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package aima.core.logic.fol.inference;
import java.util.ArrayList;
import java.util.LinkedHashSet;
import java.util.List;
import java.util.Map;
import java.util.Set;
import aima.core.logic.fol.StandardizeApart;
import aima.core.logic.fol.StandardizeApartIndexical;
import aima.core.logic.fol.StandardizeApart... |
package aima.core.logic.fol.inference;
import java.util.ArrayList;
import java.util.HashMap;
import java.util.LinkedHashSet;
import java.util.List;
import java.util.Map;
import java.util.Set;
import aima.core.logic.fol.Connectors;
import aima.core.logic.fol.inference.proof.Proof;
import aima.core.logic.fol... |
package aima.core.logic.fol.inference;
import java.util.ArrayList;
import java.util.List;
import java.util.Map;
import java.util.Set;
import aima.core.logic.fol.inference.proof.Proof;
import aima.core.logic.fol.inference.proof.ProofFinal;
import aima.core.logic.fol.inference.proof.ProofStep;
import aima.cor... |
package aima.core.logic.fol.inference;
import aima.core.logic.fol.kb.FOLKnowledgeBase;
import aima.core.logic.fol.parsing.ast.Sentence;
/**
* @author Ciaran O'Reilly
*
*/
public interface InferenceProcedure {
/**
*
* @param kb
* the knowledge base against which the query is to be m... |
package aima.core.logic.common;
import java.io.Reader;
import java.io.StringReader;
/**
* @author Ravi Mohan
*
*/
public abstract class Lexer {
protected abstract Token nextToken();
protected Reader input;
protected int lookAhead = 1;
protected int[] lookAheadBuffer;
public void setInpu... |
package aima.core.logic.common;
/**
* @author Ravi Mohan
*
*/
public class Token {
private String text;
private int type;
public Token(int type, String text) {
this.type = type;
this.text = text;
}
public String getText() {
return text;
}
public int getType() {
return type;
... |
package aima.core.logic.common;
/**
* @author Ravi Mohan
*
*/
public interface LogicTokenTypes {
static final int SYMBOL = 1;
static final int LPAREN = 2;
static final int RPAREN = 3;
static final int COMMA = 4;
static final int CONNECTOR = 5;
static final int QUANTIFIER = 6;
static... |
package aima.core.logic.common;
/**
* @author Ravi Mohan
*
*/
public interface Visitor {
} |
package aima.core.logic.common;
/**
* @author Ravi Mohan
*
*/
public abstract class Parser {
protected Lexer lexer;
protected Token[] lookAheadBuffer;
protected int lookAhead = 3;
public abstract ParseTreeNode parse(String input);
protected void fillLookAheadBuffer() {
for (int i = 0; ... |
package aima.core.logic.common;
/**
* @author Ravi Mohan
*
*/
public interface ParseTreeNode {
} |
package aima.core.learning.data;
public class DataResource {
}
|
package aima.core.learning.neural;
/**
* @author Ravi Mohan
*
*/
public interface ActivationFunction {
double activation(double parameter);
double deriv(double parameter);
}
|
package aima.core.learning.neural;
import aima.core.learning.framework.DataSet;
import aima.core.util.math.Matrix;
import aima.core.util.math.Vector;
/**
* @author Ravi Mohan
*
*/
public class FeedForwardNeuralNetwork implements FunctionApproximator {
public static final String UPPER_LIMIT_WEIGHTS = "upper_limi... |
package aima.core.learning.neural;
import aima.core.util.Util;
import aima.core.util.math.Matrix;
import aima.core.util.math.Vector;
/**
* @author Ravi Mohan
*
*/
public class Layer {
// vectors are represented by n * 1 matrices;
private final Matrix weightMatrix;
Vector biasVector, lastBiasUpdateVector;
pr... |
package aima.core.learning.neural;
import aima.core.util.math.Vector;
/**
* @author Ravi Mohan
