update dev branch

src/ml/decision_tree.py

@@ -225,14 +225,3 @@ def build_tree(self, rows, labels, value=""):
             for data, label in zip(data_subsets, label_subsets)
         ]
         return self.decision_node(question, branches, value)
-
-
-# Sample usage
-dt = DecisionTree()
-cars = dt.make_cars("car.data") # No value for filename defaults to "car.data"
-random.shuffle(cars)
-cars = cars[:1000]
-car_data = [x[:-1] for x in cars]
-car_labels = [x[-1] for x in cars]
-tree = dt.build_tree(car_data, car_labels)
-dt.print_tree(tree)

src/ml/k_means.py

@@ -2,6 +2,17 @@
 import random
 
 class KMeans:
+    """
+    An implementation of the K-Means clustering algorithm.
+
+    # Example usage
+    data = np.array([[1, 2], [1, 4], [1, 0], [10, 2], [10, 4], [10, 0]])
+    kmeans = KMeans(k=2)
+    kmeans.fit(data)
+    print("Cluster centroids:", kmeans.centroids)
+    print("Predicted clusters:", kmeans.predict(data))
+    """
+
     def __init__(self, k=2, max_iterations=100):
         self.k = k  # Number of clusters
         self.max_iterations = max_iterations  # Maximum number of iterations
@@ -50,10 +61,3 @@ def predict(self, data):
             cluster_idx = np.argmin(distances)
             predictions.append(cluster_idx)
         return predictions
-
-# Example usage
-data = np.array([[1, 2], [1, 4], [1, 0], [10, 2], [10, 4], [10, 0]])
-kmeans = KMeans(k=2)
-kmeans.fit(data)
-print("Cluster centroids:", kmeans.centroids)
-print("Predicted clusters:", kmeans.predict(data))

src/ml/linear_regression.py

@@ -1,6 +1,21 @@
 import numpy as np
 
 class LinearRegression:
+    """class for utilizing a linear regression algorithm
+
+    # # Example usage
+    # X = np.array([0, 1, 2, 3, 4, 5])
+    # y = np.array([0, 2, 4, 6, 8, 10])  # Example data
+
+    # model = LinearRegression()
+    # model.fit(X, y)
+    # predictions = model.predict(X)
+
+    # print("Coefficients:", model.coefficients)
+    # print("Intercept:", model.intercept)
+    # print("Predictions:", predictions)
+    """
+
     def __init__(self):
         self.coefficients = None
         self.intercept = None
@@ -20,15 +35,3 @@ def predict(self, X):
         if len(X.shape) == 1:
             X = X.reshape(-1, 1)
         return np.dot(X, self.coefficients) + self.intercept
-
-# Example usage
-X = np.array([0, 1, 2, 3, 4, 5])
-y = np.array([0, 2, 4, 6, 8, 10])  # Example data
-
-model = LinearRegression()
-model.fit(X, y)
-predictions = model.predict(X)
-
-print("Coefficients:", model.coefficients)
-print("Intercept:", model.intercept)
-print("Predictions:", predictions)

src/search/binary_search.py

@@ -1,3 +1,18 @@
+"""
+Binary Search algorithm
+
+# Example usage
+arr = [3, 4, 5, 6, 7, 8, 9]
+bs = BinarySearch(arr)
+target = 5
+result = bs.search(target)
+
+if result != -1:
+    print(f"Element {target} is present at index {result}.")
+else:
+    print(f"Element {target} is not present in the array.")
+"""
+
 class BinarySearch:
     def __init__(self, arr):
         self.arr = sorted(arr)  # Ensuring the array is sorted
@@ -18,14 +33,3 @@ def search(self, target):
                 right = mid - 1
 
         return -1  # Target not found
-
-# Example usage
-arr = [3, 4, 5, 6, 7, 8, 9]
-bs = BinarySearch(arr)
-target = 5
-result = bs.search(target)
-
-if result != -1:
-    print(f"Element {target} is present at index {result}.")
-else:
-    print(f"Element {target} is not present in the array.")