Added PyPI v1.0.0

QuadratiK/__init__.py

@@ -1,26 +1,22 @@
 import importlib
 
 __version__ = "1.0.0"
-submodules = [
-    "kernel_test",
-    "poisson_kernel_test",
-    "spherical_clustering",
-    "tools",
-    "ui",
-]
+submodules = ["kernel_test","poisson_kernel_test","spherical_clustering","tools","ui"]
 __all__ = submodules + [__version__]
 
 
 def __dir__():
     return __all__
 
-
 # taken from scipy
 def __getattr__(name):
     if name in submodules:
-        return importlib.import_module(f"QuadratiK.{name}")
+        return importlib.import_module(f'QuadratiK.{name}')
     else:
         try:
             return globals()[name]
         except KeyError:
-            raise AttributeError(f"Module 'QuadratiK' has no attribute '{name}'")
+            raise AttributeError(
+                f"Module 'QuadratiK' has no attribute '{name}'"
+            )
+

QuadratiK/datasets/__init__.py

@@ -1,3 +1,3 @@
 from ._dataset import load_wireless_data
 
-__all__ = ["load_wireless_data"]
+__all__ = ["load_wireless_data"]

QuadratiK/datasets/_dataset.py

@@ -16,20 +16,20 @@ def load_wireless_data(desc=False, return_X_y=False, as_dataframe=True, scaled=F
     Parameters
     ----------
 
-        desc : boolean, optional
-            If set to `True`, the function will return the description along with the data.
+        desc : boolean, optional 
+            If set to `True`, the function will return the description along with the data. 
             If set to `False`, the description will not be included. Defaults to False.
 
         return_X_y : boolean, optional
-            Determines whether the function should return the data as separate arrays (`X` and `y`).
+            Determines whether the function should return the data as separate arrays (`X` and `y`). 
             Defaults to False.
 
         as_dataframe : boolean, optional
-            Determines whether the function should return the data as a pandas DataFrame (Trues)
-            or as a numpy array (False). Defaults to True.
+            Determines whether the function should return the data as a pandas DataFrame (Trues) 
+            or as a numpy array (False). Defaults to True. 
 
         scaled : boolean, optional
-            Determines whether or not the data should be scaled. If set to True, the data will be
+            Determines whether or not the data should be scaled. If set to True, the data will be 
             divided by its Euclidean norm along each row. Defaults to False.
 
     Returns
@@ -45,9 +45,9 @@ def load_wireless_data(desc=False, return_X_y=False, as_dataframe=True, scaled=F
             Dataframe of the data with shape (n_samples, n_features + class)
 
         (desc, data, target) : tuple, if desc is True and return_X_y is True
-            A tuple of description and two numpy.ndarray. The first containing a 2D
-            array of shape (n_samples, n_features) with each row representing
-            one sample and each column representing the features. The second
+            A tuple of description and two numpy.ndarray. The first containing a 2D 
+            array of shape (n_samples, n_features) with each row representing 
+            one sample and each column representing the features. The second 
             ndarray of shape (n_samples,) containing the target samples.
 
         (desc, data) : tuple, if desc is True and as_dataframe is True
@@ -56,10 +56,10 @@ def load_wireless_data(desc=False, return_X_y=False, as_dataframe=True, scaled=F
 
     References
     ----------
-        Rohra, J.G., Perumal, B., Narayanan, S.J., Thakur, P., Bhatt, R.B. (2017).
-        User Localization in an Indoor Environment Using Fuzzy Hybrid of Particle Swarm Optimization
-        & Gravitational Search Algorithm with Neural Networks. In: Deep, K., et al. Proceedings of
-        Sixth International Conference on Soft Computing for Problem Solving. Advances in Intelligent
+        Rohra, J.G., Perumal, B., Narayanan, S.J., Thakur, P., Bhatt, R.B. (2017). 
+        User Localization in an Indoor Environment Using Fuzzy Hybrid of Particle Swarm Optimization 
+        & Gravitational Search Algorithm with Neural Networks. In: Deep, K., et al. Proceedings of 
+        Sixth International Conference on Soft Computing for Problem Solving. Advances in Intelligent 
         Systems and Computing, vol 546. Springer, Singapore. https://doi.org/10.1007/978-981-10-3322-3_27
 
     Source
@@ -73,23 +73,17 @@ def load_wireless_data(desc=False, return_X_y=False, as_dataframe=True, scaled=F
     >>> X, y = load_wireless_data(return_X_y=True)
     """
 
-    data = np.loadtxt(
-        str(
-            resources.files("QuadratiK.datasets").joinpath("data/wifi_localization.txt")
-        )
-    )
+    data = np.loadtxt(str(resources.files(
+        "QuadratiK.datasets").joinpath("data/wifi_localization.txt")))
 
     if scaled:
-        data[:, :-1] = data[:, :-1] / np.linalg.norm(
-            data[:, :-1], axis=1, keepdims=True
-        )
+        data = data/np.linalg.norm(data, axis=1, keepdims=True)
 
     feature_names = ["WS1", "WS2", "WS3", "WS4", "WS5", "WS6", "WS7", "Class"]
 
     if desc:
         desc_file = resources.files("QuadratiK.datasets").joinpath(
-            "data/wireless_localization_dataset.rst"
-        )
+            "data/wireless_localization_dataset.rst")
         fdescr = desc_file.read_text()
 
     if return_X_y:
@@ -105,5 +99,3 @@ def load_wireless_data(desc=False, return_X_y=False, as_dataframe=True, scaled=F
             return (fdescr, pd.DataFrame(data, columns=feature_names))
         else:
             return pd.DataFrame(data, columns=feature_names)
-    else:
-        return data

QuadratiK/kernel_test/__init__.py

@@ -1,4 +1,4 @@
 from ._kernel_test import KernelTest
 from ._h_selection import select_h
 
-__all__ = ["KernelTest", "select_h"]
+__all__ = ["KernelTest","select_h"]