*
*/
public interface NNTrainingScheme {
Vector processInput(FeedForwardNeuralNetwork network, Vector input);
void processError(FeedForwardNeuralNetwork network, Vector error);
void setNeuralNetwork(FunctionApproxi... |
package aima.core.learning.neural;
import java.io.BufferedReader;
import java.io.InputStreamReader;
import java.util.ArrayList;
import java.util.Arrays;
import java.util.List;
import aima.core.learning.data.DataResource;
import aima.core.learning.framework.DataSet;
import aima.core.learning.framework.Example;
import ... |
package aima.core.learning.neural;
import java.util.ArrayList;
import java.util.Arrays;
import java.util.List;
import aima.core.learning.framework.Example;
import aima.core.util.datastructure.Pair;
/**
* @author Ravi Mohan
*
*/
public class IrisDataSetNumerizer implements Numerizer {
public Pair... |
package aima.core.learning.neural;
/**
* @author Ravi Mohan
*
*/
public class HardLimitActivationFunction implements ActivationFunction {
public double activation(double parameter) {
if (parameter < 0.0) {
return 0.0;
} else {
return 1.0;
}
}
public double deriv(double parameter) {
return 0.0;
... |
package aima.core.learning.neural;
/**
* @author Ravi Mohan
*
*/
public class LogSigActivationFunction implements ActivationFunction {
public double activation(double parameter) {
return 1.0 / (1.0 + Math.pow(Math.E, (-1.0 * parameter)));
}
public double deriv(double parameter) {
// parameter = induced f... |
package aima.core.learning.neural;
import java.util.ArrayList;
import java.util.List;
import aima.core.util.math.Vector;
/**
* @author Ravi Mohan
*
*/
public class NNExample {
private final List<Double> normalizedInput, normalizedTarget;
public NNExample(List<Double> normalizedInput, List<Double> normalizedTa... |
package aima.core.learning.neural;
import java.util.ArrayList;
import java.util.List;
import aima.core.util.math.Matrix;
import aima.core.util.math.Vector;
/**
* @author Ravi Mohan
*
*/
public class LayerSensitivity {
/*
* contains sensitivity matrices and related calculations for each layer.
* Used for bac... |
package aima.core.learning.neural;
import java.util.Hashtable;
/*
* a holder for config data for neural networks and possibly for other
* learning systems.
*/
/**
* @author Ravi Mohan
*
*/
public class NNConfig {
private final Hashtable<String, Object> hash;
public NNConfig(Hashtable<String, Object> hash) ... |
package aima.core.learning.neural;
import aima.core.util.math.Matrix;
import aima.core.util.math.Vector;
/**
* @author Ravi Mohan
*
*/
public class Perceptron implements FunctionApproximator {
private final Layer layer;
private Vector lastInput;
public Perceptron(int numberOfNeurons, int numberOfInputs) {
... |
package aima.core.learning.neural;
import java.util.List;
import aima.core.learning.framework.Example;
import aima.core.util.datastructure.Pair;
/**
* @author Ravi Mohan
*
*/
public interface Numerizer {
// A Numerizer understands how to convert an example from a particular
// dataset
// into a ... |
package aima.core.learning.neural;
/**
* @author Ravi Mohan
*
*/
public class PureLinearActivationFunction implements ActivationFunction {
public double activation(double parameter) {
return parameter;
}
public double deriv(double parameter) {
return 1;
}
}
|
package aima.core.learning.neural;
import java.util.ArrayList;
/**
* @author Ravi Mohan
*
*/
public class IrisNNDataSet extends NNDataSet {
@Override
public void setTargetColumns() {
// assumed that data from file has been pre processed
// TODO this should be
// somewhere else,in the
// super class.
