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 aima.core.learning.framework.Learner.predict()

  }


  private ConstantDecisonTree majorityValue(DataSet ds) {
    Learner learner = new MajorityLearner();
    learner.train(ds);
    return new ConstantDecisonTree(learner.predict(ds.getExample(0)));
  }


  private String chooseAttribute(DataSet ds, List<String> attributeNames) {
    double greatestGain = 0.0;
    String attributeWithGreatestGain = attributeNames.get(0);

View Full Code Here

Examples of aima.core.learning.framework.Learner.predict()

  }


  private ConstantDecisonTree majorityValue(DataSet ds) {
    Learner learner = new MajorityLearner();
    learner.train(ds);
    return new ConstantDecisonTree(learner.predict(ds.getExample(0)));
  }


  private String chooseAttribute(DataSet ds, List<String> attributeNames) {
    double greatestGain = 0.0;
    String attributeWithGreatestGain = attributeNames.get(0);

View Full Code Here

Examples of aima.core.learning.inductive.DecisionList.predict()

  @Test
  public void testDecisonListWithNoTestsReturnsDefaultValue()
      throws Exception {
    DecisionList dlist = new DecisionList("Yes", "No");
    DataSet ds = DataSetFactory.getRestaurantDataSet();
    Assert.assertEquals("No", dlist.predict(ds.getExample(0)));
  }


  @Test
  public void testDecisionListWithSingleTestReturnsTestValueIfTestSuccessful()
      throws Exception {

View Full Code Here

Examples of aima.core.learning.inductive.DecisionList.predict()

    DLTest test = new DLTest();
    test.add("type", "French");


    dlist.add(test, "test1success");


    Assert.assertEquals("test1success", dlist.predict(ds.getExample(0)));
  }


  @Test
  public void testDecisionListFallsThruToNextTestIfOneDoesntMatch()
      throws Exception {

View Full Code Here

Examples of aima.core.learning.inductive.DecisionList.predict()


    DLTest test2 = new DLTest();
    test2.add("type", "French");
    dlist.add(test2, "test2success");// matches first example


    Assert.assertEquals("test2success", dlist.predict(ds.getExample(0)));
  }


  @Test
  public void testDecisionListFallsThruToDefaultIfNoTestMatches()
      throws Exception {

View Full Code Here

Examples of aima.core.learning.inductive.DecisionList.predict()


    DLTest test2 = new DLTest();
    test2.add("type", "Burger");
    dlist.add(test2, "test2success");// doesn't match first example


    Assert.assertEquals("No", dlist.predict(ds.getExample(0)));
  }


  @Test
  public void testDecisionListHandlesEmptyDataSet() throws Exception {
    // tests first base case of recursion

View Full Code Here

Examples of aima.core.learning.inductive.DecisionList.predict()

    DLTest test2 = new DLTest();
    test2.add("type", "French");
    dlist2.add(test2, "test2success");// matches first example


    DecisionList dlist3 = dlist1.mergeWith(dlist2);
    Assert.assertEquals("test2success", dlist3.predict(ds.getExample(0)));
  }
}

View Full Code Here

Examples of aima.core.learning.learners.DecisionListLearner.predict()

    DecisionListLearner learner = new DecisionListLearner("Yes", "No",
        new MockDLTestFactory(null));
    DataSet ds = DataSetFactory.getRestaurantDataSet();
    DataSet empty = ds.emptyDataSet();
    learner.train(empty);
    Assert.assertEquals("No", learner.predict(ds.getExample(0)));
    Assert.assertEquals("No", learner.predict(ds.getExample(1)));
    Assert.assertEquals("No", learner.predict(ds.getExample(2)));
  }


  @Test

View Full Code Here

Examples of aima.core.learning.learners.DecisionListLearner.predict()

        new MockDLTestFactory(null));
    DataSet ds = DataSetFactory.getRestaurantDataSet();
    DataSet empty = ds.emptyDataSet();
    learner.train(empty);
    Assert.assertEquals("No", learner.predict(ds.getExample(0)));
    Assert.assertEquals("No", learner.predict(ds.getExample(1)));
    Assert.assertEquals("No", learner.predict(ds.getExample(2)));
  }


  @Test
  public void testDecisionListLearnerReturnsFailureWhenTestsEmpty()

View Full Code Here

Examples of aima.core.learning.learners.DecisionListLearner.predict()

    DataSet ds = DataSetFactory.getRestaurantDataSet();
    DataSet empty = ds.emptyDataSet();
    learner.train(empty);
    Assert.assertEquals("No", learner.predict(ds.getExample(0)));
    Assert.assertEquals("No", learner.predict(ds.getExample(1)));
    Assert.assertEquals("No", learner.predict(ds.getExample(2)));
  }


  @Test
  public void testDecisionListLearnerReturnsFailureWhenTestsEmpty()
      throws Exception {

View Full Code Here

0 1 2 3 4 5

TOP

All source code are property of their respective owners. Java is a trademark of Sun Microsystems, Inc and owned by ORACLE Inc. Contact coftware#gmail.com.