org.deeplearning4j.nn.conf.distribution.LogNormalDistribution Maven / Gradle / Ivy
/*******************************************************************************
* Copyright (c) 2015-2018 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.conf.distribution;
import lombok.Data;
import lombok.EqualsAndHashCode;
import org.nd4j.shade.jackson.annotation.JsonCreator;
import org.nd4j.shade.jackson.annotation.JsonProperty;
/**
* A log-normal distribution, with two parameters: mean and standard deviation.
* Note: the mean and standard deviation are for the logarithm of the values.
* Put another way: if X~LogN(M,S), then mean(log(X))=M, and stdev(log(X))=S
*
*/
@EqualsAndHashCode(callSuper = false)
@Data
public class LogNormalDistribution extends Distribution {
private double mean, std;
/**
* Create a log-normal distribution
* with the given mean and std
*
* @param mean the mean
* @param std the standard deviation
*/
@JsonCreator
public LogNormalDistribution(@JsonProperty("mean") double mean, @JsonProperty("std") double std) {
this.mean = mean;
this.std = std;
}
public String toString() {
return "LogNormalDistribution(" + "mean=" + mean + ", std=" + std + ')';
}
}