semvec/pypi-packages huggingface dataset:
res-facenet-khairulimam 0.0.1
This model has been trained on VGGv2 and tested on LFW with 92% accuracy.

# ResFacenet
This package is intended as an __pytorch hub__ entry point for my trained facenet model on this repo [khrlimam/facenet](https://github.com/khrlimam/facenet).
This pretrained model can be used for anyone who want to use it for transfer learning or any other applications.

This model trained on VGGv2 dataset and tested on LFW dataset and gained 92% accuracy.

This work is distributed under MIT license, so I hope you consider the license's policy. Cheers!
squaad 2.1
Helper functions for running queries, ml pipeline, statistical analysis on SQUAAD framework

# SQUAAD ANALYSIS FRAMEWORK

## Installation

`pip install squaad`

### Releases

* V2.0 `https://github.com/fostiropoulos/squaad/releases/download/v2.0/squaad-2.0.tar.gz`


### Install from Binary
`pip install squaad-2.0.tar.gz`

## Usage

### Creating new database connection
~~~~
myConnection=db("config.json","cache")
print("Connection Status: %s"%myConnection.testConnection())
~~~~

### Config.json and Cache

* Config.json follows the following format:
~~~~
{"pgsql":{"host":"","user":"","passwd":"","db":""} }
~~~~
* Cache folder is used to save results of the queries and uses the cache next time you execute a query.

### Games-Howell Statistics Test

~~~~
stats.gamesHowellBinomial({"GROUP1":{True:100, False:3999}, "GROUP2":{True:2999,False:2939}})
~~~~~

### Classification Pipeline with KFold Usage

Parameters

* `X` Pandas dataframe with set of data. Each column is a feature
* `Y`  Labels for the set of data.
* `split_columns` (Optional) **unimplemented**, columns to split by. That is columns that can have bias, we take into consideration during splitting
* `kfolds` (Optional)  number of folds to run.
* `classifiers` (Optional)  dictionary containing classifiers to use
* `balancers` (Optional)  the balancers you want to run

### Classifiers

Default Classifiers:
* Nearest Neighbors
* Linear SVM
* RBF SVM
* Gaussian Process
* Decision Tree
* Random Forest
* Neural Net
* AdaBoost
* Naive Bayes
* QDA

### Balancers

Default Classifiers:
* Unbalanced
* SMOTE
* SMOTEEN
* SMOTETomek
* RandomUnderSampler

### ML Pipeline examples

~~~~
X=df[['locs_inc', 'cplxs_inc', 'smls_inc', 'vuls_inc', 'fbgs_inc', 'locs_dec', 'cplxs_dec', 'smls_dec', 'vuls_dec', 'fbgs_dec']]
Y=df['affiliation']
mlPipeline.classificationPipeLineKfold(X,Y)
~~~~
bnb-tx 0.0.4
Binance Chain Transactions

# Binance Chain Transaction Library
This library provides a low-level interface for interacting with different Binance Chain transactions.

## Installation
```sh
pip3 install bnb-tx
```

Or, in your `Pipfile`:
```
bnb-tx = "==0.0.4"
```


## Usage
The following is an example that uses the `ecdsa` library to sign.

```python3
import ecdsa
def uncompressed_public_key(sk):
    """ Derive uncompressed public key """
    order = sk.curve.generator.order()
    p = sk.verifying_key.pubkey.point
    x_str = ecdsa.util.number_to_string(p.x(), order)
    y_str = ecdsa.util.number_to_string(p.y(), order)
    uncompressed = b'\x04' + x_str + y_str
    return uncompressed


from binance_transaction import BnbTransaction, NewOrder, address_bytes, BUY, GTE, LIMIT_ORDER

sk = ecdsa.SigningKey.from_pem(open('secp256k1-key.pem').read())
from_address = 'bnb100dxzy02a6k7vysc5g4kk4fqamr7jhjg4m83l0'
account_number = 96025  # https://docs.binance.org/api-reference/dex-api/paths.html#apiv1accountaddress
sequence_number = 888
tx = BnbTransaction(account_number, sequence_number)
order_id = f'{address_bytes(from_address).hex().upper()}-{sequence_number + 1}'
tx.add_msg(NewOrder(from_address, order_id, 'BNB_TUSDB-888', LIMIT_ORDER, BUY, 3500000000, 500000000, GTE))
print(tx.signing_json())
sig = sk.sign_digest(tx.signing_hash())
public_key = uncompressed_public_key(sk)
tx.apply_sig(sig, public_key)
signed_transaction_bytes = tx.encode()
print(f'Signed bytes: {signed_transaction_bytes.hex()}')
```

## Support
Not all transaction types are supported.
Please consult this table for details.
If you need support, please submit a [pull request](https://github.com/trusttoken/bnb-tx-python/pulls).

|  Message Type | encode | decode | from\_obj |
|---------------|--------|--------|-----------|
| Send          | ✅     |        | ✅        |
| NewOrder      | ✅     |        | ✅        |
| CancelOrder   | ✅     |        | ✅        |
| Issue         | ✅     |        | ✅        |
| Mint          | ✅     |        | ✅        |
| Burn          | ✅     |        | ✅        |
| Freeze        | ✅     |        | ✅        |
| Unfreeze      | ✅     |        | ✅        |
| TimeLock      | ✅     |        | ✅        |
| TimeUnlock    | ✅     |        | ✅        |
| TimeRelock    | ✅     |        | ✅        |
| Proposal      | ✅     |        | ✅        |
| Vote          | ✅     |        | ✅        |



## Contributing
See [CONTRIBUTING](CONTRIBUTING.md).


## License
LGPLv3