Add Topological Sort (#1302)

* add topological sort

* fix topological sort?

* running black

* renaming file

blockchain/chinese_remainder_theorem.py

@@ -44,7 +44,7 @@ def chinese_remainder_theorem(n1, r1, n2, r2):
     (x, y) = extended_euclid(n1, n2)
     m = n1 * n2
     n = r2 * x * n1 + r1 * y * n2
-    return ((n % m + m) % m)
+    return (n % m + m) % m
 
 
 # ----------SAME SOLUTION USING InvertModulo instead ExtendedEuclid----------------
@@ -84,8 +84,8 @@ def chinese_remainder_theorem2(n1, r1, n2, r2):
 # import testmod for testing our function
 from doctest import testmod
 
-if __name__ == '__main__':
-    testmod(name='chinese_remainder_theorem', verbose=True)
-    testmod(name='chinese_remainder_theorem2', verbose=True)
-    testmod(name='invert_modulo', verbose=True)
-    testmod(name='extended_euclid', verbose=True)
+if __name__ == "__main__":
+    testmod(name="chinese_remainder_theorem", verbose=True)
+    testmod(name="chinese_remainder_theorem2", verbose=True)
+    testmod(name="invert_modulo", verbose=True)
+    testmod(name="extended_euclid", verbose=True)

blockchain/diophantine_equation.py

@@ -17,7 +17,9 @@ def diophantine(a, b, c):
 
     """
 
-    assert c % greatest_common_divisor(a, b) == 0  # greatest_common_divisor(a,b) function implemented below
+    assert (
+        c % greatest_common_divisor(a, b) == 0
+    )  # greatest_common_divisor(a,b) function implemented below
     (d, x, y) = extended_gcd(a, b)  # extended_gcd(a,b) function implemented below
     r = c / d
     return (r * x, r * y)
@@ -32,6 +34,7 @@ def diophantine(a, b, c):
 
 # n is the number of solution you want, n = 2 by default
 
+
 def diophantine_all_soln(a, b, c, n=2):
     """
     >>> diophantine_all_soln(10, 6, 14)
@@ -66,6 +69,7 @@ def diophantine_all_soln(a, b, c, n=2):
 
 # Euclid's Algorithm
 
+
 def greatest_common_divisor(a, b):
     """
     >>> greatest_common_divisor(7,5)
@@ -117,8 +121,8 @@ def extended_gcd(a, b):
 # import testmod for testing our function
 from doctest import testmod
 
-if __name__ == '__main__':
-    testmod(name='diophantine', verbose=True)
-    testmod(name='diophantine_all_soln', verbose=True)
-    testmod(name='extended_gcd', verbose=True)
-    testmod(name='greatest_common_divisor', verbose=True)
+if __name__ == "__main__":
+    testmod(name="diophantine", verbose=True)
+    testmod(name="diophantine_all_soln", verbose=True)
+    testmod(name="extended_gcd", verbose=True)
+    testmod(name="greatest_common_divisor", verbose=True)

blockchain/modular_division.py

@@ -70,6 +70,7 @@ def modular_division2(a, b, n):
 
 # Extended Euclid's Algorithm : If d divides a and b and d = a*x + b*y for integers x and y, then d = gcd(a,b)
 
+
 def extended_gcd(a, b):
     """
     >>> extended_gcd(10, 6)
@@ -116,6 +117,7 @@ def extended_euclid(a, b):
 # Euclid's Lemma :  d divides a and b, if and only if d divides a-b and b
 # Euclid's Algorithm
 
+
 def greatest_common_divisor(a, b):
     """
     >>> greatest_common_divisor(7,5)
@@ -140,10 +142,10 @@ def greatest_common_divisor(a, b):
 # Import testmod for testing our function
 from doctest import testmod
 
