io.github.ericmedvet.jgea.experimenter.builders.MultivariateRegressionProblems Maven / Gradle / Ivy

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Experimenter tool based on jgea and textual config files (with jnb).

There is a newer version: 2.7.0

/*
 * Copyright 2023 eric
 *
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 *     http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 */

package io.github.ericmedvet.jgea.experimenter.builders;

import io.github.ericmedvet.jgea.problem.regression.NumericalDataset;
import io.github.ericmedvet.jgea.problem.regression.multivariate.MultivariateRegressionFitness;
import io.github.ericmedvet.jgea.problem.regression.multivariate.MultivariateRegressionProblem;
import io.github.ericmedvet.jgea.problem.regression.univariate.UnivariateRegressionFitness;
import io.github.ericmedvet.jnb.core.Param;

import java.util.function.Supplier;

/**
 * @author "Eric Medvet" on 2023/04/30 for jgea
 */
public class MultivariateRegressionProblems {
  private MultivariateRegressionProblems() {
  }

  @SuppressWarnings("unused")
  public static MultivariateRegressionProblem fromData(
      @Param("trainingDataset") Supplier trainingDataset,
      @Param(value = "testDataset", dNPM = "ea.d.num.empty()") Supplier testDataset,
      @Param(value = "metric", dS = "mse") UnivariateRegressionFitness.Metric metric
  ) {
    return new MultivariateRegressionProblem<>(
        new MultivariateRegressionFitness(trainingDataset.get(), metric),
        new MultivariateRegressionFitness(testDataset.get(), metric)
    );
  }

}