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/*
 * Copyright (c) 2015, SRI International
 * All rights reserved.
 * Licensed under the The BSD 3-Clause License;
 * you may not use this file except in compliance with the License.
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package com.sri.ai.praise.lang.grounded.common;

import java.util.List;

import com.google.common.annotations.Beta;

/**
 * Basic representation of a Graphical Network. Contains representations common to both Markov and Bayes networks.
 * 
 * @author oreilly
 *
 */
@Beta
public interface GraphicalNetwork {
	int numberVariables();
	int cardinality(int variableIndex);
	int numberUniqueFunctionTables();
	FunctionTable getUniqueFunctionTable(int uniqueFunctionTableIdx);
	
	int numberTables();
	FunctionTable getTable(int tableIdx);
	List getVariableIndexesForTable(int tableIdx);
	List getTableIndexes(int uniqueFunctionTableIdx);
	
	default double ratioUniqueTablesToTables() {
		return ((double) numberUniqueFunctionTables()) / ((double) numberTables());
	}
	
	default int largestCardinality() {
		int result = 0;
		for (int i = 0; i < numberVariables(); i++) {
			int card = cardinality(i);
			if (card > result) {
				result = card;
			}
		}
		return result;
	}
	
	default int largestNumberOfFunctionTableEntries() {		
		int result = 0;
		for (int i = 0; i < numberUniqueFunctionTables(); i++) {
			int numEntries = getUniqueFunctionTable(i).numberEntries();
			if (numEntries > result) {
				result = numEntries;
			}
		}
 		return result;
	}
	
	default int totalNumberEntriesForAllFunctionTables() {
		int result = 0;
		for (int i = 0; i < numberTables(); i++) {
			result += getTable(i).numberEntries();
		}		
		return result;
	}
}




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