Package hivemall.io.WeightValue

Examples of hivemall.io.WeightValue.WeightValueWithCovar

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        super.close();
        if(label2model != null) {
            long numForwarded = 0L;
            long numMixed = 0L;
            if(useCovariance()) {
                final WeightValueWithCovar probe = new WeightValueWithCovar();
                final Object[] forwardMapObj = new Object[4];
                final FloatWritable fv = new FloatWritable();
                final FloatWritable cov = 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());
                        cov.set(probe.getCovariance());
                        forwardMapObj[1] = k;
                        forwardMapObj[2] = fv;
                        forwardMapObj[3] = cov;
                        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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                        }
                        Object k = c2refOI.getPrimitiveWritableObject(c2refOI.copyObject(f1));
                        float v = c3refOI.get(f2);
                        float cov = (f3 == null) ? WeightValueWithCovar.DEFAULT_COVAR
                                : c4refOI.get(f3);
                        model.set(k, new WeightValueWithCovar(v, cov, false));
                    }
                } finally {
                    IOUtils.closeQuietly(reader);
                }
            }
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    public final void close() throws HiveException {
        super.close();
        if(model != null) {
            int numForwarded = 0;
            if(useCovariance()) {
                final WeightValueWithCovar probe = new WeightValueWithCovar();
                final Object[] forwardMapObj = new Object[3];
                final FloatWritable fv = new FloatWritable();
                final FloatWritable cov = 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());
                    cov.set(probe.getCovariance());
                    forwardMapObj[0] = k;
                    forwardMapObj[1] = fv;
                    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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        float cov_x = old_cov * x;
        float new_w = old_w + coeff * cov_x * beta;
        float new_cov = old_cov - (beta * cov_x * cov_x);

        return new WeightValueWithCovar(new_w, new_cov);
    }
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        float cv = old_cov * v;
        float new_w = positive ? old_v + (alpha * cv) : old_v - (alpha * cv);
        float new_cov = old_cov - (beta * cv * cv);

        return new WeightValueWithCovar(new_w, new_cov);
    }
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        }

        float delta_w = alpha * old_cov * x;
        float new_w = positive ? old_w + delta_w : old_w - delta_w;
        float new_cov = 1.f / (1.f / old_cov + (2.f * alpha * phi * x * x));
        return new WeightValueWithCovar(new_w, new_cov);
    }
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        float cv = old_cov * v;
        float new_w = positive ? old_v + (alpha * cv) : old_v - (alpha * cv);
        float new_cov = old_cov - (beta * cv * cv);

        return new WeightValueWithCovar(new_w, new_cov);
    }
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    public final void close() throws HiveException {
        super.close();
        if(model != null) {
            int numForwarded = 0;
            if(useCovariance()) {
                final WeightValueWithCovar probe = new WeightValueWithCovar();
                final Object[] forwardMapObj = new Object[3];
                final FloatWritable fv = new FloatWritable();
                final FloatWritable cov = 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());
                    cov.set(probe.getCovariance());
                    forwardMapObj[0] = k;
                    forwardMapObj[1] = fv;
                    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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        float cv = old_cov * x;
        float new_w = old_w + (y * alpha * cv);
        float new_cov = old_cov - (beta * cv * cv);

        return new WeightValueWithCovar(new_w, new_cov);
    }
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        float cv = old_cov * x;
        float new_w = old_v + (y * alpha * cv);
        float new_cov = old_cov - (beta * cv * cv);

        return new WeightValueWithCovar(new_w, new_cov);
    }
View Full Code Here
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