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1.51763 12.61 3.59 1.31 73.29 0.58 8.5 0.0 0.0
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1.51851 13.2 3.63 1.07 72.83 0.57 8.41 0.09 0.17
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1.5159 13.24 3.34 1.47 73.1 0.39 8.22 0.0 0.0
1.5186 13.36 3.43 1.43 72.26 0.51 8.6 0.0 0.0
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1.52043 13.38 0.0 1.4 72.25 0.33 12.5 0.0 0.0
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1.51131 13.69 3.2 1.81 72.81 1.76 5.43 1.19 0.0
1.51683 14.56 0.0 1.98 73.29 0.0 8.52 1.57 0.07
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1.52152 13.12 3.58 0.9 72.2 0.23 9.82 0.0 0.16
1.523 13.31 3.58 0.82 71.99 0.12 10.17 0.0 0.03
1.51756 13.15 3.61 1.05 73.24 0.57 8.24 0.0 0.0
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1.52725 13.8 3.15 0.66 70.57 0.08 11.64 0.0 0.0
1.5241 13.83 2.9 1.17 71.15 0.08 10.79 0.0 0.0
1.52475 11.45 0.0 1.88 72.19 0.81 13.24 0.0 0.34
1.53125 10.73 0.0 2.1 69.81 0.58 13.3 3.15 0.28
1.53393 12.3 0.0 1.0 70.16 0.12 16.19 0.0 0.24
1.52222 14.43 0.0 1.0 72.67 0.1 11.52 0.0 0.08
1.52664 11.23 0.0 0.77 73.21 0.0 14.68 0.0 0.0
1.52739 11.02 0.0 0.75 73.08 0.0 14.96 0.0 0.0
1.52777 12.64 0.0 0.67 72.02 0.06 14.4 0.0 0.0
1.51892 13.46 3.83 1.26 72.55 0.57 8.21 0.0 0.14
1.51847 13.1 3.97 1.19 72.44 0.6 8.43 0.0 0.0
1.51846 13.41 3.89 1.33 72.38 0.51 8.28 0.0 0.0
1.51829 13.24 3.9 1.41 72.33 0.55 8.31 0.0 0.1
1.51673 13.3 3.64 1.53 72.53 0.65 8.03 0.0 0.29

Glass Identification Dataset

Overview

This dataset contains tabular data for classifying different types of glass based on their chemical properties. Each sample is stored in a separate text file, with features space-separated on a single line. The dataset is structured to be compatible with Lumina AI's Random Contrast Learning (RCL) algorithm via the PrismRCL application or API.

Dataset Structure

The dataset is organized into the following structure:

Glass-Identification/ train_data/ class_1/ sample_0.txt sample_1.txt ... class_2/ sample_0.txt sample_1.txt ... test_data/ class_1/ sample_0.txt sample_1.txt ... class_2/ sample_0.txt sample_1.txt ...

Note: All text file names must be unique across all class folders.

Features

  • Tabular Data: Each text file contains space-separated values representing the features of a sample.
  • Classes: There are multiple classes, each represented by a separate folder based on the type of glass.

Usage (pre-split; optimal parameters)

Here is an example of how to load the dataset using PrismRCL:

C:\PrismRCL\PrismRCL.exe chisquared rclticks=18 boxdown=1 channelpick=5 data=C:\path\to\Glass-Identification\train_data testdata=C:\path\to\Glass-Identification\test_data savemodel=C:\path\to\models\mymodel.classify log=C:\path\to\log_files stopwhendone

Explanation of Command:

  • C:\PrismRCL\PrismRCL.exe: Path to the PrismRCL executable for classification
  • chisquared: Specifies Chi-squared as the training evaluation method
  • rclticks=18: Sets the number of RCL iterations during training to 18
  • boxdown=1: Configuration parameter for training behavior
  • channelpick=5: RCL training parameter
  • data=C:\path\to\Glass-Identification\train_data: Path to the training data for Glass Identification classification
  • testdata=C:\path\to\Glass-Identification\test_data: Path to the testing data for evaluation
  • savemodel=C:\path\to\models\mymodel.classify: Path to save the resulting trained model
  • log=C:\path\to\log_files: Directory path for storing log files of the training process
  • stopwhendone: Instructs PrismRCL to end the session once training is complete

License

This dataset is licensed under the Creative Commons Attribution 4.0 International License. See the LICENSE file for more details.

Original Source

This dataset was originally sourced from the UCI Machine Learning Repository. Please cite the original source if you use this dataset in your research or applications.

Additional Information

The data values have been prepared to ensure compatibility with PrismRCL. No normalization is required as of version 2.4.0.

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