... |
package aima.core.learning.neural;
import java.util.ArrayList;
/**
* @author Ravi Mohan
*
*/
public class RabbitEyeDataSet extends NNDataSet {
@Override
public void setTargetColumns() {
// assumed that data from file has been pre processed
// TODO this should be
// somewhere else,in the
// super class.... |
package aima.core.learning.neural;
import aima.core.util.math.Vector;
/**
* @author Ravi Mohan
*
*/
public interface FunctionApproximator {
/*
* accepts input pattern and processe it returning an output value
*/
Vector processInput(Vector input);
/*
* accept an error and change the parameters to accomod... |
package aima.core.learning.neural;
import aima.core.util.math.Matrix;
import aima.core.util.math.Vector;
/**
* @author Ravi Mohan
*
*/
public class BackPropLearning implements NNTrainingScheme {
private final double learningRate;
private final double momentum;
private Layer hiddenLayer;
private Layer outputL... |
package aima.core.learning.reinforcement;
import aima.core.probability.decision.MDP;
import aima.core.probability.decision.MDPPerception;
import aima.core.probability.decision.MDPPolicy;
import aima.core.probability.decision.MDPUtilityFunction;
import aima.core.util.FrequencyCounter;
/**
* @author Ravi Mohan
*
*/... |
package aima.core.learning.reinforcement;
import aima.core.probability.Randomizer;
import aima.core.probability.decision.MDP;
import aima.core.probability.decision.MDPPerception;
/**
* @author Ravi Mohan
*
*/
public abstract class MDPAgent<STATE_TYPE, ACTION_TYPE> {
protected MDP<STATE_TYPE, ACTION_TYPE> mdp;
... |
package aima.core.learning.reinforcement;
import java.util.Hashtable;
import java.util.List;
import aima.core.probability.decision.MDP;
import aima.core.probability.decision.MDPPerception;
import aima.core.probability.decision.MDPPolicy;
import aima.core.probability.decision.MDPTransition;
import aima.core.probabilit... |
package aima.core.learning.reinforcement;
import java.util.ArrayList;
import java.util.Hashtable;
import java.util.List;
import java.util.Set;
import aima.core.probability.decision.MDPPolicy;
import aima.core.util.Util;
import aima.core.util.datastructure.Pair;
/**
* @author Ravi Mohan
*
*/
public class QTable<S... |
package aima.core.learning.reinforcement;
import java.util.Hashtable;
import java.util.List;
import aima.core.probability.decision.MDP;
import aima.core.probability.decision.MDPPerception;
import aima.core.util.FrequencyCounter;
import aima.core.util.datastructure.Pair;
/**
* @author Ravi Mohan
*
*/
public class... |
package aima.core.learning.learners;
import aima.core.learning.framework.DataSet;
import aima.core.learning.inductive.DecisionTree;
/**
* @author Ravi Mohan
*
*/
public class StumpLearner extends DecisionTreeLearner {
public StumpLearner(DecisionTree sl, String unable_to_classify) {
super(sl, unab... |
package aima.core.learning.learners;
import java.util.Hashtable;
import java.util.List;
import aima.core.learning.framework.DataSet;
import aima.core.learning.framework.Example;
import aima.core.learning.framework.Learner;
import aima.core.util.Util;
import aima.core.util.datastructure.Table;
/**
* @aut... |
package aima.core.learning.learners;
import java.util.List;
import aima.core.learning.framework.DataSet;
import aima.core.learning.framework.Example;
import aima.core.learning.framework.Learner;
import aima.core.learning.inductive.DLTest;
import aima.core.learning.inductive.DLTestFactory;
import aima.core.le... |
package aima.core.learning.learners;
import java.util.Iterator;
import java.util.List;
import aima.core.learning.framework.DataSet;
import aima.core.learning.framework.Example;
import aima.core.learning.framework.Learner;
import aima.core.learning.inductive.ConstantDecisonTree;
import aima.core.learning.indu... |
package aima.core.learning.learners;
import java.util.ArrayList;
import java.util.List;
import aima.core.learning.framework.DataSet;
import aima.core.learning.framework.Example;
import aima.core.learning.framework.Learner;
import aima.core.learning.knowledge.CurrentBestLearning;
import aima.core.learning.kno... |
package aima.core.learning.learners;
import java.util.ArrayList;
import java.util.List;
import aima.core.learning.framework.DataSet;
import aima.core.learning.framework.Example;
import aima.core.learning.framework.Learner;
import aima.core.util.Util;
/**
* @author Ravi Mohan
*
*/
public class Major... |
package aima.core.learning.framework;
/**
* @author Ravi Mohan
*
*/
public class NumericAttributeSpecification implements AttributeSpecification {
// a simple attribute representing a number reprsented as a double .
