Examples of hivemall.io.WeightValue$WeightValueParamsF2

hivemall.io.WeightValue
WeightValue with Sum of Squared Gradients

                            model = createModel();
                            label2model.put(label, model);
                        }
                        Object k = c2refOI.getPrimitiveWritableObject(c2refOI.copyObject(f1));
                        float v = c3refOI.get(f2);
                        model.set(k, new WeightValue(v, false));
                    }
                } finally {
                    IOUtils.closeQuietly(reader);
                }
            }

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                x = ObjectInspectorUtils.copyToStandardObject(f, featureInspector);
                xi = 1.f;
            }
            float old_w = model.getWeight(x);
            float new_w = old_w + (coeff * xi);
            model.set(x, new WeightValue(new_w));
        }
    }

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                    forwardMapObj[2] = cov;
                    forward(forwardMapObj);
                    numForwarded++;
                }
            } else {
                final WeightValue probe = new WeightValue();
                final Object[] forwardMapObj = new Object[2];
                final FloatWritable fv = new FloatWritable();
                final IMapIterator<Object, IWeightValue> itor = model.entries();
                while(itor.next() != -1) {
                    itor.getValue(probe);
                    if(!probe.isTouched()) {
                        continue; // skip outputting untouched weights
                    }
                    Object k = itor.getKey();
                    fv.set(probe.get());
                    forwardMapObj[0] = k;
                    forwardMapObj[1] = fv;
                    forward(forwardMapObj);
                    numForwarded++;
                }

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                k = ObjectInspectorUtils.copyToStandardObject(f, featureInspector);
                v = 1.f;
            }
            float old_trueclass_w = model2add.getWeight(k);
            float add_w = old_trueclass_w + (coeff * v);
            model2add.set(k, new WeightValue(add_w));


            if(model2sub != null) {
                float old_falseclass_w = model2sub.getWeight(k);
                float sub_w = old_falseclass_w - (coeff * v);
                model2sub.set(k, new WeightValue(sub_w));
            }
        }
    }

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                        forward(forwardMapObj);
                        numForwarded++;
                    }
                }
            } else {
                final WeightValue probe = new WeightValue();
                final Object[] forwardMapObj = new Object[3];
                final FloatWritable fv = new FloatWritable();
                for(Map.Entry<Object, PredictionModel> entry : label2model.entrySet()) {
                    Object label = entry.getKey();
                    forwardMapObj[0] = label;
                    PredictionModel model = entry.getValue();
                    numMixed += model.getNumMixed();
                    IMapIterator<Object, IWeightValue> itor = model.entries();
                    while(itor.next() != -1) {
                        itor.getValue(probe);
                        if(!probe.isTouched()) {
                            continue; // skip outputting untouched weights
                        }
                        Object k = itor.getKey();
                        fv.set(probe.get());
                        forwardMapObj[1] = k;
                        forwardMapObj[2] = fv;
                        forward(forwardMapObj);
                        numForwarded++;
                    }

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                k = ObjectInspectorUtils.copyToStandardObject(f, featureInspector);
                v = 1.f;
            }
            float old_w = model.getWeight(k);
            float new_w = old_w + (coeff * v);
            model.set(k, new WeightValue(new_w));
        }
    }

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                    forwardMapObj[2] = cov;
                    forward(forwardMapObj);
                    numForwarded++;
                }
            } else {
                final WeightValue probe = new WeightValue();
                final Object[] forwardMapObj = new Object[2];
                final FloatWritable fv = new FloatWritable();
                final IMapIterator<Object, IWeightValue> itor = model.entries();
                while(itor.next() != -1) {
                    itor.getValue(probe);
                    if(!probe.isTouched()) {
                        continue; // skip outputting untouched weights
                    }
                    Object k = itor.getKey();
                    fv.set(probe.get());
                    forwardMapObj[0] = k;
                    forwardMapObj[1] = fv;
                    forward(forwardMapObj);
                    numForwarded++;
                }

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                        if(f0 == null || f1 == null) {
                            continue; // avoid the case that key or value is null
                        }
                        Object k = keyRefOI.getPrimitiveWritableObject(keyRefOI.copyObject(f0));
                        float v = varRefOI.get(f1);
                        model.set(k, new WeightValue(v, false));
                    }
                } finally {
                    IOUtils.closeQuietly(reader);
                }
            }

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            final Random rand = new Random(43);
            for(int i = 0; i < 100000; i++) {
                Integer feature = Integer.valueOf(rand.nextInt(100));
                float weight = (float) rand.nextGaussian();
                model.set(feature, new WeightValue(weight));
            }


            waitForMixed(model, 48000, 10000L);


            int numMixed = model.getNumMixed();

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            final Random rand = new Random(43);
            for(int i = 0; i < 100000; i++) {
                Integer feature = Integer.valueOf(rand.nextInt(100));
                float weight = (float) rand.nextGaussian();
                model.set(feature, new WeightValue(weight));
            }


            waitForMixed(model, 48000, 10000L);


            int numMixed = model.getNumMixed();

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