semvec/pypi-packages huggingface dataset:
ocr-ops 0.0.0.4.3.2
OCR-Ops
Requires Python: <=3.11.7

# ocr-ops

OCR-Ops is infrastructure to perform Optimal Character Recognition (OCR) at scale on a large number of images and videos.
Built on top of the algo-ops framework, OCR-Ops is modular and extensible in its data processing operations.

Key Features:

* Supports building an OCRPipeline that can utilize multiple popular OCR annotation methods (e.g. PyTesseract, EasyOCR,
  etc.) and return the results in structured and efficient fashion within a unified framework.
* Enables multi-levels of information of the OCR application (e.g. text-only, bounding boxes, etc.)
* Allows definition of an image pre-processing pipeline (before OCR) and a text-cleaning pipeline (after OCR) of
  detected but noisy text to enable optimal and robust OCR performance.
* Supports several nice presets that are plug-and-play for the above purpose!
globalparams 1.0.2
parameters loader signelton
Requires Python: >=3.6
Keywords: parameters,storage

# paramspy
container for global (Singelton) loading and saving parameters

## Usage example
On load, need to be called once:
```
Params(LOCATION_OF_PARMAS_JSON)
```
On usage, after calling the above
```
param_dict=Params()
```

`params.json` is a nested parameters json, e.g.;
```
{
    "data": {
        "granularity": 21600,
        "header": [
            "d_price"
        ],
        "look_back": 42,
        "mean": 6.477538237436277,
        "n_test": 64,
        "std": 257.3105989979195,
        "time_decay_sec": 3000000,
        "weight_bias": 0.1
    },
    "data_collection": {
        "n_record_per_fetch": 280,
        "time_span_days": 20000
    },
    "logging": {
        "model_folder": "models",
        "output_folder": "logdir",
        "plot_every": 32
    },
    "train": {
        "batch_size": 6400,
        "learn_rate": 0.0001,
        "n_epochs": 60000
    }
}

```

<!---
deploy
```
python setup.py sdist
twine upload  dist/*
```
-->
ga-vqc 1.0.14
Genetic Algorithm for VQC ansatz search.

# GA for VQC Ansatz Search
This is a module to support Variational Quantum Circuits by optimizing the ansatz. The ansatz optimization is performed using a Genetic Algorithm, which can be parallelized with GPUs.

For a detailed example of using this package see the https://github.com/tcoulvert/QAE_4_HEP repository.


## Installation
Run the following to install:
```bash
$ pip install ga-vqc
```

## Contributors
This module was developed through the Caltech SURF program. Special thanks
to my mentor at Caltech.
- Jean-Roch (California Institute of Technology, Pasadena, CA 91125, USA)

## Usage
```python
import ga_vqc as gav

## Config (hyperparameters) for GA, see full list in example ##

vqc_main = <'Function that handles running your VQC optimization'>

# Example of allowed optimization gates, see Genepool.py for documentation
gates_dict = {"I": (1, 0), "RX": (1, 1), "CNOT": (2, 0)}
gates_probs = [0.35, 0.35, 0.3]
genepool = gav.Genepool(gates_dict, gates_probs)

vqc_config = {
    'num_qubits': 3,
    'etc': <'whatever config params your VQC model requires'>
}

ga_output_path = FILEPATH_FOR_GA_OUTPUT

config = gav.Config(vqc_main, vqc_config, genepool, ga_output_path)

# Create the GA with the given hyperparameters
ga = gav.setup(config)

# Evolve the GA and search for the best ansatz
ga.evolve()
```
Topsis-Ramjas-102117159 1.0
Topsis package
Keywords: python,topsis


# TOPSIS PACKAGE - Assignment 1
I have developed a command line python program to implement the TOPSIS.
TOPSIS (technique for order performance by similarity to ideal solution) is a useful technique in dealing with multi-attribute or multi-criteria decision making (MADM/MCDM) problems in the real world
## Installation
```pip install topsis-Ramjas-102117159```

## Usage
Please provide the filename for the CSV, including the .csv extension. After that, enter the weights vector with values separated by commas. Following the weights vector, input the impacts vector, where each element is denoted by a plus (+) or minus (-) sign. Lastly, specify the output file name along with the .csv extension.

```py -m topsis.__main__ [input_file_name.csv] [weight as string] [impact as string] [result_file_name.csv]```

## Example Usage
The below example is for the data have 5 columns.
```py -m topsis.__main__ "C:\User\...." "1,1,2,0.5,0.75" "+,+,-,-,-" "C:\User\....."```

## Example Dataset

Fund Name | P1 | P2 | P3 | P4 | P5
------------ | ------------- | ------------ | ------------- | ------------ | ------------
M1 | 0.78 | 0.61 | 5.5 | 34.7 | 10.4
M2 | 0.88 | 0.77 | 5 | 58.4 | 16.26
M3 | 0.61 | 0.37 | 5.9 | 39.9 | 11.7
M4 | 0.76 | 0.58 | 4.2 | 57.7 | 15.81
M5 | 0.84 | 0.71 | 3.2 | 48 | 13.19
M6 | 0.76 | 0.58 | 4 | 68.8 | 18.54
M7 | 0.81 | 0.66 | 6.5 | 38.2 | 11.54
M8 | 0.81 | 0.66 | 3.2 | 32.8 | 9.37

## Output Dataset
Fund Name | P1 | P2 | P3 | P4 | P5 | TOPSIS Score | Rank
------------ | ------------- | ------------ | ------------- | ------------ | ------------ | ------------ | ------------
M1 | 0.78 | 0.61 | 5.5 | 34.7 | 10.4 | 0.5303740545041122 | 4
M2 | 0.88 | 0.77 | 5 | 58.4 | 16.26 | 0.5372510220778413 | 3
M3 | 0.61 | 0.37 | 5.9 | 39.9 | 11.7 | 0.4715707210914604 | 8
M4 | 0.76 | 0.58 | 4.2 | 57.7 | 15.81 | 0.5099483054760279 | 6
M5 | 0.84 | 0.71 | 3.2 | 48 | 13.19 | 0.57723478293325 | 1
M6 | 0.76 | 0.58 | 4 | 68.8 | 18.54 | 0.49447887833737925 | 7
M7 | 0.81 | 0.66 | 6.5 | 38.2 | 11.54 | 0.5244107252631429 | 5
M8 | 0.81 | 0.66 | 3.2 | 32.8 | 9.37 | 0.5576533672285703 | 2 

<br>

## Important Points
1) There should be only numeric columns except the first column i.e. Fund Name.
2) Input file must contain atleast three columns.

<br>