private String name;
public NumericAttributeSpecification(String name) {
this.na... |
package aima.core.learning.framework;
import java.util.Hashtable;
import java.util.Iterator;
import java.util.LinkedList;
import java.util.List;
import aima.core.util.Util;
/**
* @author Ravi Mohan
*
*/
public class DataSet {
protected DataSet() {
}
public List<Example> examples;
publi... |
package aima.core.learning.framework;
import java.io.BufferedReader;
import java.io.InputStreamReader;
import java.util.Arrays;
import java.util.Hashtable;
import java.util.Iterator;
import java.util.List;
import aima.core.learning.data.DataResource;
import aima.core.util.Util;
/**
* @author Ravi Mohan... |
package aima.core.learning.framework;
import java.util.Arrays;
import java.util.List;
/**
* @author Ravi Mohan
*
*/
public class StringAttributeSpecification implements AttributeSpecification {
String attributeName;
List<String> attributePossibleValues;
public StringAttributeSpecification(Strin... |
package aima.core.learning.framework;
/**
* @author Ravi Mohan
*
*/
public interface AttributeSpecification {
boolean isValid(String string);
String getAttributeName();
Attribute createAttribute(String rawValue);
}
|
package aima.core.learning.framework;
/**
* @author Ravi Mohan
*
*/
public class StringAttribute implements Attribute {
private StringAttributeSpecification spec;
private String value;
public StringAttribute(String value, StringAttributeSpecification spec) {
this.spec = spec;
this.value = val... |
package aima.core.learning.framework;
import java.util.ArrayList;
import java.util.Iterator;
import java.util.List;
/**
* @author Ravi Mohan
*
*/
public class DataSetSpecification {
List<AttributeSpecification> attributeSpecifications;
private String targetAttribute;
public DataSetSpecificatio... |
package aima.core.learning.framework;
/**
* @author Ravi Mohan
*
*/
public class NumericAttribute implements Attribute {
double value;
private NumericAttributeSpecification spec;
public NumericAttribute(double rawvalue, NumericAttributeSpecification spec) {
this.value = rawvalue;
this.spec = ... |
package aima.core.learning.framework;
/**
* @author Ravi Mohan
*
*/
public interface Attribute {
public String valueAsString();
public String name();
}
|
package aima.core.learning.framework;
import java.util.Hashtable;
/**
* @author Ravi Mohan
*
*/
public class Example {
Hashtable<String, Attribute> attributes;
private Attribute targetAttribute;
public Example(Hashtable<String, Attribute> attributes,
Attribute targetAttribute) {
this.attr... |
package aima.core.learning.framework;
/**
* @author Ravi Mohan
*
*/
public interface Learner {
void train(DataSet ds);
String predict(Example e);
int[] test(DataSet ds);
}
|
package aima.core.learning.knowledge;
import java.util.ArrayList;
import java.util.HashMap;
import java.util.List;
import java.util.Map;
import java.util.regex.Pattern;
import aima.core.learning.framework.DataSetSpecification;
import aima.core.logic.fol.domain.FOLDomain;
/**
* @author Ciaran O'Reilly
... |
package aima.core.learning.knowledge;
import java.util.ArrayList;
import java.util.List;
import aima.core.learning.framework.Example;
import aima.core.logic.fol.Connectors;
import aima.core.logic.fol.parsing.ast.ConnectedSentence;
import aima.core.logic.fol.parsing.ast.Constant;
import aima.core.logic.fol.pa... |
package aima.core.learning.knowledge;
import java.util.List;
import aima.core.logic.fol.kb.FOLKnowledgeBase;
/**
* Artificial Intelligence A Modern Approach (3rd Edition): Figure 19.2, page 771.
