Algorithm-specific parameters
| Name | Type | Description | Notes |
|---|---|---|---|
| n_clusters | int | Number of clusters to form | [optional] [default to 8] |
| max_iter | int | Maximum number of iterations per seed point before the algorithm stops | [optional] [default to 300] |
| random_state | int | Random seed for reproducibility | [optional] [default to 42] |
| n_init | int | Number of initializations to perform | [optional] [default to 1] |
| tol | float | Convergence threshold | [optional] [default to 0.001] |
| init | str | Method for initialization ('k-means++' or 'random') | [optional] [default to 'k-means++'] |
| verbose | int | Enable verbose output | [optional] [default to 0] |
| copy_x | bool | If True, the original data is not modified | [optional] [default to True] |
| algorithm | str | Algorithm to compute pointwise distances ('auto', 'ball_tree', 'kd_tree', 'brute') | [optional] [default to 'auto'] |
| eps | float | Maximum distance for DBSCAN cluster extraction method | [optional] |
| min_samples | int | Number of samples in a neighborhood for a point to be considered a core point | [optional] [default to 5] |
| metric | str | Metric to use for distance computation | [optional] [default to 'minkowski'] |
| metric_params | Dict[str, object] | Additional keyword arguments for the metric function | [optional] |
| leaf_size | int | Leaf size passed to BallTree or KDTree | [optional] [default to 30] |
| p | float | Parameter for the Minkowski metric | [optional] [default to 2] |
| n_jobs | int | Number of parallel jobs to run (-1 means using all processors) | [optional] [default to 1] |
| min_cluster_size | float | Minimum number of samples in a cluster. Can be a fraction if < 1.0 | [optional] |
| cluster_selection_epsilon | float | A distance threshold for cluster selection. Clusters below this value will be merged | [optional] [default to 0] |
| max_cluster_size | int | Maximum number of samples in a cluster. Clusters above this size will be split | [optional] |
| alpha | float | A distance scaling parameter | [optional] [default to 1] |
| cluster_selection_method | str | Method to select clusters from the condensed tree ('eom' or 'leaf') | [optional] [default to 'eom'] |
| allow_single_cluster | bool | Allow HDBSCAN to find only a single cluster | [optional] [default to False] |
| prediction_data | bool | Whether to generate extra data for predicting cluster membership | [optional] [default to False] |
| match_reference_implementation | bool | Whether to match the reference implementation exactly | [optional] [default to False] |
| affinity | str | How to construct the affinity matrix ('nearest_neighbors', 'rbf', 'precomputed', 'precomputed_nearest_neighbors') | [optional] [default to 'rbf'] |
| memory | str | Path to the caching directory | [optional] |
| connectivity | object | [optional] | |
| compute_full_tree | str | Whether to compute the full tree ('auto', True, or False) | [optional] [default to 'auto'] |
| linkage | str | Linkage criterion ('ward', 'complete', 'average', 'single') | [optional] [default to 'ward'] |
| distance_threshold | float | The linkage distance threshold above which clusters will not be merged | [optional] |
| compute_distances | bool | Whether to compute distances between clusters | [optional] [default to False] |
| eigen_solver | str | The eigenvalue decomposition strategy ('arpack', 'lobpcg', 'amg', or None) | [optional] |
| n_components | int | Number of mixture components | [optional] [default to 1] |
| gamma | float | Kernel coefficient for rbf, poly, sigmoid, laplacian and chi2 kernels | [optional] [default to 1] |
| n_neighbors | int | Number of neighbors to use when constructing the affinity matrix using nearest neighbors | [optional] [default to 10] |
| eigen_tol | float | Stopping criterion for eigendecomposition | [optional] [default to 0] |
| assign_labels | str | Strategy to assign labels in the embedding space ('kmeans' or 'discretize') | [optional] [default to 'kmeans'] |
| degree | float | Degree of the polynomial kernel. Ignored by other kernels | [optional] [default to 3] |
| coef0 | float | Zero coefficient for polynomial and sigmoid kernels | [optional] [default to 1] |
| kernel_params | Dict[str, object] | Parameters for the kernel function | [optional] |
| covariance_type | str | Type of covariance parameters ('full', 'tied', 'diag', 'spherical') | [optional] [default to 'full'] |
| reg_covar | float | Regularization added to the diagonal of covariance | [optional] [default to 1.0E-6] |
| init_params | str | Method used to initialize weights, means and covariances ('kmeans' or 'random') | [optional] [default to 'kmeans'] |
| weights_init | List[object] | Initial weights | [optional] |
| means_init | List[object] | Initial means | [optional] |
| precisions_init | List[object] | Initial precisions | [optional] |
| warm_start | bool | If True, use the solution of the last fit as initialization | [optional] [default to False] |
| verbose_interval | int | Number of iterations between each verbose message | [optional] [default to 10] |
| bandwidth | float | Bandwidth used in the RBF kernel. If None, estimated using sklearn.cluster.estimate_bandwidth | [optional] |
| seeds | List[List[float]] | Seeds used to initialize kernels. If None, all points are used as seeds | [optional] |
| bin_seeding | bool | If true, initial kernel locations are discretized into a grid to speed up algorithm | [optional] [default to False] |
| min_bin_freq | int | Minimum number of seeds within a bin for the bin to be considered | [optional] [default to 1] |
| cluster_all | bool | If true, all points are clustered, even orphans. If false, orphans are given label -1 | [optional] [default to True] |
| max_eps | float | Maximum distance between two samples. Default (None) means no maximum distance | [optional] |
| cluster_method | str | Method to extract clusters ('xi' or 'dbscan') | [optional] [default to 'xi'] |
| 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] |
from mixpeek.models.algorithm_params import AlgorithmParams
# TODO update the JSON string below
json = "{}"
# create an instance of AlgorithmParams from a JSON string
algorithm_params_instance = AlgorithmParams.from_json(json)
# print the JSON string representation of the object
print(AlgorithmParams.to_json())
# convert the object into a dict
algorithm_params_dict = algorithm_params_instance.to_dict()
# create an instance of AlgorithmParams from a dict
algorithm_params_from_dict = AlgorithmParams.from_dict(algorithm_params_dict)