List of predict() Examples

Examples of predict()

aima.core.learning.framework.Learner.predict()
Returns the outcome predicted for the specified example @param e an example @return the outcome predicted for the specified example
aima.core.learning.inductive.DecisionList.predict()
aima.core.learning.learners.DecisionListLearner.predict()
aima.core.learning.learners.DecisionTreeLearner.predict()
com.deafgoat.ml.prognosticator.Experimenter.predict()
Does classification task on supplied data set @throws IOException If ARFF data can not be read
com.mockturtlesolutions.snifflib.invprobs.Lpreg.predict()
com.nr.ci.Svm.predict()
edu.cmu.graphchi.apps.ALSMatrixFactorization.predict()
Compute a prediction. Note: need to use the internal vertex-ids. @param leftVertex (often "user") @param rightVertex (often "movie") @return
edu.stanford.nlp.parser.nndep.DependencyParser.predict()
Determine the dependency parse of the given sentence using the loaded model. You must first load a parser before calling this method. @throws java.lang.IllegalStateException If parser has not yet been loaded and initialized(see {@link #initialize(boolean)}
gov.sandia.cognition.statistics.bayesian.KalmanFilter.predict()
org.apache.commons.math.stat.regression.SimpleRegression.predict()
Returns the "predicted" y value associated with the supplied x value, based on the data that has been added to the model when this method is activated.
predict(x) = intercept + slope * x

Preconditions:
- At least two observations (with at least two different x values) must have been added before invoking this method. If this method is invoked before a model can be estimated, Double,NaN is returned.
@param x input x value @return predicted y value
org.apache.commons.math3.filter.KalmanFilter.predict()
Predict the internal state estimation one time step ahead.
org.apache.commons.math3.stat.regression.SimpleRegression.predict()
Returns the "predicted" y value associated with the supplied x value, based on the data that has been added to the model when this method is activated.
predict(x) = intercept + slope * x

Preconditions:
- At least two observations (with at least two different x values) must have been added before invoking this method. If this method is invoked before a model can be estimated, Double,NaN is returned.
@param x input x value @return predicted y value
org.apache.spark.mllib.tree.model.DecisionTreeModel.predict()
org.apache.spark.mllib.tree.model.WeightedEnsembleModel.predict()
org.data2semantics.proppred.predictors.SVMPropertyPredictor.predict()
org.gd.spark.opendl.downpourSGD.Softmax.LR.predict()
Do predict work with multiple sample(standalone) @param x Input samples matrix @return Predict result matrix (row=x's row, column=class num)
org.grouplens.lenskit.RatingPredictor.predict()
Predict a user's rating for a single item. @param user The user ID for whom to generate a prediction. @param item The item ID whose rating is to be predicted. @return The predicted preference, or {@link Double#NaN} if no preference can bepredicted.
quickml.supervised.regressionModel.IsotonicRegression.PoolAdjacentViolatorsModel.predict()
quickml.supervised.regressionModel.LinearRegression.RidgeLinearModel.predict()
tv.floe.metronome.deeplearning.dbn.DeepBeliefNetwork.predict()
tv.floe.metronome.deeplearning.neuralnetwork.core.BaseMultiLayerNeuralNetworkVectorized.predict()
Label the probabilities of the input @param x the input to label @return a vector of probabilitiesgiven each label. This is typically of the form: [0.5, 0.5] or some other probability distribution summing to one
tv.floe.metronome.deeplearning.neuralnetwork.core.LogisticRegression.predict()
Classify input @param x the input (can either be a matrix or vector)If it's a matrix, each row is considered an example and associated rows are classified accordingly. Each row will be the likelihood of a label given that example @return a probability distribution for each row

Examples of gov.sandia.cognition.statistics.bayesian.KalmanFilter.predict()

    /*
     * Perform the filtering step
     */
    MultivariateGaussian priorPredictedState = prevState.getState().clone(); 
    final KalmanFilter kf = prevState.getFilter().clone();
    kf.predict(priorPredictedState);
    
    final InverseGammaDistribution scaleSS = prevState.getSigma2SS().clone();
    final MultivariateGaussian systemSS = prevState.getPsiSS().clone();


    final GaussianArHpWfParticle newTransState =

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Examples of gov.sandia.cognition.statistics.bayesian.KalmanFilter.predict()

