org.nd4j.linalg.api.ops.impl.summarystats.StandardDeviation Maven / Gradle / Ivy
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* * terms of the Apache License, Version 2.0 which is available at
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* * information regarding copyright ownership.
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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.imports.NoOpNameFoundException;
import org.nd4j.linalg.api.buffer.DataType;
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;
import java.util.Map;
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);
this.keepDims = keepDims;
}
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(boolean biasCorrected) {
super(biasCorrected);
}
public StandardDeviation(INDArray x, int... dimension) {
super(x, dimension);
}
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);
}
public StandardDeviation(SameDiff sameDiff, SDVariable i_v, int[] dimensions, boolean keepDims, double mean) {
super(sameDiff, i_v, dimensions, keepDims, mean);
}
public StandardDeviation(SameDiff sameDiff, SDVariable i_v, SDVariable i_v2, int[] dimensions, boolean keepDims, double mean) {
super(sameDiff, i_v, i_v2, dimensions, keepDims, mean);
}
public StandardDeviation(SameDiff sameDiff, SDVariable i_v, double mean) {
super(sameDiff, i_v, mean);
}
public StandardDeviation(SameDiff sameDiff, SDVariable i_v, int[] dimensions, double mean) {
super(sameDiff, i_v, dimensions, mean);
}
public StandardDeviation(SameDiff sameDiff, SDVariable i_v, SDVariable i_v2, int[] dimensions, double mean) {
super(sameDiff, i_v, i_v2, dimensions, mean);
}
public StandardDeviation(SameDiff sameDiff, SDVariable i_v, boolean keepDims, double mean) {
super(sameDiff, i_v, keepDims, mean);
}
public StandardDeviation(SameDiff sameDiff, SDVariable i_v, SDVariable dimensions, boolean keepDims, double mean) {
super(sameDiff, i_v, dimensions, keepDims, mean);
}
public StandardDeviation(SameDiff sameDiff, SDVariable i_v, SDVariable i_v2, double mean) {
super(sameDiff, i_v, i_v2, mean);
}
public StandardDeviation(double mean) {
super(mean);
}
public StandardDeviation(INDArray x, INDArray y, INDArray z, boolean keepDims, int[] dimensions, double mean) {
super(x, y, z, keepDims, dimensions, mean);
}
public StandardDeviation(INDArray x, double mean, int... dimensions) {
super(x, mean, dimensions);
}
public StandardDeviation(INDArray x, boolean keepDims, double mean, int... dimensions) {
super(x, keepDims, mean, dimensions);
}
public StandardDeviation(INDArray x, INDArray y, double mean, int... dimensions) {
super(x, y, mean, dimensions);
}
public StandardDeviation(INDArray x, INDArray y, INDArray z, double mean, int... dimensions) {
super(x, y, z, mean, dimensions);
}
public StandardDeviation(SameDiff sameDiff, double mean) {
super(sameDiff, mean);
}
public StandardDeviation(SameDiff sameDiff, SDVariable i_v, SDVariable i_v2, SDVariable dimensions, double mean) {
super(sameDiff, i_v, i_v2, dimensions, mean);
}
public StandardDeviation(SameDiff sameDiff, SDVariable i_v, int[] dimensions, boolean keepDims, double mean, double bias) {
super(sameDiff, i_v, dimensions, keepDims, mean, bias);
}
public StandardDeviation(SameDiff sameDiff, SDVariable i_v, SDVariable i_v2, int[] dimensions, boolean keepDims, double mean, double bias) {
super(sameDiff, i_v, i_v2, dimensions, keepDims, mean, bias);
}
public StandardDeviation(SameDiff sameDiff, SDVariable i_v, double mean, double bias) {
super(sameDiff, i_v, mean, bias);
}
public StandardDeviation(SameDiff sameDiff, SDVariable i_v, int[] dimensions, double mean, double bias) {
super(sameDiff, i_v, dimensions, mean, bias);
}
public StandardDeviation(SameDiff sameDiff, SDVariable i_v, SDVariable i_v2, int[] dimensions, double mean, double bias) {
super(sameDiff, i_v, i_v2, dimensions, mean, bias);
}
public StandardDeviation(SameDiff sameDiff, SDVariable i_v, boolean keepDims, double mean, double bias) {
super(sameDiff, i_v, keepDims, mean, bias);
}
public StandardDeviation(SameDiff sameDiff, SDVariable i_v, SDVariable dimensions, boolean keepDims, double mean, double bias) {
super(sameDiff, i_v, dimensions, keepDims, mean, bias);
}
public StandardDeviation(SameDiff sameDiff, SDVariable i_v, SDVariable i_v2, double mean, double bias) {
super(sameDiff, i_v, i_v2, mean, bias);
}
public StandardDeviation(double mean, double bias) {
super(mean, bias);
}
public StandardDeviation(INDArray x, INDArray y, INDArray z, boolean keepDims, int[] dimensions, double mean, double bias) {
super(x, y, z, keepDims, dimensions, mean, bias);
}
public StandardDeviation(INDArray x, double mean, double bias, int... dimensions) {
super(x, mean, bias, dimensions);
}
