semvec/pypi-packages huggingface dataset:
sklPCA 1.0.0
Supervised Kernel-Based Longitudinal PCA (skl-PCA)
Keywords: digital biomarkers,supervised methods,kernel methods,longitudinal methods,dimension reduction,machine learning

=======
skl-PCA
=======

This package implements Supervised Kernel-based Longitudinal Principal Components Analysis (skl-PCA) for predictor dimension reduction in longitudinal models. The software was written by members of the Mindstrong Health Data Science team:

    * Patrick Staples, PhD
    * Min Ouyang, PhD
    * Bob Dougherty, PhD
    * Greg Ryslik, PhD, FCAS, MAAA
    * Paul Dagum, MD, PhD


Please contact us at `datascience@mindstronghealth.com <datascience@mindstronghealth.com>`_.

NOTE: If you use this software in your work, please cite the following `paper <http://arxiv.org/abs/1808.06638>`_:

    Patrick Staples, Min Ouyang, Robert F. Dougherty, Gregory A. Ryslik, and Paul Dagum (2018). Supervised Kernel PCA For Longitudinal Data. http://arxiv.org/abs/1808.06638.

Installation
------------

The easiest way to install the package is via ``easy_install`` or ``pip``::

    $ pip install sklPCA

This should also take care of the dependencies (numpy, scipy, pandas, and sklearn).

Usage
-----

See examples.py for examples of simulated data, predictor reduction, fitting, and cross-validated model performance.


Copyright & License
-------------------

Copyright (c) 2018, `Mindstrong Health <http://mindstronghealth.com>`_. GNU Affero General Public License.
lipsumAPI 1.1
A lipsum.com API for Python.

# Python-Lipsum-API

Generates Lorem Ipsum from https://www.lipsum.com/

### Basic use

##### One time use

```Python
import lipsum

lipsum(mode, amount, start)
```
modes:
* words - generate x words
* paras - generate x paragraphs
* bytes - generate x characters
* lists - generate x lists

amount:
How much is generated

start:
Whether to start section with "Lorem ipsum dolor sit amet"
##### Repeated use

```Python
import lipsum


Lipsum = lipsum.LipsumGen()

Lipsum.amount(x)
Lipsum.start(True)

Lipsum.words()
Lipsum.bytes()

```

1. First we create a grabber object `LipsumGen`
2. Then we set the amount to be generated using `LipsumGen.amount`
3. Set `LipsumGen` object to start with "Lorem ipsum dolor sit amet"
4. Now we can generate Lorem Ipsum using the various functions inside our `LipsumGen` object. 
See below for various functions:
* `LipsumGen.words()`
* `LipsumGen.paras()`
* `LipsumGen.bytes()`
* `LipsumGen.lists()`

Note: you can set the amount when calling these functions `LipsumGen.words(100)`

##### Examples

Generate 100 characters of text:
```Python
import lipsum

lipsum.byte(100)
```

Generate 3 paragraphs the first of which starts with "Lorem ipsum dolor sit amet":
```Python
import lipsum

lipsum.paras(100, True)
```

Generate 10 sets of Loren Ipsum of Increasing size

```Python
import lipsum

Lipsum = lipsum.LipsumGen()
Lipsum.amount = 500

output = []

for i in range(10):
    Lipsum.amount *= 2
    output.append(Lipsum.bytes())
```

### Latest Changes
* Initial release

for full changelog click [here](https://gitlab.com/xedre/Python-Lipsum-API/blob/master/CHANGELOG)

### TODO
* CLI
    * ~~text output~~
    * file IO
* GUI
    * visual output
    * file IO