Create radial_basis_function_neural_network #12342

Swastik0710 · 2024-10-31T14:20:04Z

Implemented the Radial Basis Function Neural Network inside the neural_network folder of the repo. Feel free to tell if it require any changes.

for more information, see https://pre-commit.ci

…neural_network.py

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algorithms-keeper · 2024-10-31T14:34:07Z

neural_network/radial_basis_function_neural_network.py

+import numpy as np  # For numerical operations
+
+class RBFNN:
+    def __init__(self, input_size, hidden_size, output_size):


Please provide return type hint for the function: __init__. If the function does not return a value, please provide the type hint as: def function() -> None:

Please provide type hint for the parameter: input_size

Please provide type hint for the parameter: hidden_size

Please provide type hint for the parameter: output_size

algorithms-keeper · 2024-10-31T14:34:07Z

neural_network/radial_basis_function_neural_network.py

+        # Initialize weights for the output layer
+        self.weights = np.random.rand(hidden_size, output_size)  # Weights for output layer
+
+    def rbf(self, x, center, spread):


As there is no test file in this pull request nor any test function or class in the file neural_network/radial_basis_function_neural_network.py, please provide doctest for the function rbf

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Please provide type hint for the parameter: spread

algorithms-keeper · 2024-10-31T14:34:07Z

neural_network/radial_basis_function_neural_network.py

+        """ Radial Basis Function (Gaussian). """
+        return np.exp(-np.linalg.norm(x - center) ** 2 / (2 * spread ** 2))
+
+    def forward(self, x):


As there is no test file in this pull request nor any test function or class in the file neural_network/radial_basis_function_neural_network.py, please provide doctest for the function forward

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algorithms-keeper · 2024-10-31T14:34:07Z

neural_network/radial_basis_function_neural_network.py

+        output = np.dot(hidden_outputs, self.weights)  # Compute final output
+        return output
+
+    def train(self, X, y, epochs, learning_rate):


As there is no test file in this pull request nor any test function or class in the file neural_network/radial_basis_function_neural_network.py, please provide doctest for the function train

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algorithms-keeper · 2024-10-31T14:34:07Z

neural_network/radial_basis_function_neural_network.py

+                # Update weights
+                self.weights += learning_rate * hidden_outputs.reshape(-1, 1) * error
+
+    def predict(self, X):


As there is no test file in this pull request nor any test function or class in the file neural_network/radial_basis_function_neural_network.py, please provide doctest for the function predict

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algorithms-keeper · 2024-10-31T14:38:02Z

neural_network/radial_basis_function_neural_network.py

+import numpy as np  # For numerical operations
+
+class RBFNN:
+    def __init__(self, input_size, hidden_size, output_size):


Please provide return type hint for the function: __init__. If the function does not return a value, please provide the type hint as: def function() -> None:

Please provide type hint for the parameter: input_size

Please provide type hint for the parameter: hidden_size

Please provide type hint for the parameter: output_size

algorithms-keeper · 2024-10-31T14:38:02Z

neural_network/radial_basis_function_neural_network.py

+        # Initialize weights for the output layer
+        self.weights = rng.random((hidden_size, output_size))  # Weights for output layer
+
+    def rbf(self, x, center, spread):


As there is no test file in this pull request nor any test function or class in the file neural_network/radial_basis_function_neural_network.py, please provide doctest for the function rbf

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Please provide type hint for the parameter: center

Please provide type hint for the parameter: spread

algorithms-keeper · 2024-10-31T14:38:02Z

neural_network/radial_basis_function_neural_network.py

+        """ Radial Basis Function (Gaussian). """
+        return np.exp(-np.linalg.norm(x - center) ** 2 / (2 * spread ** 2))
+
+    def forward(self, x):


As there is no test file in this pull request nor any test function or class in the file neural_network/radial_basis_function_neural_network.py, please provide doctest for the function forward

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Please provide type hint for the parameter: x

algorithms-keeper · 2024-10-31T14:38:02Z

neural_network/radial_basis_function_neural_network.py

+        output = np.dot(hidden_outputs, self.weights)  # Compute final output
+        return output
+
+    def train(self, x_train, y_train, epochs, learning_rate):


As there is no test file in this pull request nor any test function or class in the file neural_network/radial_basis_function_neural_network.py, please provide doctest for the function train

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Please provide type hint for the parameter: learning_rate

algorithms-keeper · 2024-10-31T14:38:02Z

neural_network/radial_basis_function_neural_network.py

+                # Update weights
+                self.weights += learning_rate * hidden_outputs.reshape(-1, 1) * error
+
+    def predict(self, x_test):


As there is no test file in this pull request nor any test function or class in the file neural_network/radial_basis_function_neural_network.py, please provide doctest for the function predict

