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 tv.floe.metronome.deeplearning.neuralnetwork.core.LogisticRegression.predict()

    LogisticRegressionOptimizer opt = new LogisticRegressionOptimizer( logreg, learningRate );
    CustomConjugateGradient g = new CustomConjugateGradient(opt);
    g.optimize();
    
    
    Matrix predict = logreg.predict(x);
    //log.info(predict.toString());


    Evaluation eval = new Evaluation();
    eval.eval(y, predict);
    //log.info(eval.stats());

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Examples of tv.floe.metronome.deeplearning.neuralnetwork.core.LogisticRegression.predict()

        
        CustomConjugateGradient g = new CustomConjugateGradient(opt);
        g.optimize();
        
        
        Matrix predict = logreg.predict(xMatrix);
        //log.info(predict.toString());


        Evaluation eval = new Evaluation();
        eval.eval(yMatrix, predict);
        //log.info(eval.stats());

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Examples of tv.floe.metronome.deeplearning.neuralnetwork.core.LogisticRegression.predict()

    //NonZeroStoppingConjugateGradient g = new NonZeroStoppingConjugateGradient(opt);
    g.optimize();
    
    
    
    Matrix predict = logRegression.predict(input);


    Evaluation eval = new Evaluation();
    eval.eval(labels, predict);
    //log.info(eval.stats());
    System.out.println( eval.stats() );

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Examples of tv.floe.metronome.deeplearning.neuralnetwork.core.LogisticRegression.predict()

      //logRegression.trainWithAdagrad(x, y);
      logRegression.train(x_xor_Matrix, y_xor_Matrix, 0.001);


    }
    
    Matrix predictions = logRegression.predict(x_xor_Matrix);
    
    MatrixUtils.debug_print(predictions);
    


    //Matrix predict = logRegression.predict(x);

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Examples of tv.floe.metronome.deeplearning.neuralnetwork.core.LogisticRegression.predict()


    }
    
//    Matrix predictions = logRegression.predict(xTestMatrix);
    
    Matrix predict = logRegression.predict(xMatrix);
    //log.info(predict.toString());


    Evaluation eval = new Evaluation();
    eval.eval(yMatrix, predict);
    //log.info(eval.stats());

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Examples of tv.floe.metronome.deeplearning.neuralnetwork.core.LogisticRegression.predict()


    }
    
//    Matrix predictions = logRegression.predict(xTestMatrix);
    
    Matrix predict = logRegression.predict(input);
    //log.info(predict.toString());


    Evaluation eval = new Evaluation();
    eval.eval(labels, predict);
    //log.info(eval.stats());

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Examples of tv.floe.metronome.deeplearning.neuralnetwork.core.LogisticRegression.predict()


    }
    
//    Matrix predictions = logRegression.predict(xTestMatrix);
    
    Matrix predict = logRegression.predict(input);
    //log.info(predict.toString());


    Evaluation eval = new Evaluation();
    eval.eval(labels, predict);
    //log.info(eval.stats());

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