*
* <code>
* function CURRENT-BEST-LEARNING(examples, h) returns a hypothesis or fail
*
* if example... |
package aima.core.learning.knowledge;
import aima.core.logic.fol.parsing.ast.Sentence;
/**
* @author Ciaran O'Reilly
*
*/
public class Hypothesis {
private Sentence hypothesis = null;
public Hypothesis(Sentence hypothesis) {
this.hypothesis = hypothesis;
}
/**
* <pre>
* FORALL v (Clas... |
package aima.core.learning.inductive;
import java.util.ArrayList;
import java.util.Hashtable;
import java.util.List;
import aima.core.learning.framework.Example;
/**
* @author Ravi Mohan
*
*/
public class DecisionList {
private String positive, negative;
private List<DLTest> tests;
private ... |
package aima.core.learning.inductive;
import aima.core.learning.framework.Example;
import aima.core.util.Util;
/**
* @author Ravi Mohan
*
*/
public class ConstantDecisonTree extends DecisionTree {
// represents leaf nodes like "Yes" or "No"
private String value;
public ConstantDecisonTree(String ... |
package aima.core.learning.inductive;
import java.util.ArrayList;
import java.util.Hashtable;
import java.util.List;
import aima.core.learning.framework.DataSet;
import aima.core.learning.framework.Example;
import aima.core.util.Util;
/**
* @author Ravi Mohan
*
*/
public class DecisionTree {
priv... |
package aima.core.learning.inductive;
import java.util.Hashtable;
import aima.core.learning.framework.DataSet;
import aima.core.learning.framework.Example;
/**
* @author Ravi Mohan
*
*/
public class DLTest {
// represents a single test in the Decision List
private Hashtable<String, String> attrV... |
package aima.core.learning.inductive;
import java.util.ArrayList;
import java.util.List;
import aima.core.learning.framework.DataSet;
/**
* @author Ravi Mohan
*
*/
public class DLTestFactory {
public List<DLTest> createDLTestsWithAttributeCount(DataSet ds, int i) {
if (i != 1) {
throw new R... |
package aima.core.probability;
import java.util.ArrayList;
import java.util.HashMap;
import java.util.List;
import java.util.Map;
/**
* @author Ravi Mohan
*
*/
public class BayesNetNode {
private String variable;
List<BayesNetNode> parents, children;
ProbabilityDistribution distribution;
... |
package aima.core.probability;
import java.util.Hashtable;
/**
* @author Ravi Mohan
*
*/
public class Query {
private String queryVariable;
private Hashtable<String, Boolean> evidenceVariables;
public Query(String queryVariable, String[] evidenceVariables,
boolean[] evidenceValues) {
th... |
package aima.core.probability.reasoning;
/**
* @author Ravi Mohan
*
*/
public class Particle {
private String state;
private double weight;
public Particle(String state, double weight) {
this.state = state;
this.weight = weight;
}
public Particle(String state) {
this(state, 0);
}
public boolean h... |
package aima.core.probability.reasoning;
import java.util.Arrays;
import java.util.List;
import aima.core.probability.RandomVariable;
/**
* @author Ravi Mohan
*
*/
public class HMMFactory {
public static HiddenMarkovModel createRobotHMM() {
// Example adopted from Sebastian Thrun's "Probabilistic Robotics"
... |
package aima.core.probability.reasoning;
import java.util.Arrays;
import java.util.List;
import aima.core.probability.RandomVariable;
import aima.core.util.math.Matrix;
/**
* @author Ravi Mohan
*
*/
public class HiddenMarkovModel {
SensorModel sensorModel;
TransitionModel transitionModel;
private RandomVar... |
package aima.core.probability.reasoning;
import java.util.ArrayList;
