optimization (TheAlgorithms#1303)

data_structures/binary_tree/binary_search_tree.py

@@ -40,6 +40,7 @@ class BinarySearchTree:
     def __init__(self):
         self.root = None
 
+    # Insert a new node in Binary Search Tree with value label
     def insert(self, label):
         # Create a new Node
         new_node = Node(label, None)

linear_algebra/README.md

@@ -6,7 +6,8 @@ This module contains some useful classes and functions for dealing with linear a
 
 ## Overview  
 
-- class Vector  
+### class Vector  
+-
     - This class represents a vector of arbitray size and operations on it.  
 
     **Overview about the methods:**    
@@ -32,7 +33,8 @@ This module contains some useful classes and functions for dealing with linear a
 - function randomVector(N,a,b)
     - returns a random vector of size N, with random integer components between 'a' and 'b'.
 
-- class Matrix
+### class Matrix
+-
     - This class represents a matrix of arbitrary size and operations on it.
 
     **Overview about the methods:**  

machine_learning/scoring_functions.py

@@ -16,6 +16,16 @@
 
 # Mean Absolute Error
 def mae(predict, actual):
+    """
+    Examples(rounded for precision):
+    >>> actual = [1,2,3];predict = [1,4,3]
+    >>> np.around(mae(predict,actual),decimals = 2)
+    0.67
+
+    >>> actual = [1,1,1];predict = [1,1,1]
+    >>> mae(predict,actual)
+    0.0
+    """
     predict = np.array(predict)
     actual = np.array(actual)
 
@@ -27,6 +37,16 @@ def mae(predict, actual):
 
 # Mean Squared Error
 def mse(predict, actual):
+    """
+    Examples(rounded for precision):
+    >>> actual = [1,2,3];predict = [1,4,3]
+    >>> np.around(mse(predict,actual),decimals = 2)
+    1.33
+
+    >>> actual = [1,1,1];predict = [1,1,1]
+    >>> mse(predict,actual)
+    0.0
+    """
     predict = np.array(predict)
     actual = np.array(actual)
 
@@ -39,6 +59,16 @@ def mse(predict, actual):
 
 # Root Mean Squared Error
 def rmse(predict, actual):
+    """
+    Examples(rounded for precision):
+    >>> actual = [1,2,3];predict = [1,4,3]
+    >>> np.around(rmse(predict,actual),decimals = 2)
+    1.15
+
+    >>> actual = [1,1,1];predict = [1,1,1]
+    >>> rmse(predict,actual)
+    0.0
+    """
     predict = np.array(predict)
     actual = np.array(actual)
 
@@ -51,6 +81,16 @@ def rmse(predict, actual):
 
 # Root Mean Square Logarithmic Error
 def rmsle(predict, actual):
+    """
+    Examples(rounded for precision):
+    >>> actual = [10,10,30];predict = [10,2,30]
+    >>> np.around(rmsle(predict,actual),decimals = 2)
+    0.75
+
+    >>> actual = [1,1,1];predict = [1,1,1]
+    >>> rmsle(predict,actual)
+    0.0
+    """
     predict = np.array(predict)
     actual = np.array(actual)
 
@@ -68,15 +108,29 @@ def rmsle(predict, actual):
 
 # Mean Bias Deviation
 def mbd(predict, actual):
+    """
+    This value is Negative, if the model underpredicts,
+    positive, if it overpredicts.
+
+    Example(rounded for precision):
+
+    Here the model overpredicts
+    >>> actual = [1,2,3];predict = [2,3,4]
+    >>> np.around(mbd(predict,actual),decimals = 2)
+    50.0
+
+    Here the model underpredicts
+    >>> actual = [1,2,3];predict = [0,1,1]
+    >>> np.around(mbd(predict,actual),decimals = 2)
+    -66.67
+    """
     predict = np.array(predict)
     actual = np.array(actual)
 
     difference = predict - actual
     numerator = np.sum(difference) / len(predict)
     denumerator = np.sum(actual) / len(predict)
-    print(numerator)
-    print(denumerator)
-
+    # print(numerator, denumerator)
     score = float(numerator) / denumerator * 100
 
     return score

maths/abs.py

@@ -10,11 +10,7 @@ def abs_val(num):
     >>abs_val(0)
     0
     """
-    if num < 0:
-        return -num
-
-    # Returns if number is not < 0
-    return num
+    return -num if num < 0 else num
 
 
 def main():

maths/average_mean.py

@@ -3,17 +3,13 @@
 
 def average(nums):
     """Find mean of a list of numbers."""
-    sum = 0
-    for x in nums:
-        sum += x
-    avg = sum / len(nums)
-    print(avg)
+    avg = sum(nums) / len(nums)
     return avg
 
 
 def main():
     """Call average module to find mean of a specific list of numbers."""
-    average([2, 4, 6, 8, 20, 50, 70])
+    print(average([2, 4, 6, 8, 20, 50, 70]))
 
 
 if __name__ == "__main__":

searches/binary_search.py

@@ -43,11 +43,10 @@ def binary_search(sorted_collection, item):
         current_item = sorted_collection[midpoint]
         if current_item == item:
             return midpoint
+        elif item < current_item:
+            right = midpoint - 1
         else:
-            if item < current_item:
-                right = midpoint - 1
-            else:
-                left = midpoint + 1
+            left = midpoint + 1
     return None
 
 

sorts/selection_sort.py

@@ -35,7 +35,8 @@ def selection_sort(collection):
         for k in range(i + 1, length):
             if collection[k] < collection[least]:
                 least = k
-        collection[least], collection[i] = (collection[i], collection[least])
+        if least != i:
+            collection[least], collection[i] = (collection[i], collection[least])
     return collection