SIMILARITY: brand: euclidean_symbolic year: linear (max_diff=20) price: inverse_distance
OUTPUT: solution_repair_cost | Option | Description | |--------|-------------| | -v , --verbose | Show detailed parsing info, attribute types, and similarity mappings | | -s , --stats | Display dataset statistics: case count, attribute cardinality, missing values | | -q , --query | Run a test query against the case base (requires similarity config) | | -f , --format | Output format: table , json , csv (default: table) | | -k , --top-k | Number of nearest neighbors to retrieve (default: 5) | | -o , --output | Save output to file | | -h , --help | Show help message | 5. Example Usage Basic read $ reader cbr car_diagnosis.cbr Output: reader cbr
DOMAIN: car-diagnosis ATTRIBUTES: - brand: symbolic - engine_noise: symbolic - year: integer - price: float - is_electric: boolean CASES: case1: brand=tesla, engine_noise=quiet, year=2022, price=45000.0, is_electric=true case2: brand=bmw, engine_noise=loud, year=2019, price=32000.0, is_electric=false Overview reader cbr is a command-line utility designed
Case count: 2 Missing values: 0 Attribute 'brand': unique values [tesla, bmw] Price range: 32000.0 – 45000.0 $ reader cbr car_diagnosis.cbr --query "brand=tesla year=2021 price=40000" Output: for manga or comics)
Here’s a full, technical write-up on — a command-line tool for inspecting and validating CBR (Case-Based Reasoning) knowledge bases, commonly used in expert systems and AI research. reader cbr – CBR Knowledge Base Inspection Tool 1. Overview reader cbr is a command-line utility designed to parse, validate, and display the contents of a CBR knowledge base file . It is primarily used in academic and research-oriented CBR systems (e.g., myCBR, jColibri, or legacy CBR shells). The tool helps developers and knowledge engineers verify case structure, attribute types, similarity measures, and retrieval performance directly from the terminal. 2. Syntax reader cbr [OPTIONS] <filename.cbr> 3. Input File Format ( .cbr ) The .cbr file typically follows a structured plain-text or XML-based format. Example structure:
If you meant reader as in a for .cbr files (e.g., for manga or comics), let me know, and I’ll provide a full write‑up for that use case instead.
CBR Knowledge Base: car_diagnosis Attributes: brand, engine_noise, year, price, is_electric Total cases: 2 Similarity functions: brand(symbolic), year(linear), price(inv_dist) Output feature: solution_repair_cost $ reader cbr car_diagnosis.cbr --stats Output: