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 com.nr.ci.Svm.predict()

//        System.out.printf(test);
        k++;
      } while (test > 1.e-3 && k < 100);
      nerror=0;
      for (i=0;i<M;i++) {
        nerror += ((y[i]==1.0) != (linsvm2.predict(i) >= 0.0) ? 1 : 0);
      }
      System.out.printf("%d ",nerror);
      // Test new data
      nerror=0;
      for (j=0;j<4;j++) {   // Four quadrants

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Examples of com.nr.ci.Svm.predict()

          } else {
            yy=-1.0;
            x[0]=1.0+ndev.dev();
            x[1]=-1.0+ndev.dev();
          }
          nerror += ((yy==1.0) != (linsvm2.predict(x) >= 0.0) ? 1 : 0);
        }
      }
      System.out.printf("%d    ",nerror);
    }
    System.out.println();

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Examples of com.nr.ci.Svm.predict()

        k++;
      } while (test > 1.e-3 && k < 100);
      // Test training set
      nerror=0;
      for (i=0;i<M;i++) {
        nerror += ((y[i]==1.0) != (polysvm2.predict(i) >= 0.0) ? 1 : 0);
      }
      System.out.printf("%d ",nerror);
      // Test new data
      nerror=0;
      for (j=0;j<4;j++) {   // Four quadrants

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Examples of com.nr.ci.Svm.predict()

          } else {
            yy=-1.0;
            x[0]=1.0+ndev.dev();
            x[1]=-1.0+ndev.dev();
          }
          nerror += ((yy==1.0) != (polysvm2.predict(x) >= 0.0) ? 1 : 0);
        }
      }
      System.out.printf("%d    ",nerror);
    }
    System.out.println();

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Examples of com.nr.ci.Svm.predict()

//        System.out.printf(test);
        k++;
      } while (test > 1.e-3 && k < 100);
      nerror=0;
      for (i=0;i<M;i++) {
        nerror += ((y[i]==1.0) != (gausssvm2.predict(i) >= 0.0) ? 1 : 0);
      }
      System.out.printf("%d ",nerror);
      // Test new data
      nerror=0;
      for (j=0;j<4;j++) {   // Four quadrants

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Examples of com.nr.ci.Svm.predict()

          } else {
            yy=-1.0;
            x[0]=1.0+ndev.dev();
            x[1]=-1.0+ndev.dev();
          }
          nerror += ((yy==1.0) != (gausssvm2.predict(x) >= 0.0) ? 1 : 0);
        }
      }
      System.out.printf("%d    ",nerror);
    }
    System.out.println();

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Examples of edu.cmu.graphchi.apps.ALSMatrixFactorization.predict()

            double sumEstimatePos = 0.0;
            double sumEstimateNeg = 0.0;


            int n = Math.min(posTop.length, negTop.length);
            for(int j=0; j<n; j++) {
                sumEstimatePos += als.predict(userId, posTop[j].id);
                sumEstimateNeg += als.predict(userId, negTop[j].id);
            }




            long t = System.currentTimeMillis();

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Examples of edu.cmu.graphchi.apps.ALSMatrixFactorization.predict()

            double sumEstimateNeg = 0.0;


            int n = Math.min(posTop.length, negTop.length);
            for(int j=0; j<n; j++) {
                sumEstimatePos += als.predict(userId, posTop[j].id);
                sumEstimateNeg += als.predict(userId, negTop[j].id);
            }




            long t = System.currentTimeMillis();
            // Compute all

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Examples of edu.cmu.graphchi.apps.ALSMatrixFactorization.predict()

            // Compute all
            double allSum = 0;
            int numMovies = graphInfo.getNumRight();
            for(int m=0; m < numMovies; m++) {
                int movieId = vertexIdTranslate.forward(graphInfo.getNumLeft() + m);
                allSum += als.predict(userId, movieId);
            }


            System.out.println(i + " avg pos: " + sumEstimatePos / n + "; avg neg: " + sumEstimateNeg / n + "; all="
                    + allSum / graphInfo.getNumRight() + " (" + (System.currentTimeMillis() - t) + " ms for " + numMovies + " movies");
        }

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Examples of edu.stanford.nlp.parser.nndep.DependencyParser.predict()

    DependencyParser parser = DependencyParser.loadFromModelFile(modelPath);


    DocumentPreprocessor tokenizer = new DocumentPreprocessor(new StringReader(text));
    for (List<HasWord> sentence : tokenizer) {
      List<TaggedWord> tagged = tagger.tagSentence(sentence);
      GrammaticalStructure gs = parser.predict(tagged);


      // Print typed dependencies
      System.err.println(gs);
    }
  }

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