Contents

# Computation

## Algorithm Input

The cross entropy loss algorithm accepts the input described below. Pass the Input ID as a parameter to the methods that provide input for your algorithm. For more details, see Algorithms.
Input ID
Input
argument
Numeric table of size (
p
+ 1) x nClasses with the input argument
θ
of the objective function.
Sizes of argument, gradient, and hessian numeric tables do not depend on
intercept flag
. In case when
intercept flag
is set to
false
, computation of
θ0
value will be skipped, but sizes of tables should remain the same.
data
Numeric table of size
n
x
p
with the data
x
ij
. This parameter can be an object of any class derived from
NumericTable
.
dependentVariables
Numeric table of size
n
x 1 with dependent variables
yi
. This parameter can be an object of any class derived from
NumericTable
, except for
PackedTriangularMatrix
,
PackedSymmetricMatrix
, and
CSRNumericTable
.

## Algorithm Parameters

The cross entropy loss algorithm has the following parameters. Some of them are required only for specific values of the computation method parameter
method
:
Parameter
Default Value
Description
algorithmFPType
float
The floating-point type that the algorithm uses for intermediate computations. Can be
float
or
double
.
method
defaultDense
Performance-oriented computation method.
numberOfTerms
Not applicable
The number of terms in the objective function.
batchIndices
NULL
The numeric table of size 1 x
m
, where
m
is the batch size, with a batch of indices to be used to compute the function results. If no indices are provided, the implementation uses all the terms in the computation.
This parameter can be an object of any class derived from
NumericTable
except
PackedTriangularMatrix
and
PackedSymmetricMatrix
.
resultsToCompute
The 64-bit integer flag that specifies which characteristics of the objective function to compute.
Provide one of the following values to request a single characteristic or use bitwise OR to request a combination of the characteristics:
value
Value of the objective function
nonSmoothTermValue
Value of non-smooth term of the objective function
Gradient of the smooth term of the objective function
hessian
Hessian of smooth term of the objective function
proximalProjection
Projection of proximal operator for non-smooth term of the objective function
lipschitzConstant
Lipschitz constant of the smooth term of the objective function
hessianOverCertainFeature
Certain component of hessian diagonal
proximalProjectionOfCertainFeature
Certain component of proximal projection
interceptFlag
True
A flag that indicates a need to compute
θ
0
j
penaltyL1
0
L1 regularization coefficient
penaltyL2
0
L2 regularization coefficient
nClasses
Not applicable
Number of classes (different values of dependent variable)

## Algorithm Output

For the output of the cross entropy loss algorithm, see Output for objective functions.

## Examples

#### Product and Performance Information

1

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Notice revision #20110804