Fun Q

This project contains the source files for "Fun Q: A Functional
Introduction to Machine Learning in Q".¹

The Book

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 the
British APL Association.

The Source

Install q from Kx System's kdb+ download page and grab a
copy of the Fun Q source.

sh
$ 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.

sh
$ q funq.q -s 4

The Errors

Any typos or errors are listed here and are
incorporated into recent printings of the book as well as the
kindle version.

The Swag

Swag can be found on the Vector Sigma Spring site.

More Fun

Start q with any of the following or read the comments and run the
examples one by one.

Plotting

sh
$ q plot.q -s 4

K-Nearest Neighbors (KNN)

sh
$ q knn.q -s 4

K-Means/Medians/Medoids Clustering

sh
$ q kmeans.q -s 4

Hierarchical Agglomerative Clustering (HAC)

sh
$ q hac.q -s 4

Expectation Maximization (EM)

sh
$ q em.q -s 4

Naive Bayes

sh
$ q nb.q -s 4

Vector Space Model (tf-idf)

sh
$ q tfidf.q -s 4

Decision Tree (ID3,C4.5,CART)

sh
$ q decisiontree.q -s 4

Discrete Adaptive Boosting (AdaBoost)

sh
$ q adaboost.q -s 4

Random Forest (and Boosted Aggregating BAG)

sh
$ q randomforest.q -s 4

Linear Regression

sh
$ q linreg.q -s 4

Logistic Regression

sh
$ q logreg.q -s 4

One vs. All

sh
$ q onevsall.q -s 4

Neural Network Classification/Regression

sh
$ q nn.q -s 4

Content-Based/Collaborative Filtering (Recommender Systems)

sh
$ q recommend.q -s 4

Google PageRank

sh
$ q pagerank.q -s 4

More presentations, competitions and books by Nick Psaris can
be found at https://nick.psaris.com ↩

Funq

Fun Q

The Book

The Source

The Fun Q Environment

The Errors

The Swag

More Fun

Plotting

K-Nearest Neighbors (KNN)

K-Means/Medians/Medoids Clustering

Hierarchical Agglomerative Clustering (HAC)

Expectation Maximization (EM)

Naive Bayes

Vector Space Model (tf-idf)

Decision Tree (ID3,C4.5,CART)

Discrete Adaptive Boosting (AdaBoost)

Random Forest (and Boosted Aggregating BAG)

Linear Regression

Logistic Regression

One vs. All

Neural Network Classification/Regression

Content-Based/Collaborative Filtering (Recommender Systems)

Google PageRank

Contributors

Fun Q

The Book

The Source

The Fun Q Environment

The Errors

The Swag

More Fun

Plotting

K-Nearest Neighbors (KNN)

K-Means/Medians/Medoids Clustering

Hierarchical Agglomerative Clustering (HAC)

Expectation Maximization (EM)

Naive Bayes

Vector Space Model (tf-idf)

Decision Tree (ID3,C4.5,CART)

Discrete Adaptive Boosting (AdaBoost)

Random Forest (and Boosted Aggregating BAG)

Linear Regression

Logistic Regression

One vs. All

Neural Network Classification/Regression

Content-Based/Collaborative Filtering (Recommender Systems)

Google PageRank

Footnotes

Contributors

Related Repositories