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https://github.com/maskedsyntax/2-stage-opamp-analysis
Comparative Analysis of Machine Learning Models for Aspect Ratio Estimation of a Two-Stage Operational Amplifier
https://github.com/maskedsyntax/2-stage-opamp-analysis
analysis machine-learning opamp streamlit
Last synced: about 2 months ago
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Comparative Analysis of Machine Learning Models for Aspect Ratio Estimation of a Two-Stage Operational Amplifier
- Host: GitHub
- URL: https://github.com/maskedsyntax/2-stage-opamp-analysis
- Owner: MaskedSyntax
- License: mit
- Created: 2022-05-08T15:39:35.000Z (over 2 years ago)
- Default Branch: master
- Last Pushed: 2024-07-29T22:32:34.000Z (6 months ago)
- Last Synced: 2024-11-19T22:36:30.323Z (2 months ago)
- Topics: analysis, machine-learning, opamp, streamlit
- Language: Python
- Homepage:
- Size: 19.2 MB
- Stars: 2
- Watchers: 1
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# Aspect Ratio Estimation of a Two-Stage Operational Amplifier
This repository contains a Streamlit web application that estimates the aspect ratios of a two-stage operational amplifier using various machine learning models. The application allows users to input specific parameters and select a model to predict the aspect ratios.
## Features
- **Interactive UI**: User-friendly interface to input parameters and select models.
- **Multiple Models**: Provides predictions using different regression models including Linear Regression, Gaussian Process Regression, SVR, Decision Tree, KNN, Random Forest, and a Neural Network.
- **Visualization**: Displays predictions and aspect ratios for the selected model.
## Screenshots
## Getting Started
### Prerequisites
- Python 3.x
- Streamlit
- Keras
- Scikit-learn
- Numpy
- Pandas
- Matplotlib
### Installation
1. Clone the repository:
```bash
git clone https://github.com/Aftaab25/2-Stage-OpAmp-Analysis.git
cd 2-Stage-OpAmp-Analysis
```
2. Install the required packages:
```bash
pip install -r requirements.txt
```
3. Ensure you have the dataset `2STAGEOPAMP_DATASET.csv` in the same directory.
4. Ensure you have the trained models `model.h5` and `gaussian_model.pkl` in the same directory.
### Running the App
Run the Streamlit app using the following command:
```bash
streamlit run main.py
```
This will start the Streamlit server, and you can interact with the app in your web browser.
## Usage
1. **Input Features**:
- DC Gain
- Unity Gain Frequency (ft)
- 3-dB Frequency (f3)
- Common Mode Voltage (Vcm)
- Power Dissipation (Pdiss)
2. **Select a Model**:
- Linear Regression Model
- Gaussian Regression Model
- SVR
- Decision Tree Regressor
- KNN
- Random Forest Regressor
- Neural Network (Best)
3. **Get Predictions**: Click the 'Calculate' button to get the predicted aspect ratios for the given input features.
## Code Overview
### `main.py`
- **Imports**: Necessary libraries including Streamlit, Numpy, Pandas, Scikit-learn, and Keras.
- **Data Loading**: Loads the dataset `2STAGEOPAMP_DATASET.csv` and preprocesses it.
- **Model Loading**: Loads the pre-trained models for prediction.
- **Model Functions**: Defines functions for each machine learning model to predict aspect ratios.
- **Streamlit UI**: Creates the sidebar and main panel for user input and model selection.
## License
This project is licensed under the MIT License. See the [LICENSE](LICENSE) file for details.