com.insightml.models.AbstractBasicDoubleLearner.java Source code

Introduction

Here is the source code for com.insightml.models.AbstractBasicDoubleLearner.java

Source

/*
 * Copyright (C) 2016 Stefan Hen
 *
 * 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 com.insightml.models;

import java.util.LinkedList;
import java.util.List;

import org.apache.commons.math3.util.Pair;

import com.insightml.data.samples.Sample;
import com.insightml.utils.Arrays;
import com.insightml.utils.IArguments;
import com.insightml.utils.types.collections.DoubleArray;

public abstract class AbstractBasicDoubleLearner extends AbstractDoubleLearner<Double> {

    public AbstractBasicDoubleLearner(final IArguments arguments) {
        super(arguments);
    }

    @Override
    public final IModel<Sample, Double> run(final LearnerInput<? extends Sample, ? extends Double> input) {
        final Pair<double[], double[][]> filtered = filter(input);
        return train(filtered.getSecond(), filtered.getFirst(), input.getTrain().featureNames());
    }

    private static Pair<double[], double[][]> filter(final LearnerInput<? extends Sample, ? extends Double> input) {
        final double[][] features = input.getTrain().features();
        final Double[] expected = input.getTrain().expected(input.labelIndex);

        final DoubleArray expFiltered = new DoubleArray(expected.length);
        final List<double[]> featsFiltered = new LinkedList<>();
        for (int i = 0; i < expected.length; ++i) {
            if (expected[i] != null) {
                expFiltered.add(expected[i].doubleValue());
                featsFiltered.add(features[i]);
            }
        }
        final double[][] featsArray = Arrays.of(featsFiltered, double[].class);
        return new Pair<>(expFiltered.toArray(), featsArray);
    }

    public abstract IModel<Sample, Double> train(double[][] features, double[] expected, String[] featureNames);

}