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13.74 14.05 0.8744 5.482 3.114 2.932 4.825
14.29 14.09 0.905 5.291 3.337 2.699 4.825
14.16 14.4 0.8584 5.658 3.129 3.072 5.176
14.11 14.26 0.8722 5.52 3.168 2.688 5.219
12.08 13.23 0.8664 5.099 2.936 1.415 4.961
15.78 14.91 0.8923 5.674 3.434 5.593 5.136
11.42 12.86 0.8683 5.008 2.85 2.7 4.607
18.55 16.22 0.8865 6.153 3.674 1.738 5.894
19.15 16.45 0.889 6.245 3.815 3.084 6.185
16.17 15.38 0.8588 5.762 3.387 4.286 5.703
18.94 16.32 0.8942 6.144 3.825 2.908 5.949
19.51 16.71 0.878 6.366 3.801 2.962 6.185
18.59 16.05 0.9066 6.037 3.86 6.001 5.877
16.87 15.65 0.8648 6.139 3.463 3.696 5.967
12.26 13.6 0.8333 5.408 2.833 4.756 5.36
11.36 13.05 0.8382 5.175 2.755 4.048 5.263
12.05 13.41 0.8416 5.267 2.847 4.988 5.046
11.35 13.12 0.8291 5.176 2.668 4.337 5.132
11.55 13.1 0.8455 5.167 2.845 6.715 4.956
10.8 12.57 0.859 4.981 2.821 4.773 5.063
12.19 13.36 0.8579 5.24 2.909 4.857 5.158
15.26 14.84 0.871 5.763 3.312 2.221 5.22
14.88 14.57 0.8811 5.554 3.333 1.018 4.956
15.26 14.85 0.8696 5.714 3.242 4.543 5.314
14.03 14.16 0.8796 5.438 3.201 1.717 5.001
13.89 14.02 0.888 5.439 3.199 3.986 4.738
13.78 14.06 0.8759 5.479 3.156 3.136 4.872
14.59 14.28 0.8993 5.351 3.333 4.185 4.781
13.99 13.83 0.9183 5.119 3.383 5.234 4.781
15.69 14.75 0.9058 5.527 3.514 1.599 5.046
14.7 14.21 0.9153 5.205 3.466 1.767 4.649
12.72 13.57 0.8686 5.226 3.049 4.102 4.914
15.88 14.9 0.8988 5.618 3.507 0.7651 5.091
15.01 14.76 0.8657 5.789 3.245 1.791 5.001
16.19 15.16 0.8849 5.833 3.421 0.903 5.307
13.02 13.76 0.8641 5.395 3.026 3.373 4.825
12.74 13.67 0.8564 5.395 2.956 2.504 4.869
14.11 14.18 0.882 5.541 3.221 2.754 5.038
13.45 14.02 0.8604 5.516 3.065 3.531 5.097
13.84 13.94 0.8955 5.324 3.379 2.259 4.805
13.16 13.82 0.8662 5.454 2.975 0.8551 5.056
15.49 14.94 0.8724 5.757 3.371 3.412 5.228
14.09 14.41 0.8529 5.717 3.186 3.92 5.299
13.94 14.17 0.8728 5.585 3.15 2.124 5.012
15.05 14.68 0.8779 5.712 3.328 2.129 5.36
16.12 15.0 0.9 5.709 3.485 2.27 5.443
16.2 15.27 0.8734 5.826 3.464 2.823 5.527
17.08 15.38 0.9079 5.832 3.683 2.956 5.484
14.8 14.52 0.8823 5.656 3.288 3.112 5.309
14.28 14.17 0.8944 5.397 3.298 6.685 5.001
16.14 14.99 0.9034 5.658 3.562 1.355 5.175
13.54 13.85 0.8871 5.348 3.156 2.587 5.178
13.5 13.85 0.8852 5.351 3.158 2.249 5.176
13.16 13.55 0.9009 5.138 3.201 2.461 4.783
15.5 14.86 0.882 5.877 3.396 4.711 5.528
15.11 14.54 0.8986 5.579 3.462 3.128 5.18
13.8 14.04 0.8794 5.376 3.155 1.56 4.961
15.36 14.76 0.8861 5.701 3.393 1.367 5.132
14.99 14.56 0.8883 5.57 3.377 2.958 5.175
14.79 14.52 0.8819 5.545 3.291 2.704 5.111
14.86 14.67 0.8676 5.678 3.258 2.129 5.351
14.38 14.21 0.8951 5.386 3.312 2.462 4.956
14.43 14.4 0.8751 5.585 3.272 3.975 5.144
14.49 14.61 0.8538 5.715 3.113 4.116 5.396
14.33 14.28 0.8831 5.504 3.199 3.328 5.224
14.52 14.6 0.8557 5.741 3.113 1.481 5.487
15.03 14.77 0.8658 5.702 3.212 1.933 5.439
14.46 14.35 0.8818 5.388 3.377 2.802 5.044
14.92 14.43 0.9006 5.384 3.412 1.142 5.088
15.38 14.77 0.8857 5.662 3.419 1.999 5.222
12.11 13.47 0.8392 5.159 3.032 1.502 4.519
14.69 14.49 0.8799 5.563 3.259 3.586 5.219
11.23 12.63 0.884 4.902 2.879 2.269 4.703
12.36 13.19 0.8923 5.076 3.042 3.22 4.605
13.22 13.84 0.868 5.395 3.07 4.157 5.088
12.78 13.57 0.8716 5.262 3.026 1.176 4.782
12.88 13.5 0.8879 5.139 3.119 2.352 4.607
14.34 14.37 0.8726 5.63 3.19 1.313 5.15
14.01 14.29 0.8625 5.609 3.158 2.217 5.132
14.37 14.39 0.8726 5.569 3.153 1.464 5.3
12.73 13.75 0.8458 5.412 2.882 3.533 5.067
14.11 14.1 0.8911 5.42 3.302 2.7 5.0
16.63 15.46 0.8747 6.053 3.465 2.04 5.877
16.44 15.25 0.888 5.884 3.505 1.969 5.533
16.41 15.25 0.8866 5.718 3.525 4.217 5.618
17.99 15.86 0.8992 5.89 3.694 2.068 5.837
19.46 16.5 0.8985 6.113 3.892 4.308 6.009
19.18 16.63 0.8717 6.369 3.681 3.357 6.229
18.95 16.42 0.8829 6.248 3.755 3.368 6.148
18.83 16.29 0.8917 6.037 3.786 2.553 5.879
18.85 16.17 0.9056 6.152 3.806 2.843 6.2
17.63 15.86 0.88 6.033 3.573 3.747 5.929
19.94 16.92 0.8752 6.675 3.763 3.252 6.55
18.45 16.12 0.8921 6.107 3.769 2.235 5.794
19.38 16.72 0.8716 6.303 3.791 3.678 5.965
19.13 16.31 0.9035 6.183 3.902 2.109 5.924
19.14 16.61 0.8722 6.259 3.737 6.682 6.053
20.97 17.25 0.8859 6.563 3.991 4.677 6.316
19.06 16.45 0.8854 6.416 3.719 2.248 6.163
18.96 16.2 0.9077 6.051 3.897 4.334 5.75

