You presented 8 distinct datasets D₁, D₂, ..., D₈, each containing 14 real numbers ranging from 1 to 14,680,874.17, with corresponding target values t₁, t₂, ..., t₈. The challenge was to find a uniform mathematical approach that could approximate each target value tᵢ using only the numbers within its corresponding dataset Dᵢ.
After analysis, I formulated this as an optimization problem:
For each dataset D with target t, find:
- D is the set of available numbers
- t is the target value
- n₁, n₂, n₃ are elements from D (with replacement)
- n₃ ≠ 0
The optimization problem is solved through an exhaustive search of the solution space, evaluating the percentage error:
| Dataset | Target (t) | Error (ε) | Classification |
|---|---|---|---|
| D₁ | 309,303.86 | 0.13% | Exceptional |
| D₂ | 1,138,706.26 | 0.59% | Exceptional |
| D₃ | 25,356,398.46 | 0.74% | Exceptional |
| D₄ | 34,166,899.66 | 1.43% | Superior |
| D₅ | 786,795.00 | 0.25% | Exceptional |
| D₆ | 331,094.22 | 0.62% | Exceptional |
| D₇ | 5,888,290.46 | 0.82% | Exceptional |
| D₈ | 76,282.46 | 0.44% | Exceptional |
- Complete enumeration: 2,744 combinations per dataset (14³ minus n₃ = 0 cases)
- Total solution space explored: 21,952 combinations
- Global maximum error: 1.43%
- Datasets achieving ε < 1%: 7/8 (87.5%)
These clearly demonstrated remarkable consistency across widely varying magnitudes (76,282.46 to 34,166,899.66) while still maintaining high precision.
Make sure you have Python and virtualenv installed in your computer.
- Create the dependencies environment
virtualenv env- Start the dependencies environment
source env/bin/activate On Windows use:
source env\Scripts\activate- Install dependencies
pip install -r requirements.txt-
Your dataset should be in
data/dataset.csv -
Run the program
python src/main.pyThe program will show:
- Input data verification
- Dataset validation
- Statistical analysis
- Precision analysis
- Final results
Thanks for the opportunity,
Chris | https://github.com/0xf333