DipoleAmplitudePredictor 0.2.1

A package for making predictions for Dipole Amplitude using a pre-trained Random Forest model

Dipole Amplitude Predictor Module

Overview

The Dipole Amplitude Predictor is a module designed for predicting dipole amplitudes using a trained RandomForest model. The module is easy to install and use, providing a ready-to-use model for your applications.

Installation

To install the module via PyPI, simply use:

pip install DipoleAmplitudePredictor

Alternatively, you can refer to the code in the following GitHub repositories for more details:

• Models: Dipole-Amplitude-Prediction Models
• Module: DipoleAmplitudeModule

Usage

Once installed, you can use the module as follows:

from DipoleAmplitudePredictor import RandomForestModel
import numpy as np

# Example input array (X_new) to predict dipole amplitudes
X_new = [6.80213521e-05, 7.60105631e-05, 8.49278899e-05, 9.48961447e-05, 1.06028156e-04, 1.18472109e-04, 1.32375933e-04, 1.47911787e-04, 1.65270689e-04, 1.84666744e-04, 2.06338769e-04, 2.30553961e-04, 2.57610535e-04, 2.87841893e-04, 3.21620436e-04, 3.59362142e-04, 4.01532030e-04, 4.48649085e-04, 5.01293915e-04, 5.60114007e-04, 6.25834181e-04, 6.99262232e-04, 7.81302824e-04, 8.72963753e-04, 9.75373848e-04, 1.08979043e-03, 1.21762157e-03, 1.36043585e-03, 1.51998909e-03, 1.69823794e-03, 1.89737105e-03, 2.11982869e-03, 2.36833796e-03, 2.64594136e-03, 2.95603541e-03, 3.30241181e-03, 3.68929871e-03, 4.12141876e-03, 4.60403227e-03, 5.14301691e-03, 5.74491243e-03, 6.41702695e-03, 7.16748365e-03, 8.00535812e-03, 8.94072927e-03, 9.98485026e-03, 1.11502092e-02, 1.24507328e-02, 1.39018663e-02, 1.55208053e-02, 1.73266076e-02, 1.93404444e-02, 2.15857595e-02, 2.40885269e-02, 2.68774740e-02, 2.99843248e-02, 3.34441065e-02, 3.72953477e-02, 4.15804846e-02, 4.63459745e-02, 5.16428064e-02, 5.75264818e-02, 6.40576123e-02, 7.13017086e-02, 7.93298126e-02, 8.82180267e-02, 9.80480664e-02, 1.08906458e-01, 1.20884816e-01, 1.34078623e-01, 1.48586950e-01, 1.64510703e-01, 1.81951410e-01, 2.01008806e-01, 2.21778229e-01, 2.44347425e-01, 2.68792050e-01, 2.95171693e-01, 3.23523061e-01, 3.53855443e-01, 3.86141501e-01, 4.20312850e-01, 4.56249093e-01, 4.93774961e-01, 5.32649387e-01, 5.72566639e-01, 6.13148821e-01, 6.53954286e-01, 6.94478267e-01, 7.34171849e-01, 7.72455242e-01, 8.08748270e-01, 8.42497531e-01, 8.73213445e-01, 9.00505412e-01, 9.24111984e-01, 9.43929546e-01, 9.60016623e-01, 9.72598746e-01, 9.82034747e-01, 9.88802034e-01]
c2_value = 2.5
x_bj_target = 1e-3
X_new.append(c2_value)
X_new.append(x_bj_target)
X_new = np.array(X_new).reshape(1, -1)
# Note: Ensure that X_new has a shape of (no. of samples, 103), where the first 101 features correspond to the R_grid. Append the C2 value and the x_bj value for the prediction.

# Initialize the model and make predictions
rf_model = RandomForestModel()
predictions = rf_model.predict(X_new)  
print(predictions)
print("R values: ", rf_model.Rgrid()) # R-Grid

Note: Ensure that X_new has a shape of (no. of samples, 103), where the first 101 features correspond to the R_grid. Append the C2 value and the x_bj value for the prediction.

Refer to the GitHub repositories for additional examples and details on the input format.

Contributing

Feel free to open issues or submit pull requests if you have improvements or feature requests. You can find detailed contributions guidelines in the GitHub repositories.

License

Check the GitHub repositories for licensing details.