RoardFruit/pgm-master | Recognize.m | .m | pgm-master/PGM_Programming_Assignment_9/Recognize.m | 4,409 | utf_8 | 49b25558d67d38cc6937204f5510dd68 | % File: RecognizeActions.m
%
% Copyright (C) Daphne Koller, Stanford Univerity, 2012
function [predicted_labels] = Recognize(datasetTrain, datasetTest, G, maxIter)
% INPUTS
% datasetTrain: dataset for training models, see PA for details
% datasetTest: dataset for testing models, see PA for details
% G: graph paramete... |
github | RoardFruit/pgm-master | FactorMarginalization.m | .m | pgm-master/PGM_Programming_Assignment_9/FactorMarginalization.m | 821 | utf_8 | 9481d98b85585038c22e4faa9583992c | % FactorMarginalization Sums given variables out of a factor in log space.
% 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 cardin... |
github | RoardFruit/pgm-master | RecognizeActions.m | .m | pgm-master/PGM_Programming_Assignment_9/RecognizeActions.m | 4,489 | utf_8 | 4e632b8b222b74b71ae237f301671354 | % File: RecognizeActions.m
%
% Copyright (C) Daphne Koller, Stanford Univerity, 2012
function [accuracy, predicted_labels] = RecognizeActions(datasetTrain, datasetTest, G, maxIter)
% INPUTS
% datasetTrain: dataset for training models, see PA for details
% datasetTest: dataset for testing models, see PA for details
% ... |
github | RoardFruit/pgm-master | ComputeExactMarginalsHMM.m | .m | pgm-master/PGM_Programming_Assignment_9/ComputeExactMarginalsHMM.m | 1,613 | utf_8 | d2dad5ff1748ef025220bd22df9876ad | %COMPUTEEXACTMARGINALSHMM Runs exact inference and returns the marginals
%over all the variables and the calibrated clique tree.
% M = COMPUTEEXACTMARGINALSHMM(F) Takes a list of factors F,
% and runs exact inference and returns the calibrated clique tree (unnormalized) and
% final marginals (normalized) for the... |
github | RoardFruit/pgm-master | submit.m | .m | pgm-master/PGM_Programming_Assignment_9/submit.m | 22,612 | utf_8 | 3c90403ef5f9b0b52e55bbf559b97d22 | 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 | RoardFruit/pgm-master | VisualizeDataset.m | .m | pgm-master/PGM_Programming_Assignment_9/VisualizeDataset.m | 243 | utf_8 | ce8d4d0fc87937b06c2daa5552ff0330 | % File: VisualizeDataset.m
%
% Copyright (C) Daphne Koller, Stanford Univerity, 2012
function VisualizeDataset(Dataset)
figure
for i=1:size(Dataset,1)
img = ShowPose(reshape(Dataset(i,:,:), [10 3]));
imshow(img);
pause(0.3);
end
|
github | RoardFruit/pgm-master | AssignmentToIndex.m | .m | pgm-master/PGM_Programming_Assignment_9/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 | RoardFruit/pgm-master | submitWeb.m | .m | pgm-master/PGM_Programming_Assignment_9/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 | RoardFruit/pgm-master | FitLG.m | .m | pgm-master/PGM_Programming_Assignment_9/FitLG.m | 1,648 | utf_8 | fb35b5bf14764213e24bf53eeba348ea | % File: FitLG.m
%
% Copyright (C) Daphne Koller, Stanford Univerity, 2012
function [Beta sigma] = FitLG(X, U, W)
% Estimate parameters of the linear Gaussian model:
% X|U ~ N(Beta(1)*U(1) + ... + Beta(K)*U(K) + Beta(K+1), sigma^2);
% Note that Matlab index from 1, we can't write Beta(0). So Beta(K+1) is
% essentiall... |
github | RoardFruit/pgm-master | EM_cluster.m | .m | pgm-master/PGM_Programming_Assignment_9/EM_cluster.m | 5,447 | utf_8 | 8446f09ce75c0f3f0f149bf1513fb54f | % File: EM_cluster.m
%
% Copyright (C) Daphne Koller, Stanford Univerity, 2012
function [P loglikelihood ClassProb] = EM_cluster(poseData, G, InitialClassProb, maxIter)
% INPUTS
% poseData: N x 10 x 3 matrix, where N is number of poses;
% poseData(i,:,:) yields the 10x3 matrix for pose i.
