/*******************************************************************************
 * Copyright (c) 2015-2019 Skymind, Inc.
 *
 * 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.
 *
 * 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.
 *
 * SPDX-License-Identifier: Apache-2.0
 ******************************************************************************/

package org.deeplearning4j.nn.weights;

import lombok.EqualsAndHashCode;
import org.apache.commons.math3.util.FastMath;
import org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.linalg.factory.Nd4j;

/**
 * As per Glorot and Bengio 2010: Gaussian distribution with mean 0, variance 2.0/(fanIn + fanOut)
 *
 * @author Adam Gibson
 */
@EqualsAndHashCode
public class WeightInitXavier implements IWeightInit {

    @Override
    public INDArray init(double fanIn, double fanOut, long[] shape, char order, INDArray paramView) {
        Nd4j.randn(paramView).muli(FastMath.sqrt(2.0 / (fanIn + fanOut)));
        return paramView.reshape(order, shape);
    }
}
