README.md

Word Prediction / classification example:

Run with ./run.py

This will read the specified part of the provided textfile, tokenize the words and remove stopwords.

Then, a HTM model using the CategoryEncoder and fitting parameters is built and used to classify the read words.

The model predicts the next word for each word in a story, treating each word as a predefined category.

After running the model, tokens.txt will contain all the relevant word tokens and results.csv will list the predicted probabilities for each.

Clean directory with ./clean.py

Use this simply to reset the generated files. Strongly recommended if you intend to use this code multiple times!

Adapt this example for your needs

Feel free to modify and change the code to meet the needs for your classification-problem. You might for example change the stopwords in stopwords.txt, edit the MODEL_PARAMS or simply choose a different story/text to classify. For JACK AND THE BEANSTALK use indices 129017 - 138889.