% G: graph parameterizatio... |
github | RoardFruit/pgm-master | logsumexp.m | .m | pgm-master/PGM_Programming_Assignment_9/logsumexp.m | 341 | utf_8 | 4a201a5a36500a989a523c436ac4b69e | % File: logsumexp.m
%
% Copyright (C) Daphne Koller, Stanford Univerity, 2012
function out = logsumexp(A)
% LOGSUMEXP
% Computes log( sum( exp( ) ) ) of each row in A in a way that avoids underflow.
% If A is an N x M matrix, then out is a N x 1 vector.
pi_max = max(A, [], 2);
out = pi_max + log(sum(exp(bsxfun(@minu... |
github | RoardFruit/pgm-master | ShowPose.m | .m | pgm-master/PGM_Programming_Assignment_8/ShowPose.m | 1,432 | utf_8 | 480c7ad39d9caf52ad16fd8cc5253326 | % Copyright (C) Daphne Koller, Stanford Univerity, 2012
%
% Author: Huayan Wang, Andrew Duchi
% visualize a configuration the body parts
function img = ShowPose( pose )
% pose 10 x 3. 10 : body parts,
% 3 : y, x, alpha
pose(:,1) = pose(:,1) + 100;
pose(:,2) = pose(:,2) + 150;
pose = reshape(pose, [... |
github | RoardFruit/pgm-master | lognormpdf.m | .m | pgm-master/PGM_Programming_Assignment_8/lognormpdf.m | 159 | utf_8 | 145eafca5ce4cf649ea5bd971005f278 | %
% Copyright (C) Daphne Koller, Stanford Univerity, 2012
function val = lognormpdf(x, mu, sigma)
val = - (x - mu).^2 / (2*sigma^2) - log (sqrt(2*pi) * sigma); |
github | RoardFruit/pgm-master | VisualizeModels.m | .m | pgm-master/PGM_Programming_Assignment_8/VisualizeModels.m | 629 | utf_8 | f55ff303692ab3b671abf8d6655d3345 | % Copyright (C) Daphne Koller, Stanford Univerity, 2012
%
% Author: Huayan Wang
function VisualizeModels(P, G)
K = length(P.c);
f = figure;
while(1)
for k=1:K
subplot(1,K,k);
if size(G,3) == 1 % same graph structure for all classes
pose = SamplePose(P,G,k);
... |
github | RoardFruit/pgm-master | submit.m | .m | pgm-master/PGM_Programming_Assignment_8/submit.m | 24,472 | utf_8 | fe506b2c798ad661b509d57cbddf4bf3 | 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 | RoardFruit/pgm-master | VisualizeDataset.m | .m | pgm-master/PGM_Programming_Assignment_8/VisualizeDataset.m | 316 | utf_8 | 6cc0340af48645e3dae1185bb480284d | % Copyright (C) Daphne Koller, Stanford Univerity, 2012
%
% Author: Huayan Wang
function VisualizeDataset(Dataset)
f = figure;
for i=1:size(Dataset,1)
img = ShowPose(reshape(Dataset(i,:,:), [10 3]));
imshow(img);
pause(0.3)
if (~ishandle(f)) break; end; % quit loop when user closes the figure
end
|
github | RoardFruit/pgm-master | MaxSpanningTree.m | .m | pgm-master/PGM_Programming_Assignment_8/MaxSpanningTree.m | 2,102 | utf_8 | e2eb7e917fac0c48eb37d69923cae975 | function adj = MaxSpanningTree (weights)
% MAXSPANNINGTREE Maximum weight spanning tree
%
% adj = MaxSpanningTree(weights) takes an n-by-n weight matrix, which
% should be symmetric, and returns an adjacency list representation of
% the maximum weight spanning tree. The adjacency list will be directed,
% i.e... |
github | RoardFruit/pgm-master | submitWeb.m | .m | pgm-master/PGM_Programming_Assignment_8/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 | RoardFruit/pgm-master | SampleMultinomial.m | .m | pgm-master/PGM_Programming_Assignment_8/SampleMultinomial.m | 302 | utf_8 | 1d4d597a8553fbb1755bcb12fc4896e4 | % Copyright (C) Daphne Koller, Stanford Univerity, 2012
function sample = SampleMultinomial(probabilities)
dice = rand(1,1);
accumulate = 0;
for i=1:length(probabilities)
accumulate = accumulate + probabilities(i);
if accumulate/sum(probabilities) > dice
break
end
end
sample = i;
|
github | RoardFruit/pgm-master | ConvertAtoG.m | .m | pgm-master/PGM_Programming_Assignment_8/ConvertAtoG.m | 496 | utf_8 | 87fd4ba90b7e638012bb98d0af9ac21d | % File: ConvertAtoG.m
%
% Copyright (C) Daphne Koller, Stanford Univerity, 2012
%
% Author: Huayan Wang
function G = ConvertAtoG(A)
G = zeros(10,2);
A = A + A';
G(1,:) = [0 0];
visited = zeros(10,1);
visited(1) = 1;
cnt = 0;
while sum(visited) < 10
cnt = cnt+1;
for i=2:10
for j=1:10
