📝 Add pylint/tests badges

Author: valentingol

README.md

@@ -4,6 +4,7 @@ Principal Component Anlaysis (PCA) in PyTorch. The intention is to provide a
 simple and easy to use implementation of PCA in PyTorch, the most similar to
 the `sklearn`'s PCA as possible (in terms of API and, of course, output).
 
+[![Release](https://img.shields.io/github/v/tag/valentingol/torch_pca?label=Pypi&logo=pypi&logoColor=yellow)](https://pypi.org/project/torch_pca/)
 ![PythonVersion](https://img.shields.io/badge/python-3.8%20%7E%203.11-informational)
 ![PytorchVersion](https://img.shields.io/badge/pytorch-1.8%20%7E%201.13%20%7C%202.0+-informational)
 
@@ -17,6 +18,17 @@ the `sklearn`'s PCA as possible (in terms of API and, of course, output).
 [![Flake8](https://github.com/valentingol/torch_pca/actions/workflows/flake.yaml/badge.svg)](https://github.com/valentingol/Dinosor/actions/workflows/flake.yaml)
 [![Pydocstyle](https://github.com/valentingol/torch_pca/actions/workflows/pydocstyle.yaml/badge.svg)](https://github.com/valentingol/Dinosor/actions/workflows/pydocstyle.yaml)
 [![MyPy](https://github.com/valentingol/torch_pca/actions/workflows/mypy.yaml/badge.svg)](https://github.com/valentingol/Dinosor/actions/workflows/mypy.yaml)
+[![PyLint](https://img.shields.io/endpoint?url=https://gist.githubusercontent.com/valentingol/8fb4f3f78584e085dd7b0cca7e046d1f/raw/torch_pca_pylint.json)](https://github.com/valentingol/torch_pca/actions/workflows/pylint.yaml)
+
+[![Tests](https://github.com/valentingol/torch_pca/actions/workflows/tests.yaml/badge.svg)](https://github.com/valentingol/torch_pca/actions/workflows/tests.yaml)
+[![Coverage](https://img.shields.io/endpoint?url=https://gist.githubusercontent.com/valentingol/c5a6b5731db93da673f8e258b2669080/raw/torch_pca_tests.json)](https://github.com/valentingol/torch_pca/actions/workflows/tests.yaml)
+[![Documentation Status](https://readthedocs.org/projects/torch-pca/badge/?version=latest)](https://torch-pca.readthedocs.io/en/latest/?badge=latest)
+
+## Links
+
+Pypi project: https://pypi.org/project/torch_pca/
+
+Documentation: https://torch-pca.readthedocs.io/en/latest/
 
 ## Installation
 
@@ -31,12 +43,12 @@ Exactly like `sklearn.decomposition.PCA` but it uses PyTorch tensors as input an
 ```python
 from torch_cpa import PCA
 
-# Create like sklearn.decomposition.PCA
-pca_model = PCA(n_components=None, whiten=False)
+# Create like sklearn.decomposition.PCA, e.g.:
+pca_model = PCA(n_components=None, svd_solver='full')
 
-# Use like sklearn.decomposition.PCA
->>> new_X1 = pca_model.fit_transform(X1)
->>> new_X2 = pca_model.transform(X2)
+# Use like sklearn.decomposition.PCA, e.g.:
+>>> new_train_data = pca_model.fit_transform(train_data)
+>>> new_test_data = pca_model.transform(test_data)
 >>> print(pca.explained_variance_ratio_)
 [0.756, 0.142, 0.062, ...]
 ```