Examples of org.apache.mahout.math.Vector.iterateNonZero()

org.apache.mahout.math.Vector.iterateNonZero()
Iterates over all non-zero elements.
NOTE: Implementations may choose to reuse the Element returned for performance reasons, so if you need a copy of it, you should call {@link #getElement} for the given index @return An {@link Iterator} over all non-zero elements

        // 1. first item bias
        IntArrayList items = (IntArrayList) datamodel.getItemIDs();
        for (int i = 0; i < items.size(); i++) {
            int itemid = items.get(i);
            Vector tmp = datamodel.getVectorOfUsers(itemid);
            Iterator<Element> iter = tmp.iterateNonZero();
            double rate = 0.0;
            while (iter.hasNext()) {
                rate += (iter.next().get() - mean);
            }
            bitems.put(itemid, rate / (tmp.getNumNondefaultElements() + lamda2));

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        // 2. second user bias
        IntArrayList users = (IntArrayList) datamodel.getUserIDs();
        for (int i = 0; i < users.size(); i++) {
            int userid = users.get(i);
            Vector tmp = datamodel.getVectorOfItems(userid);
            Iterator<Element> iter = tmp.iterateNonZero();
            double rate = 0.0;
            while (iter.hasNext()) {
                Element e = iter.next();
                rate += (e.get() - mean - bitems.get(e.index()));
            }

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                Vector tmpitems = datamodel.getVectorOfItems(userid);


                PreferenceArray UserFactor = pusers.get(userid);


                // iterate to items
                Iterator<Element> itor = tmpitems.iterateNonZero();
                while (itor.hasNext()) {


                    Element e = itor.next();
                    int itemid = e.index();

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        for (int i = 0; i < users.size(); i++) {


            int userid = users.get(i);


            Vector tmp = userItemMatrix.get(userid);
            Iterator<Element> iter = tmp.iterateNonZero();


            while (iter.hasNext()) {
                Element el = iter.next();
                int itemid = el.index();
            }

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        // 1. first item bias
        IntArrayList items = (IntArrayList) datamodel.getItemIDs();
        for (int i = 0; i < items.size(); i++) {
            int itemid = items.get(i);
            Vector tmp = datamodel.getVectorOfUsers(itemid);
            Iterator<Element> iter = tmp.iterateNonZero();
            double rate = 0.0;
            while (iter.hasNext()) {
                rate += (iter.next().get() - mean);
            }
            bitems.put(itemid, rate / (tmp.getNumNondefaultElements() + lamda2));

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        // 2. second user bias
        IntArrayList users = (IntArrayList) datamodel.getUserIDs();
        for (int i = 0; i < users.size(); i++) {
            int userid = users.get(i);
            Vector tmp = datamodel.getVectorOfItems(userid);
            Iterator<Element> iter = tmp.iterateNonZero();
            double rate = 0.0;
            while (iter.hasNext()) {
                Element e = iter.next();
                rate += (e.get() - mean - bitems.get(e.index()));
            }

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                // calculate temp_Pu += sigma(yi)/sqrt(Nu);
                PreferenceArray tempUserFactor = puTemp.get(userid);
                PreferenceArray UserFactor = pusers.get(userid);
                for (int k = 0; k < parameter_k; k++) {
                    Iterator<Element> itor = tmpitems.iterateNonZero();
                    double sum = 0.0;
                    while (itor.hasNext()) {
                        Element e = itor.next();
                        int itemid = e.index();
                        sum = sum + y.get(itemid).getValue(k);

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                    sumQE.setValue(k, 0.0f);
                }


                // iterate to deal with items
                Iterator<Element> itor = tmpitems.iterateNonZero();
                while (itor.hasNext()) {


                    Element e = itor.next();
                    int itemid = e.index();

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                        // yFactor.setValue(k, preval);
                    }
                }


                // implicit factor
                itor = tmpitems.iterateNonZero();
                while (itor.hasNext()) {
                    Element e = itor.next();
                    int itemid = e.index();
                    PreferenceArray yFactor = y.get(itemid);
                    for (int k = 0; k < parameter_k; k++) {

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            VectorWritable vw = new VectorWritable();
            pointsReader = new SequenceFileDirectoryReader(docTopicsPath);
            while (pointsReader.next(k, vw)) {
                String docId = k.toString();
                Vector normGamma = vw.get();
                Iterator<Element> iter = normGamma.iterateNonZero();
                double maxTopicScore = 0.0;
                int idx = 0;
                int topic = 0;
                while(iter.hasNext()) {
                    Element e = iter.next();

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