Add license/copyright notices to LDA files

We also dd LDA to introduction notebook README list. The list of algorithms is
also rearranged in alphabetical order to match the order shown in GitHub.

Author: Chris Swierczewski

introduction_to_amazon_algorithms/README.md

@@ -3,9 +3,10 @@
 This directory includes introductory examples to Amazon SageMaker Algorithms that we have developed so far.  It seeks to provide guidance and examples on basic functionality rather than a detailed scientific review or an implementation on complex, real-world data.
 
 Example Notebooks include:
-- *linear_mnist*: Predicts whether a handwritten digit from the MNIST dataset is a 0 or not using a binary classifier from Amazon SageMaker Linear Learner.
 - *factorization_machines_mnist*: Predicts whether a handwritten digit from the MNIST dataset is a 0 or not using a binary classifier from Amazon SageMaker Factorization Machines.
-- *pca_mnist*: Uses Amazon SageMaker Principal Components Analysis (PCA) to calculate eigendigits from MNIST.
+- *lda_topic_modeling*: Topic modeling using Amazon SageMaker Latent Dirichlet Allocation (LDA) on a synthetic dataset.
+- *linear_mnist*: Predicts whether a handwritten digit from the MNIST dataset is a 0 or not using a binary classifier from Amazon SageMaker Linear Learner.
 - *ntm_synthetic*: Uses Amazon SageMaker Neural Topic Model (NTM) to uncover topics in documents from a synthetic data source, where topic distributions are known.
-- *xgboost_mnist*: Uses Amazon SageMaker XGBoost to classifiy handwritten digits from the MNIST dataset into one of the ten digits using a multi-class classifier. Both single machine and distributed use-cases are presented. 
+- *pca_mnist*: Uses Amazon SageMaker Principal Components Analysis (PCA) to calculate eigendigits from MNIST.
 - *xgboost_abalone*: Predicts the age of abalone ([Abalone dataset](https://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/regression.html)) using regression from Amazon SageMaker XGBoost.
+- *xgboost_mnist*: Uses Amazon SageMaker XGBoost to classifiy handwritten digits from the MNIST dataset into one of the ten digits using a multi-class classifier. Both single machine and distributed use-cases are presented.

introduction_to_amazon_algorithms/lda_topic_modeling/LDA-Introduction.ipynb

@@ -770,6 +770,8 @@
    "version": "3.6.3"
   }
  },
+ "notice": "Copyright 2017 Amazon.com, Inc. or its affiliates. All Rights Reserved.  Licensed under the Apache License, Version 2.0 (the \"License\"). You may not use this file except in compliance with the License. A copy of the License is located at http://aws.amazon.com/apache2.0/ or in the \"license\" file accompanying this file. This file is distributed on an \"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License."
+ },
  "nbformat": 4,
  "nbformat_minor": 2
 }

introduction_to_amazon_algorithms/lda_topic_modeling/generate_example_data.py

@@ -1,3 +1,11 @@
+# Copyright 2017 Amazon.com, Inc. or its affiliates. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License"). You may not use this file except in compliance with the License. A copy of the License is located at
+#
+#    http://aws.amazon.com/apache2.0/
+#
+# or in the "license" file accompanying this file. This file is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License.
+
 import matplotlib
 import matplotlib.pyplot as plt
 import matplotlib.cm as cm

scientific_details_of_algorithms/lda_topic_modeling/LDA-Science.ipynb

@@ -1200,6 +1200,8 @@
    "version": "3.6.3"
   }
  },
+ "notice": "Copyright 2017 Amazon.com, Inc. or its affiliates. All Rights Reserved.  Licensed under the Apache License, Version 2.0 (the \"License\"). You may not use this file except in compliance with the License. A copy of the License is located at http://aws.amazon.com/apache2.0/ or in the \"license\" file accompanying this file. This file is distributed on an \"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License."
+ },
  "nbformat": 4,
  "nbformat_minor": 2
 }

scientific_details_of_algorithms/lda_topic_modeling/generate_example_data.py

@@ -1,3 +1,11 @@
+# Copyright 2017 Amazon.com, Inc. or its affiliates. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License"). You may not use this file except in compliance with the License. A copy of the License is located at
+#
+#    http://aws.amazon.com/apache2.0/
+#
+# or in the "license" file accompanying this file. This file is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License.
+
 import matplotlib
 import matplotlib.pyplot as plt
 import matplotlib.cm as cm