if ... |
github | RoardFruit/pgm-master | IndexToAssignment.m | .m | pgm-master/PGM-Programming_Assignment_1/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 | RoardFruit/pgm-master | FactorMarginalization.m | .m | pgm-master/PGM-Programming_Assignment_1/FactorMarginalization.m | 1,663 | utf_8 | e13f6090c12d9a954c56893a8b8526d0 | % 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 | RoardFruit/pgm-master | FactorProduct.m | .m | pgm-master/PGM-Programming_Assignment_1/FactorProduct.m | 2,400 | utf_8 | cdd7f3aedfa0173fb43e9fafcc51baf4 | % 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 | RoardFruit/pgm-master | submit.m | .m | pgm-master/PGM-Programming_Assignment_1/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 | RoardFruit/pgm-master | StandardizeFactors.m | .m | pgm-master/PGM-Programming_Assignment_1/StandardizeFactors.m | 580 | utf_8 | 7a3d0747a3a6e0c14ac93de9d8daad84 | % 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 = struc... |
github | RoardFruit/pgm-master | GetValueOfAssignment.m | .m | pgm-master/PGM-Programming_Assignment_1/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 | RoardFruit/pgm-master | ComputeMarginal.m | .m | pgm-master/PGM-Programming_Assignment_1/ComputeMarginal.m | 1,409 | utf_8 | acfca93e1eeae09797944ca2648dbf94 | %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 | RoardFruit/pgm-master | AssignmentToIndex.m | .m | pgm-master/PGM-Programming_Assignment_1/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 | RoardFruit/pgm-master | submitWeb.m | .m | pgm-master/PGM-Programming_Assignment_1/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 | RoardFruit/pgm-master | ComputeJointDistribution.m | .m | pgm-master/PGM-Programming_Assignment_1/ComputeJointDistribution.m | 1,187 | utf_8 | 7895a06efc121b109820aaba0e8e9f5d | %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 | RoardFruit/pgm-master | ObserveEvidence.m | .m | pgm-master/PGM-Programming_Assignment_1/ObserveEvidence.m | 2,086 | utf_8 | 1530655648211964ad075bff5e8b2873 | % 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 | RoardFruit/pgm-master | SetValueOfAssignment.m | .m | pgm-master/PGM-Programming_Assignment_1/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 | RoardFruit/pgm-master | ComputeExactMarginalsBP.m | .m | pgm-master/PGM-Programming_Assignment_4/ComputeExactMarginalsBP.m | 1,647 | utf_8 | 451e9b8376cf11dadc170d308e631bb1 | %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 | RoardFruit/pgm-master | EliminateVar.m | .m | pgm-master/PGM-Programming_Assignment_4/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 | RoardFruit/pgm-master | IndexToAssignment.m | .m | pgm-master/PGM-Programming_Assignment_4/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 | RoardFruit/pgm-master | CliqueTreeCalibrate.m | .m | pgm-master/PGM-Programming_Assignment_4/CliqueTreeCalibrate.m | 2,820 | utf_8 | 54c591e9667fcf8b407de64fc7875510 | %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 | RoardFruit/pgm-master | FactorMarginalization.m | .m | pgm-master/PGM-Programming_Assignment_4/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 | RoardFruit/pgm-master | DecodedMarginalsToChars.m | .m | pgm-master/PGM-Programming_Assignment_4/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 | RoardFruit/pgm-master | MaxDecoding.m | .m | pgm-master/PGM-Programming_Assignment_4/MaxDecoding.m | 887 | utf_8 | 4f091e534c5df5b64aba764e74978683 | %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 | RoardFruit/pgm-master | FactorProduct.m | .m | pgm-master/PGM-Programming_Assignment_4/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 | RoardFruit/pgm-master | submit.m | .m | pgm-master/PGM-Programming_Assignment_4/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 | RoardFruit/pgm-master | StandardizeFactors.m | .m | pgm-master/PGM-Programming_Assignment_4/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 | RoardFruit/pgm-master | ComputeInitialPotentials.m | .m | pgm-master/PGM-Programming_Assignment_4/ComputeInitialPotentials.m | 2,354 | utf_8 | 77a5fe2874dc780c030856afa5b5a255 | %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 | RoardFruit/pgm-master | GetValueOfAssignment.m | .m | pgm-master/PGM-Programming_Assignment_4/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 | RoardFruit/pgm-master | GetNextCliques.m | .m | pgm-master/PGM-Programming_Assignment_4/GetNextCliques.m | 1,802 | utf_8 | b29734f0efb9a5aa49155ef80d99d370 | %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 | RoardFruit/pgm-master | FactorMaxMarginalization.m | .m | pgm-master/PGM-Programming_Assignment_4/FactorMaxMarginalization.m | 2,024 | utf_8 | 7d45c387b40a9d630826afeb61ffba82 | % 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 | RoardFruit/pgm-master | ComputeMarginal.m | .m | pgm-master/PGM-Programming_Assignment_4/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 | RoardFruit/pgm-master | AssignmentToIndex.m | .m | pgm-master/PGM-Programming_Assignment_4/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 | RoardFruit/pgm-master | CreateCliqueTree.m | .m | pgm-master/PGM-Programming_Assignment_4/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 | RoardFruit/pgm-master | submitWeb.m | .m | pgm-master/PGM-Programming_Assignment_4/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 | RoardFruit/pgm-master | ComputeJointDistribution.m | .m | pgm-master/PGM-Programming_Assignment_4/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 | RoardFruit/pgm-master | ObserveEvidence.m | .m | pgm-master/PGM-Programming_Assignment_4/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 | RoardFruit/pgm-master | SetValueOfAssignment.m | .m | pgm-master/PGM-Programming_Assignment_4/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 | RoardFruit/pgm-master | BlockLogDistribution.m | .m | pgm-master/PGM-Programming_Assignment_5/BlockLogDistribution.m | 3,141 | utf_8 | 27f9e393ddc6677c6660a82b2eb481f2 | %BLOCKLOGDISTRIBUTION
%
% LogBS = BlockLogDistribution(V, G, F, A) returns the log of a
% block-sampling array (which contains the log-unnormalized-probabilities of
% selecting each label for the block), given variables V to block-sample in
% network G with factors F and current assignment A. Note that the var... |
github | RoardFruit/pgm-master | ComputeExactMarginalsBP.m | .m | pgm-master/PGM-Programming_Assignment_5/ComputeExactMarginalsBP.m | 1,647 | utf_8 | 451e9b8376cf11dadc170d308e631bb1 | %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 | RoardFruit/pgm-master | randi.m | .m | pgm-master/PGM-Programming_Assignment_5/randi.m | 832 | utf_8 | d476049b924b22f35261e4d287bbe4b3 | % Copyright (C) Daphne Koller, Stanford University, 2012
function [num mv] = randi(arg1,arg2,arg3)
num = -1;
persistent x_i;
persistent p1;
persistent p2;
if(isempty(x_i))
x_i = 1;
p1 = 160481183;
p2 = 179424673;
end
mv=p2;
if(ischar(arg1)==1)
if(strcmp(arg1,'seed'))
if(nargin>1)
x_i = arg2;
n... |
github | RoardFruit/pgm-master | IndexToAssignment.m | .m | pgm-master/PGM-Programming_Assignment_5/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 | RoardFruit/pgm-master | CliqueTreeCalibrate.m | .m | pgm-master/PGM-Programming_Assignment_5/CliqueTreeCalibrate.m | 2,820 | utf_8 | 54c591e9667fcf8b407de64fc7875510 | %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 | RoardFruit/pgm-master | ExtractMarginalsFromSamples.m | .m | pgm-master/PGM-Programming_Assignment_5/ExtractMarginalsFromSamples.m | 1,091 | utf_8 | 529a32b21a4c107460200c7c4c5a8e6a | %EXTRACTMARGINALSFROMSAMPLES
%
% ExtractMarginalsFromSamples takes in a probabilistic network G, a list of samples, and a set
% of indices into samples that specify which samples to use in the computation of the
% marginals. The marginals are then computed using this subset of samples and returned.
