From b8a4a9fb4f7ebd9b614b23368adca155ea3a6251 Mon Sep 17 00:00:00 2001
From: Swastik Bhattacharya <152956696+Swastik0710@users.noreply.github.com>
Date: Thu, 31 Oct 2024 19:47:51 +0530
Subject: [PATCH 01/13] Create radial_basis_function_neural_network

---
 .../radial_basis_function_neural_network      | 88 +++++++++++++++++++
 1 file changed, 88 insertions(+)
 create mode 100644 neural_network/radial_basis_function_neural_network

diff --git a/neural_network/radial_basis_function_neural_network b/neural_network/radial_basis_function_neural_network
new file mode 100644
index 000000000000..fa58be6c490d
--- /dev/null
+++ b/neural_network/radial_basis_function_neural_network
@@ -0,0 +1,88 @@
+"""
+ - - - - - -- - - - - - - - - - - - - - - - - - - - - - - 
+Name - - RBFNN - Radial Basis Function Neural Network
+Goal - - Recognize Patterns in Data
+Detail: Total 3 layers neural network
+        * Input layer
+        * Hidden layer with RBF activation
+        * Output layer
+Author: Your Name
+Github: your_email@example.com
+Date: 2024.10.31
+- - - - - -- - - - - - - - - - - - - - - - - - - - - - - 
+"""
+
+import numpy as np  # For numerical operations
+
+class RBFNN:
+    def __init__(self, input_size, hidden_size, output_size):
+        """
+        Initialize the RBFNN parameters.
+        
+        :param input_size: Number of input features
+        :param hidden_size: Number of hidden units in the RBF layer
+        :param output_size: Number of output classes
+        """
+        self.input_size = input_size  # Size of input layer
+        self.hidden_size = hidden_size  # Size of hidden layer
+        self.output_size = output_size  # Size of output layer
+        
+        # Initialize centers and spread of the RBF neurons
+        self.centers = np.random.rand(hidden_size, input_size)  # Centers for RBF
+        self.spread = np.random.rand(hidden_size)  # Spread for each RBF
+
+        # 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):
+        """ Radial Basis Function (Gaussian). """
+        return np.exp(-np.linalg.norm(x - center) ** 2 / (2 * spread ** 2))
+
+    def forward(self, x):
+        """ Forward pass through the network. """
+        hidden_outputs = np.zeros(self.hidden_size)  # Outputs of hidden layer
+        for i in range(self.hidden_size):
+            hidden_outputs[i] = self.rbf(x, self.centers[i], self.spread[i])  # Compute RBF outputs
+
+        output = np.dot(hidden_outputs, self.weights)  # Compute final output
+        return output
+
+    def train(self, X, y, epochs, learning_rate):
+        """
+        Train the RBFNN model.
+        
+        :param X: Input data
+        :param y: Target output
+        :param epochs: Number of training iterations
+        :param learning_rate: Learning rate for weight updates
+        """
+        for epoch in range(epochs):
+            for i in range(len(X)):
+                x_i = X[i]
+                y_i = y[i]
+
+                # Forward pass
+                hidden_outputs = np.zeros(self.hidden_size)
+                for j in range(self.hidden_size):
+                    hidden_outputs[j] = self.rbf(x_i, self.centers[j], self.spread[j])
+
+                output = np.dot(hidden_outputs, self.weights)  # Output layer
+                
+                # Calculate the error
+                error = y_i - output
+                
+                # Update weights
+                self.weights += learning_rate * hidden_outputs.reshape(-1, 1) * error
+
+    def predict(self, X):
+        """
+        Predict outputs for given input data.
+        
+        :param X: Input data
+        :return: Predicted outputs
+        """
+        predictions = []
+        for x in X:
+            output = self.forward(x)  # Forward pass to get prediction
+            predictions.append(output)
+        return np.array(predictions)

From 923dcaec1fcc5f88c2205005763fe78246ca0b76 Mon Sep 17 00:00:00 2001
From: "pre-commit-ci[bot]"
 <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Date: Thu, 31 Oct 2024 14:20:38 +0000
Subject: [PATCH 02/13] [pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci
---
 .../radial_basis_function_neural_network         | 16 ++++++++--------
 1 file changed, 8 insertions(+), 8 deletions(-)

diff --git a/neural_network/radial_basis_function_neural_network b/neural_network/radial_basis_function_neural_network
index fa58be6c490d..73ad18479d10 100644
--- a/neural_network/radial_basis_function_neural_network
+++ b/neural_network/radial_basis_function_neural_network
@@ -1,5 +1,5 @@
 """
- - - - - - -- - - - - - - - - - - - - - - - - - - - - - - 
+ - - - - - -- - - - - - - - - - - - - - - - - - - - - - -
 Name - - RBFNN - Radial Basis Function Neural Network
 Goal - - Recognize Patterns in Data
 Detail: Total 3 layers neural network
@@ -9,7 +9,7 @@ Detail: Total 3 layers neural network
 Author: Your Name
 Github: your_email@example.com
 Date: 2024.10.31
-- - - - - -- - - - - - - - - - - - - - - - - - - - - - - 
+- - - - - -- - - - - - - - - - - - - - - - - - - - - - -
 """
 
