org.nd4j.linalg.api.ops.impl.transforms.custom.MatrixDeterminant Maven / Gradle / Ivy
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package org.nd4j.linalg.api.ops.impl.transforms.custom;
import lombok.NonNull;
import org.nd4j.autodiff.samediff.SDVariable;
import org.nd4j.autodiff.samediff.SameDiff;
import org.nd4j.common.base.Preconditions;
import org.nd4j.linalg.api.buffer.DataType;
import org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.linalg.api.ops.DynamicCustomOp;
import java.util.Collections;
import java.util.List;
public class MatrixDeterminant extends DynamicCustomOp {
public MatrixDeterminant() {
//
}
public MatrixDeterminant(@NonNull INDArray input){
super(new INDArray[]{input}, null);
}
public MatrixDeterminant(SameDiff sameDiff, SDVariable in, boolean inPlace) {
super(null, sameDiff, new SDVariable[]{in}, inPlace);
}
public MatrixDeterminant(SameDiff sameDiff, SDVariable in) {
this(sameDiff, in, false);
}
@Override
public String opName() {
return "matrix_determinant";
}
@Override
public String[] tensorflowNames() {
return new String[]{"MatrixDeterminant","BatchMatrixDeterminant"};
}
@Override
public List doDiff(List i_v) {
//TODO support rank 3+ case
//Derivative of matrix determinant
//From: Matrix Cookbook - Petersen & Pedersen
// z=det(X) then dz/dx = z * tr(X^-1)
//Unfortunately: this is NOT passing gradient checks :(
// SDVariable inverse = f().matrixInverse(arg());
// SDVariable trace = f().trace(inverse.mul(sameDiff.onesLike(arg())));
// SDVariable dOutdIn = outputVariable().mul(trace);
// return Collections.singletonList(i_v.get(0).mul(dOutdIn).mul(sameDiff.onesLike(arg())));
throw new UnsupportedOperationException("Not yet implemented");
}
@Override
public List calculateOutputDataTypes(List dataTypes){
Preconditions.checkState(dataTypes != null && dataTypes.size() == 1, "Expected exactly 1 input datatype for %s, got %s", getClass(), dataTypes);
Preconditions.checkState(dataTypes.get(0).isFPType(), "Input datatype must be a floating point type, got %s", dataTypes.get(0));
return Collections.singletonList(dataTypes.get(0));
}
}