manifest.json

{
    "title": "Subsequence Fast-MCD",
    "description": "Implementation of https://doi.org/10.2307/1270566 with sliding windows as input",
    "inputDimensionality": "univariate",
    "version": "0.3.0",
    "authors": "Sebastian Schmidl",
    "language": "Python",
    "type": "Detector",
    "learningType": "semi-supervised",
    "mainFile": "algorithm.py",
    "trainingStep": {
        "parameters": [
            {
                "name": "store_precision",
                "type": "boolean",
                "defaultValue": true,
                "optional": "true",
                "description": "Specify if the estimated precision is stored"
            },
            {
                "name": "support_fraction",
                "type": "float",
                "defaultValue": null,
                "optional": "true",
                "description": "The proportion of points to be included in the support of the raw MCD estimate. Default is None, which implies that the minimum value of support_fraction will be used within the algorithm: `(n_sample + n_features + 1) / 2`. The parameter must be in the range (0, 1)."
            },
            {
                "name": "random_state",
                "type": "int",
                "defaultValue": 42,
                "optional": "true",
                "description": "Determines the pseudo random number generator for shuffling the data."
            }
        ],
        "modelInput": "none"
    },
    "executionStep": {
        "parameters": [],
        "modelInput": "required"
    }
}