import java.util.List;
import aima.core.util.datastructure.Table;
import aima.core.util.math.Matrix;
/**
* @author Ravi Mohan
*
*/
public class SensorModel {
private Table<String, String, Double> table;
private List<String> states;
public ... |
package aima.core.probability.reasoning;
import aima.core.probability.RandomVariable;
/**
* @author Ravi Mohan
*
*/
public class HMMAgent {
private HiddenMarkovModel hmm;
private RandomVariable belief;
public HMMAgent(HiddenMarkovModel hmm) {
this.hmm = hmm;
this.belief = hmm.prior().duplicate();
}
pu... |
package aima.core.probability.reasoning;
/**
* @author Ravi Mohan
*
*/
public class HmmConstants {
public static final String PUSH_DOOR = "push";
public static final String DO_NOTHING = "do_nothing";
public static final String DOOR_CLOSED = "closed";
public static final String DOOR_OPEN = "open";
public s... |
package aima.core.probability.reasoning;
import java.util.ArrayList;
import java.util.Arrays;
import java.util.List;
import aima.core.util.datastructure.Table;
import aima.core.util.math.Matrix;
/**
* @author Ravi Mohan
*
*/
public class TransitionModel {
private Table<String, String, Double> table;
private ... |
package aima.core.probability.reasoning;
import java.util.ArrayList;
import java.util.Hashtable;
import java.util.List;
import aima.core.probability.RandomVariable;
import aima.core.probability.Randomizer;
/**
* @author Ravi Mohan
*
*/
public class ParticleSet {
private List<Particle> particles;
private Hidd... |
package aima.core.probability.reasoning;
import java.util.ArrayList;
import aima.core.probability.RandomVariable;
import aima.core.util.math.Matrix;
/**
* @author Ravi Mohan
*
*/
public class FixedLagSmoothing {
// This implementation is almost certainly wrong (see comments below).
// This is faithful to the ... |
package aima.core.probability.decision;
import java.util.ArrayList;
import java.util.Hashtable;
import java.util.List;
import aima.core.util.datastructure.Pair;
/**
* @author Ravi Mohan
*
*/
public class MDPTransitionModel<STATE_TYPE, ACTION_TYPE> {
private Hashtable<MDPTransition<STATE_TYPE, ACTION_TYPE>, Dou... |
package aima.core.probability.decision;
import java.util.List;
import aima.core.probability.Randomizer;
/**
* @author Ravi Mohan
*
*/
public interface MDPSource<STATE_TYPE, ACTION_TYPE> {
MDP<STATE_TYPE, ACTION_TYPE> asMdp();
STATE_TYPE getInitialState();
MDPTransitionModel<STATE_TYPE, ACTION_TYPE> getTrans... |
package aima.core.probability.decision;
/**
* @author Ravi Mohan
*
*/
public class MDPPerception<STATE_TYPE> {
private STATE_TYPE state;
private double reward;
public MDPPerception(STATE_TYPE state, double reward) {
this.state = state;
this.reward = reward;
}
public double getReward() {
return rewar... |
package aima.core.probability.decision;
import aima.core.environment.cellworld.CellWorld;
import aima.core.environment.cellworld.CellWorldPosition;
/**
* @author Ravi Mohan
*
*/
public class MDPFactory {
public static MDP<CellWorldPosition, String> createFourByThreeMDP() {
CellWorld cw = new CellWorld(3, 4, ... |
package aima.core.probability.decision;
import aima.core.util.datastructure.Triplet;
/**
* @author Ravi Mohan
*
*/
public class MDPTransition<STATE_TYPE, ACTION_TYPE> {
private Triplet<STATE_TYPE, ACTION_TYPE, STATE_TYPE> triplet;
public MDPTransition(STATE_TYPE initial, ACTION_TYPE action,
STATE_TYPE desti... |
package aima.core.probability.decision;
import java.util.Hashtable;
import java.util.Set;
/**
* @author Ravi Mohan
*
