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funq's Introduction

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.

$ 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

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

$ q plot.q -s 4

K-Nearest Neighbors (KNN)

$ q knn.q -s 4

K-Means/Medians/Medoids Clustering

$ q kmeans.q -s 4

Hierarchical Agglomerative Clustering (HAC)

$ q hac.q -s 4

Expectation Maximization (EM)

$ q em.q -s 4

Naive Bayes

$ 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

Linear Regression

$ q linreg.q -s 4

Logistic Regression

$ 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

Google PageRank

$ q pagerank.q -s 4

Footnotes

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

funq's People

Contributors

avec-zz avatar awilson-kx avatar danielkrizian avatar floshix avatar pindash avatar psaris avatar viveshok avatar

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funq's Issues

How to compile the code?

I work under win10, win32-bit q . Useing MSYS2 to Compile., which is armed by the following packages:

  • tar
  • make
  • patch
  • mingw-w64-i686-gcc-fortran

under the target folder, type make much errors appear, as the attach file. NEED HELP!
error.txt

possible typo in book

First congrats on the book! Looks great, very excited to go through it. Not sure if this is the right place to report typos but on p 16, section 2.4 in it refers to "3...independent features (petal and sepal measurements..." and "4...dependent feature (species label)" and then at the bottom of the page it says "we have access to the independent (species labels) and dependent (sepal and petal measurements)" The independent/dependent labels are reversed. I believe the first labels are correct but figured i'd let you know.

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