 import numpy as np  # For numerical operations
@@ -18,7 +18,7 @@ class RBFNN:
     def __init__(self, input_size, hidden_size, output_size):
         """
         Initialize the RBFNN parameters.
-        
+
         :param input_size: Number of input features
         :param hidden_size: Number of hidden units in the RBF layer
         :param output_size: Number of output classes
@@ -26,7 +26,7 @@ class RBFNN:
         self.input_size = input_size  # Size of input layer
         self.hidden_size = hidden_size  # Size of hidden layer
         self.output_size = output_size  # Size of output layer
-        
+
         # Initialize centers and spread of the RBF neurons
         self.centers = np.random.rand(hidden_size, input_size)  # Centers for RBF
         self.spread = np.random.rand(hidden_size)  # Spread for each RBF
@@ -50,7 +50,7 @@ class RBFNN:
     def train(self, X, y, epochs, learning_rate):
         """
         Train the RBFNN model.
-        
+
         :param X: Input data
         :param y: Target output
         :param epochs: Number of training iterations
@@ -67,17 +67,17 @@ class RBFNN:
                     hidden_outputs[j] = self.rbf(x_i, self.centers[j], self.spread[j])
 
                 output = np.dot(hidden_outputs, self.weights)  # Output layer
-                
+
                 # Calculate the error
                 error = y_i - output
-                
+
                 # Update weights
                 self.weights += learning_rate * hidden_outputs.reshape(-1, 1) * error
 
     def predict(self, X):
         """
         Predict outputs for given input data.
-        
+
         :param X: Input data
         :return: Predicted outputs
         """

From fa6233a1ab0b896eb42e6e304a3282f12e803631 Mon Sep 17 00:00:00 2001
From: Swastik Bhattacharya <152956696+Swastik0710@users.noreply.github.com>
Date: Thu, 31 Oct 2024 20:04:00 +0530
Subject: [PATCH 03/13] Rename radial_basis_function_neural_network to
 radial_basis_function_neural_network.py

---
 ...ion_neural_network => radial_basis_function_neural_network.py} | 0
 1 file changed, 0 insertions(+), 0 deletions(-)
 rename neural_network/{radial_basis_function_neural_network => radial_basis_function_neural_network.py} (100%)

diff --git a/neural_network/radial_basis_function_neural_network b/neural_network/radial_basis_function_neural_network.py
similarity index 100%
rename from neural_network/radial_basis_function_neural_network
rename to neural_network/radial_basis_function_neural_network.py

From 2a8b2014eeec96c283cdd5aed234c25b0aa00f4d Mon Sep 17 00:00:00 2001
From: "pre-commit-ci[bot]"
 <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Date: Thu, 31 Oct 2024 14:34:27 +0000
Subject: [PATCH 04/13] [pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci
---
 .../radial_basis_function_neural_network.py       | 15 ++++++++++-----
 1 file changed, 10 insertions(+), 5 deletions(-)

diff --git a/neural_network/radial_basis_function_neural_network.py b/neural_network/radial_basis_function_neural_network.py
index 73ad18479d10..5abab11a557b 100644
--- a/neural_network/radial_basis_function_neural_network.py
+++ b/neural_network/radial_basis_function_neural_network.py
@@ -14,6 +14,7 @@
 
 import numpy as np  # For numerical operations
 
+
 class RBFNN:
     def __init__(self, input_size, hidden_size, output_size):
         """
@@ -32,17 +33,21 @@ def __init__(self, input_size, hidden_size, output_size):
         self.spread = np.random.rand(hidden_size)  # Spread for each RBF
 
         # Initialize weights for the output layer
-        self.weights = np.random.rand(hidden_size, output_size)  # Weights for output layer
+        self.weights = np.random.rand(
+            hidden_size, output_size
+        )  # Weights for output layer
 
     def rbf(self, x, center, spread):
-        """ Radial Basis Function (Gaussian). """
-        return np.exp(-np.linalg.norm(x - center) ** 2 / (2 * spread ** 2))
+        """Radial Basis Function (Gaussian)."""
+        return np.exp(-(np.linalg.norm(x - center) ** 2) / (2 * spread**2))
 
     def forward(self, x):
-        """ Forward pass through the network. """
+        """Forward pass through the network."""
         hidden_outputs = np.zeros(self.hidden_size)  # Outputs of hidden layer
         for i in range(self.hidden_size):
-            hidden_outputs[i] = self.rbf(x, self.centers[i], self.spread[i])  # Compute RBF outputs
+            hidden_outputs[i] = self.rbf(
+                x, self.centers[i], self.spread[i]
+            )  # Compute RBF outputs
 
         output = np.dot(hidden_outputs, self.weights)  # Compute final output
         return output

From a3bf6259ff2de48957442d04ae17d50ac760ba54 Mon Sep 17 00:00:00 2001
From: Swastik Bhattacharya <152956696+Swastik0710@users.noreply.github.com>
Date: Thu, 31 Oct 2024 20:07:54 +0530
Subject: [PATCH 05/13] Update radial_basis_function_neural_network.py

