Add RBFNN implementation with Iris classification #12793
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| # Step 3: Solve output weights using least squares | ||
| self.weights = np.linalg.pinv(rbf_activations).dot(y_data) | ||
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| def predict(self, x): |
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As there is no test file in this pull request nor any test function or class in the file neural_network/rbfnn.py, please provide doctest for the function predict
Please provide return type hint for the function: predict. If the function does not return a value, please provide the type hint as: def function() -> None:
Please provide descriptive name for the parameter: x
Please provide type hint for the parameter: x
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This PR introduces a clean implementation of a Radial Basis Function Neural Network (RBFNN) for classification, demonstrated on the Iris dataset. Key Features: Gaussian RBF activation in the hidden layer. KMeans clustering for center initialization. Least-squares fitting for training output weights. Do tell, if you'd like enhancements such as regularization, cross-validation, or multi-dataset support as well! |
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Add RBFNN implementation with Iris classification