update licenses, tests

Author: j-bac

doc/installation.rst

@@ -8,7 +8,7 @@ PyPI
 
 Install scikit-dimension from PyPI_ using::
 
-    pip install scikit-dimension #will come with first release
+    pip install scikit-dimension
 
 Development Version
 ^^^^^^^^^^^^^^^^^^^

doc/release_notes.rst

@@ -4,6 +4,6 @@
 Release Notes
 =============
 
-Version 0.1.1 :small:`December 2020`
+Version 0.2 :small:`December 2020`
 -----------------------------------
-First alpha release of skdim.
+First release of skdim.

setup.py

@@ -47,21 +47,19 @@
 MAINTAINER = "Jonathan Bac"
 MAINTAINER_EMAIL = "[email protected]"
 URL = "https://github.com/j-bac/scikit-dimension"
-LICENSE = "new BSD"
+LICENSE = "BSD-3-clause"
 DOWNLOAD_URL = "https://github.com/j-bac/scikit-dimension"
 VERSION = __version__
 INSTALL_REQUIRES = ["numpy", "scipy", "scikit-learn"]
 CLASSIFIERS = [
     "Intended Audience :: Science/Research",
-    "Intended Audience :: Developers",
     "License :: OSI Approved",
     "Programming Language :: Python",
-    "Topic :: Software Development",
     "Topic :: Scientific/Engineering",
     "Operating System :: Microsoft :: Windows",
-    "Operating System :: POSIX",
     "Operating System :: Unix",
     "Operating System :: MacOS",
+    "Programming Language :: Python :: 3.6",
     "Programming Language :: Python :: 3.7",
     "Programming Language :: Python :: 3.8",
 ]

skdim/_commonfuncs.py

@@ -563,32 +563,32 @@ def fit_predict_pw(
             return self.dimension_pw_
 
 
-def mean_local_id(local_id, knnidx):
-    """
-    Compute the mean ID of all neighborhoods in which a point appears
-
-    Parameters
-    ----------
-    local_id : list or np.array
-        list of local ID for each point
-    knnidx : np.array 
-        indices of kNN for each point returned by function get_nn
-
-    Results
-    -------
-    dimension_pw_smooth_ : np.array
-        list of mean local ID for each point
-
-    """
-    dimension_pw_smooth_ = np.zeros(len(local_id))
-    for point_i in range(len(local_id)):
-        # get all points which have this point in their neighbourhoods
-        all_neighborhoods_with_point_i = np.append(
-            np.where(knnidx == point_i)[0], point_i
-        )
-        # get the mean local ID of these points
-        dimension_pw_smooth_[point_i] = local_id[all_neighborhoods_with_point_i].mean()
-    return dimension_pw_smooth_
+# def mean_local_id(local_id, knnidx):
+#    """
+#    Compute the mean ID of all neighborhoods in which a point appears
+#
+#    Parameters
+#    ----------
+#    local_id : list or np.array
+#        list of local ID for each point
+#    knnidx : np.array
+#        indices of kNN for each point returned by function get_nn
+#
+#    Results
+#    -------
+#    dimension_pw_smooth_ : np.array
+#        list of mean local ID for each point
+#
+#    """
+#    dimension_pw_smooth_ = np.zeros(len(local_id))
+#    for point_i in range(len(local_id)):
+#        # get all points which have this point in their neighbourhoods
+#        all_neighborhoods_with_point_i = np.append(
+#            np.where(knnidx == point_i)[0], point_i
+#        )
+#        # get the mean local ID of these points
+#        dimension_pw_smooth_[point_i] = local_id[all_neighborhoods_with_point_i].mean()
+#    return dimension_pw_smooth_
 
 
 def binom_coeff(n, k):

skdim/_version.py

@@ -29,4 +29,4 @@
 # OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
 # OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
 #
-__version__ = "0.1.1"
+__version__ = "0.2"

skdim/id/_DANCo.py

@@ -50,6 +50,7 @@
 
 
 class DANCo(GlobalEstimator):
+    # SPDX-License-Identifier: MIT, 2017 Kerstin Johnsson [IDJohnsson]_
     """Intrinsic dimension estimation using the Dimensionality from Angle and Norm Concentration algorithm. [Ceruti2012]_ [IDLombardi]_ [IDJohnsson]_ 
 
     Parameters

skdim/id/_ESS.py

@@ -46,6 +46,7 @@
 
 
 class ESS(LocalEstimator):
+    # SPDX-License-Identifier: MIT, 2017 Kerstin Johnsson [IDJohnsson]_
     """Intrinsic dimension estimation using the Expected Simplex Skewness algorithm. [Johnsson2015]_ [IDJohnsson]_
     The ESS method assumes that the data is local, i.e. that it is a neighborhood taken from a larger data set, such that the curvature and the noise within the neighborhood is relatively small. In the ideal case (no noise, no curvature) this is equivalent to the data being uniformly distributed over a hyper ball. 
     

skdim/id/_KNN.py

@@ -36,6 +36,7 @@
 
 
 class KNN(GlobalEstimator):
+    # SPDX-License-Identifier: MIT, 2017 Kerstin Johnsson [IDJohnsson]_
     """Intrinsic dimension estimation using the kNN algorithm. [Carter2010]_ [IDJohnsson]_
 
     This is a simplified version of the kNN dimension estimation method described by Carter et al. (2010), 

skdim/id/_MLE.py

@@ -38,6 +38,7 @@
 
 
 class MLE(LocalEstimator):
+    # SPDX-License-Identifier: MIT, 2017 Kerstin Johnsson [IDJohnsson]_
     """Intrinsic dimension estimation using the Maximum Likelihood algorithm. [Haro2008]_ [Hill1975]_ [Levina2005]_ [IDJohnsson]_
 
     The estimators are based on the referenced paper by Haro et al. (2008), using the assumption that there is a single manifold. 

skdim/id/_MiND_ML.py

@@ -37,6 +37,7 @@
 
 
 class MiND_ML(GlobalEstimator):
+    # SPDX-License-Identifier: MIT, 2017 Kerstin Johnsson [IDJohnsson]_
     """Intrinsic dimension estimation using the MiND_MLk and MiND_MLi algorithms. [Rozza2012]_ [IDJohnsson]_
     
     Parameters