semvec/pypi-packages huggingface dataset:
cloudx-nac-api 1.3.7
CloudX Digital Edge IaaS API package

# Securyi Anomaly Detector Micro Service

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```bash
pip install cloudx_naas_api
```

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```bash
make build push
```

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limobauwzan 2.0.5
sdfee
Requires Python: >=3.6, <4
Keywords: sample,setuptools,development

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memtrace2npz 0.0.1
A package for converting binary output of DynamoRIO's memtrace client to npz.
Requires Python: >=3.6

# memtrace2npz

This project is a utility for converting the output of DynamoRIO's memtrace-x86 client to an npz file. The output can be compressed into a zip file as well. 

## Installation

```
pip3 install memtrace2npz
```

## Usage

```
memtrace2npz < -i inputfile > [ -o outfile ] [ -h ] [ -f ]
```

You must specifiy the input file with `-i`. You may optionally specify an outputfile -- if you don't, the output file will be in the current directory with the basename of your input file, and with the extension changed to `.npz`.

You may skip the check for the existence of the outfile with `-f`. 

## Testing

```
pytest
```
limobauwzam 2.0.5
sdfee
Requires Python: >=3.6, <4
Keywords: sample,setuptools,development

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lqa 0.0.4
A package for automatically adjusting learning rate in the training of deep learning.
Requires Python: >=3.6

# Local Quadratic Approximation (LQA)
In deep learning tasks, the learning rate determines the update step size in each iteration, which plays a critical role in gradient-based optimization. However, the determination of the appropriate learning rate in practice typically replies on subjective judgement. In this work, we propose a novel optimization method based on local quadratic approximation (LQA). In each update step, given the gradient direction, we locally approximate the loss function by a standard quadratic function of the learning rate. Then, we propose an approximation step to obtain a nearly optimal learning rate in a computationally efficient way. The proposed LQA method has three important features. First, the learning rate is automatically determined in each update step. Second, it is dynamically adjusted according to the current loss function value and the parameter estimates. Third, with the gradient direction fixed, the proposed method leads to nearly the greatest reduction in terms of the loss function. Extensive experiments have been conducted to prove the strengths of the proposed LQA method. The detailed description of the LQA method can be found at https://arxiv.org/abs/2004.03260.

## How to use LQA
### 1. Import the package
```
import lqa
```

### 2. Initialize a LQA worker and train your model. 

The following example shows the necessary parameters: 

* model: your model (based on tensorflow.keras)

* epochs: the number of epochs

* train: training dataset (e.g., [X0, Y0], or a data generator)

* test: testing (or validation) dataset (e.g., [X1, Y1], or a data generator)

* loss: loss function

```
bestmodel = lqa.gd(model, epochs=10, train=[X0,X1], test=[Y0,Y1]], loss='sparse_categorical_crossentropy')
```

### 3. Get the tuned model from the worker.

The tuned model and training records are organized as attributes of the LQA worker.
```
# tuned model
bestmodel.model

# training records, including:
# history[train_loss] - loss on the training dataset
# history[train_acc] - accuracy on the training dataset
# history[test_loss] - loss on the testing dataset
# history[test_acc] - accuracy on the testing dataset
bestmodel.history
```
StateMint 2.1.1
A library for finding State Space models of dynamical systems.
Requires Python: >=2.7

## Python Package

[![Build Status](https://travis-ci.org/CameronDevine/StateMint.svg?branch=master)](https://travis-ci.org/CameronDevine/StateMint)
[![PyPI](https://img.shields.io/pypi/v/StateMint.svg)](https://pypi.org/project/StateMint/)
[![Documentation Status](https://readthedocs.org/projects/statemint/badge/?version=latest)](https://statemint.readthedocs.io/en/latest/?badge=latest)
[![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/camerondevine/StateMint/master?filepath=python%2FExample.ipynb)

The StateMint Python package is an implementation based on [SymPy](http://www.sympy.org) which allows this code to easily combined with simulation and analysis code, or integrated into other systems.
The package provides a `Solve` function which takes the input variables, state variable elemental equations, non-state variable elemental equations, constraint equations, and output variables and returns a Python object with multiple forms of the solution.
These outputs can be further analyzed with SymPy or converted to Numpy objects for easy simulation or numerical analysis.
The full documentation for the code is available on [readthedocs.io](https://statemint.readthedocs.io/en/latest/).
An [example](https://github.com/CameronDevine/StateMint/blob/master/python/Example.ipynb) Jupyter notebook (also available on [binder](https://github.com/CameronDevine/StateMint/blob/master/python/Example.ipynb)) is included with the package.
Because this package is available on PyPI the latest release can be easily installed by running `pip install StateMint`.