本文整理汇总了Java中ca.pfv.spmf.algorithms.frequentpatterns.zart.AlgoZart.printStatistics方法的典型用法代码示例。如果您正苦于以下问题:Java AlgoZart.printStatistics方法的具体用法?Java AlgoZart.printStatistics怎么用?Java AlgoZart.printStatistics使用的例子?那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类ca.pfv.spmf.algorithms.frequentpatterns.zart.AlgoZart
的用法示例。
在下文中一共展示了AlgoZart.printStatistics方法的9个代码示例,这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞,您的评价将有助于系统推荐出更棒的Java代码示例。
示例1: runAlgorithm
import ca.pfv.spmf.algorithms.frequentpatterns.zart.AlgoZart; //导入方法依赖的package包/类
@Override
public void runAlgorithm(String[] parameters, String inputFile, String outputFile) throws IOException {
System.out.println("STEP 1: APPLY ZART TO FIND CLOSED ITEMSETS AND GENERATORS");
double minsup = getParamAsDouble(parameters[0]);
double minconf = getParamAsDouble(parameters[1]);
TransactionDatabase database = new TransactionDatabase();
try {
database.loadFile(inputFile);
} catch (Exception e) {
e.printStackTrace();
}
// Applying the Zart algorithm
AlgoZart zart = new AlgoZart();
TZTableClosed results = zart.runAlgorithm(database, minsup);
zart.printStatistics();
System.out.println("STEP 2 : CALCULATING MNR ASSOCIATION RULES");
// Run the algorithm to generate MNR rules
AlgoMNRRules algoMNR = new AlgoMNRRules();
algoMNR.runAlgorithm(outputFile, minconf, results, database.size());
algoMNR.printStatistics();
}
示例2: runAlgorithm
import ca.pfv.spmf.algorithms.frequentpatterns.zart.AlgoZart; //导入方法依赖的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]);
TransactionDatabase database = new TransactionDatabase();
try {
database.loadFile(inputFile);
} catch (Exception e) {
e.printStackTrace();
}
// Applying the Zart algorithm
AlgoZart zart = new AlgoZart();
TZTableClosed results = zart.runAlgorithm(database, minsup);
zart.printStatistics();
// Generate IGB association rules
AlgoIGB algoIGB = new AlgoIGB();
algoIGB.runAlgorithm(results, database.getTransactions().size(),
minconf, outputFile);
algoIGB.printStatistics();
}
示例3: main
import ca.pfv.spmf.algorithms.frequentpatterns.zart.AlgoZart; //导入方法依赖的package包/类
public static void main(String[] args) throws IOException {
System.out.println("STEP 1 : EXECUTING THE ZART ALGORITHM TO FIND CLOSED ITEMSETS AND MINIMUM GENERATORS");
String input = fileToPath("contextIGB.txt");
String output = "C:\\patterns\\IGB_association_rules.txt";
TransactionDatabase database = new TransactionDatabase();
try {
database.loadFile(input);
} catch (Exception e) {
e.printStackTrace();
}
// Applying the Zart algorithm
AlgoZart zart = new AlgoZart();
double minsup = 0.5;
TZTableClosed results = zart.runAlgorithm(database, minsup);
zart.printStatistics();
System.out.println("STEP 2 : RUNNING THE IGB ALGORITHM");
// Apply the IGB algorithm
double minconf = 0.61; // minimum confidence
AlgoIGB algoIGB = new AlgoIGB();
algoIGB.runAlgorithm(results, database.getTransactions().size(), minconf, output);
algoIGB.printStatistics();
}
示例4: main
import ca.pfv.spmf.algorithms.frequentpatterns.zart.AlgoZart; //导入方法依赖的package包/类
public static void main(String[] args) throws IOException {
String input = fileToPath("contextZart.txt"); // the database
String output = "C://patterns//zart_output.txt"; // the path for saving the frequent itemsets found
// Load a binary context
TransactionDatabase context = new TransactionDatabase();
context.loadFile(input);
// Apply the Zart algorithm
