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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.
* *
* * SPDX-License-Identifier: Apache-2.0
* *****************************************************************************
*/
//================== GENERATED CODE - DO NOT MODIFY THIS FILE ==================
package org.nd4j.autodiff.samediff.ops;
import static org.nd4j.autodiff.samediff.ops.SDValidation.isSameType;
import java.lang.String;
import org.nd4j.autodiff.samediff.SDVariable;
import org.nd4j.autodiff.samediff.SameDiff;
import org.nd4j.linalg.api.buffer.DataType;
public class SDLinalg extends SDOps {
public SDLinalg(SameDiff sameDiff) {
super(sameDiff);
}
/**
* Computes the Cholesky decomposition of one or more square matrices.
*
* @param input Input tensor with inner-most 2 dimensions forming square matrices (NUMERIC type)
* @return output Transformed tensor (NUMERIC type)
*/
public SDVariable cholesky(SDVariable input) {
SDValidation.validateNumerical("Cholesky", "input", input);
return new org.nd4j.linalg.api.ops.impl.transforms.Cholesky(sd,input).outputVariable();
}
/**
* Computes the Cholesky decomposition of one or more square matrices.
*
* @param name name May be null. Name for the output variable
* @param input Input tensor with inner-most 2 dimensions forming square matrices (NUMERIC type)
* @return output Transformed tensor (NUMERIC type)
*/
public SDVariable cholesky(String name, SDVariable input) {
SDValidation.validateNumerical("Cholesky", "input", input);
SDVariable out = new org.nd4j.linalg.api.ops.impl.transforms.Cholesky(sd,input).outputVariable();
return sd.updateVariableNameAndReference(out, name);
}
/**
* Solver for linear squares problems.
*
* @param matrix input tensor (NUMERIC type)
* @param rhs input tensor (NUMERIC type)
* @param l2_reguralizer regularizer
* @param fast fast mode, defaults to True
* @return output Transformed tensor (FLOATING_POINT type)
*/
public SDVariable lstsq(SDVariable matrix, SDVariable rhs, double l2_reguralizer, boolean fast) {
SDValidation.validateNumerical("Lstsq", "matrix", matrix);
SDValidation.validateNumerical("Lstsq", "rhs", rhs);
return new org.nd4j.linalg.api.ops.custom.Lstsq(sd,matrix, rhs, l2_reguralizer, fast).outputVariable();
}
/**
* Solver for linear squares problems.
*
* @param name name May be null. Name for the output variable
* @param matrix input tensor (NUMERIC type)
* @param rhs input tensor (NUMERIC type)
* @param l2_reguralizer regularizer
* @param fast fast mode, defaults to True
* @return output Transformed tensor (FLOATING_POINT type)
*/
public SDVariable lstsq(String name, SDVariable matrix, SDVariable rhs, double l2_reguralizer,
boolean fast) {
SDValidation.validateNumerical("Lstsq", "matrix", matrix);
SDValidation.validateNumerical("Lstsq", "rhs", rhs);
SDVariable out = new org.nd4j.linalg.api.ops.custom.Lstsq(sd,matrix, rhs, l2_reguralizer, fast).outputVariable();
return sd.updateVariableNameAndReference(out, name);
}
/**
* Solver for linear squares problems.
*
* @param matrix input tensor (NUMERIC type)
* @param rhs input tensor (NUMERIC type)
* @param l2_reguralizer regularizer
* @return output Transformed tensor (FLOATING_POINT type)
*/
public SDVariable lstsq(SDVariable matrix, SDVariable rhs, double l2_reguralizer) {
SDValidation.validateNumerical("Lstsq", "matrix", matrix);
SDValidation.validateNumerical("Lstsq", "rhs", rhs);
return new org.nd4j.linalg.api.ops.custom.Lstsq(sd,matrix, rhs, l2_reguralizer, true).outputVariable();
}
/**
* Solver for linear squares problems.
*
* @param name name May be null. Name for the output variable
* @param matrix input tensor (NUMERIC type)
* @param rhs input tensor (NUMERIC type)
* @param l2_reguralizer regularizer
* @return output Transformed tensor (FLOATING_POINT type)
*/
public SDVariable lstsq(String name, SDVariable matrix, SDVariable rhs, double l2_reguralizer) {
SDValidation.validateNumerical("Lstsq", "matrix", matrix);
SDValidation.validateNumerical("Lstsq", "rhs", rhs);
SDVariable out = new org.nd4j.linalg.api.ops.custom.Lstsq(sd,matrix, rhs, l2_reguralizer, true).outputVariable();
return sd.updateVariableNameAndReference(out, name);
}
/**
* Computes LU decomposition.
