/**
 * Copyright (C) 2013-2017 Vasilis Vryniotis 
 *
 * 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.datumbox.framework.core.machinelearning.preprocessing;

import com.datumbox.framework.common.Configuration;
import com.datumbox.framework.common.concurrency.StreamMethods;
import com.datumbox.framework.common.dataobjects.*;
import com.datumbox.framework.common.storage.interfaces.BigMap;
import com.datumbox.framework.common.storage.interfaces.StorageEngine;
import com.datumbox.framework.core.common.dataobjects.Dataframe;
import com.datumbox.framework.core.common.dataobjects.Record;
import com.datumbox.framework.core.machinelearning.common.abstracts.AbstractTrainer;
import com.datumbox.framework.core.machinelearning.common.abstracts.transformers.AbstractScaler;
import com.datumbox.framework.core.statistics.descriptivestatistics.Descriptives;

import java.util.Map;
import java.util.Set;
import java.util.stream.Stream;

/**
 * Rescales the numerical features of the dataset between 0 and 1.
 *
 * @author Vasilis Vryniotis 
 */
public class MinMaxScaler extends AbstractScaler {

    /** {@inheritDoc} */
    public static class ModelParameters extends AbstractScaler.AbstractModelParameters {
        private static final long serialVersionUID = 1L;

        /**
         * The minimum value of each numerical variable.
         */
        @BigMap(keyClass=Object.class, valueClass=Double.class, mapType= StorageEngine.MapType.HASHMAP, storageHint= StorageEngine.StorageHint.IN_MEMORY, concurrent=true)
        private Map minColumnValues;

        /**
         * The maximum value of each numerical variable.
         */
        @BigMap(keyClass=Object.class, valueClass=Double.class, mapType= StorageEngine.MapType.HASHMAP, storageHint= StorageEngine.StorageHint.IN_MEMORY, concurrent=true)
        private Map maxColumnValues;

        /**
         * @param storageEngine
         * @see AbstractTrainer.AbstractModelParameters#AbstractModelParameters(StorageEngine)
         */
        protected ModelParameters(StorageEngine storageEngine) {
            super(storageEngine);
        }

        /**
         * Getter for the minimum values of the columns.
         *
         * @return
         */
        public Map getMinColumnValues() {
            return minColumnValues;
        }

        /**
         * Setter for the minimum values of the columns.
         *
         * @param minColumnValues
         */
        protected void setMinColumnValues(Map minColumnValues) {
            this.minColumnValues = minColumnValues;
        }

        /**
         * Getter for the maximum values of the columns.
         *
         * @return
         */
        public Map getMaxColumnValues() {
            return maxColumnValues;
        }

        /**
         * Setter for the maximum values of the columns.
         *
         * @param maxColumnValues
         */
        protected void setMaxColumnValues(Map maxColumnValues) {
            this.maxColumnValues = maxColumnValues;
        }

    }

    /** {@inheritDoc} */
    public static class TrainingParameters extends AbstractScaler.AbstractTrainingParameters {
        private static final long serialVersionUID = 1L;

    }

    /**
     * @param trainingParameters
     * @param configuration
     * @see AbstractTrainer#AbstractTrainer(AbstractTrainer.AbstractTrainingParameters, Configuration)
     */
    protected MinMaxScaler(TrainingParameters trainingParameters, Configuration configuration) {
        super(trainingParameters, configuration);
    }

    /**
     * @param storageName
     * @param configuration
     * @see AbstractTrainer#AbstractTrainer(String, Configuration)
     */
    protected MinMaxScaler(String storageName, Configuration configuration) {
        super(storageName, configuration);
    }

    /** {@inheritDoc} */
    @Override
    protected void _fit(Dataframe trainingData) {
        ModelParameters modelParameters = knowledgeBase.getModelParameters();
        Map minColumnValues = modelParameters.getMinColumnValues();
        Map maxColumnValues = modelParameters.getMaxColumnValues();
        boolean scaleResponse = knowledgeBase.getTrainingParameters().getScaleResponse();

        Set supportedXDataTypes = getSupportedXDataTypes();
        Stream