    MultivariateGaussian posteriorState = prevState.getState().clone(); 
    // TODO FIXME gross hack!
//    posteriorState.getCovariance().scaleEquals(sigma2Sample);
//    kf.setMeasurementCovariance(Iy.scale(sigma2Sample));
//    kf.setModelCovariance(Ix.scale(sigma2Sample));
    kf.predict(posteriorState);
    kf.update(posteriorState, data.getObservedValue());
//    kf.setMeasurementCovariance(Iy);
//    kf.setModelCovariance(Ix);

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Examples of gov.sandia.cognition.statistics.bayesian.KalmanFilter.predict()

    /*
     * Perform the filtering step
     */
    MultivariateGaussian priorPredictedState = prevState.getState().clone(); 
    KalmanFilter kf = Iterables.get(prevState.getHmm().getStateFilters(), predClass);
    kf.predict(priorPredictedState);
    
    final DlmHiddenMarkovModel newHmm = prevState.getHmm().clone();
    final InverseGammaDistribution invScaleSS = prevState.getInvScaleSS().clone();
    final List<MultivariateGaussian> psiSS = 
        ObjectUtil.cloneSmartElementsAsArrayList(prevState.getPsiSS());

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Examples of org.apache.commons.math.stat.regression.SimpleRegression.predict()

        SimpleRegression regression = new SimpleRegression();
        regression.addData(point1.RT, point1.RT2);
        regression.addData(point2.RT, point2.RT2);
        double rt = point1.RT + 1;
        while (rt < point2.RT) {
          RTs newPoint = new RTs(rt, regression.predict(rt));
          list.add(newPoint);
          rt++;
        }


      }

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Examples of org.apache.commons.math.stat.regression.SimpleRegression.predict()

        SimpleRegression regression = new SimpleRegression();
        regression.addData(point1.RT, point1.RT2);
        regression.addData(point2.RT, point2.RT2);
        double rt = point1.RT + 1;
        while (rt < point2.RT) {
          RTs newPoint = new RTs(rt, regression.predict(rt));
          list.add(newPoint);
          rt++;
        }


      }

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Examples of org.apache.commons.math3.filter.KalmanFilter.predict()

            final double[] state = filter.getStateEstimation();
            kalmanX.add(state[0]);
            kalmanY.add(state[2]);


            // update the kalman filter with the measurements
            filter.predict(controlVector);
            filter.correct(new double[] { mx, 0, my, 0 } );
        }


        chart.setXAxisTitle("Distance (m)");
        chart.setYAxisTitle("Height (m)");

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Examples of org.apache.commons.math3.filter.KalmanFilter.predict()

            measuredVoltageSeries.add(measuredVoltage);


            kalmanVoltageSeries.add(filter.getStateEstimation()[0]);
            covSeries.add(filter.getErrorCovariance()[0][0]);


            filter.predict();
            filter.correct(new double[] { measuredVoltage });
        }


        chart1.setYAxisTitle("Voltage");
        chart1.setXAxisTitle("Iteration");

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Examples of org.apache.commons.math3.stat.regression.SimpleRegression.predict()

        if (cost == 0) break;
        regression.addData(Math.log(cost), m);


    }
    
    double d = regression.predict(Math.log(costThreshold));
    
    return qualifiedWage/d;
    
  }

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Examples of org.apache.spark.mllib.tree.model.DecisionTreeModel.predict()


    // Evaluate model on training instances and compute training error
    JavaPairRDD<Double, Double> predictionAndLabel =
      data.mapToPair(new PairFunction<LabeledPoint, Double, Double>() {
        @Override public Tuple2<Double, Double> call(LabeledPoint p) {
          return new Tuple2<Double, Double>(model.predict(p.features()), p.label());
        }
      });
    Double trainErr =
      1.0 * predictionAndLabel.filter(new Function<Tuple2<Double, Double>, Boolean>() {
        @Override public Boolean call(Tuple2<Double, Double> pl) {

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Examples of org.apache.spark.mllib.tree.model.WeightedEnsembleModel.predict()


      // Evaluate model on training instances and compute training error
      JavaPairRDD<Double, Double> predictionAndLabel =
          data.mapToPair(new PairFunction<LabeledPoint, Double, Double>() {
            @Override public Tuple2<Double, Double> call(LabeledPoint p) {
              return new Tuple2<Double, Double>(model.predict(p.features()), p.label());
            }
          });
      Double trainErr =
          1.0 * predictionAndLabel.filter(new Function<Tuple2<Double, Double>, Boolean>() {
            @Override public Boolean call(Tuple2<Double, Double> pl) {

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