double minsup = 0.4;
AlgoZart zart = new AlgoZart();
TZTableClosed results = zart.runAlgorithm(context, minsup);
TFTableFrequent frequents = zart.getTableFrequent();
zart.printStatistics();
zart.saveResultsToFile(output);
}
示例5: runAlgorithm
import ca.pfv.spmf.algorithms.frequentpatterns.zart.AlgoZart; //导入方法依赖的package包/类
@Override
public void runAlgorithm(String[] parameters, String inputFile, String outputFile) throws IOException {
double minsup = getParamAsDouble(parameters[0]);
// Load a binary context
TransactionDatabase context = new TransactionDatabase();
context.loadFile(inputFile);
// Apply the Zart algorithm
AlgoZart zart = new AlgoZart();
zart.runAlgorithm(context, minsup);
zart.printStatistics();
zart.saveResultsToFile(outputFile);
}
示例6: main
import ca.pfv.spmf.algorithms.frequentpatterns.zart.AlgoZart; //导入方法依赖的package包/类
public static void main(String[] args) throws IOException {
System.out.println("STEP 1 : EXECUTING THE ZART ALGORITHM TO FIND CLOSED ITEMSETS AND MINIMUM GENERATORS");
String input = fileToPath("contextIGB.txt");
String output = null;
// Note : we set the output to null because we choose to keep
// the result into memory instead of saving to a file, for this
// example.
TransactionDatabase database = new TransactionDatabase();
try {
database.loadFile(input);
} catch (Exception e) {
e.printStackTrace();
}
// Applying the Zart algorithm
AlgoZart zart = new AlgoZart();
double minsup = 0.5;
TZTableClosed results = zart.runAlgorithm(database, minsup);
zart.printStatistics();
System.out.println("STEP 2 : RUNNING THE IGB ALGORITHM");
// Apply the IGB algorithm
double minconf = 0.61; // minimum confidence
AlgoIGB algoIGB = new AlgoIGB();
Rules rules = algoIGB.runAlgorithm(results, database.getTransactions().size(), minconf,output);
algoIGB.printStatistics();
rules.printRules(database.getTransactions().size());
}
示例7: main
import ca.pfv.spmf.algorithms.frequentpatterns.zart.AlgoZart; //导入方法依赖的package包/类
public static void main(String[] args) throws IOException {
System.out.println("STEP 1 : EXECUTING THE ZART ALGORITHM TO FIND CLOSED ITEMSETS AND MINIMUM GENERATORS");
String input = fileToPath("contextZart.txt");
String output = null;
// Note, above, we set the output file path to null
// because we want to keep the result into memory for this example
double minsup = 0.6; // minimum support
double minconf = 0.6; // minimum confidence
TransactionDatabase database = new TransactionDatabase();
try {
database.loadFile(input);
} catch (Exception e) {
e.printStackTrace();
}
database.printDatabase();
// Applying the Zart algorithm
AlgoZart zart = new AlgoZart();
TZTableClosed results = zart.runAlgorithm(database, minsup);
zart.printStatistics();
// // PRINT RESULTS FROM THE ZART ALGORITHM
// int countClosed=0;
// int countGenerators=0;
// System.out.println("===================");
// for(int i=0; i< results.levels.size(); i++){
// System.out.println("LEVEL : " + i);
// for(Itemset closed : results.levels.get(i)){
// System.out.println(" CLOSED : " + closed.toString() + " supp : " + closed.getAbsoluteSupport());
// countClosed++;
// System.out.println(" GENERATORS : ");
// for(Itemset generator : results.mapGenerators.get(closed)){
// countGenerators++;
// System.out.println(" =" + generator.toString());
// }
// }
// }
System.out.println("STEP 2 : CALCULATING MNR ASSOCIATION RULES");
// Create MNR rules.
