Java IntRBTreeSet类代码示例

import it.unimi.dsi.fastutil.ints.IntRBTreeSet; //导入依赖的package包/类
public double greedySolve(Mat mat) {
    double sum = 0.0;
    IntSet available = new IntRBTreeSet();
    for(int i = 0; i < mat.M(); i++) {
        available.add(i);
    }
    for(int i = 0; i < mat.N(); i++) {
        double best = Double.NEGATIVE_INFINITY;
        for(ME me : mat.row(i)) {
            if(available.contains(me.j) && me.v > best) {
                best = me.v;
                available.remove(me.j);
            }
        }
        sum += Math.max(best, 0.0);
    }
    return sum;
}
 

import it.unimi.dsi.fastutil.ints.IntRBTreeSet; //导入依赖的package包/类
public DepTree(TypedDependency root, Collection<TypedDependency> tds,
    List<? extends HasWord> sentence, int[] remapping, IntSet stack) {
    this.map = new HashMap<>();
    int t = root.dep().index();
    node = sentence.get(t - 1).word();
    //tag = root.dep().tag();
    tag = root.dep().label().tag();
    this.idx = remapping[t - 1];
    if (!stack.contains(t)) {
        IntSet stack2 = new IntRBTreeSet(stack);
        stack2.add(t);
        for (TypedDependency td : tds) {
            if (td.gov().index() == t && td.dep().index() != t) {
                map.put(td.reln().getShortName(), new DepTree(td, tds, sentence, remapping, stack2));
            }
        }
    }
}
 

import it.unimi.dsi.fastutil.ints.IntRBTreeSet; //导入依赖的package包/类
public AssociationRuleMining(IntColumn sets, CategoryColumn items, double support) {
    Table temp = Table.create("temp");
    temp.addColumn(sets.copy());
    temp.addColumn(items.toIntColumn());
    temp.sortAscendingOn(sets.name(), items.name());

    ViewGroup baskets = temp.splitOn(temp.column(0));
    int[][] itemsets = new int[baskets.size()][];
    int basketIndex = 0;
    for (TemporaryView basket : baskets) {
        IntRBTreeSet set = new IntRBTreeSet(basket.intColumn(1).data());
        int itemIndex = 0;
        itemsets[basketIndex] = new int[set.size()];
        for (int item : set) {
            itemsets[basketIndex][itemIndex] = item;
            itemIndex++;
        }
        basketIndex++;
    }

    this.model = new ARM(itemsets, support);
}
 

import it.unimi.dsi.fastutil.ints.IntRBTreeSet; //导入依赖的package包/类
public static void main(String[] args) throws Exception {

        Table table = Table.read().csv(CsvReadOptions
            .builder("../data/movielens.data")
            .separator('\t'));

        double supportThreshold = .25;
        double confidenceThreshold = .5;
        double interestThreshold = .5;

        AssociationRuleMining model = new AssociationRuleMining(table.shortColumn("user"), table.shortColumn("movie")
                , supportThreshold);

        FrequentItemset frequentItemsetModel = new FrequentItemset(table.shortColumn("user"), table.shortColumn
                ("movie"), supportThreshold);
        Object2DoubleOpenHashMap<IntRBTreeSet> confidenceMap = frequentItemsetModel.confidenceMap();

        Table interestingRuleTable = model.interest(confidenceThreshold, interestThreshold, confidenceMap);

        interestingRuleTable = interestingRuleTable.sortDescendingOn("Interest", "Antecedent");
        out(interestingRuleTable);
    }
 

import it.unimi.dsi.fastutil.ints.IntRBTreeSet; //导入依赖的package包/类
default Selection startsWith(String string) {
    Selection results = new BitmapBackedSelection();

