semvec/pypi-packages huggingface dataset:
py-payhero 0.0.2
A Python SDK for interacting with the PayHero API
License: MIT
Requires Python: >=3.6


![image](https://github.com/user-attachments/assets/7d03da7d-f2e2-4dac-b2da-1c73d1dc9949)
<div align="center">
    <h2>PAYHERO PYTHON SDK </h2>
</div>
<p align="center">
    <em>A Python wrapper for interacting with the PayHero API </em>
</p>

<p align="center">
<a href="https://pypi.org/project/py-payhero" target="_blank">
    <img src="https://img.shields.io/pypi/v/py-payhero?color=%2334D058&label=pypi%20package" alt="Package version">
</a>
<a href="https://pypi.org/project/py-payhero" target="_blank">
    <img src="https://img.shields.io/pypi/pyversions/p.svg?color=%2334D058" alt="Supported Python versions">
</a>
</p>


---

**Documentation**: <a href="https://google.com" target="_blank">https://fast.m</a>

**Source Code**: <a href="https://github.com/njeru-codes/PayHero-API-SDK-" target="_blank">https://github.com/njeru-codes/PayHero-API-SDK-</a>

---

py-payhero is a lightweight python sdk wrapper for interacting with payhero APIs.
features:
  - list 1
  - feature 2

### installation
```bash
  pip install py-payhero
```

### usage 
`for detailed usage of this package head over to documentation page`
```python
  from PayHero import API

  app = API("username", "password")

  service_balance = app.get_service_balance()
  

```

### Contributing
Contributions are welcome! Please follow these steps:
- Fork the repository.
- Create a new branch (git checkout -b feature-branch).
- Make your changes and commit them (git commit -m 'Add new feature').
- Push to the branch (git push origin feature-branch).
- Create a pull request
CSMHP 0.1.5
Cluster-based Superposed Marked Hawkes Process
License: MIT
Requires Python: >=3.8

# CSMHP
This package is an implementation of:<br />
Cluster-based Superposed Marked Hawkes Process (CIBer)  <br />



The `CSMHP` package implements a novel Hawkes process model in PyTorch for modeling event sequence with high dimension of covariates, especially categorical ones. This model is useful in various fields such as finance, healthcare, and cyber-risk analysis, where temporal event sequences need to be modeled and analyzed. The package allows training a Hawkes process with clustering-based probability estimates.

## Features

- **Hawkes Process Model**: A self-exciting point process that models events whose intensity is influenced by prior events.
- **Clustering Model Integration**: Integrates clustering algorithms to infer event type probabilities.
- **Parameter Optimization**: Supports optimization of the model parameters (`mu`, `gamma`, `alpha_kernel`, `beta_kernel`) using gradient-based methods.
- **Event Simulation**: Implements the thinning algorithm for simulating future event times based on the trained model.
- **Mini-batch Optimization**: Option for mini-batch optimization to handle large datasets efficiently.

## Installation

To install the `CSMHP` package, you can use `pip`:

```bash
pip install CSMHP

```python
import torch
import numpy as np
from sklearn.ensemble import RandomForestClassifier
from CSMHP import CSM_Hawkes

# Initialize parameters
T = 1000  # Length of observation window
num_clusters = 3  # Number of event clusters
params = None  # Optionally provide initial parameters
X_train = np.random.rand(100, 5)  # Example training data with 100 events and 5 features
y_train = np.random.randint(0, num_clusters, size=100)

# Define a clustering model (e.g., KMeans)
cluster_model = RandomForestClassifier(n_estimators=50, max_depth=2)
cluster_model.fit(X_train, y_train)
probability = cluster_model.predict(X_train)

# Initialize CSM_Hawkes model
hawkes_model = CSMHP(T=T, num_clusters=num_clusters, params=params, 
                          model=cluster_model, X_train=X_train, step=0.01, unit='Week')

# Fit the model (train the probability and parameters)
event_times = np.random.rand(100) * T  # Example event times
hawkes_model.fit_param(event_times, probability epoch=20)
hawkes_model.fit_prob(event_times, probability, step=0.5, epoch=5)
hawkes_model.fit_param(event_times, None, epoch=20)
hawkes_model.fit_prob(event_times, None, step=0.5, epoch=5)
hawkes_model.fit_param(event_times, None, epoch=10)
capeditor 0.6.6
Wagtail based CAP composer
License: MIT
Requires Python: >=3.9

## ⚠️ Important Note

This package has been deprecated in favor of [Alertwise](https://github.com/wmo-raf/alertwise).

Please follow the instructions in the [Alertwise](https://github.com/wmo-raf/alertwise) repository to install the new
package.