%
% Samples... |
github | RoardFruit/pgm-master | randsample.m | .m | pgm-master/PGM-Programming_Assignment_5/randsample.m | 1,542 | utf_8 | c010fcf86cbd91a7dc9bb6cc67f2d289 | %randsample(V,n,true,distribution) returns a set of n values sampled
% at random from the integers 1 through V with replacement using distribution
% 'distribution'
%
% replacing true with false causes sampling w/out replacement
% omitting the distribution causes a default to the uniform distribution
%
% Copyright (C) ... |
github | RoardFruit/pgm-master | ClusterGraphCalibrate.m | .m | pgm-master/PGM-Programming_Assignment_5/ClusterGraphCalibrate.m | 3,994 | utf_8 | 835edc342e6b31e3bd9ea8928fb1d507 | % CLUSTERGRAPHCALIBRATE Loopy belief propagation for cluster graph calibration.
% P = CLUSTERGRAPHCALIBRATE(P, useSmart) calibrates a given cluster graph, G,
% and set of of factors, F. The function returns the final potentials for
% each cluster.
% The cluster graph data structure has the following fields:
% ... |
github | RoardFruit/pgm-master | FactorMarginalization.m | .m | pgm-master/PGM-Programming_Assignment_5/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 | RoardFruit/pgm-master | VisualizeMCMCMarginals.m | .m | pgm-master/PGM-Programming_Assignment_5/VisualizeMCMCMarginals.m | 2,554 | utf_8 | a52cdac6ed53a41190ef3528b2068e49 | % VISUALIZEMCMCMARGINALS
%
% This function accepts a list of sample lists, each from a different MCMC run. It then visualizes
% the estimated marginals for each variable in V over the lifetime of the MCMC run.
%
% samples_list - a list of sample lists; each sample list is a m-by-n matrix where m is the
% number of sam... |
github | RoardFruit/pgm-master | smooth.m | .m | pgm-master/PGM-Programming_Assignment_5/smooth.m | 431 | utf_8 | e49120038d1ea24bfca2efe6051e172a | % Copyright (C) Daphne Koller, Stanford University, 2012
function [YY] = smooth(Y,window)
if(~exist('window','var'))
window =5;
end
if(mod(window,2)==0)
window = window+1;
end
mid = (window+1)/2;
len = length(Y);
Smoother =zeros(len);
for i=1:len
dev = min([mid-1 min([i-1 len-i])]);
% dev
Smoother(i,(i-dev... |
github | RoardFruit/pgm-master | FactorProduct.m | .m | pgm-master/PGM-Programming_Assignment_5/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 | RoardFruit/pgm-master | LogProbOfJointAssignment.m | .m | pgm-master/PGM-Programming_Assignment_5/LogProbOfJointAssignment.m | 341 | utf_8 | 3b24866daccab9ccaa5bde84906c38d2 | % Returns the log probability of an assignment A in a distribution defined by factors F
%
% Copyright (C) Daphne Koller, Stanford University, 2012
function logp = LogProbOfJointAssignment(F, A)
% work in log-space to prevent underflow
logp = 0.0;
for i = 1:length(F)
logp = logp + log(GetValueOfAssignment(F(i), A,... |
github | RoardFruit/pgm-master | MHSWTrans.m | .m | pgm-master/PGM-Programming_Assignment_5/MHSWTrans.m | 4,272 | utf_8 | b25596cbd17ae5706be42003039457f4 | % MHSWTRANS
%
% MCMC Metropolis-Hastings transition function that
% utilizes the Swendsen-Wang proposal distribution.