---
 .../radial_basis_function_neural_network.py   | 56 +++++++++----------
 1 file changed, 26 insertions(+), 30 deletions(-)

diff --git a/neural_network/radial_basis_function_neural_network.py b/neural_network/radial_basis_function_neural_network.py
index 5abab11a557b..4c889596e434 100644
--- a/neural_network/radial_basis_function_neural_network.py
+++ b/neural_network/radial_basis_function_neural_network.py
@@ -1,5 +1,5 @@
 """
- - - - - - -- - - - - - - - - - - - - - - - - - - - - - -
+ - - - - - -- - - - - - - - - - - - - - - - - - - - - - - 
 Name - - RBFNN - Radial Basis Function Neural Network
 Goal - - Recognize Patterns in Data
 Detail: Total 3 layers neural network
@@ -9,17 +9,16 @@
 Author: Your Name
 Github: your_email@example.com
 Date: 2024.10.31
-- - - - - -- - - - - - - - - - - - - - - - - - - - - - -
+- - - - - -- - - - - - - - - - - - - - - - - - - - - - - 
 """
 
 import numpy as np  # For numerical operations
 
-
 class RBFNN:
     def __init__(self, input_size, hidden_size, output_size):
         """
         Initialize the RBFNN parameters.
-
+        
         :param input_size: Number of input features
         :param hidden_size: Number of hidden units in the RBF layer
         :param output_size: Number of output classes
@@ -27,44 +26,41 @@ def __init__(self, input_size, hidden_size, output_size):
         self.input_size = input_size  # Size of input layer
         self.hidden_size = hidden_size  # Size of hidden layer
         self.output_size = output_size  # Size of output layer
-
+        
+        rng = np.random.default_rng()  # Create a random number generator
         # Initialize centers and spread of the RBF neurons
-        self.centers = np.random.rand(hidden_size, input_size)  # Centers for RBF
-        self.spread = np.random.rand(hidden_size)  # Spread for each RBF
+        self.centers = rng.random((hidden_size, input_size))  # Centers for RBF
+        self.spread = rng.random(hidden_size)  # Spread for each RBF
 
         # Initialize weights for the output layer
-        self.weights = np.random.rand(
-            hidden_size, output_size
-        )  # Weights for output layer
+        self.weights = rng.random((hidden_size, output_size))  # Weights for output layer
 
     def rbf(self, x, center, spread):
-        """Radial Basis Function (Gaussian)."""
-        return np.exp(-(np.linalg.norm(x - center) ** 2) / (2 * spread**2))
+        """ Radial Basis Function (Gaussian). """
+        return np.exp(-np.linalg.norm(x - center) ** 2 / (2 * spread ** 2))
 
     def forward(self, x):
-        """Forward pass through the network."""
+        """ Forward pass through the network. """
         hidden_outputs = np.zeros(self.hidden_size)  # Outputs of hidden layer
         for i in range(self.hidden_size):
-            hidden_outputs[i] = self.rbf(
-                x, self.centers[i], self.spread[i]
-            )  # Compute RBF outputs
+            hidden_outputs[i] = self.rbf(x, self.centers[i], self.spread[i])  # Compute RBF outputs
 
         output = np.dot(hidden_outputs, self.weights)  # Compute final output
         return output
 
-    def train(self, X, y, epochs, learning_rate):
+    def train(self, x_train, y_train, epochs, learning_rate):
         """
         Train the RBFNN model.
-
-        :param X: Input data
-        :param y: Target output
+        
+        :param x_train: Input data
+        :param y_train: Target output
         :param epochs: Number of training iterations
         :param learning_rate: Learning rate for weight updates
         """
-        for epoch in range(epochs):
-            for i in range(len(X)):
-                x_i = X[i]
-                y_i = y[i]
+        for _ in range(epochs):  # Use underscore for unused loop variable
+            for i in range(len(x_train)):
+                x_i = x_train[i]
+                y_i = y_train[i]
 
                 # Forward pass
                 hidden_outputs = np.zeros(self.hidden_size)
@@ -72,22 +68,22 @@ def train(self, X, y, epochs, learning_rate):
                     hidden_outputs[j] = self.rbf(x_i, self.centers[j], self.spread[j])
 
                 output = np.dot(hidden_outputs, self.weights)  # Output layer
-
+                
                 # Calculate the error
                 error = y_i - output
-
+                
                 # Update weights
                 self.weights += learning_rate * hidden_outputs.reshape(-1, 1) * error
 
-    def predict(self, X):
+    def predict(self, x_test):
         """
         Predict outputs for given input data.
-
-        :param X: Input data
+        
+        :param x_test: Input data
         :return: Predicted outputs
         """
         predictions = []
-        for x in X:
+        for x in x_test:
             output = self.forward(x)  # Forward pass to get prediction
             predictions.append(output)
         return np.array(predictions)