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algorithms-keeper · 2024-10-31T17:47:34Z

neural_network/adaptive_resonance_theory_1.py

+import numpy as np
+
+class ART1:
+    def __init__(self, num_features, vigilance=0.8):


Please provide return type hint for the function: __init__. If the function does not return a value, please provide the type hint as: def function() -> None:

Please provide type hint for the parameter: num_features

Please provide type hint for the parameter: vigilance

algorithms-keeper · 2024-10-31T17:47:34Z

neural_network/adaptive_resonance_theory_1.py

+        self.vigilance = vigilance
+        self.weights = []  # Stores the weights for clusters
+
+    def _similarity(self, x, w):


As there is no test file in this pull request nor any test function or class in the file neural_network/adaptive_resonance_theory_1.py, please provide doctest for the function _similarity

Please provide return type hint for the function: _similarity. If the function does not return a value, please provide the type hint as: def function() -> None:

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Please provide type hint for the parameter: x

Please provide descriptive name for the parameter: w

Please provide type hint for the parameter: w

algorithms-keeper · 2024-10-31T17:47:34Z

neural_network/adaptive_resonance_theory_1.py

+        """
+        return np.sum(np.minimum(x, w)) / np.sum(x)
+
+    def _weight_update(self, x, w):


As there is no test file in this pull request nor any test function or class in the file neural_network/adaptive_resonance_theory_1.py, please provide doctest for the function _weight_update

Please provide return type hint for the function: _weight_update. If the function does not return a value, please provide the type hint as: def function() -> None:

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Please provide type hint for the parameter: w

algorithms-keeper · 2024-10-31T17:47:34Z

neural_network/adaptive_resonance_theory_1.py

+        """
+        return np.minimum(x, w)
+
+    def train(self, data):


As there is no test file in this pull request nor any test function or class in the file neural_network/adaptive_resonance_theory_1.py, please provide doctest for the function train

Please provide return type hint for the function: train. If the function does not return a value, please provide the type hint as: def function() -> None:

Please provide type hint for the parameter: data

algorithms-keeper · 2024-10-31T17:47:34Z

neural_network/adaptive_resonance_theory_1.py

+            if not assigned:
+                self.weights.append(x.copy())
+
+    def predict(self, x):


As there is no test file in this pull request nor any test function or class in the file neural_network/adaptive_resonance_theory_1.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

algorithms-keeper · 2024-10-31T17:47:35Z

neural_network/radial_basis_function_neural_network.py

+
+
+class RBFNN:
+    def __init__(self, input_size, hidden_size, output_size):


Please provide return type hint for the function: __init__. If the function does not return a value, please provide the type hint as: def function() -> None:

Please provide type hint for the parameter: input_size

Please provide type hint for the parameter: hidden_size

Please provide type hint for the parameter: output_size

algorithms-keeper · 2024-10-31T17:47:36Z

neural_network/radial_basis_function_neural_network.py

+            (hidden_size, output_size)
+        )  # Weights for output layer
+
+    def rbf(self, x, center, spread):


As there is no test file in this pull request nor any test function or class in the file neural_network/radial_basis_function_neural_network.py, please provide doctest for the function rbf

Please provide return type hint for the function: rbf. 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

Please provide type hint for the parameter: center

Please provide type hint for the parameter: spread

algorithms-keeper · 2024-10-31T17:47:36Z

neural_network/radial_basis_function_neural_network.py

+        """Radial Basis Function (Gaussian)."""
+        return np.exp(-(np.linalg.norm(x - center) ** 2) / (2 * spread**2))
+
+    def forward(self, x):


As there is no test file in this pull request nor any test function or class in the file neural_network/radial_basis_function_neural_network.py, please provide doctest for the function forward

Please provide return type hint for the function: forward. 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

algorithms-keeper · 2024-10-31T17:47:36Z

neural_network/radial_basis_function_neural_network.py

+        output = np.dot(hidden_outputs, self.weights)  # Compute final output
+        return output
+
+    def train(self, x_train, y_train, epochs, learning_rate):


As there is no test file in this pull request nor any test function or class in the file neural_network/radial_basis_function_neural_network.py, please provide doctest for the function train

Please provide return type hint for the function: train. If the function does not return a value, please provide the type hint as: def function() -> None:

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Please provide type hint for the parameter: y_train

Please provide type hint for the parameter: epochs

Please provide type hint for the parameter: learning_rate

algorithms-keeper · 2024-10-31T17:47:36Z

neural_network/radial_basis_function_neural_network.py

+                # Update weights
+                self.weights += learning_rate * hidden_outputs.reshape(-1, 1) * error
+
+    def predict(self, x_test):


As there is no test file in this pull request nor any test function or class in the file neural_network/radial_basis_function_neural_network.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:

