| Package | Description |
|---|---|
| org.nd4j.linalg.api.ops | |
| org.nd4j.linalg.api.ops.impl.reduce.floating | |
| org.nd4j.linalg.api.ops.impl.reduce3 |
| Modifier and Type | Class and Description |
|---|---|
class |
BaseReduceFloatOp |
| Modifier and Type | Class and Description |
|---|---|
class |
AMean
Calculate the absolute mean of the given vector
|
class |
Bias
Calculate a bias
|
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 |
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
|
Copyright © 2019. All rights reserved.