/
Distributions.java
65 lines (61 loc) · 2.98 KB
/
Distributions.java
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
/*
* ******************************************************************************
* *
* *
* * 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.
* *
* * SPDX-License-Identifier: Apache-2.0
* *****************************************************************************
*/
package org.deeplearning4j.nn.conf.distribution;
import org.nd4j.linalg.factory.Nd4j;
public class Distributions {
private Distributions() {}
public static org.nd4j.linalg.api.rng.distribution.Distribution createDistribution(Distribution dist) {
if (dist == null)
return null;
if (dist instanceof NormalDistribution) {
NormalDistribution nd = (NormalDistribution) dist;
return Nd4j.getDistributions().createNormal(nd.getMean(), nd.getStd());
}
if (dist instanceof GaussianDistribution) {
GaussianDistribution nd = (GaussianDistribution) dist;
return Nd4j.getDistributions().createNormal(nd.getMean(), nd.getStd());
}
if (dist instanceof UniformDistribution) {
UniformDistribution ud = (UniformDistribution) dist;
return Nd4j.getDistributions().createUniform(ud.getLower(), ud.getUpper());
}
if (dist instanceof BinomialDistribution) {
BinomialDistribution bd = (BinomialDistribution) dist;
return Nd4j.getDistributions().createBinomial(bd.getNumberOfTrials(), bd.getProbabilityOfSuccess());
}
if (dist instanceof LogNormalDistribution) {
LogNormalDistribution lnd = (LogNormalDistribution) dist;
return Nd4j.getDistributions().createLogNormal(lnd.getMean(), lnd.getStd());
}
if (dist instanceof TruncatedNormalDistribution) {
TruncatedNormalDistribution tnd = (TruncatedNormalDistribution) dist;
return Nd4j.getDistributions().createTruncatedNormal(tnd.getMean(), tnd.getStd());
}
if (dist instanceof OrthogonalDistribution) {
OrthogonalDistribution od = (OrthogonalDistribution) dist;
return Nd4j.getDistributions().createOrthogonal(od.getGain());
}
if (dist instanceof ConstantDistribution) {
ConstantDistribution od = (ConstantDistribution) dist;
return Nd4j.getDistributions().createConstant(od.getValue());
}
throw new RuntimeException("unknown distribution type: " + dist.getClass());
}
}