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Source files for "Fun Q: A Functional Introduction to Machine Learning in Q"

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# Fun Q

This project contains the source files for "Fun Q: A Functional
Introduction to Machine Learning in Q".[^fn1]

## The Book

**Fun Q** can be purchased on [Amazon][BOOK] and [Amazon
UK][BOOKUK]. A [Kindle version][KINDLE] is also available.  Books may
be purchased in quantity and/or special sales by contacting the
publisher, [Vector Sigma][VECSIG].  Read a [review][REVIEW] by [Daniel
Krizian][KRIZIAN] published by [Vector][VECTOR], the Journal of the
British APL Association.

[BOOK]: https://www.amazon.com/dp/1734467509 "Fun Q book"
[BOOKUK]: https://www.amazon.co.uk/gp/product/1734467509 "Fun Q book"
[KINDLE]: https://www.amazon.com/dp/B08R5W95WF "Fun Q e-book"
[VECSIG]: mailto:sales@vector-sigma.com "Vector Sigma e-mail"
[REVIEW]: https://vector.org.uk/book-review-fun-q-a-functional-introduction-to-machine-learning-in-q/ "Fun Q book review"
[KRIZIAN]: https://www.linkedin.com/in/danielkrizian/ "Daniel Krizian"
[VECTOR]: https://vector.org.uk/ "Vector - Journal of the British APL Association"

## The Source

Install `q` from Kx System's kdb+ [download page][DOWNLOAD] and grab a
copy of the **Fun Q** [source]({{site.github.repository_url}}).

[DOWNLOAD]: https://kx.com/developers/download-licenses/ "Download Q"

```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](errata.adoc) 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][SPRING].

[SPRING]: https://vectorsigma.creator-spring.com/ "Vector Sigma Swag"

## 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
```

[^fn1]: More presentations, competitions and books by Nick Psaris can
    be found at <https://nick.psaris.com>

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