Parameters for OPTICS clustering algorithm.
| Name | Type | Description | Notes |
|---|---|---|---|
| min_samples | int | Number of samples in a neighborhood for a point to be considered a core point | [optional] [default to 5] |
| max_eps | float | Maximum distance between two samples. Default (None) means no maximum distance | [optional] |
| metric | str | Metric to use for distance computation | [optional] [default to 'minkowski'] |
| p | float | Parameter for the Minkowski metric | [optional] [default to 2] |
| metric_params | Dict[str, object] | Additional keyword arguments for the metric function | [optional] |
| cluster_method | str | Method to extract clusters ('xi' or 'dbscan') | [optional] [default to 'xi'] |
| eps | float | Maximum distance for DBSCAN cluster extraction method | [optional] |
| xi | float | Minimum steepness on the reachability plot for cluster boundary (xi method) | [optional] [default to 0.05] |
| predecessor_correction | bool | Correct clusters based on predecessors (xi method) | [optional] [default to True] |
| min_cluster_size | float | Minimum number of samples in a cluster. Can be a fraction if < 1.0 | [optional] |
| algorithm | str | Algorithm to compute pointwise distances ('auto', 'ball_tree', 'kd_tree', 'brute') | [optional] [default to 'auto'] |
| leaf_size | int | Leaf size passed to BallTree or KDTree | [optional] [default to 30] |
| n_jobs | int | Number of parallel jobs to run (-1 means using all processors) | [optional] [default to 1] |
from mixpeek.models.optics_params import OPTICSParams
# TODO update the JSON string below
json = "{}"
# create an instance of OPTICSParams from a JSON string
optics_params_instance = OPTICSParams.from_json(json)
# print the JSON string representation of the object
print(OPTICSParams.to_json())
# convert the object into a dict
optics_params_dict = optics_params_instance.to_dict()
# create an instance of OPTICSParams from a dict
optics_params_from_dict = OPTICSParams.from_dict(optics_params_dict)