-if __name__ == '__main__':
-    testmod(name='modular_division', verbose=True)
-    testmod(name='modular_division2', verbose=True)
-    testmod(name='invert_modulo', verbose=True)
-    testmod(name='extended_gcd', verbose=True)
-    testmod(name='extended_euclid', verbose=True)
-    testmod(name='greatest_common_divisor', verbose=True)
+if __name__ == "__main__":
+    testmod(name="modular_division", verbose=True)
+    testmod(name="modular_division2", verbose=True)
+    testmod(name="invert_modulo", verbose=True)
+    testmod(name="extended_gcd", verbose=True)
+    testmod(name="extended_euclid", verbose=True)
+    testmod(name="greatest_common_divisor", verbose=True)

graphs/g_topological_sort.py

@@ -0,0 +1,47 @@
+# Author: Phyllipe Bezerra (https://github.com/pmba)
+
+clothes = {
+    0: "underwear",
+    1: "pants",
+    2: "belt",
+    3: "suit",
+    4: "shoe",
+    5: "socks",
+    6: "shirt",
+    7: "tie",
+    8: "clock",
+}
+
+graph = [[1, 4], [2, 4], [3], [], [], [4], [2, 7], [3], []]
+
+visited = [0 for x in range(len(graph))]
+stack = []
+
+
+def print_stack(stack, clothes):
+    order = 1
+    while stack:
+        cur_clothe = stack.pop()
+        print(order, clothes[cur_clothe])
+        order += 1
+
+
+def dfs(u, visited, graph):
+    visited[u] = 1
+    for v in graph[u]:
+        if not visited[v]:
+            dfs(v, visited, graph)
+
+    stack.append(u)
+
+
+def top_sort(graph, visited):
+    for v in range(len(graph)):
+        if not visited[v]:
+            dfs(v, visited, graph)
+
+
+if __name__ == "__main__":
+    top_sort(graph, visited)
+    print(stack)
+    print_stack(stack, clothes)

machine_learning/k_nearest_neighbours.py

@@ -5,12 +5,13 @@ from sklearn.model_selection import train_test_split
 
 data = datasets.load_iris()
 
-X = np.array(data['data'])
-y = np.array(data['target'])
-classes = data['target_names']
+X = np.array(data["data"])
+y = np.array(data["target"])
+classes = data["target_names"]
 
 X_train, X_test, y_train, y_test = train_test_split(X, y)
 
+
 def euclidean_distance(a, b):
     """
     Gives the euclidean distance between two points
@@ -21,6 +22,7 @@ def euclidean_distance(a, b):
     """
     return np.linalg.norm(np.array(a) - np.array(b))
 
+
 def classifier(train_data, train_target, classes, point, k=5):
     """
     Classifies the point using the KNN algorithm
@@ -43,13 +45,13 @@ def classifier(train_data, train_target, classes, point, k=5):
     for data_point in data:
         distance = euclidean_distance(data_point[0], point)
         distances.append((distance, data_point[1]))
-    # Choosing 'k' points with the least distances. 
+    # Choosing 'k' points with the least distances.
     votes = [i[1] for i in sorted(distances)[:k]]
-    # Most commonly occuring class among them 
+    # Most commonly occuring class among them
     # is the class into which the point is classified
     result = Counter(votes).most_common(1)[0][0]
     return classes[result]
 
 
 if __name__ == "__main__":
-    print(classifier(X_train, y_train, classes, [4.4, 3.1, 1.3, 1.4]))
+    print(classifier(X_train, y_train, classes, [4.4, 3.1, 1.3, 1.4]))

maths/fibonacci_sequence_recursion.py

@@ -6,7 +6,7 @@ def recur_fibo(n):
     >>> [recur_fibo(i) for i in range(12)]
     [0, 1, 1, 2, 3, 5, 8, 13, 21, 34, 55, 89]
     """
-    return n if n <= 1 else recur_fibo(n-1) + recur_fibo(n-2)
+    return n if n <= 1 else recur_fibo(n - 1) + recur_fibo(n - 2)
 
 
 def main():

maths/sum_of_arithmetic_series.py

@@ -8,7 +8,7 @@ def sum_of_series(first_term, common_diff, num_of_terms):
     >>> sum_of_series(1, 10, 100)
     49600.0
     """
-    sum = ((num_of_terms/2)*(2*first_term+(num_of_terms-1)*common_diff))
+    sum = (num_of_terms / 2) * (2 * first_term + (num_of_terms - 1) * common_diff)
     # formula for sum of series
     return sum
 
@@ -19,4 +19,5 @@ def main():
 
 if __name__ == "__main__":
     import doctest
+
     doctest.testmod()

sorts/stooge_sort.py

@@ -33,6 +33,7 @@ def stooge(arr, i, h):
         # Recursively sort first 2/3 elements
         stooge(arr, i, (h - t))
 
+
 if __name__ == "__main__":
     user_input = input("Enter numbers separated by a comma:\n").strip()
     unsorted = [int(item) for item in user_input.split(",")]