This project contains the source files for “Fun Q: A Functional Introduction to Machine Learning in Q”.1
Fun Q can be purchased on Amazon and Amazon UK. A Kindle version is also available. Books may be purchased in quantity and/or special sales by contacting the publisher, Vector Sigma. Read a review by Daniel Krizian published by Vector, the Journal of British APL Association.
q from Kx System’s kdb+ download
page and grab a copy of the
Fun Q source.
$ git clone https://github.com/psaris/funq
The Fun Q Environment
The following command starts the q interpreter with all Fun Q libraries loaded and 4 secondary threads for parallel computing.
$ q funq.q -s 4
Any typos or errors are listed here and are incorporated into recent printings of the book as well as the kindle version.
Swag can be found on the Vector Sigma Teespring site.
Start q with any of the following or read the comments and run the examples one by one.
$ q plot.q -s 4
K-Nearest Neighbors (KNN)
$ q knn.q -s 4
$ q kmeans.q -s 4
Hierarchical Agglomerative Clustering (HAC)
$ q hac.q -s 4
Expectation Maximization (EM)
$ q em.q -s 4
$ q nb.q -s 4
Vector Space Model (tf-idf)
$ q tfidf.q -s 4
Decision Tree (ID3,C4.5,CART)
$ q decisiontree.q -s 4
Discrete Adaptive Boosting (AdaBoost)
$ q adaboost.q -s 4
Random Forest (and Boosted Aggregating BAG)
$ q randomforest.q -s 4
$ q linreg.q -s 4
$ q logreg.q -s 4
One vs. All
$ q onevsall.q -s 4
Neural Network Classification/Regression
$ q nn.q -s 4
Content-Based/Collaborative Filtering (Recommender Systems)
$ q recommend.q -s 4
$ q pagerank.q -s 4