*/
public class MDPPolicy<STATE_TYPE, ACTION_TYPE> {
Hashtable<STATE_TYPE, ACTION_TYPE> stateToAction;
public MDPPolicy() {
stateToAction = new Hashtable<STATE_TYPE, ACTION_TYPE>();
}
publi... |
package aima.core.probability.decision;
import java.util.Hashtable;
/**
* @author Ravi Mohan
*
*/
public class MDPRewardFunction<STATE_TYPE> {
Hashtable<STATE_TYPE, Double> stateToReward;
public MDPRewardFunction() {
stateToReward = new Hashtable<STATE_TYPE, Double>();
}
public double getRewardFor(STATE_T... |
package aima.core.probability.decision;
import java.util.List;
import aima.core.probability.Randomizer;
import aima.core.util.datastructure.Pair;
/**
* @author Ravi Mohan
*
*/
public class MDP<STATE_TYPE, ACTION_TYPE> {
private STATE_TYPE initialState;
private MDPTransitionModel<STATE_TYPE, ACTION_TYPE> trans... |
package aima.core.probability.decision;
import java.util.Hashtable;
/**
* @author Ravi Mohan
*
*/
public class MDPUtilityFunction<STATE_TYPE> {
private Hashtable<STATE_TYPE, Double> hash;
public MDPUtilityFunction() {
hash = new Hashtable<STATE_TYPE, Double>();
}
public Double getUtility(STATE_TYPE state)... |
package aima.core.probability;
import java.util.Hashtable;
import aima.core.util.Util;
/**
* @author Ravi Mohan
*
*/
public class EnumerateJointAsk {
public static double[] ask(Query q, ProbabilityDistribution pd) {
double[] probDist = new double[2];
Hashtable<String, Boolean> h = q.getEviden... |
package aima.core.probability;
import java.util.Hashtable;
import java.util.Iterator;
import java.util.List;
import aima.core.util.Util;
/**
* @author Ravi Mohan
*
*/
public class EnumerationAsk {
public static double[] ask(Query q, BayesNet net) {
Hashtable<String, Boolean> evidenceVariables ... |
package aima.core.probability;
import java.util.ArrayList;
import java.util.Hashtable;
import java.util.Iterator;
import java.util.List;
import aima.core.util.Util;
/**
* @author Ravi Mohan
*
*/
public class BayesNet {
private List<BayesNetNode> roots = new ArrayList<BayesNetNode>();
private L... |
package aima.core.probability;
/**
* @author Ravi Mohan
*
*/
public interface Randomizer {
public double nextDouble();
}
|
package aima.core.probability;
import java.util.ArrayList;
import java.util.HashMap;
import java.util.LinkedHashMap;
import java.util.List;
import java.util.Map;
/**
* @author Ravi Mohan
*
*/
public class ProbabilityDistribution {
private List<Row> rows = new ArrayList<Row>();
// <VariableName:Dis... |
package aima.core.probability;
import java.util.ArrayList;
import java.util.Hashtable;
import java.util.List;
import aima.core.probability.reasoning.HiddenMarkovModel;
import aima.core.probability.reasoning.Particle;
import aima.core.probability.reasoning.ParticleSet;
import aima.core.util.Util;
import aima.core.util... |
package aima.core.probability;
import java.util.Random;
/**
* @author Ravi Mohan
*
*/
public class JavaRandomizer implements Randomizer {
static Random r = new Random();
public double nextDouble() {
return r.nextDouble();
}
}
|
package aima.core.util;
import java.util.ArrayList;
import java.util.Hashtable;
import java.util.List;
import java.util.Random;
/**
* @author Ravi Mohan
*
*/
public class Util {
public static final String NO = "No";
public static final String YES = "Yes";
private static Random r = new Random(... |
package aima.core.util.math;
import java.util.List;
/**
* @author Ciaran O'Reilly see:
* http://demonstrations.wolfram.com/MixedRadixNumberRepresentations/
* for useful example.
