Patchwork PR: GenerateDocstring

K-Means/KMeans.py

@@ -23,6 +23,16 @@ class KMeans:
     """
 
     def __init__(self, K=5, max_iters=100, plot_steps=False):
+        """Initialize the KMeans clustering algorithm.
+
+        Args:
+            K (int): The number of clusters to form. Default is 5.
+            max_iters (int): The maximum number of iterations for the algorithm. Default is 100.
+            plot_steps (bool): Whether to plot the clustering steps. Default is False.
+
+        Returns:
+            None: This method initializes the object and doesn't return anything.
+        """
         self.K = K
         self.max_iters = max_iters
         self.plot_steps = plot_steps

Linear Regression/LinearRegression.py

@@ -10,6 +10,15 @@ class LinearRegression:
     """
 
     def __init__(self, learning_rate=0.001, n_iters=1000) -> None:
+        """Initialize the linear regression model.
+
+        Args:
+            learning_rate float: The learning rate for gradient descent optimization. Default is 0.001.
+            n_iters int: The number of iterations for the optimization process. Default is 1000.
+
+        Returns:
+            None: This method initializes the model parameters and doesn't return anything.
+        """
         self.learning_rate = learning_rate
         self.n_iters = n_iters
         self.weights = None

Logistic Regression/LogisticRegression.py

@@ -1,6 +1,14 @@
 import numpy as np
 
 def sigmoid(x):
+    """Computes the sigmoid function for the given input.
+
+    Args:
+        x (numpy.ndarray or float): The input value or array of values.
+
+    Returns:
+        numpy.ndarray or float: The sigmoid of the input, with values in the range (0, 1).
+    """
     return 1 / (1 + np.exp(-x))
 
 class LogisticRegression:
@@ -13,6 +21,15 @@ class LogisticRegression:
     n_iters : int, optional (default=1000)
         The number of iterations to run gradient descent.
 
+    """Initialize the linear regression model.
+
+    Args:
+        learning_rate float: The step size for gradient descent optimization. Default is 0.001.
+        n_iters int: The number of iterations for the optimization process. Default is 1000.
+
+    Returns:
+        None: This method initializes the model parameters but does not return anything.
+    """
     Attributes:
     weights : ndarray of shape (n_features,)
         Coefficients of the model.

Perceptron/Perceptron.py

@@ -38,6 +38,16 @@ class Perceptron:
     """
 
     def __init__(self, learning_rate=0.01, n_iters=1000):
+        """
+        Initializes the Perceptron model with specified learning rate and number of iterations.
+
+        Args:
+            learning_rate (float): The learning rate for weight updates. Default is 0.01.
+            n_iters (int): The number of iterations for training. Default is 1000.
+
+        Returns:
+            None
+        """
         self.lr = learning_rate
         self.n_iters = n_iters
         self.activation_func = unit_step_func