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/*
 * Copyright 2019 Amazon.com, Inc. or its affiliates. All Rights Reserved.
 *
 * Licensed under the Apache License, Version 2.0 (the "License"). You may not use this file except in compliance
 * with the License. A copy of the License is located at
 *
 * http://aws.amazon.com/apache2.0/
 *
 * or in the "license" file accompanying this file. This file 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 ai.djl.training.initializer;

import ai.djl.ndarray.NDArray;
import ai.djl.ndarray.NDManager;
import ai.djl.ndarray.types.DataType;
import ai.djl.ndarray.types.Shape;

/**
 * {@code NormalInitializer} initializes weights with random values sampled from a normal
 * distribution with a mean of zero and standard deviation of {@code sigma}. Default standard
 * deviation is 0.01.
 */
public class NormalInitializer implements Initializer {

    private float sigma;

    /** Creates an instance of {@code NormalInitializer} with a default sigma of 0.01. */
    public NormalInitializer() {
        this.sigma = 0.01f;
    }

    /**
     * Creates a Normal initializer.
     *
     * @param sigma the standard deviation of the normal distribution
     */
    public NormalInitializer(float sigma) {
        this.sigma = sigma;
    }

    /** {@inheritDoc} */
    @Override
    public NDArray initialize(NDManager manager, Shape shape, DataType dataType) {
        return manager.randomNormal(0.0f, sigma, shape, dataType, manager.getDevice());
    }
}