Modifier and Type | Interface and Description |
---|---|
interface |
LossFunction
A loss function for computing
the delta between two arrays
|
interface |
ReduceBoolOp
ReduceLong take any type in, and return BOOL type
|
interface |
ReduceFloatOp
ReduceLong take any type in, and return FLOATING-POINT type
|
interface |
ReduceLongOp
ReduceLong take any type in, and return LONG type
|
interface |
ReduceSameOp
ReduceLong take any type in, and return same type
|
Modifier and Type | Class and Description |
---|---|
class |
BaseReduceBoolOp |
class |
BaseReduceFloatOp |
class |
BaseReduceLongOp |
class |
BaseReduceOp
Base class for accumulation, initiates the initial entry
with respect to the child class.
|
class |
BaseReduceSameOp |
Modifier and Type | Method and Description |
---|---|
ReduceOp |
DefaultOpExecutioner.execAndReturn(ReduceOp op) |
ReduceOp |
OpExecutioner.execAndReturn(ReduceOp op)
Execute and return the result from an accumulation
|
Modifier and Type | Method and Description |
---|---|
abstract INDArray |
DefaultOpExecutioner.exec(ReduceOp op) |
INDArray |
OpExecutioner.exec(ReduceOp reduceOp)
Execute a reduceOp, possibly along one or more dimensions
|
ReduceOp |
DefaultOpExecutioner.execAndReturn(ReduceOp op) |
ReduceOp |
OpExecutioner.execAndReturn(ReduceOp op)
Execute and return the result from an accumulation
|
Constructor and Description |
---|
PostulateMetaOp(ScalarOp opA,
ReduceOp opB) |
PostulateMetaOp(TransformOp opA,
ReduceOp opB) |
ReduceMetaOp(ScalarOp opA,
ReduceOp opB) |
ReduceMetaOp(ScalarOp opA,
ReduceOp opB,
int... dimensions) |
Modifier and Type | Class and Description |
---|---|
class |
All
Boolean AND accumulation
|
class |
Any
Boolean AND pairwise transform
|
class |
IsInf
IsInf function
|
class |
IsNaN
IsInf function
|
Modifier and Type | Class and Description |
---|---|
class |
AMean
Calculate the absolute mean of the given vector
|
class |
Entropy
Entropy Op - returns the entropy (information gain, or uncertainty of a random variable).
|
class |
LogEntropy
Log Entropy Op - returns the log entropy (information gain, or uncertainty of a random variable).
|
class |
Mean
Calculate the mean of the vector
|
class |
Norm1
Sum of absolute values
|
class |
Norm2
Sum of squared values (real)
Sum of squared complex modulus (complex)
|
class |
NormMax
The max absolute value
|
class |
ShannonEntropy
Non-normalized Shannon Entropy Op - returns the entropy (information gain, or uncertainty of a random variable).
|
class |
SquaredNorm
Squared norm (sum_i x_i^2) reduction operation
|
Modifier and Type | Class and Description |
---|---|
class |
CountNonZero
Count the number of non-zero elements
|
class |
CountZero
Count the number of zero elements
|
class |
MatchCondition
This operation returns number of elements matching specified condition
|
Modifier and Type | Class and Description |
---|---|
class |
AMax
Calculate the absolute max over a vector
|
class |
AMin
Calculate the absolute minimum over a vector
|
class |
ASum
Absolute sum the components
|
class |
Max
Calculate the max over an array
|
class |
Min
Calculate the min over an array
|
class |
Prod
Prod the components
|
class |
Sum
Sum the components
|
Modifier and Type | Class and Description |
---|---|
class |
BaseReduce3Op
Manhattan distance
|
class |
CosineDistance
Cosine distance
Note that you need to initialize
a scaling constant equal to the norm2 of the
vector
|
class |
CosineSimilarity
Cosine similarity
Note that you need to initialize
a scaling constant equal to the norm2 of the
vector
|
class |
Dot
Dot product.
|
class |
EqualsWithEps
Operation for fast INDArrays equality checks
|
class |
EuclideanDistance
Euclidean distance
|
class |
HammingDistance
Hamming distance (simple)
|
class |
JaccardDistance
Jaccard distance (dissimilarity)
|
class |
ManhattanDistance
Manhattan distance
|
Modifier and Type | Class and Description |
---|---|
class |
StandardDeviation
Standard deviation (sqrt of variance)
|
class |
Variance
Variance with bias correction.
|
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