nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/impl/summarystats/Variance.java
/*
* ******************************************************************************
* *
* *
* * 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
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*/
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.common.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.ops.OpContext;
import org.nd4j.linalg.api.ops.impl.reduce.bp.VarianceBp;
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;
public class Variance extends BaseReduceOp {
protected double mean, bias;
protected boolean biasCorrected = true;
public Variance(SameDiff sameDiff, SDVariable i_v, long[] dimensions, boolean keepDims, double mean) {
super(sameDiff, i_v, dimensions, keepDims);
this.mean = mean;
}
public Variance(SameDiff sameDiff, SDVariable i_v, SDVariable i_v2, long[] dimensions, boolean keepDims, double mean) {
super(sameDiff, i_v, i_v2, dimensions, keepDims);
this.mean = mean;
}
public Variance(SameDiff sameDiff, SDVariable i_v, double mean) {
super(sameDiff, i_v);
this.mean = mean;
}
public Variance(SameDiff sameDiff, SDVariable i_v, long[] dimensions, double mean) {
super(sameDiff, i_v, dimensions);
this.mean = mean;
}
public Variance(SameDiff sameDiff, SDVariable i_v, SDVariable i_v2, long[] dimensions, double mean) {
super(sameDiff, i_v, i_v2, dimensions);
this.mean = mean;
}
public Variance(SameDiff sameDiff, SDVariable i_v, boolean keepDims, double mean) {
super(sameDiff, i_v, keepDims);
this.mean = mean;
}
public Variance(SameDiff sameDiff, SDVariable i_v, SDVariable dimensions, boolean keepDims, double mean) {
super(sameDiff, i_v, dimensions, keepDims);
this.mean = mean;
}
public Variance(SameDiff sameDiff, SDVariable i_v, SDVariable i_v2, double mean) {
super(sameDiff, i_v, i_v2);
this.mean = mean;
}
public Variance(double mean) {
this.mean = mean;
}
public Variance(INDArray x, INDArray y, INDArray z, boolean keepDims, long[] dimensions, double mean) {
super(x, y, z, keepDims, dimensions);
this.mean = mean;
}
public Variance(INDArray x, double mean, long... dimensions) {
super(x, dimensions);
this.mean = mean;
}
public Variance(INDArray x, boolean keepDims, double mean, long... dimensions) {
super(x, keepDims, dimensions);
this.mean = mean;
}
public Variance(INDArray x, INDArray y, double mean, long... dimensions) {
super(x, y, dimensions);
this.mean = mean;
}
public Variance(INDArray x, INDArray y, INDArray z, double mean, long... dimensions) {
super(x, y, z, dimensions);
this.mean = mean;
}
public Variance(SameDiff sameDiff, double mean) {
super(sameDiff);
this.mean = mean;
}
public Variance(SameDiff sameDiff, SDVariable i_v, SDVariable i_v2, SDVariable dimensions, double mean) {
super(sameDiff, i_v, i_v2, dimensions);
this.mean = mean;
}
public Variance(SameDiff sameDiff, SDVariable i_v, long[] dimensions, boolean keepDims, double mean, double bias) {
super(sameDiff, i_v, dimensions, keepDims);
this.mean = mean;
this.bias = bias;
}
public Variance(SameDiff sameDiff, SDVariable i_v, SDVariable i_v2, long[] dimensions, boolean keepDims, double mean, double bias) {
super(sameDiff, i_v, i_v2, dimensions, keepDims);
this.mean = mean;
this.bias = bias;
}
public Variance(SameDiff sameDiff, SDVariable i_v, double mean, double bias) {
super(sameDiff, i_v);
this.mean = mean;
this.bias = bias;
}
public Variance(SameDiff sameDiff, SDVariable i_v, long[] dimensions, double mean, double bias) {
super(sameDiff, i_v, dimensions);
this.mean = mean;
this.bias = bias;
}
public Variance(SameDiff sameDiff, SDVariable i_v, SDVariable i_v2, long[] dimensions, double mean, double bias) {
super(sameDiff, i_v, i_v2, dimensions);
this.mean = mean;
this.bias = bias;
}
public Variance(SameDiff sameDiff, SDVariable i_v, boolean keepDims, double mean, double bias) {
super(sameDiff, i_v, keepDims);
this.mean = mean;
this.bias = bias;
}
