geosnap.analyze.cluster module¶
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geosnap.analyze.cluster.
affinity_propagation
(X, damping=0.8, preference=-1000, max_iter=500, convergence_iter=15, copy=True, affinity='euclidean', verbose=False, **kwargs)[source]¶ Clustering with Affinity Propagation.
- Parameters
- Xarray-like
n x k attribute data
- preferencearray-like, shape (n_samples,) or float, optional,
default: None
The preference parameter passed to scikit-learn’s affinity propagation algorithm
- damping: float, optional, default: 0.8
The damping parameter passed to scikit-learn’s affinity propagation algorithm
- max_iterint, optional, default: 1000
Maximum number of iterations
- Returns
- model: sklearn AffinityPropagation instance
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geosnap.analyze.cluster.
azp
(X, w, n_clusters=5, **kwargs)[source]¶ AZP clustering algorithm
- Parameters
- Xarray-like
n x k attribute data
- wPySAL W instance
spatial weights matrix
- n_clustersint, optional, default: 5
The number of clusters to form.
- Returns
- model: region AZP instance
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geosnap.analyze.cluster.
gaussian_mixture
(X, n_clusters=5, covariance_type='full', best_model=False, max_clusters=10, random_state=None, **kwargs)[source]¶ Clustering with Gaussian Mixture Model
- Parameters
- Xarray-like
n x k attribute data
- n_clustersint, optional, default: 5
The number of clusters to form.
- covariance_type: str, optional, default: “full”“
The covariance parameter passed to scikit-learn’s GaussianMixture algorithm
- best_model: bool, optional, default: False
Option for finding endogenous K according to Bayesian Information Criterion
- max_clusters: int, optional, default:10
The max number of clusters to test if using best_model option
- random_state: int, optional, default: None
The seed used to generate replicable results
- Returns
- model: sklearn GaussianMixture instance
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geosnap.analyze.cluster.
hdbscan
(X, min_cluster_size=5, gen_min_span_tree=True, **kwargs)[source]¶ Clustering with Hierarchical DBSCAN
- Parameters
- Xarray-like
n x k attribute data
- min_cluster_sizeint, default: 5
the minimum number of points necessary to generate a cluster
- gen_min_span_treebool
Description of parameter gen_min_span_tree (the default is True).
- Returns
- model: hdbscan HDBSCAN instance
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geosnap.analyze.cluster.
kmeans
(X, n_clusters, init='k-means++', n_init=10, max_iter=300, tol=0.0001, verbose=0, random_state=None, copy_x=True, n_jobs=None, algorithm='auto', precompute_distances='auto', **kwargs)[source]¶ K-Means clustering.
- Parameters
- Xarray-like
n x k attribute data
- n_clustersint, optional, default: 8
The number of clusters to form as well as the number of centroids to generate.
- Returns
- model: sklearn KMeans instance
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geosnap.analyze.cluster.
max_p
(X, w, threshold_variable='count', threshold=10, **kwargs)[source]¶ Max-p clustering algorithm [DAR12]
- Parameters
- Xarray-like
n x k attribute data
- wPySAL W instance
spatial weights matrix
- threshold_variablestr, default:”count”
attribute variable to use as floor when calculate
- thresholdint, default:10
integer that defines the upper limit of a variable that can be grouped into a single region
- Returns
- model: region MaxPRegionsHeu instance
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geosnap.analyze.cluster.
skater
(X, w, n_clusters=5, floor=-inf, trace=False, islands='increase', **kwargs)[source]¶ SKATER spatial clustering algorithm.
- Parameters
- Xarray-like
n x k attribute data
- wPySAL W instance
spatial weights matrix
- n_clustersint, optional, default: 5
The number of clusters to form.
- floortype
TODO.
- tracetype
TODO.
- islandstype
TODO.
- Returns
- model: skater SKATER instance
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geosnap.analyze.cluster.
spectral
(X, n_clusters, eigen_solver=None, random_state=None, n_init=10, gamma=1.0, affinity='rbf', n_neighbors=10, eigen_tol=0.0, assign_labels='kmeans', degree=3, coef0=1, kernel_params=None, n_jobs=-1, **kwargs)[source]¶ Short summary.
- Parameters
- Xarral-like
n x k attribute data
- n_clusterstype
The number of clusters to form as well as the number of centroids to generate.
- eigen_solvertype
Description of parameter eigen_solver (the default is None).
- random_statetype
Description of parameter random_state (the default is None).
- n_inittype
Description of parameter n_init (the default is 10).
- gammatype
Description of parameter gamma (the default is 1.0).
- affinitytype
Description of parameter affinity (the default is ‘rbf’).
- n_neighborstype
Description of parameter n_neighbors (the default is 10).
- eigen_toltype
Description of parameter eigen_tol (the default is 0.0).
- assign_labelstype
Description of parameter assign_labels (the default is ‘kmeans’).
- degreetype
Description of parameter degree (the default is 3).
- coef0type
Description of parameter coef0 (the default is 1).
- kernel_paramstype
Description of parameter kernel_params (the default is None).
- n_jobstype
Description of parameter n_jobs (the default is -1).
- **kwargstype
Description of parameter **kwargs.
- Returns
- model: sklearn SpectralClustering instance
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geosnap.analyze.cluster.
spenc
(X, w, n_clusters=5, gamma=1, **kwargs)[source]¶ Spatially encouraged spectral clustering
[wolf2018]
- Parameters
- Xarray-like
n x k attribute data
- wPySAL W instance
spatial weights matrix
- n_clustersint, optional, default: 5
The number of clusters to form.
- gammaint, default:1
TODO.
- Returns
- model: spenc SPENC instance
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geosnap.analyze.cluster.
ward
(X, n_clusters=5, **kwargs)[source]¶ Agglomerative clustering using Ward linkage.
- Parameters
- Xarray-like
n x k attribute data
- n_clustersint, optional, default: 8
The number of clusters to form.
- Returns
- model: sklearn AgglomerativeClustering instance
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geosnap.analyze.cluster.
ward_spatial
(X, w, n_clusters=5, **kwargs)[source]¶ - Agglomerative clustering using Ward linkage with a spatial connectivity
constraint
- Parameters
- Xarray-like
n x k attribute data
- wPySAL W instance
spatial weights matrix
- n_clustersint, optional, default: 5
The number of clusters to form.
- Returns
- model: sklearn AgglomerativeClustering instance