Examples#

This section provides complete, worked examples for each Tabular template.

Note

Example projects are installed with NeurEco as zip files and are opened from the desktop app’s example gallery. See Finding Example Data below.

Available Examples#

Regression Examples#

Energy Consumption

Predict house energy consumption based on environmental factors (temperature, wind, humidity, etc.)

Classification Examples#

Cancer RNA Classification

Classify cancer types based on RNA gene expression data

Compression Examples#

Heaviside Signal Compression

Compress and reconstruct signal data, demonstrating the autoencoder capabilities

Other Available Test Cases#

NeurEco includes three more Tabular test cases, each with a ready-to-use project and a Python tutorial script (TutorialScript.py):

  • Airfoil RANS Flow Fields (Regression, AirfRANS): predicts the whole pressure field around 2D airfoils from the flow and airfoil parameters, using the “Is the output a field” option

  • Metamaterial Antennas (Regression, MetamaterialAntennas): predicts the performance of an antenna (return loss, radiated power, gain, bandwidth, …) from its design parameters

  • Wall Following Robot (Classification, WallFollowingRobot): predicts the move a robot needs to follow a wall from the readings of its 24 ultrasound sensors

Finding Example Data#

Each example project is a zip file (for example EnergyConsumption.zip) in:

  • Windows: <install folder>\testsuite\ (by default C:\Program Files\Adagos\NeurEco-5.0\testsuite\)

  • Linux: /opt/neureco/share/adagos/.neurecotestsuite/

  • Python Package: the pip package does not include the examples. The example zip files come with the Windows installer or portable ZIP and with the Linux neureco package (folders above). Extract one and run its Python tutorial scripts (for example TutorialScript.py) from the extracted folder.

To open an example in the desktop app, choose it in the example gallery (“Extract an example project”). The app extracts the zip file into a folder you choose and opens the project.

Running Examples#

Each example includes:

  1. Dataset description — What the data represents

  2. Complete code — GUI, Python API, and CLI instructions

  3. Expected results — What to expect when running the example

  4. Variations — Ideas for modifying the example

Use these examples as starting points for your own projects.