mml-models-regression-model.7.50.0.Final.source-code.KiePMMLUpdateResultMethodTemplate.tmpl Maven / Gradle / Ivy

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KiePMML Model for Regression implementation

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/*
 * Copyright 2020 Red Hat, Inc. and/or its affiliates.
 *
 * 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 org.kie.pmml.models.regression.evaluator;


import java.util.concurrent.atomic.AtomicReference;

import org.apache.commons.math3.distribution.NormalDistribution;


public class KiePMMLUpdateResultMethodTemplate {


    protected void updateSOFTMAXResult(final AtomicReference toUpdate) {
        toUpdate.updateAndGet(y -> 1.0 / (1.0 + Math.exp(-y)));
    }

    protected void updateLOGITResult(final AtomicReference toUpdate) {
        toUpdate.updateAndGet(y -> 1.0 / (1.0 + Math.exp(-y)));
    }

    protected void updateEXPResult(final AtomicReference toUpdate) {
        toUpdate.updateAndGet(Math::exp);
    }

    protected void updatePROBITResult(final AtomicReference toUpdate) {
        toUpdate.updateAndGet(y -> new NormalDistribution().cumulativeProbability(y));
    }

    protected void updateCLOGLOGResult(final AtomicReference toUpdate) {
        toUpdate.updateAndGet(y -> 1.0 - Math.exp(-Math.exp(y)));
    }

    protected void updateCAUCHITResult(final AtomicReference toUpdate) {
        toUpdate.updateAndGet(y -> 0.5 + (1 / Math.PI) * Math.atan(y));
    }

    protected void updateNONEResult(final AtomicReference toUpdate) {
        // NO OP
    }
}