docs

Author: j-bac

doc/references.rst

@@ -6,12 +6,15 @@ Articles
 .. [Albergante2019] Albergante, L., *et al.* (2019),
    *Estimating the effective dimension of large biological datasets using Fisher separability analysis.*,
    `2019 International Joint Conference on Neural Networks, IEEE`.
-.. [Amsaleg2018] Amsaleg, L., *et al.* (2018)
+.. [Amsaleg2018] Amsaleg, L., *et al.* (2018),
    *Extreme-value-theoretic estimation of local intrinsic dimensionality.*
    `DAMI, 32(6):1768–1805.`
-.. [Amsaleg2019], Amsaleg, L., *et al.* (2019)
+.. [Amsaleg2019], Amsaleg, L., *et al.* (2019),
    *Intrinsic dimensionality estimation within tight localities.*,
    `Proceedings of the SIAM International Conference on Data Mining (SDM), pages 181–189, Calgary, Alberta, Canada`
+.. [Campadelli2015] Campadelli *et al.* (2015),
+   *Intrinsic Dimension Estimation: Relevant Techniques and a Benchmark Framework.*
+   `Mathematical Problems in Engineering`.
 .. [Cangelosi2007] Cangelosi, R., and Goriely, A. (2007),
    *Component retention in principal component analysis with application to cDNA microarray data.*,
    `Biol. Direct 2:2.`

skdim/datasets.py

@@ -127,7 +127,7 @@ def lineDiskBall(n, random_state=None):
     Generates a sample from a uniform distribution on a line, an oblong disk and an oblong ball
     Translated from ldbl function in Hideitsu Hino's package
 
-        Parameters
+    Parameters
     ----------
     n: int 
         Number of data points.
@@ -247,21 +247,12 @@ def dl(r):
 
 class BenchmarkManifolds:
     """
-    Generates a commonly used benchmark set of synthetic manifolds with known intrinsic dimension described by Hein et al. and Campadelli et al.
+    Generates a commonly used benchmark set of synthetic manifolds with known intrinsic dimension described by Hein et al. and Campadelli et al. [Campadelli2015]_
 
-    Attributes
+    Parameters
     ----------
     noise_type : str, 'uniform' or 'gaussian'
         Type of noise to generate
-
-    References
-    ----------
-
-    Campadelli et al., Intrinsic Dimension Estimation: Relevant Techniques and 
-    a Benchmark Framework, https://doi.org/10.1155/2015/759567
-
-    M. Hein and J.-Y. Audibert, IntrinsicDity estimation of submanifolds in Euclidean space, 
-    Proceedings of the 22nd Internatical Conference on Machine Learning (ICML), 289--296. (Eds.) L. de Raedt and S. Wrobel (2005). 
     """
 
     # class modified and adapted from https://github.com/stat-ml/GeoMLE