/*
 *  ******************************************************************************
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 *  * This program and the accompanying materials are made available under the
 *  * terms of the Apache License, Version 2.0 which is available at
 *  * https://www.apache.org/licenses/LICENSE-2.0.
 *  *
 *  *  See the NOTICE file distributed with this work for additional
 *  *  information regarding copyright ownership.
 *  * 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.
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package org.deeplearning4j.nn.adapters;

import lombok.val;
import org.nd4j.adapters.OutputAdapter;
import org.nd4j.common.base.Preconditions;
import org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.linalg.factory.Nd4j;

public class ArgmaxAdapter implements OutputAdapter<int[]> {

    /**
     * This method does conversion from INDArrays to int[], where each element will represents position of the highest element in output INDArray
     * I.e. Array of {0.25, 0.1, 0.5, 0.15} will return int array with length of 1, and value {2}
     *
     * @param outputs
     * @return
     */
    @Override
    public int[] apply(INDArray... outputs) {
        Preconditions.checkArgument(outputs.length == 1, "Argmax adapter can have only 1 output");
        val array = outputs[0];
        Preconditions.checkArgument(array.rank() < 3, "Argmax adapter requires 2D or 1D output");
        val result = array.rank() == 2 ? new int[(int) array.size(0)] : new int[1];

        if (array.rank() == 2) {
            val t = Nd4j.argMax(array, 1);
            for (int e = 0; e < t.length(); e++)
                result[e] = (int) t.getDouble(e);
        } else
            result[0] = (int) Nd4j.argMax(array, Integer.MAX_VALUE).getDouble(0);

        return result;
    }
}
