semvec/pypi-packages huggingface dataset:
icol 0.13.0
Iterative Correlation Learning implementation
License: MIT
Requires Python: >=3.8

# icol
** Iterative Correlation Learning in Python **

`icol` allows one to fit extremly sparse linear models from very high dimensional datasets in a computationally efficient manner. We also include two feature expansion methods, allowing icol to be used as a Symbolic Regression tool.

---

## Installation 

Install via pip:

``` bash
pip3 install icol
```


## Example
```python

import numpy as np

from icol.icol import PolynomialFeaturesICL, AdaptiveLASSO, ICL, rmse

# Example Data: 10 features, three of which are used to form a degree 3 polynomial with 4 terms. 
random_state = 0
n = 100
p = 10
rung = 3
s = 5
d = 4

np.random.seed(random_state)
X_train = np.random.normal(size=(n, p))

y = lambda X: X[:, 0] + 2*X[:, 1]**2 - X[:, 0]*X[:, 1] + 3*X[:, 2]**3

y_train = y(X_train)

# Initialise and fit the ICL model
FE = PolynomialFeaturesICL(rung=rung, include_bias=False)
so = AdaptiveLASSO(gamma=1, fit_intercept=False)

X_train_transformed = FE.fit_transform(X_train, y)
feature_names = FE.get_feature_names_out()

icl = ICL(s=s, so=so, d=d, fit_intercept=True, normalize=True, pool_reset=False)
icl.fit(X_train_transformed, y_train, feature_names=feature_names, verbose=False)

# Compute the train and test error and print the model to verify that we have reproduced the data generating function
print(icl)
print(icl.__repr__())

y_hat_train = icl.predict(X_train_transformed)

print("Train rmse: " + str(rmse(y_hat_train, y_train)))

X_test = np.random.normal(size=(100*n, p))
X_test_transformed = FE.transform(X_test)
y_test = y(X_test)
y_hat_test = icl.predict(X_test_transformed)
print("Test rmse: " + str(rmse(y_hat_test, y_test)))

```
navcli 0.4.1
可交互、可探索的浏览器命令行工具，专为 AI Agent 设计
License: MIT
Requires Python: >=3.9
Keywords: browser, cli, automation, ai-agent, playwright

# NavCLI - Goals & Vision

## Core Goal

**Enable AI Agents to browse the web like humans.**

Existing solutions (HTTP APIs, headless browser scripts, Playwright MCP) have limitations:
- No support for JS-rendered SPAs
- No session persistence
- Lack of interactive exploration

NavCLI's positioning: **An interactive, explorable browser CLI**

## Core Value

| Feature | What NavCLI Solves |
|---------|-------------------|
| JS Rendering | Full SPA support |
| Session Persistence | Cookies, session maintained |
| Interactive CLI | Agent can explore while operating |
| Token Optimization | Lightweight elements + on-demand text/html |

## Typical Workflow

```bash
> g https://example.com          # Navigate
> elements                       # Observe interactive elements
> c .btn-login                   # Click login
> t #email "test@example.com"    # Type email
> t #password "123456"           # Type password
> c button[type="submit"]       # Submit
> text                           # Confirm result
```

Agent can: **Navigate → Observe → Interact → Feedback → Continue**

## Vision

Become the **standard browser interaction layer** for AI Agents, enabling any Agent to control browsers via command-line interface:
- Form filling, login authentication
- Information scraping, content exploration
- Complex multi-step business processes

## Related Documentation

- [Skill File Download](https://github.com/wumu2013/navcli/blob/main/docs/skill.md)
- [Project Homepage](https://make.datavoid.fun/navcli/)
- [PRD Product Requirements](./NAVCLI_PRD.md)