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Merge pull request #78 from IDSIA/dev
Release 0.2.0
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| Original file line number | Diff line number | Diff line change |
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| @@ -1,52 +1,51 @@ | ||
| package examples; | ||
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| import ch.idsia.crema.factor.bayesian.BayesianDefaultFactor; | ||
| import ch.idsia.crema.factor.bayesian.BayesianFactor; | ||
| import ch.idsia.crema.model.graphical.BayesianNetwork; | ||
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| public class BNdefinition { | ||
| public static void main(String[] args) { | ||
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| BayesianNetwork model = new BayesianNetwork(); | ||
| BayesianFactor[] f = new BayesianFactor[5]; | ||
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| // Winter? | ||
| int A = model.addVariable(2); | ||
| f[A] = new BayesianFactor(model.getDomain(A), new double[]{.6, .4}, false); | ||
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| // Sprinkler? | ||
| int B = model.addVariable(2); | ||
| model.addParent(B, A); | ||
| f[B] = new BayesianFactor(model.getDomain(A, B), false); | ||
| f[B].setData(new int[]{B, A}, new double[]{.2, .8, .75, .25}); | ||
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| // Rain? | ||
| int C = model.addVariable(2); | ||
| model.addParent(C, A); | ||
| f[C] = new BayesianFactor(model.getDomain(A, C), false); | ||
| f[C].setData(new int[]{C, A}, new double[]{.8, .2, .1, .9}); | ||
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| // Wet Grass? | ||
| int D = model.addVariable(2); | ||
| model.addParent(D, B); | ||
| model.addParent(D, C); | ||
| f[D] = new BayesianFactor(model.getDomain(B, C, D), false); | ||
| f[D].setData(new int[]{D, B, C}, new double[]{.95, .05, .9, .1, .8, .2, 0, 1}); | ||
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| // Slippery Road? | ||
| int E = model.addVariable(2); | ||
| model.addParent(E, C); | ||
| f[E] = new BayesianFactor(model.getDomain(C, E), false); | ||
| f[E].setData(new int[]{E, C}, new double[]{.7, .3, 0, 1}); | ||
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| model.setFactors(f); | ||
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| System.out.println(model); | ||
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| for(int x: model.getVariables()){ | ||
| for(int y: model.getChildren(x)){ | ||
| System.out.print("("+x+"-->"+y+")"); | ||
| } | ||
| } | ||
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| } | ||
| public static void main(String[] args) { | ||
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| BayesianNetwork model = new BayesianNetwork(); | ||
| BayesianFactor[] f = new BayesianFactor[5]; | ||
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| // Winter? | ||
| int A = model.addVariable(2); | ||
| f[A] = new BayesianDefaultFactor(model.getDomain(A), new double[]{.6, .4}); | ||
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| // Sprinkler? | ||
| int B = model.addVariable(2); | ||
| model.addParent(B, A); | ||
| f[B] = new BayesianDefaultFactor(model.getDomain(A, B), new int[]{B, A}, new double[]{.2, .8, .75, .25}); | ||
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| // Rain? | ||
| int C = model.addVariable(2); | ||
| model.addParent(C, A); | ||
| f[C] = new BayesianDefaultFactor(model.getDomain(A, C), new int[]{C, A}, new double[]{.8, .2, .1, .9}); | ||
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| // Wet Grass? | ||
| int D = model.addVariable(2); | ||
| model.addParent(D, B); | ||
| model.addParent(D, C); | ||
| f[D] = new BayesianDefaultFactor(model.getDomain(B, C, D), new int[]{D, B, C}, new double[]{.95, .05, .9, .1, .8, .2, 0, 1}); | ||
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| // Slippery Road? | ||
| int E = model.addVariable(2); | ||
| model.addParent(E, C); | ||
| f[E] = new BayesianDefaultFactor(model.getDomain(C, E), new int[]{E, C}, new double[]{.7, .3, 0, 1}); | ||
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| model.setFactors(f); | ||