From c2a5cd0cdc8116b34404478282ccd4553f8417d0 Mon Sep 17 00:00:00 2001
From: "pre-commit-ci[bot]"
 <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Date: Thu, 31 Oct 2024 14:38:21 +0000
Subject: [PATCH 06/13] [pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci
---
 .../radial_basis_function_neural_network.py   | 31 +++++++++++--------
 1 file changed, 18 insertions(+), 13 deletions(-)

diff --git a/neural_network/radial_basis_function_neural_network.py b/neural_network/radial_basis_function_neural_network.py
index 4c889596e434..68e3ecbd2aa3 100644
--- a/neural_network/radial_basis_function_neural_network.py
+++ b/neural_network/radial_basis_function_neural_network.py
@@ -1,5 +1,5 @@
 """
- - - - - - -- - - - - - - - - - - - - - - - - - - - - - - 
+ - - - - - -- - - - - - - - - - - - - - - - - - - - - - -
 Name - - RBFNN - Radial Basis Function Neural Network
 Goal - - Recognize Patterns in Data
 Detail: Total 3 layers neural network
@@ -9,16 +9,17 @@
 Author: Your Name
 Github: your_email@example.com
 Date: 2024.10.31
-- - - - - -- - - - - - - - - - - - - - - - - - - - - - - 
+- - - - - -- - - - - - - - - - - - - - - - - - - - - - -
 """
 
 import numpy as np  # For numerical operations
 
+
 class RBFNN:
     def __init__(self, input_size, hidden_size, output_size):
         """
         Initialize the RBFNN parameters.
-        
+
         :param input_size: Number of input features
         :param hidden_size: Number of hidden units in the RBF layer
         :param output_size: Number of output classes
@@ -26,24 +27,28 @@ def __init__(self, input_size, hidden_size, output_size):
         self.input_size = input_size  # Size of input layer
         self.hidden_size = hidden_size  # Size of hidden layer
         self.output_size = output_size  # Size of output layer
-        
+
         rng = np.random.default_rng()  # Create a random number generator
         # Initialize centers and spread of the RBF neurons
         self.centers = rng.random((hidden_size, input_size))  # Centers for RBF
         self.spread = rng.random(hidden_size)  # Spread for each RBF
 
         # Initialize weights for the output layer
-        self.weights = rng.random((hidden_size, output_size))  # Weights for output layer
+        self.weights = rng.random(
+            (hidden_size, output_size)
+        )  # Weights for output layer
 
     def rbf(self, x, center, spread):
-        """ Radial Basis Function (Gaussian). """
-        return np.exp(-np.linalg.norm(x - center) ** 2 / (2 * spread ** 2))
+        """Radial Basis Function (Gaussian)."""
+        return np.exp(-(np.linalg.norm(x - center) ** 2) / (2 * spread**2))
 
     def forward(self, x):
-        """ Forward pass through the network. """
+        """Forward pass through the network."""
         hidden_outputs = np.zeros(self.hidden_size)  # Outputs of hidden layer
         for i in range(self.hidden_size):
-            hidden_outputs[i] = self.rbf(x, self.centers[i], self.spread[i])  # Compute RBF outputs
+            hidden_outputs[i] = self.rbf(
+                x, self.centers[i], self.spread[i]
+            )  # Compute RBF outputs
 
         output = np.dot(hidden_outputs, self.weights)  # Compute final output
         return output
@@ -51,7 +56,7 @@ def forward(self, x):
     def train(self, x_train, y_train, epochs, learning_rate):
         """
         Train the RBFNN model.
-        
+
         :param x_train: Input data
         :param y_train: Target output
         :param epochs: Number of training iterations
@@ -68,17 +73,17 @@ def train(self, x_train, y_train, epochs, learning_rate):
                     hidden_outputs[j] = self.rbf(x_i, self.centers[j], self.spread[j])
 
                 output = np.dot(hidden_outputs, self.weights)  # Output layer
-                
+
                 # Calculate the error
                 error = y_i - output
-                
+
                 # Update weights
                 self.weights += learning_rate * hidden_outputs.reshape(-1, 1) * error
 
     def predict(self, x_test):
         """
         Predict outputs for given input data.
-        
+
         :param x_test: Input data
         :return: Predicted outputs
         """

From f6028cc6bf9f929d04f3dc3c87509e6f169404a7 Mon Sep 17 00:00:00 2001
From: Swastik Bhattacharya <152956696+Swastik0710@users.noreply.github.com>
Date: Thu, 31 Oct 2024 23:13:46 +0530
Subject: [PATCH 07/13] Created adaptive_resonance_thoery_1.py

---
 neural_network/adaptive_resonance_thoery_1.py | 95 +++++++++++++++++++
 1 file changed, 95 insertions(+)
 create mode 100644 neural_network/adaptive_resonance_thoery_1.py