*
* @param input input tensor (NUMERIC type)
* @return output (FLOATING_POINT type)
*/
public SDVariable lu(SDVariable input) {
SDValidation.validateNumerical("Lu", "input", input);
return new org.nd4j.linalg.api.ops.custom.Lu(sd,input).outputVariable();
}
/**
* Computes LU decomposition.
*
* @param name name May be null. Name for the output variable
* @param input input tensor (NUMERIC type)
* @return output (FLOATING_POINT type)
*/
public SDVariable lu(String name, SDVariable input) {
SDValidation.validateNumerical("Lu", "input", input);
SDVariable out = new org.nd4j.linalg.api.ops.custom.Lu(sd,input).outputVariable();
return sd.updateVariableNameAndReference(out, name);
}
/**
* Performs matrix mutiplication on input tensors.
*
* @param a input tensor (NUMERIC type)
* @param b input tensor (NUMERIC type)
* @return output (FLOATING_POINT type)
*/
public SDVariable matmul(SDVariable a, SDVariable b) {
SDValidation.validateNumerical("Matmul", "a", a);
SDValidation.validateNumerical("Matmul", "b", b);
return new org.nd4j.linalg.api.ops.impl.reduce.Mmul(sd,a, b).outputVariable();
}
/**
* Performs matrix mutiplication on input tensors.
*
* @param name name May be null. Name for the output variable
* @param a input tensor (NUMERIC type)
* @param b input tensor (NUMERIC type)
* @return output (FLOATING_POINT type)
*/
public SDVariable matmul(String name, SDVariable a, SDVariable b) {
SDValidation.validateNumerical("Matmul", "a", a);
SDValidation.validateNumerical("Matmul", "b", b);
SDVariable out = new org.nd4j.linalg.api.ops.impl.reduce.Mmul(sd,a, b).outputVariable();
return sd.updateVariableNameAndReference(out, name);
}
/**
* Copy a tensor setting outside a central band in each innermost matrix.
*
* @param input input tensor (NUMERIC type)
* @param minLower lower diagonal count
* @param maxUpper upper diagonal count
*/
public SDVariable[] matrixBandPart(SDVariable input, int minLower, int maxUpper) {
SDValidation.validateNumerical("MatrixBandPart", "input", input);
return new org.nd4j.linalg.api.ops.custom.MatrixBandPart(sd,input, minLower, maxUpper).outputVariables();
}
/**
* Copy a tensor setting outside a central band in each innermost matrix.
*
* @param names names May be null. Arrays of names for the output variables.
* @param input input tensor (NUMERIC type)
* @param minLower lower diagonal count
* @param maxUpper upper diagonal count
*/
public SDVariable[] matrixBandPart(String[] names, SDVariable input, int minLower, int maxUpper) {
SDValidation.validateNumerical("MatrixBandPart", "input", input);
SDVariable[] out = new org.nd4j.linalg.api.ops.custom.MatrixBandPart(sd,input, minLower, maxUpper).outputVariables();
return sd.updateVariableNamesAndReferences(out, names);
}
/**
* Computes the QR decompositions of input matrix.
*
* @param input input tensor (NUMERIC type)
* @param full full matrices mode
*/
public SDVariable[] qr(SDVariable input, boolean full) {
SDValidation.validateNumerical("Qr", "input", input);
return new org.nd4j.linalg.api.ops.impl.transforms.custom.Qr(sd,input, full).outputVariables();
}
/**
* Computes the QR decompositions of input matrix.
*
* @param names names May be null. Arrays of names for the output variables.
* @param input input tensor (NUMERIC type)
* @param full full matrices mode
*/
public SDVariable[] qr(String[] names, SDVariable input, boolean full) {
SDValidation.validateNumerical("Qr", "input", input);
SDVariable[] out = new org.nd4j.linalg.api.ops.impl.transforms.custom.Qr(sd,input, full).outputVariables();
return sd.updateVariableNamesAndReferences(out, names);
}
/**
* Computes the QR decompositions of input matrix.
