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  1. README.md +29 -1
  2. chess_rock_vs_pawn.py +143 -0
  3. kr-vs-kp.data +0 -0
  4. kr-vs-kp.names +66 -0
README.md CHANGED
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  ---
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- license: cc
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ---
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ---
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+ language:
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+ - en
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+ tags:
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+ - chess
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+ - tabular_classification
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+ - binary_classification
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+ - multiclass_classification
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+ pretty_name: Adult
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+ size_categories:
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+ - 1K<n<10K
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+ task_categories: # Full list at https://github.com/huggingface/hub-docs/blob/main/js/src/lib/interfaces/Types.ts
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+ - tabular-classification
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+ configs:
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+ - chess
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  ---
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+ # Adult
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+ The [Adult dataset](https://archive.ics.uci.edu/ml/datasets/Adult) from the [UCI ML repository](https://archive.ics.uci.edu/ml/datasets).
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+ Census dataset including personal characteristic of a person, and their income threshold.
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+
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+ # Configurations and tasks
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+ | **Configuration** | **Task** | **Description** |
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+ |-------------------|---------------------------|--------------------------|
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+ | chess | Binary classification | Can the white piece win? |
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+
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+ # Usage
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+ ```python
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+ from datasets import load_dataset
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+
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+ dataset = load_dataset("mstz/chess", "chess")["train"]
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+ ```
chess_rock_vs_pawn.py ADDED
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+ """Chess"""
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+
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+ from typing import List
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+
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+ import datasets
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+
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+ import pandas
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+
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+
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+ VERSION = datasets.Version("1.0.0")
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+ _BASE_FEATURE_NAMES = [
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+ "bkblk",
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+ "bknwy",
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+ "bkon8",
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+ "bkona",
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+ "bkspr",
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+ "bkxbq",
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+ "bkxcr",
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+ "bkxwp",
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+ "blxwp",
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+ "bxqsq",
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+ "cntxt",
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+ "dsopp",
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+ "dwipd",
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+ "hdchk",
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+ "katri",
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+ "mulch",
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+ "qxmsq",
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+ "r2ar8",
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+ "reskd",
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+ "reskr",
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+ "rimmx",
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+ "rkxwp",
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+ "rxmsq",
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+ "simpl",
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+ "skach",
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+ "skewr",
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+ "skrxp",
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+ "spcop",
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+ "stlmt",
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+ "thrsk",
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+ "wkcti",
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+ "wkna8",
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+ "wknck",
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+ "wkovl",
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+ "wkpos",
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+ "white_wins"
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+ ]
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+
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+ DESCRIPTION = "Chess dataset from the UCI ML repository."
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+ _HOMEPAGE = "https://archive.ics.uci.edu/ml/datasets/Chess"
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+ _URLS = ("https://huggingface.co/datasets/mstz/chess/raw/chess.csv")
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+ _CITATION = """
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+ @misc{misc_chess_(king-rook_vs._king-pawn)_22,
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+ title = {{Chess (King-Rook vs. King-Pawn)}},
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+ year = {1989},
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+ howpublished = {UCI Machine Learning Repository},
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+ note = {{DOI}: \\url{10.24432/C5DK5C}}
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+ }"""
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+
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+ # Dataset info
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+ urls_per_split = {
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+ "train": "https://huggingface.co/datasets/mstz/chess/raw/main/kr-vs-kp.data"
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+ }
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+ features_types_per_config = {
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+ "chess": {
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+ "bkblk": datasets.Value("string"),
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+ "bknwy": datasets.Value("string"),
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+ "bkon8": datasets.Value("string"),
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+ "bkona": datasets.Value("string"),
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+ "bkspr": datasets.Value("string"),
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+ "bkxbq": datasets.Value("string"),
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+ "bkxcr": datasets.Value("string"),
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+ "bkxwp": datasets.Value("string"),
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+ "blxwp": datasets.Value("string"),
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+ "bxqsq": datasets.Value("string"),
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+ "cntxt": datasets.Value("string"),
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+ "dsopp": datasets.Value("string"),
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+ "dwipd": datasets.Value("string"),
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+ "hdchk": datasets.Value("string"),
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+ "katri": datasets.Value("string"),
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+ "mulch": datasets.Value("string"),
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+ "qxmsq": datasets.Value("string"),
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+ "r2ar8": datasets.Value("string"),
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+ "reskd": datasets.Value("string"),
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+ "reskr": datasets.Value("string"),
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+ "rimmx": datasets.Value("string"),
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+ "rkxwp": datasets.Value("string"),
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+ "rxmsq": datasets.Value("string"),
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+ "simpl": datasets.Value("string"),
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+ "skach": datasets.Value("string"),
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+ "skewr": datasets.Value("string"),
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+ "skrxp": datasets.Value("string"),
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+ "spcop": datasets.Value("string"),
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+ "stlmt": datasets.Value("string"),
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+ "thrsk": datasets.Value("string"),
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+ "wkcti": datasets.Value("string"),
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+ "wkna8": datasets.Value("string"),
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+ "wknck": datasets.Value("string"),
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+ "wkovl": datasets.Value("string"),
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+ "wkpos": datasets.Value("string"),
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+ "white_wins": datasets.ClassLabel(num_classes=2, names=("no", "yes"))
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+ }
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+ }
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+ features_per_config = {k: datasets.Features(features_types_per_config[k]) for k in features_types_per_config}
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+
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+
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+ class ChessConfig(datasets.BuilderConfig):
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+ def __init__(self, **kwargs):
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+ super(ChessConfig, self).__init__(version=VERSION, **kwargs)
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+ self.features = features_per_config[kwargs["name"]]
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+
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+
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+ class Chess(datasets.GeneratorBasedBuilder):
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+ # dataset versions
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+ DEFAULT_CONFIG = "chess"
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+ BUILDER_CONFIGS = [
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+ ChessConfig(name="chess",
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+ description="Chess for binary classification.")