diff --git a/neural_network/adaptive_resonance_thoery_1.py b/neural_network/adaptive_resonance_thoery_1.py
new file mode 100644
index 000000000000..5215f62cfe1e
--- /dev/null
+++ b/neural_network/adaptive_resonance_thoery_1.py
@@ -0,0 +1,95 @@
+"""
+- - - - - -- - - - - - - - - - - - - - - - - - - - - - - 
+Name - - ART1 - Adaptive Resonance Theory 1
+Goal - - Cluster Binary Data
+Detail: Unsupervised clustering model using a vigilance parameter
+        to control cluster formation in binary datasets.
+        * Initialize with features and vigilance threshold
+        * Train to form clusters based on input patterns
+        * Predict for assigning new inputs to clusters
+Author: Your Name
+Github: your_email@example.com
+Date: 2024.10.31
+- - - - - -- - - - - - - - - - - - - - - - - - - - - - - 
+"""
+import numpy as np
+
+class ART1:
+    def __init__(self, num_features, vigilance=0.8):
+        """
+        Initialize the ART1 model with the number of features and the vigilance parameter.
+        
+        Parameters:
+        num_features (int): Number of features in input binary data.
+        vigilance (float): Vigilance parameter to control cluster formation (0 < vigilance <= 1).
+        """
+        self.num_features = num_features
+        self.vigilance = vigilance
+        self.weights = []  # Stores the weights for clusters
+    
+    def _similarity(self, x, w):
+        """
+        Calculate similarity between input vector x and weight vector w.
+        
+        Parameters:
+        x (np.array): Input binary vector.
+        w (np.array): Cluster weight vector.
+        
+        Returns:
+        float: Similarity value based on the intersection over the input length.
+        """
+        return np.sum(np.minimum(x, w)) / np.sum(x)
+    
+    def _weight_update(self, x, w):
+        """
+        Update weights for a cluster based on input vector.
+        
+        Parameters:
+        x (np.array): Input binary vector.
+        w (np.array): Cluster weight vector.
+        
+        Returns:
+        np.array: Updated weight vector.
+        """
+        return np.minimum(x, w)
+    
+    def train(self, data):
+        """
+        Train the ART1 model to form clusters based on the vigilance parameter.
+        
+        Parameters:
+        data (np.array): Binary dataset with each row as a sample.
+        """
+        for x in data:
+            assigned = False
+            for i, w in enumerate(self.weights):
+                if self._similarity(x, w) >= self.vigilance:
+                    self.weights[i] = self._weight_update(x, w)
+                    assigned = True
+                    break
+            if not assigned:
+                self.weights.append(x.copy())
+    
+    def predict(self, x):
+        """
+        Predict the cluster for a new input vector or classify it as a new cluster.
+        
+        Parameters:
+        x (np.array): Input binary vector.
+        
+        Returns:
+        int: Cluster index for the input or -1 if classified as a new cluster.
+        """
+        for i, w in enumerate(self.weights):
+            if self._similarity(x, w) >= self.vigilance:
+                return i
+        return -1
+    
+    def get_weights(self):
+        """
+        Retrieve the weight vectors of the clusters.
+        
+        Returns:
+        list: List of weight vectors for each cluster.
+        """
+        return self.weights

From 6d8e719bb126a2a7c8132c9f6764d534ba2197de Mon Sep 17 00:00:00 2001
From: Swastik Bhattacharya <152956696+Swastik0710@users.noreply.github.com>
Date: Thu, 31 Oct 2024 23:14:12 +0530
Subject: [PATCH 08/13] Rename adaptive_resonance_thoery_1.py to
 adaptive_resonance_theory_1.py

---
 ...ptive_resonance_thoery_1.py => adaptive_resonance_theory_1.py} | 0
 1 file changed, 0 insertions(+), 0 deletions(-)
 rename neural_network/{adaptive_resonance_thoery_1.py => adaptive_resonance_theory_1.py} (100%)

diff --git a/neural_network/adaptive_resonance_thoery_1.py b/neural_network/adaptive_resonance_theory_1.py
similarity index 100%
rename from neural_network/adaptive_resonance_thoery_1.py
rename to neural_network/adaptive_resonance_theory_1.py

From 4b5fa398cc0a217bc9685034b77be3e374b1c488 Mon Sep 17 00:00:00 2001
From: "pre-commit-ci[bot]"
 <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Date: Thu, 31 Oct 2024 17:44:24 +0000
Subject: [PATCH 09/13] [pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci
---
 neural_network/adaptive_resonance_theory_1.py | 34 ++++++++++---------
 1 file changed, 18 insertions(+), 16 deletions(-)

diff --git a/neural_network/adaptive_resonance_theory_1.py b/neural_network/adaptive_resonance_theory_1.py
index 5215f62cfe1e..4d5c303931ee 100644
--- a/neural_network/adaptive_resonance_theory_1.py
+++ b/neural_network/adaptive_resonance_theory_1.py
@@ -1,5 +1,5 @@
 """
-- - - - - -- - - - - - - - - - - - - - - - - - - - - - - 
+- - - - - -- - - - - - - - - - - - - - - - - - - - - - -
 Name - - ART1 - Adaptive Resonance Theory 1
 Goal - - Cluster Binary Data
 Detail: Unsupervised clustering model using a vigilance parameter
@@ -10,15 +10,17 @@
 Author: Your Name
 Github: your_email@example.com
 Date: 2024.10.31
-- - - - - -- - - - - - - - - - - - - - - - - - - - - - - 
+- - - - - -- - - - - - - - - - - - - - - - - - - - - - -
 """
+
 import numpy as np
 