*
* @param input input tensor (NUMERIC type)
*/
public SDVariable[] qr(SDVariable input) {
SDValidation.validateNumerical("Qr", "input", input);
return new org.nd4j.linalg.api.ops.impl.transforms.custom.Qr(sd,input, false).outputVariables();
}
/**
* Computes the QR decompositions of input matrix.
*
* @param names names May be null. Arrays of names for the output variables.
* @param input input tensor (NUMERIC type)
*/
public SDVariable[] qr(String[] names, SDVariable input) {
SDValidation.validateNumerical("Qr", "input", input);
SDVariable[] out = new org.nd4j.linalg.api.ops.impl.transforms.custom.Qr(sd,input, false).outputVariables();
return sd.updateVariableNamesAndReferences(out, names);
}
/**
* Solver for systems of linear equations.
*
* @param matrix input tensor (NUMERIC type)
* @param rhs input tensor (NUMERIC type)
* @param adjoint adjoint mode, defaults to False
* @return output Output tensor (FLOATING_POINT type)
*/
public SDVariable solve(SDVariable matrix, SDVariable rhs, boolean adjoint) {
SDValidation.validateNumerical("Solve", "matrix", matrix);
SDValidation.validateNumerical("Solve", "rhs", rhs);
return new org.nd4j.linalg.api.ops.custom.LinearSolve(sd,matrix, rhs, adjoint).outputVariable();
}
/**
* Solver for systems of linear equations.
*
* @param name name May be null. Name for the output variable
* @param matrix input tensor (NUMERIC type)
* @param rhs input tensor (NUMERIC type)
* @param adjoint adjoint mode, defaults to False
* @return output Output tensor (FLOATING_POINT type)
*/
public SDVariable solve(String name, SDVariable matrix, SDVariable rhs, boolean adjoint) {
SDValidation.validateNumerical("Solve", "matrix", matrix);
SDValidation.validateNumerical("Solve", "rhs", rhs);
SDVariable out = new org.nd4j.linalg.api.ops.custom.LinearSolve(sd,matrix, rhs, adjoint).outputVariable();
return sd.updateVariableNameAndReference(out, name);
}
/**
* Solver for systems of linear equations.
*
* @param matrix input tensor (NUMERIC type)
* @param rhs input tensor (NUMERIC type)
* @return output Output tensor (FLOATING_POINT type)
*/
public SDVariable solve(SDVariable matrix, SDVariable rhs) {
SDValidation.validateNumerical("Solve", "matrix", matrix);
SDValidation.validateNumerical("Solve", "rhs", rhs);
return new org.nd4j.linalg.api.ops.custom.LinearSolve(sd,matrix, rhs, false).outputVariable();
}
/**
* Solver for systems of linear equations.
*
* @param name name May be null. Name for the output variable
* @param matrix input tensor (NUMERIC type)
* @param rhs input tensor (NUMERIC type)
* @return output Output tensor (FLOATING_POINT type)
*/
public SDVariable solve(String name, SDVariable matrix, SDVariable rhs) {
SDValidation.validateNumerical("Solve", "matrix", matrix);
SDValidation.validateNumerical("Solve", "rhs", rhs);
SDVariable out = new org.nd4j.linalg.api.ops.custom.LinearSolve(sd,matrix, rhs, false).outputVariable();
return sd.updateVariableNameAndReference(out, name);
}
/**
* Solver for systems of linear questions.
*
* @param matrix input tensor (NUMERIC type)
* @param rhs input tensor (NUMERIC type)
* @param lower defines whether innermost matrices in matrix are lower or upper triangular
* @param adjoint adjoint mode
* @return output (FLOATING_POINT type)
*/
public SDVariable triangularSolve(SDVariable matrix, SDVariable rhs, boolean lower,
boolean adjoint) {
SDValidation.validateNumerical("TriangularSolve", "matrix", matrix);
SDValidation.validateNumerical("TriangularSolve", "rhs", rhs);
return new org.nd4j.linalg.api.ops.custom.TriangularSolve(sd,matrix, rhs, lower, adjoint).outputVariable();
}
/**
* Solver for systems of linear questions.
