Selects num_to_sample rows of input using the KMeans++ criterion.
tf.raw_ops.KmeansPlusPlusInitialization(
points, num_to_sample, seed, num_retries_per_sample, name=None
)
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.
Args |
|---|
points
|
A Tensor of type float32.
Matrix of shape (n, d). Rows are assumed to be input points.
|
num_to_sample
|
A Tensor of type int64.
Scalar. The number of rows to sample. This value must not be larger than n.
|
seed
|
A Tensor of type int64.
Scalar. Seed for initializing the random number generator.
|
num_retries_per_sample
|
A Tensor of type int64.
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.
|
name
|
A name for the operation (optional).
|
Returns |
|---|
A Tensor of type float32.
|