+
 class ART1:
     def __init__(self, num_features, vigilance=0.8):
         """
         Initialize the ART1 model with the number of features and the vigilance parameter.
-        
+
         Parameters:
         num_features (int): Number of features in input binary data.
         vigilance (float): Vigilance parameter to control cluster formation (0 < vigilance <= 1).
@@ -26,37 +28,37 @@ def __init__(self, num_features, vigilance=0.8):
         self.num_features = num_features
         self.vigilance = vigilance
         self.weights = []  # Stores the weights for clusters
-    
+
     def _similarity(self, x, w):
         """
         Calculate similarity between input vector x and weight vector w.
-        
+
         Parameters:
         x (np.array): Input binary vector.
         w (np.array): Cluster weight vector.
-        
+
         Returns:
         float: Similarity value based on the intersection over the input length.
         """
         return np.sum(np.minimum(x, w)) / np.sum(x)
-    
+
     def _weight_update(self, x, w):
         """
         Update weights for a cluster based on input vector.
-        
+
         Parameters:
         x (np.array): Input binary vector.
         w (np.array): Cluster weight vector.
-        
+
         Returns:
         np.array: Updated weight vector.
         """
         return np.minimum(x, w)
-    
+
     def train(self, data):
         """
         Train the ART1 model to form clusters based on the vigilance parameter.
-        
+
         Parameters:
         data (np.array): Binary dataset with each row as a sample.
         """
@@ -69,14 +71,14 @@ def train(self, data):
                     break
             if not assigned:
                 self.weights.append(x.copy())
-    
+
     def predict(self, x):
         """
         Predict the cluster for a new input vector or classify it as a new cluster.
-        
+
         Parameters:
         x (np.array): Input binary vector.
-        
+
         Returns:
         int: Cluster index for the input or -1 if classified as a new cluster.
         """
@@ -84,11 +86,11 @@ def predict(self, x):
             if self._similarity(x, w) >= self.vigilance:
                 return i
         return -1
-    
+
     def get_weights(self):
         """
         Retrieve the weight vectors of the clusters.
-        
+
         Returns:
         list: List of weight vectors for each cluster.
         """

From 3d4150abf4faeeeddb3f3d29cd07b4341345373a Mon Sep 17 00:00:00 2001
From: Swastik Bhattacharya <152956696+Swastik0710@users.noreply.github.com>
Date: Thu, 31 Oct 2024 23:17:26 +0530
Subject: [PATCH 10/13] Update adaptive_resonance_theory_1.py

---
 neural_network/adaptive_resonance_theory_1.py | 31 ++++++++++---------
 1 file changed, 16 insertions(+), 15 deletions(-)

diff --git a/neural_network/adaptive_resonance_theory_1.py b/neural_network/adaptive_resonance_theory_1.py
index 4d5c303931ee..6febab500835 100644
--- a/neural_network/adaptive_resonance_theory_1.py
+++ b/neural_network/adaptive_resonance_theory_1.py
@@ -15,12 +15,11 @@
 
 import numpy as np
 
-
 class ART1:
     def __init__(self, num_features, vigilance=0.8):
         """
         Initialize the ART1 model with the number of features and the vigilance parameter.
-
+        
         Parameters:
         num_features (int): Number of features in input binary data.
         vigilance (float): Vigilance parameter to control cluster formation (0 < vigilance <= 1).
@@ -28,69 +27,71 @@ def __init__(self, num_features, vigilance=0.8):
         self.num_features = num_features
         self.vigilance = vigilance
         self.weights = []  # Stores the weights for clusters
-
+    
     def _similarity(self, x, w):
         """
         Calculate similarity between input vector x and weight vector w.
-
+        
         Parameters:
         x (np.array): Input binary vector.
         w (np.array): Cluster weight vector.
-
+        
         Returns:
         float: Similarity value based on the intersection over the input length.
         """
         return np.sum(np.minimum(x, w)) / np.sum(x)
-
+    
     def _weight_update(self, x, w):
         """
         Update weights for a cluster based on input vector.
-
+        
         Parameters:
         x (np.array): Input binary vector.
         w (np.array): Cluster weight vector.
-
+        
         Returns:
         np.array: Updated weight vector.
         """
         return np.minimum(x, w)
-
+    
     def train(self, data):
         """
         Train the ART1 model to form clusters based on the vigilance parameter.
-
+        
         Parameters:
         data (np.array): Binary dataset with each row as a sample.
         """
         for x in data:
             assigned = False
             for i, w in enumerate(self.weights):
+                # Split the line here to satisfy the line-length requirement
                 if self._similarity(x, w) >= self.vigilance:
                     self.weights[i] = self._weight_update(x, w)
                     assigned = True
                     break
             if not assigned:
                 self.weights.append(x.copy())
-
+    
     def predict(self, x):
         """
         Predict the cluster for a new input vector or classify it as a new cluster.
-
+        
         Parameters:
         x (np.array): Input binary vector.
-
+        
         Returns:
         int: Cluster index for the input or -1 if classified as a new cluster.
         """
         for i, w in enumerate(self.weights):
+            # Split the line here to satisfy the line-length requirement
             if self._similarity(x, w) >= self.vigilance:
                 return i
         return -1
-
+    
     def get_weights(self):
         """
         Retrieve the weight vectors of the clusters.
-
+        
         Returns:
         list: List of weight vectors for each cluster.
         """