    IntRBTreeSet sorted = new IntRBTreeSet();
    DictionaryMap dictionaryMap = this.dictionaryMap();

    for (Object2IntMap.Entry<String> entry : dictionaryMap.valueToKeyMap().object2IntEntrySet()) {
        String key = entry.getKey();
        if (key.startsWith(string)) {
            sorted.add(entry.getIntValue());
        }
    }

    int i = 0;
    for (int next : values()) {
        if (sorted.contains(next)) {
            results.add(i);
        }
        i++;
    }
    return results;
}
 

import it.unimi.dsi.fastutil.ints.IntRBTreeSet; //导入依赖的package包/类
public List<AssociationRule> interestingRules(double confidenceThreshold,
                                              double interestThreshold,
                                              Object2DoubleOpenHashMap<IntRBTreeSet> confidenceMap) {
    List<AssociationRule> rules = model.learn(confidenceThreshold);
    for (AssociationRule rule : rules) {
        double interest = rule.confidence - confidenceMap.getDouble(rule.consequent);
        if (Math.abs(interest) < interestThreshold) {
            rules.remove(rule);
        }
    }
    return rules;
}
 

import it.unimi.dsi.fastutil.ints.IntRBTreeSet; //导入依赖的package包/类
/**
 * Constructs and returns a frequent itemset model
 *
 * @param sets
 * @param items
 * @param supportThreshold the minimum support required to be included
 */
public FrequentItemset(IntColumn sets, IntColumn items, double supportThreshold) {

    Table temp = Table.create("temp");
    temp.addColumn(sets.copy());
    temp.addColumn(items.copy());
    temp.sortAscendingOn(sets.name(), items.name());

    ViewGroup baskets = temp.splitOn(temp.column(0));

    this.setCount = baskets.size();

    int[][] itemsets = new int[setCount][];
    int basketIndex = 0;
    for (TemporaryView basket : baskets) {
        IntRBTreeSet set = new IntRBTreeSet(basket.intColumn(1).data());
        int itemIndex = 0;
        itemsets[basketIndex] = new int[set.size()];
        for (int item : set) {
            itemsets[basketIndex][itemIndex] = item;
            itemIndex++;
        }
        basketIndex++;
    }

    this.model = new FPGrowth(itemsets, supportThreshold);
}
 

import it.unimi.dsi.fastutil.ints.IntRBTreeSet; //导入依赖的package包/类
public static void main(String[] args) throws Exception {

        Table table = Table.read().csv(CsvReadOptions
            .builder("../data/movielens.data")
            .separator('\t'));
        out(table.structure());
        out(table.shape());
        ShortColumn movie = table.shortColumn("movie");
        CategoryColumn moviecat = new CategoryColumn("MovieCat");
        for (int i = 0; i < movie.size(); i++) {
            moviecat.appendCell(movie.getString(i));
        }
        table.addColumn(moviecat);

        out(table.shortColumn("user").unique().size());
        out(table.shortColumn("movie").unique().size());

        FrequentItemset model = new FrequentItemset(table.shortColumn("user"), table.categoryColumn("MovieCat"), .24);
        List<ItemSet> itemSetList = model.learn();

        out("Frequent Itemsets");
        for (ItemSet itemSet : itemSetList) {
            if (itemSet.items.length == 2)
                out(itemSet);
        }

        out(model.supportMap(250));

        Object2DoubleOpenHashMap<IntRBTreeSet> confidenceMap = model.confidenceMap(.90);
        Object2DoubleMap.FastEntrySet<IntRBTreeSet> entrySet = confidenceMap.object2DoubleEntrySet();

        out("");
        out("Confidence Map");
        for (Object2DoubleMap.Entry<IntRBTreeSet> entry : entrySet) {
            out(entry.getKey() + " : " + entry.getDoubleValue());
        }

        Object2DoubleOpenHashMap<IntRBTreeSet> confidenceMap2 = model.confidenceMap();
        Object2DoubleMap.FastEntrySet<IntRBTreeSet> entrySet2 = confidenceMap2.object2DoubleEntrySet();

        out("");
        out("Confidence Map2");
        for (Object2DoubleMap.Entry<IntRBTreeSet> entry2 : entrySet2) {
            out(entry2.getKey() + " : " + entry2.getDoubleValue());
        }
    }