public StandardDeviation(INDArray x, boolean keepDims, double mean, double bias, int... dimensions) {
super(x, keepDims, mean, bias, dimensions);
}
public StandardDeviation(INDArray x, INDArray y, double mean, double bias, int... dimensions) {
super(x, y, mean, bias, dimensions);
}
public StandardDeviation(INDArray x, INDArray y, INDArray z, double mean, double bias, int... dimensions) {
super(x, y, z, mean, bias, dimensions);
}
public StandardDeviation(SameDiff sameDiff, double mean, double bias) {
super(sameDiff, mean, bias);
}
public StandardDeviation(SameDiff sameDiff, SDVariable i_v, SDVariable i_v2, SDVariable dimensions, double mean, double bias) {
super(sameDiff, i_v, i_v2, dimensions, mean, bias);
}
public StandardDeviation(SameDiff sameDiff, SDVariable i_v, int[] dimensions, boolean keepDims, double mean, double bias, boolean biasCorrected) {
super(sameDiff, i_v, dimensions, keepDims, mean, bias, biasCorrected);
}
public StandardDeviation(SameDiff sameDiff, SDVariable i_v, SDVariable i_v2, int[] dimensions, boolean keepDims, double mean, double bias, boolean biasCorrected) {
super(sameDiff, i_v, i_v2, dimensions, keepDims, mean, bias, biasCorrected);
}
public StandardDeviation(SameDiff sameDiff, SDVariable i_v, double mean, double bias, boolean biasCorrected) {
super(sameDiff, i_v, mean, bias, biasCorrected);
}
public StandardDeviation(SameDiff sameDiff, SDVariable i_v, int[] dimensions, double mean, double bias, boolean biasCorrected) {
super(sameDiff, i_v, dimensions, mean, bias, biasCorrected);
}
public StandardDeviation(SameDiff sameDiff, SDVariable i_v, SDVariable i_v2, int[] dimensions, double mean, double bias, boolean biasCorrected) {
super(sameDiff, i_v, i_v2, dimensions, mean, bias, biasCorrected);
}
public StandardDeviation(SameDiff sameDiff, SDVariable i_v, boolean keepDims, double mean, double bias, boolean biasCorrected) {
super(sameDiff, i_v, keepDims, mean, bias, biasCorrected);
}
public StandardDeviation(SameDiff sameDiff, SDVariable i_v, SDVariable dimensions, boolean keepDims, double mean, double bias, boolean biasCorrected) {
super(sameDiff, i_v, dimensions, keepDims, mean, bias, biasCorrected);
}
public StandardDeviation(SameDiff sameDiff, SDVariable i_v, SDVariable i_v2, double mean, double bias, boolean biasCorrected) {
super(sameDiff, i_v, i_v2, mean, bias, biasCorrected);
}
public StandardDeviation(double mean, double bias, boolean biasCorrected) {
super(mean, bias, biasCorrected);
}
public StandardDeviation(INDArray x, INDArray y, INDArray z, boolean keepDims, int[] dimensions, double mean, double bias, boolean biasCorrected) {
super(x, y, z, keepDims, dimensions, mean, bias, biasCorrected);
}
public StandardDeviation(INDArray x, double mean, double bias, boolean biasCorrected, int... dimensions) {
super(x, mean, bias, biasCorrected, dimensions);
}
public StandardDeviation(INDArray x, boolean keepDims, double mean, double bias, boolean biasCorrected, int... dimensions) {
super(x, keepDims, mean, bias, biasCorrected, dimensions);
}
public StandardDeviation(INDArray x, INDArray y, double mean, double bias, boolean biasCorrected, int... dimensions) {
super(x, y, mean, bias, biasCorrected, dimensions);
}
public StandardDeviation(INDArray x, INDArray y, INDArray z, double mean, double bias, boolean biasCorrected, int... dimensions) {
super(x, y, z, mean, bias, biasCorrected, dimensions);
}
public StandardDeviation(SameDiff sameDiff, double mean, double bias, boolean biasCorrected) {
super(sameDiff, mean, bias, biasCorrected);
}
public StandardDeviation(SameDiff sameDiff, SDVariable i_v, SDVariable i_v2, SDVariable dimensions, double mean, double bias, boolean biasCorrected) {
super(sameDiff, i_v, i_v2, dimensions, mean, bias, biasCorrected);
}
@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 void setPropertiesForFunction(Map properties) {
Boolean isEmptyReduce = getBooleanFromProperty("isEmptyReduce",properties);
if(isEmptyReduce != null) {
this.isEmptyReduce = isEmptyReduce;
}
Boolean biasCorrected = getBooleanFromProperty("biasCorrected",properties);
if(biasCorrected != null) {
this.biasCorrected = biasCorrected;
}
Double mean = getDoubleValueFromProperty("mean",properties);
if(mean != null) {
this.mean = mean;
}
Boolean keepDims = getBooleanFromProperty("keepDims",properties);
if(keepDims != null) {
this.keepDims = keepDims;
}
Boolean isComplex = getBooleanFromProperty("isComplex",properties);
if(isComplex != null) {
this.isComplex = isComplex;
}
Double bias = getDoubleValueFromProperty("bias",properties);
if(bias != null) {
this.bias = bias;
}
}
@Override
public List calculateOutputDataTypes(List dataTypes) {
return super.calculateOutputDataTypes(dataTypes);
}
@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;
}
}