*/
public class MixedRadixNumber extends Number {
//
private static final long serialVersionUID = 1L;
//
... |
package aima.core.util.math;
/**
* LU Decomposition.
* <P>
* For an m-by-n matrix A with m >= n, the LU decomposition is an m-by-n unit
* lower triangular matrix L, an n-by-n upper triangular matrix U, and a
* permutation vector piv of length m so that A(piv,:) = L*U. If m < n, then L
* is m-by-m and U is m-by-n... |
package aima.core.util.math;
import java.io.BufferedReader;
import java.io.PrintWriter;
import java.io.StreamTokenizer;
import java.text.DecimalFormat;
import java.text.DecimalFormatSymbols;
import java.text.NumberFormat;
import java.util.List;
import java.util.Locale;
/**
* Jama = Java Matrix class.
* <P>
* The J... |
package aima.core.util.math;
import java.util.List;
/**
* @author Ravi Mohan
*
*/
public class Vector extends Matrix {
private static final long serialVersionUID = 1L;
// Vector is modelled as a matrix with a single column;
public Vector(int size) {
super(size, 1);
}
public Vector(List<Double> lst) {
s... |
package aima.core.util.datastructure;
import java.util.Collection;
import java.util.LinkedList;
/**
* Artificial Intelligence A Modern Approach (3rd Edition): pg 80.<br>
*
* First-in, first-out or FIFO queue, which pops the oldest element of the queue;
*/
/**
* @author Ravi Mohan
* @author Ciaran... |
package aima.core.util.datastructure;
/**
* Note: If looking at a rectangle - the coordinate (x=0, y=0) will be the top left hand corner.
* This corresponds with Java's AWT coordinate system.
*/
/**
* @author Ravi Mohan
*
*/
public class XYLocation {
public enum Direction {
North, South, East, ... |
package aima.core.util.datastructure;
import java.util.ArrayList;
import java.util.Hashtable;
import java.util.List;
/**
* Represents a directed labeled graph. Vertices are represented by their unique
* labels and labeled edges by means of nested hashtables. Variant of class
* {@code aima.util.Table}. Thi... |
package aima.core.util.datastructure;
/**
* Simplified version of <code>java.awt.geom.Point2D</code>. We do not want
* dependencies to presentation layer packages here.
*
* @author R. Lunde
*/
public class Point2D {
private double x;
private double y;
public Point2D(double x, double y) {
this.... |
package aima.core.util.datastructure;
/**
* @author Ravi Mohan
*
*/
public class Triplet<X, Y, Z> {
private final X x;
private final Y y;
private final Z z;
public Triplet(X x, Y y, Z z) {
this.x = x;
this.y = y;
this.z = z;
}
public X getFirst() {
return x;
}
public Y getSecond() {
return y... |
package aima.core.util.datastructure;
import java.util.Hashtable;
import java.util.List;
/**
* @author Ravi Mohan
*
*/
public class Table<RowHeaderType, ColumnHeaderType, ValueType> {
private List<RowHeaderType> rowHeaders;
private List<ColumnHeaderType> columnHeaders;
private Hashtable<RowHeaderTy... |
package aima.core.util.datastructure;
import java.util.Collection;
import java.util.Comparator;
import java.util.SortedSet;
/**
* Artificial Intelligence A Modern Approach (3rd Edition): pg 80.<br>
*
* The priority queue, which pops the element of the queue with the highest
* priority according to some... |
package aima.core.util.datastructure;
import java.util.Collection;
import java.util.LinkedList;
/**
* Artificial Intelligence A Modern Approach (3rd Edition): pg 80.<br>
*
* Last-in, first-out or LIFO queue (also known as a stack), which pops the newest element of the queue;
*/
/**
* @author Ravi M... |
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