3.2.0

• Fix to issues relating to unsupported versions of scipy
• Updates to tests no-longer supported by the Python equivalent functions

3.1.0

• Update to FRESH functionality to be more efficient in distributed applications
• Fix to df2tab to handle nulls appropriately in date columns
• Fix to tsPlot functionality

Addition of stats library in tgz releases

Addition of stats library to packaged release (#95)

* Addition of stats folder for .tgz releases

* length update for FRESH functionality

3.0.1

Addition of stats library for docker image deployment

3.0.0

• Refactor coding/commenting style to be up to date with coding standards
• Addition of stats section. This includes functionality such as
  - OLS/WLS fit/predict functionality
  - Transfer of percentile/describe function from utility folder to stats folder
  - Expansion of the .ml.describe` function to allow users more flexibility by having a user configurable json file
• Change function names to camel case. Any functions that were affected by this change are defined within functionMapping.json. These functions are still callable until the next release of the ML Toolkit. If the old versions are called a warning message will be sent to stdout
• Scaling and transformation preprocessing functions were amended to now contain a fit/transform/fitTransform key. Any functions affected by this changed are defined within functionMapping.json. These functions are still callable until the next release of the ML Toolkit. If the old versions are called a warning message will be sent to stdout.
• All functions containing a predict/update/transform key as output, must now take config as the initial input which is of type dictionary and has a modelInfo key
• The contents within Freshs' hyperparam.txt file were converted to a json file hyperparameters.json
• The utility functions within the clustering library were moved to clust/utils.q
• init.q can now be loaded before initialization of ml.q
• All README files were updated to reflect that the toolkit is not in its BETA release stages
• Test script was added to check that length of code in files did not exceed 80 chars filelength.t
• Tests are now run in appveyor/travis by calling testFiles.bat. This will be updated when any new test folder is added to the toolkit
• All tests were updated to reflect these changes

2.0.0

What’s New:

Time series functionality:

• Addition of time series models implemented in q
  • AR, ARMA, ARIMA, SARIMA and ARCH.
• Time series feature engineering techniques (windowed and lagged feature generation.
• Data stationarity testing

Graph/pipeline resources:

• Framework for the development of modularised kdb+ workflows and executable pipeline structures

Optimization:

• Implementation of the Broyden-Fletcher-Goldfarb-Shanno algorithm for function minimization

Grid Search:

• Random and pseudo random (Sobol) number generated parameter set functionality providing an alternative to exhaustive grid search.

Clustering:

• Implementation of k-means clustering now uses early stopping

Updates:

Clustering:

• Fit / predict / update style function calls rather than just fit+predict as previously to allow models to be deployed for classification on incoming data.

Initial release candidate for version 2.0.0 (update)

Additive update, including clustering updates

• Fit / predict / update style function calls rather than just fit+predict as previously to allow models to be deployed for classification on incoming data.

Initial release candidate for version 2.0.0

What’s New:
Time series functionality:

• Addition of time series models implemented in q
  • AR, ARMA, ARIMA, SARIMA and ARCH.
• Time series feature engineering techniques (windowed and lagged feature generation.
• Data stationarity testing

Graph/pipeline resources:

• Framework for the development of modularised kdb+ workflows and executable pipeline structures

Optimization:

• Implementation of the Broyden-Fletcher-Goldfarb-Shanno algorithm for function minimization

Grid Search:

• Random and pseudo random (Sobol) number generated parameter set functionality providing an alternative to exhaustive grid search.
  Clustering:
• Implementation of k-means clustering now uses early stopping

Updates:

Clustering:

• Fit / predict / update style function calls rather than just fit+predict as previously to allow models to be deployed for classification on incoming data.

Initial Stable Release

Promotion of machine learning toolkit to 1.0.0 from 1.0.0-rc.4

Modification to tab2df to handle single character columns

This release covers two changes to the interface

1. Fix a minor bug in .ml.tab2df relating to the incorrect conversion of 'c' columns

// Define a table which will highlight the incorrect behaviour
q)tab:([]s:`a`b`c;j:1 2 3;c:"ABC")
// Old behaviour (duplicating 'ABC')
q)print .ml.tab2df tab
   s  j    c
0  a  1  ABC
1  b  2  ABC
2  c  3  ABC
// New behaviour
q)print .ml.tab2df tab  
   s  j    c
0  a  1  A
1  b  2  B
2  c  3  C

2. Minor change to test scripts for continuous integration purposes due to update in python side default behaviour