public Variance(SameDiff sameDiff, SDVariable i_v, SDVariable dimensions, boolean keepDims, double mean, double bias) {
super(sameDiff, i_v, dimensions, keepDims);
this.mean = mean;
this.bias = bias;
}
public Variance(SameDiff sameDiff, SDVariable i_v, SDVariable i_v2, double mean, double bias) {
super(sameDiff, i_v, i_v2);
this.mean = mean;
this.bias = bias;
}
public Variance(double mean, double bias) {
this.mean = mean;
this.bias = bias;
}
public Variance(INDArray x, INDArray y, INDArray z, boolean keepDims, long[] dimensions, double mean, double bias) {
super(x, y, z, keepDims, dimensions);
this.mean = mean;
this.bias = bias;
}
public Variance(INDArray x, double mean, double bias, long... dimensions) {
super(x, dimensions);
this.mean = mean;
this.bias = bias;
}
public Variance(INDArray x, boolean keepDims, double mean, double bias, long... dimensions) {
super(x, keepDims, dimensions);
this.mean = mean;
this.bias = bias;
}
public Variance(INDArray x, INDArray y, double mean, double bias, long... dimensions) {
super(x, y, dimensions);
this.mean = mean;
this.bias = bias;
}
public Variance(INDArray x, INDArray y, INDArray z, double mean, double bias, long... dimensions) {
super(x, y, z, dimensions);
this.mean = mean;
this.bias = bias;
}
public Variance(SameDiff sameDiff, double mean, double bias) {
super(sameDiff);
this.mean = mean;
this.bias = bias;
}
public Variance(SameDiff sameDiff, SDVariable i_v, SDVariable i_v2, SDVariable dimensions, double mean, double bias) {
super(sameDiff, i_v, i_v2, dimensions);
this.mean = mean;
this.bias = bias;
}
public Variance(SameDiff sameDiff, SDVariable i_v, long[] dimensions, boolean keepDims, double mean, double bias, boolean biasCorrected) {
super(sameDiff, i_v, dimensions, keepDims);
this.mean = mean;
this.bias = bias;
this.biasCorrected = biasCorrected;
}
public Variance(SameDiff sameDiff, SDVariable i_v, SDVariable i_v2, long[] dimensions, boolean keepDims, double mean, double bias, boolean biasCorrected) {
super(sameDiff, i_v, i_v2, dimensions, keepDims);
this.mean = mean;
this.bias = bias;
this.biasCorrected = biasCorrected;
}
public Variance(SameDiff sameDiff, SDVariable i_v, double mean, double bias, boolean biasCorrected) {
super(sameDiff, i_v);
this.mean = mean;
this.bias = bias;
this.biasCorrected = biasCorrected;
}
public Variance(SameDiff sameDiff, SDVariable i_v, long[] dimensions, double mean, double bias, boolean biasCorrected) {
super(sameDiff, i_v, dimensions);
this.mean = mean;
this.bias = bias;
this.biasCorrected = biasCorrected;
}
public Variance(SameDiff sameDiff, SDVariable i_v, SDVariable i_v2, long[] dimensions, double mean, double bias, boolean biasCorrected) {
super(sameDiff, i_v, i_v2, dimensions);
this.mean = mean;
this.bias = bias;
this.biasCorrected = biasCorrected;
}
public Variance(SameDiff sameDiff, SDVariable i_v, boolean keepDims, double mean, double bias, boolean biasCorrected) {
super(sameDiff, i_v, keepDims);
this.mean = mean;
this.bias = bias;
this.biasCorrected = biasCorrected;
}
public Variance(SameDiff sameDiff, SDVariable i_v, SDVariable dimensions, boolean keepDims, double mean, double bias, boolean biasCorrected) {
super(sameDiff, i_v, dimensions, keepDims);
this.mean = mean;
this.bias = bias;
this.biasCorrected = biasCorrected;
}
public Variance(SameDiff sameDiff, SDVariable i_v, SDVariable i_v2, double mean, double bias, boolean biasCorrected) {
super(sameDiff, i_v, i_v2);
this.mean = mean;
this.bias = bias;
this.biasCorrected = biasCorrected;
}
public Variance(double mean, double bias, boolean biasCorrected) {
this.mean = mean;
this.bias = bias;
this.biasCorrected = biasCorrected;
}
public Variance(INDArray x, INDArray y, INDArray z, boolean keepDims, long[] dimensions, double mean, double bias, boolean biasCorrected) {
super(x, y, z, keepDims, dimensions);
this.mean = mean;
this.bias = bias;
this.biasCorrected = biasCorrected;
}
public Variance(INDArray x, double mean, double bias, boolean biasCorrected, long... dimensions) {
