Home
Reference Guides
Explore the Extended Definitions, OML Guides, Block Library, API Guide and Glossary.
Reference Guide for OpenMatrix Language Functions
The Reference Guide contains documentation for all functions supported in the OpenMatrix language.
Machine Learning Commands
Preprocessing

Welcome
What's New
Discover new features and enhancements.
Get Started
New to Twin Activate? Learn the basics here.
Tutorials
Start using Twin Activate with our interactive tutorials.
User Guide
Learn about the features and functionality available in Twin Activate.
Reference Guides
Explore the Extended Definitions, OML Guides, Block Library, API Guide and Glossary.
- Extended Definitions for Advanced Users
  PDF file with in-depth information on key topics in the User's Guide.
- Scripting Guide for OpenMatrix Language
  OpenMatrix is a mathematical scripting language.
- Reference Guide for OpenMatrix Language Functions
  The Reference Guide contains documentation for all functions supported in the OpenMatrix language.
  - ABF Commands
  - Altair Integration Commands
  - Audio Commands
  - CAE Commands
  - CAE Reader Commands
  - CAE Reader 3D Commands
  - CAE Writer Commands
  - Calculus Commands
  - Computer Vision Commands
  - Communication Commands
  - Control System Commands
  - Core Minimal Interpreter Commands
  - Data Structure Commands
  - Differential Equations Commands
  - Elementary Math Commands
  - File I/O Commands
  - Geometry Commands
  - GUI Commands
  - HDF5 Reader Commands
  - HDF5 Writer Commands
  - hstpyfit Commands
  - Image Commands
  - JSON Commands
  - Library Manager Commands
  - Linear Algebra Commands
  - Logical Commands
  - Machine Learning Commands
    - Apriori
    - Association Rules
    - Classification
    - Clustering
    - Decomposition
    - Distance
    - Evaluation Metrics
    - K-Mode Clustering
    - Model Selection
    - Preprocessing
      - binarize
        Binarizes the input records so that all the values of the input are either 0 or 1.
      - binarizetransform
        Binarize the test records so that all the values of the input are either 0 or 1.
      - labelbinarizer
        Binarize labels in one vs all fashion.
      - labelbinarizertransform
        Binarize labels in one vs all fashion.
      - labelencoder
        Encodes labels such that all lies between 0 and n -1 classes.
      - labelencodertransform
        Encodes labels such that all lies between 0 and n -1 classes.
      - impute
        Fills the missing values in the dataset with one of the following strategies (see Inputs section).
      - imputetransform
        Fill missing values of the test records.
      - maxabsnormalize
        Normalizes dataset using Max Absolute Normalization. It scales and translates each feature individually such that the maximal absolute value of each feature in the training set will be 1.0 after normalization.
      - maxabsnormalizetransform
        Normalizes test dataset using values computed on training data by 'maxAbsNormalize' function.
      - minmaxnormalize
        Normalizes dataset into specified range of values using Min Max Normalization. It transforms the dataset by scaling each feature to a given range.
      - minmaxnormalizetransform
        Normalizes test dataset using values computed on training data by 'minMaxNormalize' function.
      - polyfeatures
        Generates polynomial features of specified degree.
      - polyfeaturestransform
        Generate polynomial features for test data.
      - standardize
        Standardize the dataset so that each column has its mean as 0. It gives equal weight to all features.
      - standardizetransform
        Standardize the test dataset using values computed by 'standardize' function.
      - splitdata
        Splits the dataset into training and test set based on the given ratio.
    - Regression
  - Optimization Commands
  - Plotting Commands
  - Polynomial Math Commands
  - Signal Processing Commands
  - Statistical Analysis Commands
  - String Operations Commands
  - System Commands
  - Table Commands
  - Time Commands
  - Trigonometry Commands
  - OML to Python Bridge Commands
  - Python to OML Bridge Commands
- Block Reference Guide
  Describes all of the blocks in the installed Twin Activate library.
- Function Guide for Twin Activate
  Provides information about developing and simulating models through the Twin Activate Application Program Interface.
- Glossary
  Key terms associated with the software.
Frequently Asked Questions
You've got questions? We've got answers!

Preprocessing

Attention: Available only with Twin Activate commercial edition.

The following utility functions are available to change raw feature data into a representation that is more suitable for the downstream estimators:

Encoding

Categorical

Label Binarizer (labelbinarizer, labelbinarizertransform)
Label Encoder (labelencoder, labelencodertransform)

Numerical

Binarizer (binarize, binarizetransform)
Polynomial Features Generator (polyfeatures, polyfeaturestransform)

Imputation

impute
imputetransform

Normalization

Max Absolute Normalizer (maxabsnormalize, maxabsnormalizetransform)
MinMax Absolute Normalizer (minmaxnormalize)
Standardizer (standardize, standardizetransform)

Split Data

splitdata