% A - The current joint assignment. This should be
% updated to be the next assignment
% G - The network
% F - List of all factors
% variant - a number (1 or 2) indicating the variant of Swe... |
github | RoardFruit/pgm-master | submit.m | .m | pgm-master/PGM-Programming_Assignment_5/submit.m | 37,742 | utf_8 | af0d03d5e8bbc6f624767b9f623ea8f8 | 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 | RoardFruit/pgm-master | ConstructRandNetwork.m | .m | pgm-master/PGM-Programming_Assignment_5/ConstructRandNetwork.m | 1,907 | utf_8 | eecd8be58b912f3050983100aa964b07 | %
% This is a script that constructs the toy network and outputs toy_network and toy_factors into
% the environment. You should modify this file to change the parameters of the toy network.
%
% In this file, on_diag_weight represents the weight of the on-diagonal elements
% in your pairwise CPDs (weight of agreement) ... |
github | RoardFruit/pgm-master | NaiveGetNextClusters.m | .m | pgm-master/PGM-Programming_Assignment_5/NaiveGetNextClusters.m | 1,337 | utf_8 | 4c0440156c06acd7c81feb028ea45e2c | %NAIVEGETNEXTCLUSTERS Takes in a node adjacency matrix and returns the indices
% of the nodes between which the m+1th message should be passed.
%
% Output [i j]
% i = the origin of the m+1th message
% j = the destination of the m+1th message
%
% This method should iterate over the messages in increasing o... |
github | RoardFruit/pgm-master | ComputeInitialPotentials.m | .m | pgm-master/PGM-Programming_Assignment_5/ComputeInitialPotentials.m | 2,155 | utf_8 | 4e9e6a70ffa892c07c08a35886997c9b | %COMPUTEINITIALPOTENTIALS Sets up the cliques in the clique tree that is
%passed in as a parameter.
% P = COMPUTEINITIALPOTENTIALS(C) Takes the clique tree C which is a
% struct with three fields:
% - nodes: represents the cliques in the tree.
% - edges: represents the adjacency matrix of the tree.
% - facto... |
github | RoardFruit/pgm-master | MHUniformTrans.m | .m | pgm-master/PGM-Programming_Assignment_5/MHUniformTrans.m | 1,025 | utf_8 | 53ca4f7dd2ec4457da23252d3c1dc269 | % MHUNIFORMTRANS
%
% MCMC Metropolis-Hastings transition function that
% utilizes the uniform proposal distribution.
% A - The current joint assignment. This should be
% updated to be the next assignment
% G - The network
% F - List of all factors
%
% Copyright (C) Daphne Koller, Stanford University, 2012
f... |
github | RoardFruit/pgm-master | GetValueOfAssignment.m | .m | pgm-master/PGM-Programming_Assignment_5/GetValueOfAssignment.m | 837 | utf_8 | 5be53ec21ee2dc20753b783db51acd8c | %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 | RoardFruit/pgm-master | ComputeApproxMarginalsBP.m | .m | pgm-master/PGM-Programming_Assignment_5/ComputeApproxMarginalsBP.m | 2,511 | utf_8 | 7ec81a88af5fae6cfbf1630a5d456e01 | % COMPUTEAPPROXMARGINALSBP Computation of approximate marginals using Loopy BP
% M = COMPUTEAPPROXMARGINALSBP(F,E ) returns the approximate marginals over
% each variable in F given the evidence E.
% .
% The Factor list F has the following fields:
% - .var: indices of variables in the specified cluste... |
github | RoardFruit/pgm-master | rand.m | .m | pgm-master/PGM-Programming_Assignment_5/rand.m | 442 | utf_8 | 305621a863b56b3e13a94716c53e494b | % Copyright (C) Daphne Koller, Stanford University, 2012
function [val] = rand(arg1,arg2);
val = -1;
gran = 1e6;
if(nargin>0&&ischar(arg1))
if(nargin==1)
arg2=1;
end
randi(arg1,arg2);
val=0;
else
if(nargin==0)
val = randi(1e6)/(1e6);
else
if(nargin==1)
if(length(arg1)>1)
arg2=arg... |
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