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1.5175 12.82 3.55 1.49 72.75 0.54 8.52 0.0 0.19 |
1.51966 14.77 3.75 0.29 72.02 0.03 9.0 0.0 0.0 |
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1.51818 13.72 0.0 0.56 74.45 0.0 10.99 0.0 0.0 |
1.51708 13.72 3.68 1.81 72.06 0.64 7.88 0.0 0.0 |
1.51687 13.23 3.54 1.48 72.84 0.56 8.1 0.0 0.0 |
1.52614 13.7 0.0 1.36 71.24 0.19 13.44 0.0 0.1 |
1.51851 13.2 3.63 1.07 72.83 0.57 8.41 0.09 0.17 |
1.51409 14.25 3.09 2.08 72.28 1.1 7.08 0.0 0.0 |
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 |
1.51779 13.64 3.65 0.65 73.0 0.06 8.93 0.0 0.0 |
1.51655 13.41 3.39 1.28 72.64 0.52 8.65 0.0 0.0 |
1.52043 13.38 0.0 1.4 72.25 0.33 12.5 0.0 0.0 |
1.51852 14.09 2.19 1.66 72.67 0.0 9.32 0.0 0.0 |
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 |
1.51658 14.8 0.0 1.99 73.11 0.0 8.28 1.71 0.0 |
1.52101 13.64 4.49 1.1 71.78 0.06 8.75 0.0 0.0 |
1.51761 13.89 3.6 1.36 72.73 0.48 7.83 0.0 0.0 |
1.51571 12.72 3.46 1.56 73.2 0.67 8.09 0.0 0.24 |
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1.51589 12.88 3.43 1.4 73.28 0.69 8.05 0.0 0.24 |
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1.51784 12.68 3.67 1.16 73.11 0.61 8.7 0.0 0.0 |
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1.51926 13.2 3.33 1.28 72.36 0.6 9.14 0.0 0.11 |
1.51837 13.14 2.84 1.28 72.85 0.55 9.07 0.0 0.0 |
1.51778 13.21 2.81 1.29 72.98 0.51 9.02 0.0 0.09 |
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1.51824 12.87 3.48 1.29 72.95 0.6 8.43 0.0 0.0 |
1.51754 13.48 3.74 1.17 72.99 0.59 8.03 0.0 0.0 |
1.51754 13.39 3.66 1.19 72.79 0.57 8.27 0.0 0.11 |
1.51905 13.6 3.62 1.11 72.64 0.14 8.76 0.0 0.0 |
1.51743 13.3 3.6 1.14 73.09 0.58 8.17 0.0 0.0 |
1.52172 13.51 3.86 0.88 71.79 0.23 9.54 0.0 0.11 |
1.52227 14.17 3.81 0.78 71.35 0.0 9.69 0.0 0.0 |
1.52172 13.48 3.74 0.9 72.01 0.18 9.61 0.0 0.07 |
1.52099 13.69 3.59 1.12 71.96 0.09 9.4 0.0 0.0 |
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1.52152 13.05 3.65 0.87 72.32 0.19 9.85 0.0 0.17 |
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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1.51755 13.0 3.6 1.36 72.99 0.57 8.4 0.0 0.11 |
1.5173 12.35 2.72 1.63 72.87 0.7 9.23 0.0 0.0 |
1.5182 12.62 2.76 0.83 73.81 0.35 9.42 0.0 0.2 |
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 |
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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 classificationchisquared: Specifies Chi-squared as the training evaluation methodrclticks=18: Sets the number of RCL iterations during training to 18boxdown=1: Configuration parameter for training behaviorchannelpick=5: RCL training parameterdata=C:\path\to\Glass-Identification\train_data: Path to the training data for Glass Identification classificationtestdata=C:\path\to\Glass-Identification\test_data: Path to the testing data for evaluationsavemodel=C:\path\to\models\mymodel.classify: Path to save the resulting trained modellog=C:\path\to\log_files: Directory path for storing log files of the training processstopwhendone: 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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