updated license information in all source files

LICENSE

@@ -1,6 +1,6 @@
 MIT License
 
-Copyright (c) 2021 Tommaso Carraro
+Copyright (c) 2021-2024 Tommaso Carraro
 
 Permission is hereby granted, free of charge, to any person obtaining a copy
 of this software and associated documentation files (the "Software"), to deal

examples/1-binary_classification.ipynb

@@ -9,6 +9,8 @@
     }
    },
    "source": [
+    "This notebook is licensed under the MIT License. See the [LICENSE file](https://github.com/tommasocarraro/LTNtorch/blob/main/LICENSE) in the project root for details.\n",
+    "\n",
     "# The reach of Logic Tensor Networks\n",
     "\n",
     "**Premise:** we suggest going through the tutorials (`/tutorial` folder) before going through these examples.\n",

examples/2-multi_class_single_label_classification.ipynb

@@ -3,6 +3,8 @@
   {
    "cell_type": "markdown",
    "source": [
+    "This notebook is licensed under the MIT License. See the [LICENSE file](https://github.com/tommasocarraro/LTNtorch/blob/main/LICENSE) in the project root for details.\n",
+    "\n",
     "## Multi-class single label classification\n",
     "\n",
     "The natural extension of binary classification is a multi-class classification task.\n",

examples/3-multi_class_multi_label_classification.ipynb

@@ -3,6 +3,8 @@
   {
    "cell_type": "markdown",
    "source": [
+    "This notebook is licensed under the MIT License. See the [LICENSE file](https://github.com/tommasocarraro/LTNtorch/blob/main/LICENSE) in the project root for details.\n",
+    "\n",
     "## Multi-class multi-label classification\n",
     "\n",
     "We now turn to multi-label classification, whereby multiple labels can be assigned to each example. As a first example\n",
@@ -527,4 +529,4 @@
  },
  "nbformat": 4,
  "nbformat_minor": 0
-}
+}

examples/4-semi-supervised_pattern_recognition.ipynb

@@ -9,6 +9,8 @@
     }
    },
    "source": [
+    "This notebook is licensed under the MIT License. See the [LICENSE file](https://github.com/tommasocarraro/LTNtorch/blob/main/LICENSE) in the project root for details.\n",
+    "\n",
     "## Semi-supervised pattern recognition\n",
     "\n",
     "Let us now explore two, more elaborate, classification tasks, which showcase the\n",

examples/5-regression.ipynb

@@ -3,6 +3,8 @@
   {
    "cell_type": "markdown",
    "source": [
+    "This notebook is licensed under the MIT License. See the [LICENSE file](https://github.com/tommasocarraro/LTNtorch/blob/main/LICENSE) in the project root for details.\n",
+    "\n",
     "## Regression\n",
     "\n",
     "Another important problem in Machine Learning is regression, where a relationship is estimated between one independent variable $X$ and a continuous dependent variable $Y$.\n",
@@ -375,4 +377,4 @@
  },
  "nbformat": 4,
  "nbformat_minor": 0
-}
+}

examples/6-clustering.ipynb

@@ -3,6 +3,8 @@
   {
    "cell_type": "markdown",
    "source": [
+    "This notebook is licensed under the MIT License. See the [LICENSE file](https://github.com/tommasocarraro/LTNtorch/blob/main/LICENSE) in the project root for details.\n",
+    "\n",
     "## Clustering\n",
     "\n",
     "In unsupervised learning, labels are either not available or are not used for learning. Clustering is a form of\n",
@@ -393,4 +395,4 @@
  },
  "nbformat": 4,
  "nbformat_minor": 0
-}
+}

examples/7-learning_embeddings_with_LTN.ipynb

@@ -3,6 +3,8 @@
   {
    "cell_type": "markdown",
    "source": [
+    "This notebook is licensed under the MIT License. See the [LICENSE file](https://github.com/tommasocarraro/LTNtorch/blob/main/LICENSE) in the project root for details.\n",
+    "\n",
     "## Learning embeddings with LTN\n",
     "\n",
     "A classic example of Statistical Relational Learning is the smokers-friends-cancer example introduced in the paper\n",
@@ -597,4 +599,4 @@
  },
  "nbformat": 4,
  "nbformat_minor": 0
-}
+}