AlgoMNRRules algoMNR = new AlgoMNRRules();
Rules rules = algoMNR.runAlgorithm(output, minconf, results, database.size());
rules.printRules(database.size());
}
示例8: main
import ca.pfv.spmf.algorithms.frequentpatterns.zart.AlgoZart; //导入方法依赖的package包/类
public static void main(String[] args) throws IOException {
// Load a binary context
TransactionDatabase context = new TransactionDatabase();
context.loadFile(fileToPath("contextZart.txt"));
// Apply the Zart algorithm
double minsup = 0.4;
AlgoZart zart = new AlgoZart();
TZTableClosed results = zart.runAlgorithm(context, minsup);
TFTableFrequent frequents = zart.getTableFrequent();
zart.printStatistics();
// PRINTING RESULTS
int countClosed=0;
int countGenerators=0;
System.out.println("======= List of closed itemsets and their generators ============");
for(int i=0; i< results.levels.size(); i++){
System.out.println("LEVEL (SIZE) : " + i);
for(Itemset closed : results.levels.get(i)){
System.out.println(" CLOSED : " + closed.toString() + " supp : " + closed.getAbsoluteSupport());
countClosed++;
System.out.println(" GENERATORS : ");
for(Itemset generator : results.mapGenerators.get(closed)){
countGenerators++;
System.out.println(" =" + generator.toString());
}
}
}
System.out.println(" NUMBER OF CLOSED : " + countClosed + " NUMBER OF GENERATORS : " + countGenerators );
// SECOND, WE PRINT THE LIST OF ALL FREQUENT ITEMSETS
System.out.println("======= List of all frequent itemsets ============");
int countFrequent =0;
for(int i=0; i< frequents.levels.size(); i++){
System.out.println("LEVEL (SIZE) : " + i);
for(Itemset itemset : frequents.levels.get(i)){
countFrequent++;
System.out.println(" ITEMSET : " + itemset.toString() + " supp : " + itemset.getAbsoluteSupport());
}
}
System.out.println("NB OF FREQUENT ITEMSETS : " + countFrequent);
}
示例9: main
import ca.pfv.spmf.algorithms.frequentpatterns.zart.AlgoZart; //导入方法依赖的package包/类
public static void main(String[] args) throws IOException {
System.out.println("STEP 1 : EXECUTING THE ZART ALGORITHM TO FIND CLOSED ITEMSETS AND MINIMUM GENERATORS");
String input = fileToPath("contextZart.txt");
String output = "C:\\patterns\\MNR_association_rules.txt";
//
double minsup = 0.6; // minimum support
double minconf = 0.6; // minimum confidence
TransactionDatabase database = new TransactionDatabase();
try {
database.loadFile(input);
} catch (Exception e) {
e.printStackTrace();
}
database.printDatabase();
// Applying the Zart algorithm
AlgoZart zart = new AlgoZart();
TZTableClosed results = zart.runAlgorithm(database, minsup);
zart.printStatistics();
// // PRINT RESULTS FROM THE ZART ALGORITHM
// int countClosed=0;
// int countGenerators=0;
// System.out.println("===================");
// for(int i=0; i< results.levels.size(); i++){
// System.out.println("LEVEL : " + i);
// for(Itemset closed : results.levels.get(i)){
// System.out.println(" CLOSED : " + closed.toString() + " supp : " + closed.getAbsoluteSupport());
// countClosed++;
// System.out.println(" GENERATORS : ");
// for(Itemset generator : results.mapGenerators.get(closed)){
// countGenerators++;
// System.out.println(" =" + generator.toString());
// }
// }
// }
System.out.println("STEP 2 : CALCULATING MNR ASSOCIATION RULES");
// Run the algorithm to generate MNR rules
AlgoMNRRules algoMNR = new AlgoMNRRules();
algoMNR.runAlgorithm(output, minconf, results, database.size());
}