public final class
KmeansPlusPlusInitialization
Selects num_to_sample rows of input using the KMeans++ criterion.
Rows of points are assumed to be input points. One row is selected at random. Subsequent rows are sampled with probability proportional to the squared L2 distance from the nearest row selected thus far till num_to_sample rows have been sampled.
Public Methods
| Output<Float> |
asOutput()
Returns the symbolic handle of a tensor.
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| static KmeansPlusPlusInitialization | |
| Output<Float> |
samples()
Matrix of shape (num_to_sample, d).
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Inherited Methods
Public Methods
public Output<Float> asOutput ()
Returns the symbolic handle of a tensor.
Inputs to TensorFlow operations are outputs of another TensorFlow operation. This method is used to obtain a symbolic handle that represents the computation of the input.
public static KmeansPlusPlusInitialization create (Scope scope, Operand<Float> points, Operand<Long> numToSample, Operand<Long> seed, Operand<Long> numRetriesPerSample)
Factory method to create a class wrapping a new KmeansPlusPlusInitialization operation.
Parameters
| scope | current scope |
|---|---|
| points | Matrix of shape (n, d). Rows are assumed to be input points. |
| numToSample | Scalar. The number of rows to sample. This value must not be larger than n. |
| seed | Scalar. Seed for initializing the random number generator. |
| numRetriesPerSample | Scalar. For each row that is sampled, this parameter specifies the number of additional points to draw from the current distribution before selecting the best. If a negative value is specified, a heuristic is used to sample O(log(num_to_sample)) additional points. |
Returns
- a new instance of KmeansPlusPlusInitialization