*
* @param name name May be null. Name for the output variable
* @param matrix input tensor (NUMERIC type)
* @param rhs input tensor (NUMERIC type)
* @param lower defines whether innermost matrices in matrix are lower or upper triangular
* @param adjoint adjoint mode
* @return output (FLOATING_POINT type)
*/
public SDVariable triangularSolve(String name, SDVariable matrix, SDVariable rhs, boolean lower,
boolean adjoint) {
SDValidation.validateNumerical("TriangularSolve", "matrix", matrix);
SDValidation.validateNumerical("TriangularSolve", "rhs", rhs);
SDVariable out = new org.nd4j.linalg.api.ops.custom.TriangularSolve(sd,matrix, rhs, lower, adjoint).outputVariable();
return sd.updateVariableNameAndReference(out, name);
}
/**
* Computes pairwise cross product.
*
* @param a (NUMERIC type)
* @param b (NUMERIC type)
* @return output (FLOATING_POINT type)
*/
public SDVariable cross(SDVariable a, SDVariable b) {
SDValidation.validateNumerical("cross", "a", a);
SDValidation.validateNumerical("cross", "b", b);
return new org.nd4j.linalg.api.ops.impl.shape.Cross(sd,a, b).outputVariable();
}
/**
* Computes pairwise cross product.
*
* @param name name May be null. Name for the output variable
* @param a (NUMERIC type)
* @param b (NUMERIC type)
* @return output (FLOATING_POINT type)
*/
public SDVariable cross(String name, SDVariable a, SDVariable b) {
SDValidation.validateNumerical("cross", "a", a);
SDValidation.validateNumerical("cross", "b", b);
SDVariable out = new org.nd4j.linalg.api.ops.impl.shape.Cross(sd,a, b).outputVariable();
return sd.updateVariableNameAndReference(out, name);
}
/**
* Calculates diagonal tensor.
*
* @param input (NUMERIC type)
* @return output (FLOATING_POINT type)
*/
public SDVariable diag(SDVariable input) {
SDValidation.validateNumerical("diag", "input", input);
return new org.nd4j.linalg.api.ops.impl.shape.Diag(sd,input).outputVariable();
}
/**
* Calculates diagonal tensor.
*
* @param name name May be null. Name for the output variable
* @param input (NUMERIC type)
* @return output (FLOATING_POINT type)
*/
public SDVariable diag(String name, SDVariable input) {
SDValidation.validateNumerical("diag", "input", input);
SDVariable out = new org.nd4j.linalg.api.ops.impl.shape.Diag(sd,input).outputVariable();
return sd.updateVariableNameAndReference(out, name);
}
/**
* Calculates diagonal tensor.
*
* @param input (NUMERIC type)
* @return output (FLOATING_POINT type)
*/
public SDVariable diag_part(SDVariable input) {
SDValidation.validateNumerical("diag_part", "input", input);
return new org.nd4j.linalg.api.ops.impl.shape.DiagPart(sd,input).outputVariable();
}
/**
* Calculates diagonal tensor.
*
* @param name name May be null. Name for the output variable
* @param input (NUMERIC type)
* @return output (FLOATING_POINT type)
*/
public SDVariable diag_part(String name, SDVariable input) {
SDValidation.validateNumerical("diag_part", "input", input);
SDVariable out = new org.nd4j.linalg.api.ops.impl.shape.DiagPart(sd,input).outputVariable();
return sd.updateVariableNameAndReference(out, name);
}
/**
* Calculates log of determinant.
*
* @param input (NUMERIC type)
* @return output (FLOATING_POINT type)
*/
public SDVariable logdet(SDVariable input) {
SDValidation.validateNumerical("logdet", "input", input);
return new org.nd4j.linalg.api.ops.custom.Logdet(sd,input).outputVariable();
}
/**
* Calculates log of determinant.
*
* @param name name May be null. Name for the output variable
* @param input (NUMERIC type)
* @return output (FLOATING_POINT type)
*/
public SDVariable logdet(String name, SDVariable input) {
SDValidation.validateNumerical("logdet", "input", input);
SDVariable out = new org.nd4j.linalg.api.ops.custom.Logdet(sd,input).outputVariable();
return sd.updateVariableNameAndReference(out, name);
}
/**
* Matrix multiplication: out = mmul(x,y)
* Supports specifying transpose argument to perform operation such as mmul(a^T, b), etc.
