semvec/pypi-packages huggingface dataset:
llmtask 0.0.2
LLM Downstream Task Evaluation Tool.

# LLM Task

## Introduction



## Quick Start

```bash
pip install llmtask
```


```python
import random

from llmtask import TaskGenerator

choices = ("A", "B", "C", "D")

TG = TaskGenerator("mmlu", max_shot=4)

for task in TG:
    TG.feedback(random.choice(choices))

print(TG.summary())
```
lino-voga 23.12.0
A Lino Django application for managing courses, participants and meeting rooms


Lino Voga is a `Lino <https://www.lino-framework.org>`__ application
for managing courses, participants and meeting rooms.

- The central project homepage is https://voga.lino-framework.org

- Online demo site at https://voga1e.lino-framework.org
  and https://voga1r.lino-framework.org

- Technical specs are at https://www.lino-framework.org/specs/voga

- This is an integral part of the Lino framework, which is documented
  at https://www.lino-framework.org

- The changelog is at https://www.lino-framework.org/changes

- For introductions, commercial information and hosting solutions
  see https://www.saffre-rumma.net

- This is a sustainably free open-source project. Your contributions are
  welcome.  See https://community.lino-framework.org for details.
igPCA 1.0.1
Integrative Generalized Principle Analysis
Requires Python: >=3.6

# Intergative Generalized Principle Componenet Analysis (igPCA)

Intergative Generalized Principle Componenet Analysis (igPCA) is a framework for joint dimensionality reduction of double-structure data. The algorithm details wiil be available soon in out manuscript:
    Xie X and Ma J (2023). * Structured dimensionality reduction for multi-view microbiome data*

`igPCA` is a python implementation of the proposed framework.

## Installation

```bash
$ pip install igPCA
```

## Usage

`igPCA` can be used to perform joint dimensinality reduction for two dataset X1 and X2 as follows:

```python
from .igPCA import igPCA
import matplotlib.pyplot as plt

model = igPCA(X1, X2, H, Q1, Q2, r1, r2)
model.fit(r0 = r0)
```

In this simple example, H, Q1 and Q2 are kernel matrices characterzing X1 and X2. The total rank for X1 and X2 are r1 and r2, respectively. r0 is the joint rank between X1 and X2.