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algorithms-keeper · 2024-10-31T17:53:59Z

neural_network/adaptive_resonance_theory_1.py

+import numpy as np
+
+class ART1:
+    def __init__(self, num_features, vigilance=0.8):


Please provide return type hint for the function: __init__. If the function does not return a value, please provide the type hint as: def function() -> None:

Please provide type hint for the parameter: num_features

Please provide type hint for the parameter: vigilance

algorithms-keeper · 2024-10-31T17:53:59Z

neural_network/adaptive_resonance_theory_1.py

+        self.vigilance = vigilance
+        self.weights = []  # Stores the weights for clusters
+
+    def _similarity(self, x, w):


As there is no test file in this pull request nor any test function or class in the file neural_network/adaptive_resonance_theory_1.py, please provide doctest for the function _similarity

Please provide return type hint for the function: _similarity. 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

Please provide descriptive name for the parameter: w

Please provide type hint for the parameter: w

algorithms-keeper · 2024-10-31T17:53:59Z

neural_network/adaptive_resonance_theory_1.py

+        """
+        return np.sum(np.minimum(x, w)) / np.sum(x)
+
+    def _weight_update(self, x, w):


As there is no test file in this pull request nor any test function or class in the file neural_network/adaptive_resonance_theory_1.py, please provide doctest for the function _weight_update

Please provide return type hint for the function: _weight_update. If the function does not return a value, please provide the type hint as: def function() -> None:

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Please provide type hint for the parameter: w

algorithms-keeper · 2024-10-31T17:53:59Z

neural_network/adaptive_resonance_theory_1.py

+        """
+        return np.minimum(x, w)
+
+    def train(self, data):


As there is no test file in this pull request nor any test function or class in the file neural_network/adaptive_resonance_theory_1.py, please provide doctest for the function train

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Please provide type hint for the parameter: data

algorithms-keeper · 2024-10-31T17:53:59Z

neural_network/adaptive_resonance_theory_1.py

+            if not assigned:
+                self.weights.append(x.copy())
+
+    def predict(self, x):


As there is no test file in this pull request nor any test function or class in the file neural_network/adaptive_resonance_theory_1.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:

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algorithms-keeper · 2024-10-31T17:54:00Z

neural_network/radial_basis_function_neural_network.py

+
+
+class RBFNN:
+    def __init__(self, input_size, hidden_size, output_size):


Please provide return type hint for the function: __init__. If the function does not return a value, please provide the type hint as: def function() -> None:

Please provide type hint for the parameter: input_size

Please provide type hint for the parameter: hidden_size

Please provide type hint for the parameter: output_size

algorithms-keeper · 2024-10-31T17:54:00Z

neural_network/radial_basis_function_neural_network.py

+            (hidden_size, output_size)
+        )  # Weights for output layer
+
+    def rbf(self, x, center, spread):


As there is no test file in this pull request nor any test function or class in the file neural_network/radial_basis_function_neural_network.py, please provide doctest for the function rbf

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Please provide type hint for the parameter: center

Please provide type hint for the parameter: spread

algorithms-keeper · 2024-10-31T17:54:00Z

neural_network/radial_basis_function_neural_network.py

+        """Radial Basis Function (Gaussian)."""
+        return np.exp(-(np.linalg.norm(x - center) ** 2) / (2 * spread**2))
+
+    def forward(self, x):


As there is no test file in this pull request nor any test function or class in the file neural_network/radial_basis_function_neural_network.py, please provide doctest for the function forward

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algorithms-keeper · 2024-10-31T17:54:00Z

neural_network/radial_basis_function_neural_network.py

+        output = np.dot(hidden_outputs, self.weights)  # Compute final output
+        return output
+
+    def train(self, x_train, y_train, epochs, learning_rate):


As there is no test file in this pull request nor any test function or class in the file neural_network/radial_basis_function_neural_network.py, please provide doctest for the function train

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Please provide type hint for the parameter: learning_rate

algorithms-keeper · 2024-10-31T17:54:00Z

neural_network/radial_basis_function_neural_network.py

+                # Update weights
+                self.weights += learning_rate * hidden_outputs.reshape(-1, 1) * error
+
+    def predict(self, x_test):


As there is no test file in this pull request nor any test function or class in the file neural_network/radial_basis_function_neural_network.py, please provide doctest for the function predict

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cclauss · 2024-11-01T08:10:56Z

Closing require_type_hints PRs to prepare for Hacktoberfest

Swastik0710 and others added 2 commits October 31, 2024 19:47

Create radial_basis_function_neural_network

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Rename radial_basis_function_neural_network to radial_basis_function_…

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…neural_network.py

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Swastik0710 and others added 3 commits October 31, 2024 23:13

Created adaptive_resonance_thoery_1.py

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cclauss closed this Nov 1, 2024



		class RBFNN:
		def __init__(self, input_size, hidden_size, output_size):

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