From a65dcc25414c49af6cbc1a80e30a6df73713f5b7 Mon Sep 17 00:00:00 2001
From: "pre-commit-ci[bot]"
 <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Date: Thu, 31 Oct 2024 17:47:50 +0000
Subject: [PATCH 11/13] [pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci
---
 neural_network/adaptive_resonance_theory_1.py | 29 ++++++++++---------
 1 file changed, 15 insertions(+), 14 deletions(-)

diff --git a/neural_network/adaptive_resonance_theory_1.py b/neural_network/adaptive_resonance_theory_1.py
index 6febab500835..11499c0d051d 100644
--- a/neural_network/adaptive_resonance_theory_1.py
+++ b/neural_network/adaptive_resonance_theory_1.py
@@ -15,11 +15,12 @@
 
 import numpy as np
 
+
 class ART1:
     def __init__(self, num_features, vigilance=0.8):
         """
         Initialize the ART1 model with the number of features and the vigilance parameter.
-        
+
         Parameters:
         num_features (int): Number of features in input binary data.
         vigilance (float): Vigilance parameter to control cluster formation (0 < vigilance <= 1).
@@ -27,37 +28,37 @@ def __init__(self, num_features, vigilance=0.8):
         self.num_features = num_features
         self.vigilance = vigilance
         self.weights = []  # Stores the weights for clusters
-    
+
     def _similarity(self, x, w):
         """
         Calculate similarity between input vector x and weight vector w.
-        
+
         Parameters:
         x (np.array): Input binary vector.
         w (np.array): Cluster weight vector.
-        
+
         Returns:
         float: Similarity value based on the intersection over the input length.
         """
         return np.sum(np.minimum(x, w)) / np.sum(x)
-    
+
     def _weight_update(self, x, w):
         """
         Update weights for a cluster based on input vector.
-        
+
         Parameters:
         x (np.array): Input binary vector.
         w (np.array): Cluster weight vector.
-        
+
         Returns:
         np.array: Updated weight vector.
         """
         return np.minimum(x, w)
-    
+
     def train(self, data):
         """
         Train the ART1 model to form clusters based on the vigilance parameter.
-        
+
         Parameters:
         data (np.array): Binary dataset with each row as a sample.
         """
@@ -71,14 +72,14 @@ def train(self, data):
                     break
             if not assigned:
                 self.weights.append(x.copy())
-    
+
     def predict(self, x):
         """
         Predict the cluster for a new input vector or classify it as a new cluster.
-        
+
         Parameters:
         x (np.array): Input binary vector.
-        
+
         Returns:
         int: Cluster index for the input or -1 if classified as a new cluster.
         """
@@ -87,11 +88,11 @@ def predict(self, x):
             if self._similarity(x, w) >= self.vigilance:
                 return i
         return -1
-    
+
     def get_weights(self):
         """
         Retrieve the weight vectors of the clusters.
-        
+
         Returns:
         list: List of weight vectors for each cluster.
         """

From b52bc75e0e3504ec12637b50b457cc787ed0bb60 Mon Sep 17 00:00:00 2001
From: Swastik Bhattacharya <152956696+Swastik0710@users.noreply.github.com>
Date: Thu, 31 Oct 2024 23:23:53 +0530
Subject: [PATCH 12/13] Update adaptive_resonance_theory_1.py

---
 neural_network/adaptive_resonance_theory_1.py | 43 ++++++++++---------
 1 file changed, 22 insertions(+), 21 deletions(-)

diff --git a/neural_network/adaptive_resonance_theory_1.py b/neural_network/adaptive_resonance_theory_1.py
index 11499c0d051d..9152b6283b93 100644
--- a/neural_network/adaptive_resonance_theory_1.py
+++ b/neural_network/adaptive_resonance_theory_1.py
@@ -1,5 +1,5 @@
 """
-- - - - - -- - - - - - - - - - - - - - - - - - - - - - -
+- - - - - -- - - - - - - - - - - - - - - - - - - - - - - 
 Name - - ART1 - Adaptive Resonance Theory 1
 Goal - - Cluster Binary Data
 Detail: Unsupervised clustering model using a vigilance parameter
@@ -10,17 +10,16 @@
 Author: Your Name
 Github: your_email@example.com
 Date: 2024.10.31
-- - - - - -- - - - - - - - - - - - - - - - - - - - - - -
+- - - - - -- - - - - - - - - - - - - - - - - - - - - - - 
 """
 
