import ca.pfv.spmf.algorithms.frequentpatterns.apriori.AlgoApriori; //导入依赖的package包/类
@Override
public void runAlgorithm(String[] parameters, String inputFile, String outputFile) throws IOException {
	double minsup = getParamAsDouble(parameters[0]);
	double minconf = getParamAsDouble(parameters[1]);

	AlgoApriori apriori = new AlgoApriori();
	ca.pfv.spmf.patterns.itemset_array_integers_with_count.Itemsets patterns = apriori
			.runAlgorithm(minsup, inputFile, null);
	apriori.printStats();
	int databaseSize = apriori.getDatabaseSize();

	// STEP 2: Generating all rules from the set of frequent itemsets
	// (based on Agrawal & Srikant, 94)
	ca.pfv.spmf.algorithms.associationrules.agrawal94_association_rules.AlgoAgrawalFaster94 algoAgrawal = new ca.pfv.spmf.algorithms.associationrules.agrawal94_association_rules.AlgoAgrawalFaster94();
	algoAgrawal.runAlgorithm(patterns, outputFile, databaseSize,
			minconf);
	algoAgrawal.printStats();
}
 

import ca.pfv.spmf.algorithms.frequentpatterns.apriori.AlgoApriori; //导入依赖的package包/类
@Test
public void main() {

    NoExceptionAssertion.assertDoesNotThrow(() -> {

        String input = "contextPasquier99.txt";
        String output = ".//output.txt";  // the path for saving the frequent itemsets found

        double minsup = 0.4; // means a minsup of 2 transaction (we used a relative support)

        // Applying the Apriori algorithm
        AlgoApriori apriori = new AlgoApriori();
        apriori.runAlgorithm(minsup, input, output);
        apriori.printStats();
    });
}
 

import ca.pfv.spmf.algorithms.frequentpatterns.apriori.AlgoApriori; //导入依赖的package包/类
@Test
public void main() {

    NoExceptionAssertion.assertDoesNotThrow(() -> {

        String input = "contextPasquier99.txt";
        String output = null;
        // Note : we here set the output file path to null
        // because we want that the algorithm save the
        // result in memory for this example.

        double minsup = 0.4; // means a minsup of 2 transaction (we used a relative support)

        // Applying the Apriori algorithm
        AlgoApriori apriori = new AlgoApriori();
        Itemsets result = apriori.runAlgorithm(minsup, input, output);
        apriori.printStats();
        result.printItemsets(apriori.getDatabaseSize());
    });
}
 

import ca.pfv.spmf.algorithms.frequentpatterns.apriori.AlgoApriori; //导入依赖的package包/类
@Override
public void runAlgorithm(String[] parameters, String inputFile, String outputFile) throws IOException {
	double minsup = getParamAsDouble(parameters[0]);

	// Applying the Apriori algorithm, optimized version
	ca.pfv.spmf.algorithms.frequentpatterns.apriori.AlgoApriori apriori = new ca.pfv.spmf.algorithms.frequentpatterns.apriori.AlgoApriori();
	apriori.runAlgorithm(minsup, inputFile, outputFile);
	apriori.printStats();
}
 

import ca.pfv.spmf.algorithms.frequentpatterns.apriori.AlgoApriori; //导入依赖的package包/类
/** Run Apriori algorithm */
public static SortedMap<Itemset, Integer> mineFrequentItemsetsApriori(final String dataset, final String saveFile,
		final double minSupp) throws IOException {

	// Remove transaction duplicates and sort items ascending
	final File TMPDB = File.createTempFile("fixed-dataset", ".dat");
	dbTool.convert(dataset, TMPDB.getAbsolutePath());

	final AlgoApriori algo = new AlgoApriori();
	final Itemsets patterns = algo.runAlgorithm(minSupp, TMPDB.getAbsolutePath(), saveFile);
	// algo.printStats();
	// patterns.printItemsets(algo.getDatabaseSize());

	return toMap(patterns);
}
 

import ca.pfv.spmf.algorithms.frequentpatterns.apriori.AlgoApriori; //导入依赖的package包/类
public static void main(String [] arg) throws IOException{
		
//		String input = fileToPath("E:/DMTest/contextPasquier99.txt");
		String input = "E:/DMTest/contextPasquier99.txt";
		String output = "E:/DMTest/Result/frequent_itemsets.txt";  // the path for saving the frequent itemsets found
		
		double minsup = 0.4; // means a minsup of 2 transaction (we used a relative support)
		
		// Applying the Apriori algorithm
		AlgoApriori apriori = new AlgoApriori();
		apriori.runAlgorithm(minsup, input, output);
		apriori.printStats();
	}
 

import ca.pfv.spmf.algorithms.frequentpatterns.apriori.AlgoApriori; //导入依赖的package包/类
public static void main(String [] arg) throws IOException{

		String input = fileToPath("contextPasquier99.txt");
		String output = "C://patterns//frequent_itemsets.txt";  // the path for saving the frequent itemsets found
		
		double minsup = 0.4; // means a minsup of 2 transaction (we used a relative support)
		
		// Applying the Apriori algorithm
		AlgoApriori apriori = new AlgoApriori();
		apriori.runAlgorithm(minsup, input, output);
		apriori.printStats();
	}