*
* @param x First input variable (NUMERIC type)
* @param y Second input variable (NUMERIC type)
* @param transposeX Transpose x (first argument)
* @param transposeY Transpose y (second argument)
* @param transposeZ Transpose result array
* @return output (NUMERIC type)
*/
public SDVariable mmul(SDVariable x, SDVariable y, boolean transposeX, boolean transposeY,
boolean transposeZ) {
SDValidation.validateNumerical("mmul", "x", x);
SDValidation.validateNumerical("mmul", "y", y);
return new org.nd4j.linalg.api.ops.impl.reduce.Mmul(sd,x, y, transposeX, transposeY, transposeZ).outputVariable();
}
/**
* Matrix multiplication: out = mmul(x,y)
* Supports specifying transpose argument to perform operation such as mmul(a^T, b), etc.
*
* @param name name May be null. Name for the output variable
* @param x First input variable (NUMERIC type)
* @param y Second input variable (NUMERIC type)
* @param transposeX Transpose x (first argument)
* @param transposeY Transpose y (second argument)
* @param transposeZ Transpose result array
* @return output (NUMERIC type)
*/
public SDVariable mmul(String name, SDVariable x, SDVariable y, boolean transposeX,
boolean transposeY, boolean transposeZ) {
SDValidation.validateNumerical("mmul", "x", x);
SDValidation.validateNumerical("mmul", "y", y);
SDVariable out = new org.nd4j.linalg.api.ops.impl.reduce.Mmul(sd,x, y, transposeX, transposeY, transposeZ).outputVariable();
return sd.updateVariableNameAndReference(out, name);
}
/**
* Matrix multiplication: out = mmul(x,y)
* Supports specifying transpose argument to perform operation such as mmul(a^T, b), etc.
*
* @param x First input variable (NUMERIC type)
* @param y Second input variable (NUMERIC type)
* @return output (NUMERIC type)
*/
public SDVariable mmul(SDVariable x, SDVariable y) {
SDValidation.validateNumerical("mmul", "x", x);
SDValidation.validateNumerical("mmul", "y", y);
return new org.nd4j.linalg.api.ops.impl.reduce.Mmul(sd,x, y, false, false, false).outputVariable();
}
/**
* Matrix multiplication: out = mmul(x,y)
* Supports specifying transpose argument to perform operation such as mmul(a^T, b), etc.
*
* @param name name May be null. Name for the output variable
* @param x First input variable (NUMERIC type)
* @param y Second input variable (NUMERIC type)
* @return output (NUMERIC type)
*/
public SDVariable mmul(String name, SDVariable x, SDVariable y) {
SDValidation.validateNumerical("mmul", "x", x);
SDValidation.validateNumerical("mmul", "y", y);
SDVariable out = new org.nd4j.linalg.api.ops.impl.reduce.Mmul(sd,x, y, false, false, false).outputVariable();
return sd.updateVariableNameAndReference(out, name);
}
/**
* Calculates singular value decomposition.
*
* @param input (NUMERIC type)
* @param fullUV
* @param computeUV
* @param switchNum
* @return output (FLOATING_POINT type)
*/
public SDVariable svd(SDVariable input, boolean fullUV, boolean computeUV, int switchNum) {
SDValidation.validateNumerical("svd", "input", input);
return new org.nd4j.linalg.api.ops.impl.transforms.custom.Svd(sd,input, fullUV, computeUV, switchNum).outputVariable();
}
/**
* Calculates singular value decomposition.
*
* @param name name May be null. Name for the output variable
* @param input (NUMERIC type)
* @param fullUV
* @param computeUV
* @param switchNum
* @return output (FLOATING_POINT type)
*/
public SDVariable svd(String name, SDVariable input, boolean fullUV, boolean computeUV,
int switchNum) {
SDValidation.validateNumerical("svd", "input", input);
SDVariable out = new org.nd4j.linalg.api.ops.impl.transforms.custom.Svd(sd,input, fullUV, computeUV, switchNum).outputVariable();
return sd.updateVariableNameAndReference(out, name);
}
/**
* Calculates singular value decomposition.
*
* @param input (NUMERIC type)
* @param fullUV
* @param computeUV
* @return output (FLOATING_POINT type)
*/
public SDVariable svd(SDVariable input, boolean fullUV, boolean computeUV) {
SDValidation.validateNumerical("svd", "input", input);
return new org.nd4j.linalg.api.ops.impl.transforms.custom.Svd(sd,input, fullUV, computeUV, 16).outputVariable();
}
/**
* Calculates singular value decomposition.
