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/*******************************************************************************
* 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.nd4j.linalg.api.ops.impl.summarystats;
import lombok.val;
import org.nd4j.autodiff.samediff.SDVariable;
import org.nd4j.autodiff.samediff.SameDiff;
import org.nd4j.base.Preconditions;
import org.nd4j.imports.NoOpNameFoundException;
import org.nd4j.linalg.api.buffer.DataType;
import org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.linalg.api.ops.BaseReduceOp;
import org.nd4j.linalg.api.shape.LongShapeDescriptor;
import org.nd4j.linalg.api.shape.Shape;
import org.nd4j.linalg.exception.ND4JIllegalStateException;
import org.nd4j.linalg.factory.Nd4j;
import java.util.ArrayList;
import java.util.Collections;
import java.util.List;
/**
* Variance with bias correction.
* Bias can either be divided by n or adjusted with:
* (currentResult - (pow(bias, 2.0) / n())) / (n() - 1.0);
*
* @author Adam Gibson
*/
public class Variance extends BaseReduceOp {
protected double mean, bias;
protected boolean biasCorrected = true;
public Variance(SameDiff sameDiff, SDVariable i_v, boolean biasCorrected, boolean keepDims, int[] dimensions) {
super(sameDiff, i_v, dimensions, keepDims);
this.biasCorrected = biasCorrected;
defineDimensions(dimensions);
}
public Variance() {
}
public Variance(boolean biasCorrected) {
this.biasCorrected = biasCorrected;
}
public Variance(INDArray x, int... dimension) {
this(x, true, dimension);
}
public Variance(INDArray x, INDArray z, boolean biasCorrected, int... dimensions) {
this(x, z, true, false, dimensions);
this.biasCorrected = biasCorrected;
}
public Variance(INDArray x, boolean biasCorrected, int... dimensions) {
super(x);
this.biasCorrected = biasCorrected;
defineDimensions(dimensions);
}
public Variance(INDArray x, INDArray z, boolean biasCorrected, boolean keepDims, int... dimensions) {
super(x, null, z, keepDims, dimensions);
this.biasCorrected = biasCorrected;
defineDimensions(dimensions);
}
@Override
public INDArray noOp() {
return Nd4j.zerosLike(x());
}
@Override
public int opNum() {
return 0;
}
@Override
public String opName() {
return "var";
}
public boolean isBiasCorrected() {
return biasCorrected;
}
public void setBiasCorrected(boolean biasCorrected) {
this.biasCorrected = biasCorrected;
}
@Override
public List doDiff(List grad) {
//If out = var(in) then:
//dL/dIn = dL/dOut * dOut/dIn
// with dOut/dIn = (in-mean) * 2/(n-1)
return Collections.singletonList(f().varianceBp(arg(), grad.get(0), biasCorrected, keepDims, dimensions));
}
@Override
public String onnxName() {
throw new NoOpNameFoundException("No onnx opName found for " + opName());
}
@Override
public String tensorflowName(){
throw new NoOpNameFoundException("No tensorflow op opName found for " + opName());
}
@Override
public Type getOpType() {
return Type.VARIANCE;
}
@Override
public DataType resultType() {
if (this.x() != null && this.x().isR())
return this.x().dataType();
if(this.arg() != null){
return this.arg().dataType();
}
return Nd4j.defaultFloatingPointType();
}
@Override
public boolean validateDataTypes() {
if (!x().isR())
return false;
if (y() != null && !y().isR())
return false;
if (z() != null && !z().isR())
return false;
return true;
}
@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;
}
@Override
public Type opType(){
return Type.VARIANCE;
}
public List calculateOutputDataTypes(List dataTypes){
Preconditions.checkState(dataTypes != null && dataTypes.size() == 1, "Expected exactly 1 input datatype for %s, got input %s", getClass(), dataTypes);
//Variance and stdev reduction: Always FP out, but if FP in is float/double/half then it's float/double/half out
//If not FP in, then return default FP type out
if(dataTypes.get(0).isFPType())
return dataTypes;
return Collections.singletonList(Nd4j.defaultFloatingPointType());
}
}