Optimized recursive_bubble_sort (#2410)

* optimized recursive_bubble_sort

* Fixed doctest error due whitespace

* reduce loop times for optimization

* fixup! Format Python code with psf/black push

Co-authored-by: github-actions <${GITHUB_ACTOR}@users.noreply.github.com>

machine_learning/decision_tree.py

@@ -135,8 +135,7 @@ class Decision_Tree:
 
 
 class Test_Decision_Tree:
-    """Decision Tres test class
-    """
+    """Decision Tres test class"""
 
     @staticmethod
     def helper_mean_squared_error_test(labels, prediction):

machine_learning/k_means_clust.py

@@ -132,12 +132,12 @@ def kmeans(
     data, k, initial_centroids, maxiter=500, record_heterogeneity=None, verbose=False
 ):
     """This function runs k-means on given data and initial set of centroids.
-       maxiter: maximum number of iterations to run.(default=500)
-       record_heterogeneity: (optional) a list, to store the history of heterogeneity
-                             as function of iterations
-                             if None, do not store the history.
-       verbose: if True, print how many data points changed their cluster labels in
-                             each iteration"""
+    maxiter: maximum number of iterations to run.(default=500)
+    record_heterogeneity: (optional) a list, to store the history of heterogeneity
+                          as function of iterations
+                          if None, do not store the history.
+    verbose: if True, print how many data points changed their cluster labels in
+                          each iteration"""
     centroids = initial_centroids[:]
     prev_cluster_assignment = None
 

machine_learning/linear_discriminant_analysis.py

@@ -153,7 +153,7 @@ def calculate_variance(items: list, means: list, total_count: int) -> float:
 def predict_y_values(
     x_items: list, means: list, variance: float, probabilities: list
 ) -> list:
-    """ This function predicts new indexes(groups for our data)
+    """This function predicts new indexes(groups for our data)
     :param x_items: a list containing all items(gaussian distribution of all classes)
     :param means: a list containing real mean values of each class
     :param variance: calculated value of variance by calculate_variance function

machine_learning/linear_regression.py

@@ -12,7 +12,7 @@ import requests
 
 
 def collect_dataset():
-    """ Collect dataset of CSGO
+    """Collect dataset of CSGO
     The dataset contains ADR vs Rating of a Player
     :return : dataset obtained from the link, as matrix
     """
@@ -32,7 +32,7 @@ def collect_dataset():
 
 
 def run_steep_gradient_descent(data_x, data_y, len_data, alpha, theta):
-    """ Run steep gradient descent and updates the Feature vector accordingly_
+    """Run steep gradient descent and updates the Feature vector accordingly_
     :param data_x   : contains the dataset
     :param data_y   : contains the output associated with each data-entry
     :param len_data : length of the data_
@@ -51,7 +51,7 @@ def run_steep_gradient_descent(data_x, data_y, len_data, alpha, theta):
 
 
 def sum_of_square_error(data_x, data_y, len_data, theta):
-    """ Return sum of square error for error calculation
+    """Return sum of square error for error calculation
     :param data_x    : contains our dataset
     :param data_y    : contains the output (result vector)
     :param len_data  : len of the dataset
@@ -66,7 +66,7 @@ def sum_of_square_error(data_x, data_y, len_data, theta):
 
 
 def run_linear_regression(data_x, data_y):
-    """ Implement Linear regression over the dataset
+    """Implement Linear regression over the dataset
     :param data_x  : contains our dataset
     :param data_y  : contains the output (result vector)
     :return        : feature for line of best fit (Feature vector)