# probkit
Toolkit for modifying probabilities and shaping curves.
## Features
- **Tunable sigmoid curves** - Transform distributions with controllable steepness/flatness
- **Probability modification** - Scale probabilities using ratios with proper mathematical behavior
- **Pure functional design** - Deterministic functions for precise control
- **Random sampling** - Convenient random versions for generating samples from curves
- **Zero dependencies** - Only uses Python standard library
- **Robust validation** - Input validation and comprehensive error handling
- **Well tested** - Thoroughly unit tested with edge case coverage
## Quick Start
### Deterministic Functions
```python
from probkit import ntsig, biased_curve, modified_probability
# Sigmoid-like curve through (0,0), (0.5,0.5), (1,1)
y = ntsig(k=0.5, x=0.3) # k controls steepness
# Custom curve between any two points
y = biased_curve(k=0.2, a=10, b=100, x=0.7) # From (0,10) to (1,100)
# Modify probability with a ratio
new_prob = modified_probability(0.3, 1.5) # Scale 30% by 1.5x
new_prob = modified_probability(0.3, 3, 2) # Scale 30% by ratio 3/2
```
### Random Sampling
```python
import probkit.sampling
# Optionally set seed for reproducible results
sampling.seed(42)
# Sample from curves with random x values
sample = sampling.sample_ntsig(k=0.5)
sample = sampling.sample_biased_curve(k=0.2, a=10, b=100)
# Generate multiple samples
samples = [sampling.sample_ntsig(0.3) for _ in range(1000)]
```
## API Reference
### Curve Functions
- **`ntsig(k, x)`** - Normalized tunable sigmoid. Negative k is flat (logit-like), positive k is steep (sigmoid-like)
- **`nthsig(k, x)`** - Normalized tunable half-sigmoid. Negative k is convex, positive k is concave
- **`biased_curve(k, a, b, x)`** - Custom curve between points (0,a) and (1,b) with bias k
### Probability Functions
- **`modified_probability(k, a, b=None)`** - Scale probability by ratio `a` (or `a/b` if b provided) with proper saturation
### Random Sampling
- **`probkit.sampling.seed(value)`** - Set seed for reproducible random sampling
- **`probkit.sampling.sample_ntsig(k)`** - Sample from ntsig with random x
- **`probkit.sampling.sample_nthsig(k)`** - Sample from nthsig with random x
- **`probkit.sampling.sample_biased_curve(k, a, b)`** - Sample from biased_curve with random x
### Utilities
- **`clamp(val, min_val, max_val)`** - Constrain value to range
- **`transform_range(x, old_range, new_range)`** - Linear transformation between ranges
- **`effective_ratio(a, b)`** - Safe division with edge case handling
## Use Cases
- **Game development** - Procedural generation, difficulty curves, loot tables
- **Simulations** - Monte Carlo methods, statistical modeling
- **Data science** - Distribution transformation, probability weighting
- **Machine learning** - Custom activation functions, data preprocessing
## Install
Clone this repo or copy the `probkit` folder into your project. No external dependencies required.
```bash
# Example: install with pip from local folder
pip install .
```
## Testing
Run all tests with:
```bash
python -m unittest discover tests -v
```
## Contributing
Pull requests and suggestions welcome! Open an issue or PR on GitHub.
## License
MIT License. See LICENSE file for details.
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