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| System.out.println(model); | ||
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| for (int x : model.getVariables()) { | ||
| for (int y : model.getChildren(x)) { | ||
| System.out.print("(" + x + "-->" + y + ")"); | ||
| } | ||
| } | ||
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| } | ||
| } | ||
| //54 |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -1,53 +1,61 @@ | ||
| package examples; | ||
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| import ch.idsia.crema.core.Domain; | ||
| import ch.idsia.crema.core.DomainBuilder; | ||
| import ch.idsia.crema.factor.bayesian.BayesianDefaultFactor; | ||
| import ch.idsia.crema.factor.bayesian.BayesianFactor; | ||
| import ch.idsia.crema.factor.bayesian.BayesianFactorFactory; | ||
| import ch.idsia.crema.model.graphical.BayesianNetwork; | ||
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| public class BayesianFactors { | ||
| public static void main(String[] args) { | ||
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| // Create domain of 2 variables of sizes 2,3 | ||
| Domain d1 = DomainBuilder.var(0, 1).size(2, 3); | ||
| public static void main(String[] args) { | ||
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| // Some operations over a domain | ||
| d1.getVariables(); // vector of variables (int[]) | ||
| d1.getSize(); // number of variables | ||
| d1.getCardinality(0); // size of a specific variable | ||
| d1.getSizeAt(0); | ||
| // Create domain of 2 variables of sizes 2,3 | ||
| Domain d1 = DomainBuilder.var(0, 1).size(2, 3); | ||
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| // Some operations over a domain | ||
| d1.getVariables(); // vector of variables (int[]) | ||
| d1.getSize(); // number of variables | ||
| d1.getCardinality(0); // size of a specific variable | ||
| d1.getSizeAt(0); | ||
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| // Crate a factor over the domain | ||
| BayesianFactor f = new BayesianFactor(d1); // P([0, 1]) -> P([X|Y]) | ||
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| f.getDomain(); | ||
| // Crate a factor over the domain and set the data (i.e., values) of the factor | ||
| BayesianFactor f = new BayesianDefaultFactor(d1, // P([0, 1]) -> P([X|Y]) | ||
| new double[]{ | ||
| // x1 x2 | ||
| 0.2, 0.8, // y1 | ||
| 0.5, 0.5, // y2 | ||
| 0.1, 0.9 // y3 | ||
| }); | ||
| f.getDomain(); | ||
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| // Set the data (i.e., values) of the factor | ||
| // x1 x2 | ||
| f.setData(new double[]{0.2, 0.8, //y1 | ||
| 0.5, 0.5, //y2 | ||
| 0.1, 0.9 //y3 | ||
| }); | ||
| // or using the dedicated Factory class: | ||
| f = BayesianFactorFactory.factory() | ||
| .domain(d1) | ||
| .data(new double[]{0.2, 0.8, 0.5, 0.5, 0.1, 0.9}) | ||
| .get(); | ||
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| f.marginalize(0).getData(); // = double[3] { 1.0, 1.0, 1.0 } | ||
| f.marginalize(0).getData(); // = double[3] { 1.0, 1.0, 1.0 } | ||
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| ////// A factor can also be defined in the context of a specific model | ||
| ////// A factor can also be defined in the context of a specific model | ||
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| // Define the BN | ||
| BayesianNetwork model = new BayesianNetwork(); | ||
| model.addVariables(2, 3); | ||
| model.addParent(0, 1); | ||
| // Define the BN | ||
| BayesianNetwork model = new BayesianNetwork(); | ||
| model.addVariables(2, 3); | ||
| model.addParent(0, 1); | ||
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| // extract a domain with varaiables 0 and 1 in the BN | ||
| Domain d2 = model.getDomain(0, 1); | ||
| // extract a domain with varaiables 0 and 1 in the BN | ||
| Domain d2 = model.getDomain(0, 1); | ||
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| // Create an empty factor with that domain | ||
| f = new BayesianFactor(model.getDomain(0, 1)); | ||
| // Create an empty factor with that domain | ||
| f = new BayesianDefaultFactor(model.getDomain(0, 1), null); | ||
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| } | ||
| } | ||
| } | ||
| //53 | ||
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