*
* @param name name May be null. Name for the output variable
* @param input (NUMERIC type)
* @param fullUV
* @param computeUV
* @return output (FLOATING_POINT type)
*/
public SDVariable svd(String name, SDVariable input, boolean fullUV, boolean computeUV) {
SDValidation.validateNumerical("svd", "input", input);
SDVariable out = new org.nd4j.linalg.api.ops.impl.transforms.custom.Svd(sd,input, fullUV, computeUV, 16).outputVariable();
return sd.updateVariableNameAndReference(out, name);
}
/**
* An array with ones at and below the given diagonal and zeros elsewhere.
*
* @param dataType Data type
* @param row
* @param column
* @param diagonal
* @return output (FLOATING_POINT type)
*/
public SDVariable tri(DataType dataType, int row, int column, int diagonal) {
return new org.nd4j.linalg.api.ops.custom.Tri(sd,dataType, row, column, diagonal).outputVariable();
}
/**
* An array with ones at and below the given diagonal and zeros elsewhere.
*
* @param name name May be null. Name for the output variable
* @param dataType Data type
* @param row
* @param column
* @param diagonal
* @return output (FLOATING_POINT type)
*/
public SDVariable tri(String name, DataType dataType, int row, int column, int diagonal) {
SDVariable out = new org.nd4j.linalg.api.ops.custom.Tri(sd,dataType, row, column, diagonal).outputVariable();
return sd.updateVariableNameAndReference(out, name);
}
/**
* An array with ones at and below the given diagonal and zeros elsewhere.
*
* @param row
* @param column
* @return output (FLOATING_POINT type)
*/
public SDVariable tri(int row, int column) {
return new org.nd4j.linalg.api.ops.custom.Tri(sd,DataType.FLOAT, row, column, 0).outputVariable();
}
/**
* An array with ones at and below the given diagonal and zeros elsewhere.
*
* @param name name May be null. Name for the output variable
* @param row
* @param column
* @return output (FLOATING_POINT type)
*/
public SDVariable tri(String name, int row, int column) {
SDVariable out = new org.nd4j.linalg.api.ops.custom.Tri(sd,DataType.FLOAT, row, column, 0).outputVariable();
return sd.updateVariableNameAndReference(out, name);
}
/**
* Upper triangle of an array. Return a copy of a input tensor with the elements below the k-th diagonal zeroed.
*
* @param input (NUMERIC type)
* @param diag
* @return output (FLOATING_POINT type)
*/
public SDVariable triu(SDVariable input, int diag) {
SDValidation.validateNumerical("triu", "input", input);
return new org.nd4j.linalg.api.ops.custom.Triu(sd,input, diag).outputVariable();
}
/**
* Upper triangle of an array. Return a copy of a input tensor with the elements below the k-th diagonal zeroed.
*
* @param name name May be null. Name for the output variable
* @param input (NUMERIC type)
* @param diag
* @return output (FLOATING_POINT type)
*/
public SDVariable triu(String name, SDVariable input, int diag) {
SDValidation.validateNumerical("triu", "input", input);
SDVariable out = new org.nd4j.linalg.api.ops.custom.Triu(sd,input, diag).outputVariable();
return sd.updateVariableNameAndReference(out, name);
}
/**
* Upper triangle of an array. Return a copy of a input tensor with the elements below the k-th diagonal zeroed.
*
* @param input (NUMERIC type)
* @return output (FLOATING_POINT type)
*/
public SDVariable triu(SDVariable input) {
SDValidation.validateNumerical("triu", "input", input);
return new org.nd4j.linalg.api.ops.custom.Triu(sd,input, 0).outputVariable();
}
/**
* Upper triangle of an array. Return a copy of a input tensor with the elements below the k-th diagonal zeroed.
*
* @param name name May be null. Name for the output variable
* @param input (NUMERIC type)
* @return output (FLOATING_POINT type)
*/
public SDVariable triu(String name, SDVariable input) {
SDValidation.validateNumerical("triu", "input", input);
SDVariable out = new org.nd4j.linalg.api.ops.custom.Triu(sd,input, 0).outputVariable();
return sd.updateVariableNameAndReference(out, name);
}
}