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+ ]
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+
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+
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+ def _info(self):
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+ info = datasets.DatasetInfo(description=DESCRIPTION, citation=_CITATION, homepage=_HOMEPAGE,
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+ features=features_per_config[self.config.name])
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+
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+ return info
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+
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+ def _split_generators(self, dl_manager: datasets.DownloadManager) -> List[datasets.SplitGenerator]:
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+ downloads = dl_manager.download_and_extract(urls_per_split)
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+
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+ return [
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+ datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": downloads["train"]})
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+ ]
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+
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+ def _generate_examples(self, filepath: str):
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+ data = pandas.read_csv(filepath)
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+ data = self.preprocess(data, config=self.config.name)
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+
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+ for row_id, row in data.iterrows():
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+ data_row = dict(row)
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+
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+ yield row_id, data_row
kr-vs-kp.data ADDED
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kr-vs-kp.names ADDED
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+ 1. Title: Chess End-Game -- King+Rook versus King+Pawn on a7
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+ (usually abbreviated KRKPA7). The pawn on a7 means it is one square
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+ away from queening. It is the King+Rook's side (white) to move.
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+
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+ 2. Sources:
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+ (a) Database originally generated and described by Alen Shapiro.
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+ (b) Donor/Coder: Rob Holte ([email protected]). The database
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+ was supplied to Holte by Peter Clark of the Turing Institute
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+ in Glasgow ([email protected]).
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+ (c) Date: 1 August 1989
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+
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+ 3. Past Usage:
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+ - Alen D. Shapiro (1983,1987), "Structured Induction in Expert Systems",
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+ Addison-Wesley. This book is based on Shapiro's Ph.D. thesis (1983)
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+ at the University of Edinburgh entitled "The Role of Structured
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+ Induction in Expert Systems".
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+ - Stephen Muggleton (1987), "Structuring Knowledge by Asking Questions",
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+ pp.218-229 in "Progress in Machine Learning", edited by I. Bratko
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+ and Nada Lavrac, Sigma Press, Wilmslow, England SK9 5BB.
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+ - Robert C. Holte, Liane Acker, and Bruce W. Porter (1989),
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+ "Concept Learning and the Problem of Small Disjuncts",
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+ Proceedings of IJCAI. Also available as technical report AI89-106,
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+ Computer Sciences Department, University of Texas at Austin,
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+ Austin, Texas 78712.
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+
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+ 4. Relevant Information:
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+ The dataset format is described below. Note: the format of this
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+ database was modified on 2/26/90 to conform with the format of all
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+ the other databases in the UCI repository of machine learning databases.
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+
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+ 5. Number of Instances: 3196 total
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+
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+ 6. Number of Attributes: 36
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+
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+ 7. Attribute Summaries:
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+ Classes (2): -- White-can-win ("won") and White-cannot-win ("nowin").
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+ I believe that White is deemed to be unable to win if the Black pawn
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+ can safely advance.
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+ Attributes: see Shapiro's book.
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+
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+ 8. Missing Attributes: -- none
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+
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+ 9. Class Distribution:
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+ In 1669 of the positions (52%), White can win.
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+ In 1527 of the positions (48%), White cannot win.
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+
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+ The format for instances in this database is a sequence of 37 attribute values.
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+ Each instance is a board-descriptions for this chess endgame. The first
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+ 36 attributes describe the board. The last (37th) attribute is the
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+ classification: "win" or "nowin". There are 0 missing values.
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+ A typical board-description is
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+
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+ f,f,f,f,f,f,f,f,f,f,f,f,l,f,n,f,f,t,f,f,f,f,f,f,f,t,f,f,f,f,f,f,f,t,t,n,won
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+
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+ The names of the features do not appear in the board-descriptions.
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+ Instead, each feature correponds to a particular position in the
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+ feature-value list. For example, the head of this list is the value
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+ for the feature "bkblk". The following is the list of features, in
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+ the order in which their values appear in the feature-value list:
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+
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+ [bkblk,bknwy,bkon8,bkona,bkspr,bkxbq,bkxcr,bkxwp,blxwp,bxqsq,cntxt,dsopp,dwipd,
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+ hdchk,katri,mulch,qxmsq,r2ar8,reskd,reskr,rimmx,rkxwp,rxmsq,simpl,skach,skewr,
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+ skrxp,spcop,stlmt,thrsk,wkcti,wkna8,wknck,wkovl,wkpos,wtoeg]
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+
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+ In the file, there is one instance (board position) per line.
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+