tech.tablesaw.api.ml.classification.Lda Maven / Gradle / Ivy

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/*
 * 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 tech.tablesaw.api.ml.classification;

import com.google.common.base.Preconditions;
import smile.classification.LDA;
import tech.tablesaw.api.IntConvertibleColumn;
import tech.tablesaw.api.NumericColumn;
import tech.tablesaw.util.DoubleArrays;

import java.util.SortedSet;
import java.util.TreeSet;

public class Lda extends AbstractClassifier {

    private final LDA classifierModel;

    private Lda(LDA classifierModel) {
        this.classifierModel = classifierModel;
    }

    public static Lda learn(IntConvertibleColumn labels, NumericColumn... predictors) {
        LDA classifierModel = new LDA(DoubleArrays.to2dArray(predictors), labels.toIntArray());
        return new Lda(classifierModel);
    }

    public static Lda learn(IntConvertibleColumn labels, double[] priors, NumericColumn... predictors) {
        LDA classifierModel = new LDA(DoubleArrays.to2dArray(predictors), labels.toIntArray(), priors);
        return new Lda(classifierModel);
    }

    public static Lda learn(IntConvertibleColumn labels, double[] priors, double tolerance, NumericColumn... predictors) {
        LDA classifierModel = new LDA(DoubleArrays.to2dArray(predictors), labels.toIntArray(), priors, tolerance);
        return new Lda(classifierModel);
    }

    public int predict(double[] data) {
        return classifierModel.predict(data);
    }

    public ConfusionMatrix predictMatrix(IntConvertibleColumn labels, NumericColumn... predictors) {
        Preconditions.checkArgument(predictors.length > 0);

        SortedSet