Wheat Seeds Dataset

Overview

This dataset contains tabular data for classifying different varieties of wheat seeds. 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:

Wheat-Seeds/
    train_data/
        class_1/
            sample_0.txt
            sample_1.txt
            ...
        class_2/
            sample_0.txt
            sample_1.txt
            ...
        class_3/
            sample_0.txt
            sample_1.txt
            ...
    test_data/
        class_1/
            sample_0.txt
            sample_1.txt
            ...
        class_2/
            sample_0.txt
            sample_1.txt
            ...
        class_3/
            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 three classes, each represented by a separate folder.

Thank you for providing the command and its explanation. I'll ensure that the 'usage' section is consistent with the included command. Here's a revised version of the usage section:

Usage (pre-split; optimal parameters)

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

C:\PrismRCL\PrismRCL.exe naivebayes rclticks=7 boxdown=0 channelpick=5 data=C:\path\to\Wheat-Seeds\train_data testdata=C:\path\to\Wheat-Seeds\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
  • naivebayes: Specifies Naive Bayes as the training evaluation method
  • rclticks=7: Sets the number of RCL iterations during training to 7
  • boxdown=0: RCL training parameter
  • channelpick=5: RCL training parameter
  • data=C:\path\to\Wheat-Seeds\train_data: Path to the training data for wheat seeds classification
  • testdata=C:\path\to\Wheat-Seeds\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

This usage section now accurately reflects the provided command and includes a consistent explanation for each parameter.

License

This dataset is licensed under the Creative Commons Attribution 4.0 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. Dua, D. and Graff, C. (2019). UCI Machine Learning Repository [https://archive.ics.uci.edu/dataset/236/seeds]. Irvine, CA: University of California, School of Information and Computer Science.


## 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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