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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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* * SPDX-License-Identifier: Apache-2.0
* *****************************************************************************
*/
package org.nd4j.linalg.api.ops.impl.summarystats;
import lombok.val;
import org.nd4j.autodiff.samediff.SDVariable;
import org.nd4j.autodiff.samediff.SameDiff;
import org.nd4j.imports.NoOpNameFoundException;
import org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.linalg.api.ops.impl.reduce.bp.StandardDeviationBp;
import org.nd4j.linalg.api.shape.LongShapeDescriptor;
import org.nd4j.linalg.api.shape.Shape;
import org.nd4j.linalg.exception.ND4JIllegalStateException;
import java.util.ArrayList;
import java.util.Collections;
import java.util.List;
public class StandardDeviation extends Variance {
public StandardDeviation(SameDiff sameDiff, SDVariable i_v, boolean biasCorrected, boolean keepDims, int[] dimensions) {
super(sameDiff, i_v, biasCorrected, keepDims, dimensions );
}
public StandardDeviation(INDArray x, boolean biasCorrected, boolean keepDims, int... dimension) {
super(x, biasCorrected, dimension);
this.keepDims = keepDims;
}
public StandardDeviation(INDArray x, boolean biasCorrected, int... dimension) {
super(x, biasCorrected, dimension);
}
public StandardDeviation() {
}
public StandardDeviation(INDArray x) {
super(x);
}
public StandardDeviation(INDArray x, INDArray z, boolean biasCorrected, int... dimension) {
super(x, z, biasCorrected, dimension);
}
public StandardDeviation(INDArray x, INDArray z, boolean newFormat, boolean keepDims, int[] dimensions) {
super(x, z, newFormat, keepDims, dimensions);
}
@Override
public int opNum() {
return 1;
}
@Override
public String opName() {
return "std";
}
@Override
public String onnxName(){
throw new NoOpNameFoundException("No onnx op opName found for " + opName());
}
@Override
public String tensorflowName(){
throw new NoOpNameFoundException("No tensorflow op opName found for " + opName());
}
@Override
public Type getOpType() {
return Type.SUMMARYSTATS;
}
@Override
public Type opType(){
return Type.SUMMARYSTATS;
}
@Override
public List doDiff(List grad) {
//Here: calculating dL/dIn given dL/dOut (i.e., i_v1) and input/output
//If out = stdev(in) then:
//dL/dIn = dL/dOut * dOut/dIn
//dOut/dIn_i = (in_i-mean)/(stdev * (n-1))
return new StandardDeviationBp(sameDiff, arg(), grad.get(0), biasCorrected, keepDims, dimensions).outputs();
}
@Override
public List calculateOutputShape() {
if(args().length < 1) {
throw new ND4JIllegalStateException("Unable to compute input shape. No arguments found.");
}
long[] argShape = arg().getShape();
if (argShape == null && x() == null) {
return Collections.emptyList();
}
long[] inputShape = (argShape == null || Shape.isPlaceholderShape(argShape) ? x().shape() : argShape);
val ret = new ArrayList(1);
val reducedShape = Shape.getReducedShape(inputShape,dimensions, isKeepDims());
ret.add(LongShapeDescriptor.fromShape(reducedShape, resultType()));
return ret;
}
}