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Changes to Discrete Examples

release/4.3a0
Abhijit Kundu 2012-06-06 03:25:56 +00:00
parent 3768efd7d3
commit 59960a8d14
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119
examples/DiscreteBayesNet_FG.cpp Normal file
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/* ----------------------------------------------------------------------------
* GTSAM Copyright 2010, Georgia Tech Research Corporation,
* Atlanta, Georgia 30332-0415
* All Rights Reserved
* Authors: Frank Dellaert, et al. (see THANKS for the full author list)
* See LICENSE for the license information
* -------------------------------------------------------------------------- */
/**
* @file DiscreteBayesNet_FG.cpp
* @brief Discrete Bayes Net example using Factor Graphs
* @author Abhijit
* @date Jun 4, 2012
*
* We use the famous Rain/Cloudy/Sprinkler Example of [Russell & Norvig, 2009, p529]
* You may be familiar with other graphical model packages like BNT (available
* at http://bnt.googlecode.com/svn/trunk/docs/usage.html) where this is used as an
* example. The following demo is same as that in the above link, except that
* everything is using GTSAM.
*/
#include <gtsam/discrete/DiscreteFactorGraph.h>
#include <gtsam/discrete/DiscreteSequentialSolver.h>
#include <iomanip>
using namespace std;
using namespace gtsam;
int main(int argc, char **argv) {
// We assume binary state variables
// we have 0 == "False" and 1 == "True"
const size_t nrStates = 2;
// define variables
DiscreteKey Cloudy(1, nrStates), Sprinkler(2, nrStates), Rain(3, nrStates),
WetGrass(4, nrStates);
// create Factor Graph of the bayes net
DiscreteFactorGraph graph;
// add factors
graph.add(Cloudy, "0.5 0.5"); //P(Cloudy)
graph.add(Cloudy & Sprinkler, "0.5 0.5 0.9 0.1"); //P(Sprinkler | Cloudy)
graph.add(Cloudy & Rain, "0.8 0.2 0.2 0.8"); //P(Rain | Cloudy)
graph.add(Sprinkler & Rain & WetGrass,
"1 0 0.1 0.9 0.1 0.9 0.001 0.99"); //P(WetGrass | Sprinkler, Rain)
// Alternatively we can also create a DiscreteBayesNet, add DiscreteConditional
// factors and create a FactorGraph from it. (See testDiscreteBayesNet.cpp)
// Since this is a relatively small distribution, we can as well print
// the whole distribution..
cout << "Distribution of Example: " << endl;
cout << setw(11) << "Cloudy(C)" << setw(14) << "Sprinkler(S)" << setw(10)
<< "Rain(R)" << setw(14) << "WetGrass(W)" << setw(15) << "P(C,S,R,W)"
<< endl;
for (size_t a = 0; a < nrStates; a++)
for (size_t m = 0; m < nrStates; m++)
for (size_t h = 0; h < nrStates; h++)
for (size_t c = 0; c < nrStates; c++) {
DiscreteFactor::Values values;
values[Cloudy.first] = c;
values[Sprinkler.first] = h;
values[Rain.first] = m;
values[WetGrass.first] = a;
double prodPot = graph(values);
cout << boolalpha << setw(8) << (bool) c << setw(14)
<< (bool) h << setw(12) << (bool) m << setw(13)
<< (bool) a << setw(16) << prodPot << endl;
}
// "Most Probable Explanation", i.e., configuration with largest value
DiscreteSequentialSolver solver(graph);
DiscreteFactor::sharedValues optimalDecoding = solver.optimize();
cout <<"\nMost Probable Explanation (MPE):" << endl;
cout << boolalpha << "Cloudy = " << (bool)(*optimalDecoding)[Cloudy.first]
<< " Sprinkler = " << (bool)(*optimalDecoding)[Sprinkler.first]
<< " Rain = " << boolalpha << (bool)(*optimalDecoding)[Rain.first]
<< " WetGrass = " << (bool)(*optimalDecoding)[WetGrass.first]<< endl;
// "Inference" We show an inference query like: probability that the Sprinkler was on;
// given that the grass is wet i.e. P( S | W=1) =?
cout << "\nInference Query: Probability of Sprinkler being on given Grass is Wet" << endl;
// Method 1: we can compute the joint marginal P(S,W) and from that we can compute
// P(S | W=1) = P(S,W=1)/P(W=1) We do this in following three steps..
//Step1: Compute P(S,W)
DiscreteFactorGraph jointFG;
jointFG = *solver.jointFactorGraph(DiscreteKeys(Sprinkler & WetGrass).indices());
DecisionTreeFactor probSW = jointFG.product();
//Step2: Compute P(W)
DecisionTreeFactor probW = *solver.marginalFactor(WetGrass.first);
//Step3: Computer P(S | W=1) = P(S,W=1)/P(W=1)
DiscreteFactor::Values values;
values[WetGrass.first] = 1;
//print P(S=0|W=1)
values[Sprinkler.first] = 0;
cout << "P(S=0|W=1) = " << probSW(values)/probW(values) << endl;
//print P(S=1|W=1)
values[Sprinkler.first] = 1;
cout << "P(S=1|W=1) = " << probSW(values)/probW(values) << endl;
// TODO: Method 2 : One way is to modify the factor graph to
// incorporate the evidence node and compute the marginal
// TODO: graph.addEvidence(Cloudy,0);
return 0;
}

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examples/UGM_chain.cpp Normal file
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/* ----------------------------------------------------------------------------
* GTSAM Copyright 2010, Georgia Tech Research Corporation,
* Atlanta, Georgia 30332-0415
* All Rights Reserved
* Authors: Frank Dellaert, et al. (see THANKS for the full author list)
* See LICENSE for the license information
* -------------------------------------------------------------------------- */
/**
* @file small.cpp
* @brief UGM (undirected graphical model) examples: chain
* @author Frank Dellaert
* @author Abhijit
*
* See http://www.di.ens.fr/~mschmidt/Software/UGM/chain.html
* for more explanation. This code demos the same example using GTSAM.
*/
#include <gtsam/discrete/DiscreteFactorGraph.h>
#include <gtsam/discrete/DiscreteSequentialSolver.h>
#include <iomanip>
using namespace std;
using namespace gtsam;
int main(int argc, char** argv) {
// Set Number of Nodes in the Graph
const int nrNodes = 50;
// Each node takes 1 of 7 possible states denoted by 0-6 in following order:
// ["VideoGames" "Industry" "GradSchool" "VideoGames(with PhD)"
// "Industry(with PhD)" "Academia" "Deceased"]
const size_t nrStates = 7;
// define variables
vector<DiscreteKey> nodes;
for (int i = 0; i < nrNodes; i++){
DiscreteKey dk(i, nrStates);
nodes.push_back(dk);
}
// create graph
DiscreteFactorGraph graph;
// add node potentials
graph.add(nodes[0], ".3 .6 .1 0 0 0 0");
for (int i = 1; i < nrNodes; i++)
graph.add(nodes[i], "1 1 1 1 1 1 1");
const std::string edgePotential = ".08 .9 .01 0 0 0 .01 "
".03 .95 .01 0 0 0 .01 "
".06 .06 .75 .05 .05 .02 .01 "
"0 0 0 .3 .6 .09 .01 "
"0 0 0 .02 .95 .02 .01 "
"0 0 0 .01 .01 .97 .01 "
"0 0 0 0 0 0 1";
// add edge potentials
for (int i = 0; i < nrNodes - 1; i++)
graph.add(nodes[i] & nodes[i + 1], edgePotential);
cout << "Created Factor Graph with " << nrNodes << " variable nodes and "
<< graph.size() << " factors (Unary+Edge).";
// "Decoding", i.e., configuration with largest value
// We use sequential variable elimination
DiscreteSequentialSolver solver(graph);
DiscreteFactor::sharedValues optimalDecoding = solver.optimize();
optimalDecoding->print("\nMost Probable Explanation (optimalDecoding)\n");
// "Inference" Computing marginals for each node
cout << "\nComputing Node Marginals .." << endl;
for (vector<DiscreteKey>::iterator itr = nodes.begin(); itr != nodes.end();
++itr) {
//Compute the marginal
Vector margProbs = solver.marginalProbabilities(*itr);
//Print the marginals
cout << "Node#" << setw(4) << itr->first << " : ";
print(margProbs);
cout << endl;
}
return 0;
}

16
examples/UGM_small.cpp
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@ -69,6 +69,22 @@ int main(int argc, char** argv) {
DiscreteFactor::sharedValues optimalDecoding = solver.optimize(); DiscreteFactor::sharedValues optimalDecoding = solver.optimize();
optimalDecoding->print("\noptimalDecoding"); optimalDecoding->print("\noptimalDecoding");
// "Inference" Computing marginals
cout << "\nComputing Node Marginals .." << endl;
Vector margProbs;
margProbs = solver.marginalProbabilities(Cathy);
print(margProbs, "Cathy's Node Marginal:");
margProbs = solver.marginalProbabilities(Heather);
print(margProbs, "Heather's Node Marginal");
margProbs = solver.marginalProbabilities(Mark);
print(margProbs, "Mark's Node Marginal");
margProbs = solver.marginalProbabilities(Allison);
print(margProbs, "Allison's Node Marginal");
return 0; return 0;
} }