ltn/__init__.py

@@ -1,3 +1,6 @@
+# Copyright (c) 2021-2024 Tommaso Carraro
+# Licensed under the MIT License. See LICENSE file in the project root for details.
+
 from ltn.core import Variable, Predicate, Constant, Function, Connective, diag, undiag, Quantifier, \
     LTNObject, process_ltn_objects, LambdaModel
 import torch

ltn/core.py

@@ -1,3 +1,6 @@
+# Copyright (c) 2021-2024 Tommaso Carraro
+# Licensed under the MIT License. See LICENSE file in the project root for details.
+
 """
 The `ltn.core` module contains the main functionalities of LTNtorch. In particular, it contains the definitions of
 constants, variables, predicates, functions, connectives, and quantifiers.

ltn/fuzzy_ops.py

@@ -1,3 +1,6 @@
+# Copyright (c) 2021-2024 Tommaso Carraro
+# Licensed under the MIT License. See LICENSE file in the project root for details.
+
 """
 .. _fuzzyop:
 

setup.py

@@ -1,3 +1,6 @@
+# Copyright (c) 2021-2024 Tommaso Carraro
+# Licensed under the MIT License. See LICENSE file in the project root for details.
+
 from setuptools import setup, find_packages
 
 with open("README.md", "r") as fh:

tests/tests.py

@@ -1,3 +1,6 @@
+# Copyright (c) 2021-2024 Tommaso Carraro
+# Licensed under the MIT License. See LICENSE file in the project root for details.
+
 import copy
 
 import pytest

tutorials/1-grounding_non_logical_symbols.ipynb

@@ -3,6 +3,8 @@
   {
    "cell_type": "markdown",
    "source": [
+    "This notebook is licensed under the MIT License. See the [LICENSE file](https://github.com/tommasocarraro/LTNtorch/blob/main/LICENSE) in the project root for details.\n",
+    "\n",
     "# Grounding in Logic Tensor Networks (LTN)\n",
     "\n",
     "## Real Logic\n",
@@ -734,4 +736,4 @@
  },
  "nbformat": 4,
  "nbformat_minor": 0
-}
+}

tutorials/2-grounding_connectives.ipynb

@@ -3,6 +3,8 @@
   {
    "cell_type": "markdown",
    "source": [
+    "This notebook is licensed under the MIT License. See the [LICENSE file](https://github.com/tommasocarraro/LTNtorch/blob/main/LICENSE) in the project root for details.\n",
+    "\n",
     "# Grounding in LTN (continuation)\n",
     "\n",
     "This tutorial explains how to ground connectives and quantifiers in LTN. It expects some familiarity with the first tutorial on grounding non-logical symbols (constants, variables, functions and predicates)."
@@ -827,4 +829,4 @@
  },
  "nbformat": 4,
  "nbformat_minor": 0
-}
+}

tutorials/2b-operators-and-gradients.ipynb

@@ -3,6 +3,8 @@
   {
    "cell_type": "markdown",
    "source": [
+    "This notebook is licensed under the MIT License. See the [LICENSE file](https://github.com/tommasocarraro/LTNtorch/blob/main/LICENSE) in the project root for details.\n",
+    "\n",
     "## Complementary Notebook: Appropriate Operators to Approximate Connectives and Quantifiers\n",
     "\n",
     "This notebook is a complement to the tutorial on operators (2-grounding_connectives.ipynb).\n",
@@ -460,4 +462,4 @@
  },
  "nbformat": 4,
  "nbformat_minor": 0
-}
+}

tutorials/3-knowledgebase-and-learning.ipynb

@@ -3,6 +3,8 @@
   {
    "cell_type": "markdown",
    "source": [
+    "This notebook is licensed under the MIT License. See the [LICENSE file](https://github.com/tommasocarraro/LTNtorch/blob/main/LICENSE) in the project root for details.\n",
+    "\n",
     "# Learning in LTN\n",
     "\n",
     "This tutorial explains the concept of learning in Logic Tensor Networks.\n",
@@ -639,4 +641,4 @@
  },
  "nbformat": 4,
  "nbformat_minor": 0
-}
+}