org.bytedeco.javacpp

Class tensorflow.MatrixSolveLs

java.lang.Object

org.bytedeco.javacpp.Pointer

org.bytedeco.javacpp.tensorflow.MatrixSolveLs

All Implemented Interfaces:

AutoCloseable

Enclosing class:

tensorflow

@Namespace(value="tensorflow::ops")
 @NoOffset
public static class tensorflow.MatrixSolveLs
extends Pointer

Solves one or more linear least-squares problems. matrix is a tensor of shape [..., M, N] whose inner-most 2 dimensions form real or complex matrices of size [M, N]. Rhs is a tensor of the same type as matrix and shape [..., M, K]. The output is a tensor shape [..., N, K] where each output matrix solves each of the equations matrix[..., :, :] * output[..., :, :] = rhs[..., :, :] in the least squares sense. We use the following notation for (complex) matrix and right-hand sides in the batch: matrix=\\(A \in \mathbb{C}^{m \times n}\\), rhs=\\(B \in \mathbb{C}^{m \times k}\\), output=\\(X \in \mathbb{C}^{n \times k}\\), l2_regularizer=\\(\lambda \in \mathbb{R}\\). If fast is True, then the solution is computed by solving the normal equations using Cholesky decomposition. Specifically, if \\(m \ge n\\) then \\(X = (A^H A + \lambda I)^{-1} A^H B\\), which solves the least-squares problem \\(X = \mathrm{argmin}_{Z \in \Re^{n \times k} } ||A Z - B||_F^2 + \lambda ||Z||_F^2\\). If \\(m \lt n\\) then output is computed as \\(X = A^H (A A^H + \lambda I)^{-1} B\\), which (for \\(\lambda = 0\\)) is the minimum-norm solution to the under-determined linear system, i.e. \\(X = \mathrm{argmin}_{Z \in \mathbb{C}^{n \times k} } ||Z||_F^2 \\), subject to \\(A Z = B\\). Notice that the fast path is only numerically stable when \\(A\\) is numerically full rank and has a condition number \\(\mathrm{cond}(A) \lt \frac{1}{\sqrt{\epsilon_{mach} } }\\) or \\(\lambda\\) is sufficiently large. If fast is False an algorithm based on the numerically robust complete orthogonal decomposition is used. This computes the minimum-norm least-squares solution, even when \\(A\\) is rank deficient. This path is typically 6-7 times slower than the fast path. If fast is False then l2_regularizer is ignored. Arguments: * scope: A Scope object * matrix: Shape is [..., M, N]. * rhs: Shape is [..., M, K]. * l2_regularizer: Scalar tensor. \compatibility(numpy) Equivalent to np.linalg.lstsq \end_compatibility Returns: * Output: Shape is [..., N, K].

Nested Class Summary

Nested Classes

Modifier and Type	Class and Description
static class	tensorflow.MatrixSolveLs.Attrs Optional attribute setters for MatrixSolveLs

Nested classes/interfaces inherited from class org.bytedeco.javacpp.Pointer

Pointer.CustomDeallocator, Pointer.Deallocator, Pointer.NativeDeallocator

Field Summary

Fields inherited from class org.bytedeco.javacpp.Pointer

address, capacity, limit, position

Constructor Summary

Constructors

Constructor and Description
MatrixSolveLs(Pointer p) Pointer cast constructor.
MatrixSolveLs(tensorflow.Scope scope, tensorflow.Input matrix, tensorflow.Input rhs, tensorflow.Input l2_regularizer)
MatrixSolveLs(tensorflow.Scope scope, tensorflow.Input matrix, tensorflow.Input rhs, tensorflow.Input l2_regularizer, tensorflow.MatrixSolveLs.Attrs attrs)

Method Summary

Modifier and Type	Method and Description
tensorflow.Input	asInput()
tensorflow.Output	asOutput()
static tensorflow.MatrixSolveLs.Attrs	Fast(boolean x)
tensorflow.Node	node()
tensorflow.Operation	operation()
tensorflow.MatrixSolveLs	operation(tensorflow.Operation operation)
tensorflow.Output	output()
tensorflow.MatrixSolveLs	output(tensorflow.Output output)

Methods inherited from class org.bytedeco.javacpp.Pointer

address, asBuffer, asByteBuffer, availablePhysicalBytes, calloc, capacity, capacity, close, deallocate, deallocate, deallocateReferences, deallocator, deallocator, equals, fill, formatBytes, free, hashCode, isNull, limit, limit, malloc, maxBytes, maxPhysicalBytes, memchr, memcmp, memcpy, memmove, memset, offsetof, parseBytes, physicalBytes, position, position, put, realloc, setNull, sizeof, toString, totalBytes, totalPhysicalBytes, withDeallocator, zero

Methods inherited from class java.lang.Object

clone, finalize, getClass, notify, notifyAll, wait, wait, wait

Constructor Detail

MatrixSolveLs

public MatrixSolveLs(Pointer p)

Pointer cast constructor. Invokes Pointer.Pointer(Pointer).

MatrixSolveLs

public MatrixSolveLs(@Const @ByRef
                     tensorflow.Scope scope,
                     @ByVal
                     tensorflow.Input matrix,
                     @ByVal
                     tensorflow.Input rhs,
                     @ByVal
                     tensorflow.Input l2_regularizer)

MatrixSolveLs

public MatrixSolveLs(@Const @ByRef
                     tensorflow.Scope scope,
                     @ByVal
                     tensorflow.Input matrix,
                     @ByVal
                     tensorflow.Input rhs,
                     @ByVal
                     tensorflow.Input l2_regularizer,
                     @Const @ByRef
                     tensorflow.MatrixSolveLs.Attrs attrs)

Method Detail

asOutput

@ByVal
 @Name(value="operator tensorflow::Output")
public tensorflow.Output asOutput()

asInput

@ByVal
 @Name(value="operator tensorflow::Input")
public tensorflow.Input asInput()

node

public tensorflow.Node node()

Fast

@ByVal
public static tensorflow.MatrixSolveLs.Attrs Fast(@Cast(value="bool")
                                                          boolean x)

operation

@ByRef
public tensorflow.Operation operation()

operation

public tensorflow.MatrixSolveLs operation(tensorflow.Operation operation)

output

@ByRef
public tensorflow.Output output()

output

public tensorflow.MatrixSolveLs output(tensorflow.Output output)