super(x, dimensions);
this.mean = mean;
this.bias = bias;
this.biasCorrected = biasCorrected;
}
public Variance(INDArray x, boolean keepDims, double mean, double bias, boolean biasCorrected, long... dimensions) {
super(x, keepDims, dimensions);
this.mean = mean;
this.bias = bias;
this.biasCorrected = biasCorrected;
}
public Variance(INDArray x, INDArray y, double mean, double bias, boolean biasCorrected, long... dimensions) {
super(x, y, dimensions);
this.mean = mean;
this.bias = bias;
this.biasCorrected = biasCorrected;
}
public Variance(INDArray x, INDArray y, INDArray z, double mean, double bias, boolean biasCorrected, long... dimensions) {
super(x, y, z, dimensions);
this.mean = mean;
this.bias = bias;
this.biasCorrected = biasCorrected;
}
public Variance(SameDiff sameDiff, double mean, double bias, boolean biasCorrected) {
super(sameDiff);
this.mean = mean;
this.bias = bias;
this.biasCorrected = biasCorrected;
}
public Variance(SameDiff sameDiff, SDVariable i_v, SDVariable i_v2, SDVariable dimensions, double mean, double bias, boolean biasCorrected) {
super(sameDiff, i_v, i_v2, dimensions);
this.mean = mean;
this.bias = bias;
this.biasCorrected = biasCorrected;
}
public Variance(SameDiff sameDiff, SDVariable i_v, boolean biasCorrected, boolean keepDims, long[] 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, long... dimension) {
this(x, true, dimension);
}
public Variance(INDArray x, INDArray z, boolean biasCorrected, long... dimensions) {
this(x, z, true, false, dimensions);
this.biasCorrected = biasCorrected;
}
public Variance(INDArray x, boolean biasCorrected, boolean keepDims, long... dimensions) {
this(x, null, biasCorrected, keepDims, dimensions);
}
public Variance(INDArray x, boolean biasCorrected, long... dimensions) {
super(x,dimensions);
this.biasCorrected = biasCorrected;
}
public Variance(INDArray x, INDArray z, boolean biasCorrected, boolean keepDims, long... dimensions) {
super(x, null, z, keepDims, dimensions);
this.biasCorrected = biasCorrected;
}
@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<SDVariable> doDiff(List<SDVariable> grad) {
//If out = var(in) then:
//dL/dIn = dL/dOut * dOut/dIn
// with dOut/dIn = (in-mean) * 2/(n-1)
return new VarianceBp(sameDiff, arg(), grad.get(0), biasCorrected, keepDims, dimensions).outputs();
}
@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() {
return resultType(null);
}
@Override
public DataType resultType(OpContext oc) {
INDArray x = oc != null ? oc.getInputArray(0) : x();
if (x != null && x.isR())
return x.dataType();
if(this.arg() != null){
return this.arg().dataType();
}
return Nd4j.defaultFloatingPointType();
}
@Override
public boolean validateDataTypes(OpContext oc) {
INDArray x = oc != null ? oc.getInputArray(0) : x();
if (x != null && !x.isR()) {
return false;
}
INDArray y = oc != null ? oc.getInputArray(1) : y();
if (y != null && !y.isR())
return false;
INDArray z = oc != null ? oc.getOutputArray(0) : z();
return !(z != null && !z.isR());
}
@Override
public List<LongShapeDescriptor> calculateOutputShape() {
return calculateOutputShape(null);
}
@Override
public List<LongShapeDescriptor> calculateOutputShape(OpContext oc) {
INDArray x = oc != null ? oc.getInputArray(0) : x();
if(oc == null && args().length < 1) {
throw new ND4JIllegalStateException("Unable to compute input shape. No arguments found.");
}
long[] argShape = x.shape();
if (argShape == null && x == null) {
return Collections.emptyList();
}
long[] inputShape = (argShape == null || Shape.isPlaceholderShape(argShape) ? x.shape() : argShape);
val ret = new ArrayList<LongShapeDescriptor>(1);
val reducedShape = Shape.getReducedShape(inputShape,dimensions, isKeepDims());
ret.add(LongShapeDescriptor.fromShape(reducedShape, resultType()));
return ret;
}
@Override
public Type opType(){
return Type.VARIANCE;
}
@Override
public List<DataType> calculateOutputDataTypes(List<DataType> 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());
}
}