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All Samples(6)  |  Call(3)  |  Derive(0)  |  Import(3)

src/n/i/nipy-0.3.0/nipy/labs/utils/reproducibility_measures.py   nipy(Download)
                0 is bad
    """
    from ...algorithms.utils.fast_distance import euclidean_distance as ed
    if data == None:
        if target == None:
        return 0.
 
    dmatrix = ed(data, target) / sigma
    sensitivity = dmatrix.min(0)
    sensitivity = np.exp( - 0.5 * sensitivity ** 2)

src/p/y/pypreprocess-HEAD/pypreprocess/external/nipy_labs/utils/reproducibility_measures.py   pypreprocess(Download)
                0 is bad
    """
    from ...algorithms.utils.fast_distance import euclidean_distance as ed
    if data == None:
        if target == None:
        return 0.
 
    dmatrix = ed(data, target) / sigma
    sensitivity = dmatrix.min(0)
    sensitivity = np.exp( - 0.5 * sensitivity ** 2)

src/n/i/nipy-0.3.0/nipy/labs/spatial_models/structural_bfls.py   nipy(Download)
    def homogeneity(self):
        """ returns the mean distance between points within each LR
        """
        from ...algorithms.utils.fast_distance import euclidean_distance
 
                h[k] = 0
            else:
                edk = euclidean_distance(pk)
                h[k] = edk.sum() / (sk * (sk - 1))
        return h