Convex-hull-peeling depth#

DepthEucl.qhpeeling(x: ndarray | None = None, evaluate_dataset: bool = False) → ndarray[source]

Calculates the convex hull peeling depth of points w.r.t. a multivariate data set.

Usage

qhpeeling(x, data)

Arguments
x: array-like or None, default=None

Matrix of objects (numerical vector as one object) whose depth is to be calculated; each row contains a d-variate point. Should have the same dimension as data.

evaluate_datasetbool, default=False

Determines if dataset loaded will be evaluated. Automatically sets x to dataset

References

  • Barnett, V. (1976). The ordering of multivariate data. Journal of the Royal Statistical Society, Series A, 139, 318–355.

  • Eddy, W. F. (1981). Graphics for the multivariate two-sample problem: Comment. Journal of the American Statistical Association, 76, 287–289.

Examples
>>> from depth.model import DepthEucl
>>> mat1=[[1, 0, 0, 0, 0],[0, 2, 0, 0, 0],[0, 0, 3, 0, 0],[0, 0, 0, 2, 0],[0, 0, 0, 0, 1]]
>>> mat2=[[1, 0, 0, 0, 0],[0, 1, 0, 0, 0],[0, 0, 1, 0, 0],[0, 0, 0, 1, 0],[0, 0, 0, 0, 1]]
>>> x = np.random.multivariate_normal([1,1,1,1,1], mat2, 10)
>>> data = np.random.multivariate_normal([0,0,0,0,0], mat1, 100)
>>> model=DepthEucl().load_dataset(data)
>>> model.qhpeeling(x)
[0.   0.   0.   0.   0.   0.   0.01 0.   0.   0.01]

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