semvec/pypi-packages huggingface dataset:
fast-elm-kernel 1.2
Extreme Learning Machine kernels for ML

# Fast ELM Kernel
This package provides fast calculation of an Extreme Learning Machine kernel. ELM kernels are parameter free gaussian kernels.

## Usage
`pip install fast-elm-kernel`

```python
from fast_elm_kernel import elm_kernel_vec, elm_kernel
import numpy as np

# Note that for vector operations elm_kernel_vec is prefered and faster
X = np.array([
  [17, 24, 1, 8, 15],
  [23, 5, 7, 14, 16],
  [4, 6, 13, 20, 22],
  [10, 12, 19, 21, 3],
  [11, 18, 25, 2, 9]
])

print(elm_kernel(X, X))

print(elm_kernel_vec(np.array([1, 2, 3]), np.array([100, 0.5, 0.3])))

```

## LICENSE
MIT License

Copyright (c) [2022] [Moritz Heusinger]

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
streamlit-firebase-remote-config-component 0.0.4
Firebase Remote Config and Google Analytics for Streamlit
Requires Python: >=3.1


        A streamlit component which enables fatching remote config variables from firebase and also enables google analytics.
        Usage:
        
        from fbrc import fbrc
        configParams:dict = fbrc(FirebaseConfig)

        #or add user id fro firebase analytics
        configParams:dict = fbrc(FirebaseConfig, userid)
trading-simulator 1.0.1
Simulation on financial market transaction (eg. Stock buy&sell).
Requires Python: >=3.7

# trn-sim
Simulation on financial market transaction (eg. Stock buy&sell). 
Imagining you have a trading account, you can do buy & sell actions. You can build the trn history once you take the action and provide functions for calculation of FF(Fund Flow) and GL(Gain & Loss). 

# Version 1.0
A very simple and conding version. You have to write your own python logic to decide when you buy or sell. But it will help you recording and computing your result.