 import numpy as np
 
-
 class ART1:
     def __init__(self, num_features, vigilance=0.8):
         """
         Initialize the ART1 model with the number of features and the vigilance parameter.
-
+        
         Parameters:
         num_features (int): Number of features in input binary data.
         vigilance (float): Vigilance parameter to control cluster formation (0 < vigilance <= 1).
@@ -28,71 +27,73 @@ def __init__(self, num_features, vigilance=0.8):
         self.num_features = num_features
         self.vigilance = vigilance
         self.weights = []  # Stores the weights for clusters
-
+    
     def _similarity(self, x, w):
         """
         Calculate similarity between input vector x and weight vector w.
-
+        
         Parameters:
         x (np.array): Input binary vector.
         w (np.array): Cluster weight vector.
-
+        
         Returns:
         float: Similarity value based on the intersection over the input length.
         """
         return np.sum(np.minimum(x, w)) / np.sum(x)
-
+    
     def _weight_update(self, x, w):
         """
         Update weights for a cluster based on input vector.
-
+        
         Parameters:
         x (np.array): Input binary vector.
         w (np.array): Cluster weight vector.
-
+        
         Returns:
         np.array: Updated weight vector.
         """
         return np.minimum(x, w)
-
+    
     def train(self, data):
         """
         Train the ART1 model to form clusters based on the vigilance parameter.
-
+        
         Parameters:
         data (np.array): Binary dataset with each row as a sample.
         """
         for x in data:
             assigned = False
             for i, w in enumerate(self.weights):
-                # Split the line here to satisfy the line-length requirement
-                if self._similarity(x, w) >= self.vigilance:
+                # Check similarity and update weights if similarity exceeds vigilance
+                similarity = self._similarity(x, w)
+                if similarity >= self.vigilance:
                     self.weights[i] = self._weight_update(x, w)
                     assigned = True
                     break
             if not assigned:
                 self.weights.append(x.copy())
-
+    
     def predict(self, x):
         """
         Predict the cluster for a new input vector or classify it as a new cluster.
-
+        
         Parameters:
         x (np.array): Input binary vector.
-
+        
         Returns:
         int: Cluster index for the input or -1 if classified as a new cluster.
         """
         for i, w in enumerate(self.weights):
-            # Split the line here to satisfy the line-length requirement
-            if self._similarity(x, w) >= self.vigilance:
+            # Check similarity for prediction
+            similarity = self._similarity(x, w)
+            if similarity >= self.vigilance:
                 return i
         return -1
-
+    
     def get_weights(self):
         """
         Retrieve the weight vectors of the clusters.
-
+        
         Returns:
         list: List of weight vectors for each cluster.
         """

From 8196fd24d38e0e9060b9f672557cac45d3db36ce Mon Sep 17 00:00:00 2001
From: "pre-commit-ci[bot]"
 <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Date: Thu, 31 Oct 2024 17:54:16 +0000
Subject: [PATCH 13/13] [pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci
---
 neural_network/adaptive_resonance_theory_1.py | 33 ++++++++++---------
 1 file changed, 17 insertions(+), 16 deletions(-)

diff --git a/neural_network/adaptive_resonance_theory_1.py b/neural_network/adaptive_resonance_theory_1.py
index 9152b6283b93..abff2cf7e294 100644
--- a/neural_network/adaptive_resonance_theory_1.py
+++ b/neural_network/adaptive_resonance_theory_1.py
@@ -1,5 +1,5 @@
 """
-- - - - - -- - - - - - - - - - - - - - - - - - - - - - - 
+- - - - - -- - - - - - - - - - - - - - - - - - - - - - -
 Name - - ART1 - Adaptive Resonance Theory 1
 Goal - - Cluster Binary Data
 Detail: Unsupervised clustering model using a vigilance parameter
@@ -10,16 +10,17 @@
 Author: Your Name
 Github: your_email@example.com
 Date: 2024.10.31
-- - - - - -- - - - - - - - - - - - - - - - - - - - - - - 
+- - - - - -- - - - - - - - - - - - - - - - - - - - - - -
 """
 
 import numpy as np
 
+
 class ART1:
     def __init__(self, num_features, vigilance=0.8):
         """
         Initialize the ART1 model with the number of features and the vigilance parameter.
-        
+
         Parameters:
         num_features (int): Number of features in input binary data.
         vigilance (float): Vigilance parameter to control cluster formation (0 < vigilance <= 1).
@@ -27,37 +28,37 @@ def __init__(self, num_features, vigilance=0.8):
         self.num_features = num_features
         self.vigilance = vigilance
         self.weights = []  # Stores the weights for clusters
-    
+
     def _similarity(self, x, w):
         """
         Calculate similarity between input vector x and weight vector w.
-        
+
         Parameters:
         x (np.array): Input binary vector.
         w (np.array): Cluster weight vector.
-        
+
         Returns:
         float: Similarity value based on the intersection over the input length.
         """
         return np.sum(np.minimum(x, w)) / np.sum(x)
-    
+
     def _weight_update(self, x, w):
         """
         Update weights for a cluster based on input vector.
-        
+
         Parameters:
         x (np.array): Input binary vector.
         w (np.array): Cluster weight vector.
-        
+
         Returns:
         np.array: Updated weight vector.
         """
         return np.minimum(x, w)
-    
+
     def train(self, data):
         """
         Train the ART1 model to form clusters based on the vigilance parameter.
-        
+
         Parameters:
         data (np.array): Binary dataset with each row as a sample.
         """
@@ -72,14 +73,14 @@ def train(self, data):
                     break
             if not assigned:
                 self.weights.append(x.copy())
-    
+
     def predict(self, x):
         """
         Predict the cluster for a new input vector or classify it as a new cluster.
-        
+
         Parameters:
         x (np.array): Input binary vector.
-        
+
         Returns:
         int: Cluster index for the input or -1 if classified as a new cluster.
         """
@@ -89,11 +90,11 @@ def predict(self, x):
             if similarity >= self.vigilance:
                 return i
         return -1
-    
+
     def get_weights(self):
         """
         Retrieve the weight vectors of the clusters.
-        
+
         Returns:
         list: List of weight vectors for each cluster.
         """