Metadata-Version: 2.1
Name: nn_error_metrics
Version: 1.0.0
Summary: A collection of neural network machine learning error metrics.
Home-page: https://github.com/arif-x/nn-error_metrics-metrics
Author: Ariffudin
Author-email: sudo.ariffudin@email.com
License: MIT
Project-URL: Source, https://github.com/arif-x/nn-error_metrics-metrics
Project-URL: Source Code, https://github.com/arif-x/nn-error_metrics-metrics
Keywords: nn neural-network metrics nn-error_metrics-metrics
Platform: UNKNOWN
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: Operating System :: OS Independent
Description-Content-Type: text/plain

# Example Usage
```
# Import the required functions from your package
from nn_metrics.metrics import (
    mean_absolute_percentage_error,
    mean_absolute_error,
    mean_squared_error,
    root_mean_squared_error,
    binary_cross_entropy,
    categorical_correntropy,
    sparse_categorical_crossentropy
)

# Example usage:
actual = [10, 20, 30, 40, 50]
predicted = [12, 18, 28, 41, 48]

# Calculate and print error metrics
print("Mean Absolute Percentage Error (MAPE):", mean_absolute_percentage_error(actual, predicted))
print("Mean Absolute Error (MAE):", mean_absolute_error(actual, predicted))
print("Mean Squared Error (MSE):", mean_squared_error(actual, predicted))
print("Root Mean Squared Error (RMSE):", root_mean_squared_error(actual, predicted))
```

