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Chapter01/chapter1.py

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+def binary_search(arr, low, high, key):
+    while low <= high:
+        mid = int(low + (high - low)/2)  
+        if arr[mid] == key:
+            return mid
+        elif arr[mid] < key:
+            low = mid + 1
+        else:
+            high = mid - 1
+    return -1  
+
+
+arr = [ 2, 3, 4, 2, 10, 40] 
+x = 10 
+result = binary_search(arr, 0, len(arr)-1, x)  
+print(result)

Chapter02/binary_search.py

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+# Linear search program to search an element, return the index position of the #array  
+
+def searching(search_arr, x):
+    for i in range(len(search_arr)):
+        if search_arr[i] == x:            
+            return i       
+    return -1
+
+
+search_arr = [3, 4, 1, 6, 14]  
+x=4
+print("Index position for the element x is:", searching(search_arr, x)) 

Chapter02/linear_search.py

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+import sys
+
+def matrix_chain(mat, i, j):
+    if i == j:     
+        return 0     
+    minimum_computations = sys.maxsize    
+    for k in range(i, j):   
+        count = (MatrixChain(mat, i, k) + MatrixChain(mat, k+1, j)+ mat[i-1] * mat[k] * mat[j])     
+        if count < minimum_computations:    
+            minimum_computations = count   
+        return minimum_computations  
+        
+matrix_sizes = [20, 30, 45, 50]  
+print("Minimum multiplications are", MatrixChain(matrix_sizes , 1, len(matrix_sizes)-1))  

Chapter02/matrix_chain.py

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+def merge_sort(unsorted_list):
+    if len(unsorted_list) == 1:
+        return unsorted_list
+    mid_point = int(len(unsorted_list)/2)    
+    first_half = unsorted_list[:mid_point]   
+    second_half = unsorted_list[mid_point:]   
+
+    half_a = merge_sort(first_half)   
+    half_b = merge_sort(second_half)   
+
+    return merge(half_a, half_b) 
+  
+  
+def merge(first_sublist, second_sublist): 
+    i = j = 0 
+    merged_list = [] 
+    while i < len(first_sublist) and j < len(second_sublist): 
+        if first_sublist[i] < second_sublist[j]: 
+            merged_list.append(first_sublist[i])   
+            i += 1   
+        else: 
+            merged_list.append(second_sublist[j])   
+            j += 1 
+    
+    while i < len(first_sublist):   
+        merged_list.append(first_sublist[i])   
+        i += 1   
+    
+    while j < len(second_sublist): 
+        merged_list.append(second_sublist[j])   
+        j += 1 
+    return merged_list   
+  
+ 
+a= [11, 12, 7, 41, 61, 13, 16, 14] 
+print(merge_sort(a))
+ 

Chapter02/merge_sort.py

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+def fib(n):
+  if n <= 1:
+    return 1
+  else:
+    return fib(n-1) + fib(n-2)
+
+for i in range(5):
+  print(fib(i)) 

Chapter03/Fibonacci_series.py

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+import sys
+def MatrixChain(mat, i, j):
+    if i == j: 
+        return 0
+    minimum_computations = sys.maxsize
+    for k in range(i, j):
+        count = (MatrixChain(mat, i, k) + MatrixChain(mat, k+1, j)+ mat[i-1] * mat[k] * mat[j])
+    if count < minimum_computations:    
+        minimum_computations = count   
+    return minimum_computations  
+
+
+matrix_sizes = [20, 30, 45, 50]  
+print("Minimum multiplications are", MatrixChain(matrix_sizes , 1, len(matrix_sizes)-1))  

Chapter03/MatrixChain_multiplication.py

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+def binary_search(arr, low, high, key):
+    while low <= high:  
+        mid = int(low + (high - low)/2)
+        if arr[mid] == key:  
+            return mid  
+        elif arr[mid] < key:  
+            low = mid + 1  
+        else:  
+            high = mid - 1  
+    return -1  
+
+
+arr = [ 2, 3, 4, 2, 10, 40] 
+x = 10 
+result = binary_search(arr, 0, len(arr)-1, x)  
+print(result) 

Chapter03/binary_search.py

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+
+def get_shortest_distance(table, vertex): 
+        shortest_distance = table[vertex][DISTANCE] 
+        return shortest_distance 
+    
+def set_shortest_distance(table, vertex, new_distance): 
+        table[vertex][DISTANCE] = new_distance 
+        
+def set_previous_node(table, vertex, previous_node): 
+        table[vertex][PREVIOUS_NODE] = previous_node 
+
+def get_distance(graph, first_vertex, second_vertex): 
+        return graph[first_vertex][second_vertex] 
+        
+def get_next_node(table, visited_nodes): 
+        unvisited_nodes = list(set(table.keys()).difference(set(visited_nodes))) 
+        assumed_min = table[unvisited_nodes[0]][DISTANCE] 
+        min_vertex = unvisited_nodes[0] 
+        for node in unvisited_nodes: 
+            if table[node][DISTANCE] < assumed_min: 
+                assumed_min = table[node][DISTANCE] 
+                min_vertex = node 
+        return min_vertex 
+
+def find_shortest_path(graph, table, origin): 
+   visited_nodes = [] 
+   current_node = origin 
+   starting_node = origin 
+   while True: 
+        adjacent_nodes = graph[current_node] 
+        if set(adjacent_nodes).issubset(set(visited_nodes)): 
+            # Nothing here to do. All adjacent nodes have been visited. 
+            pass 
+        else: 
+            unvisited_nodes = set(adjacent_nodes).difference(set(visited_nodes)) 
+            for vertex in unvisited_nodes: 
+                distance_from_starting_node = get_shortest_distance(table, vertex) 
+                if distance_from_starting_node == INFINITY and current_node == starting_node: 
+                    total_distance = get_distance(graph, vertex, current_node) 
+                else: 
+                    total_distance = get_shortest_distance (table, 
+                    current_node) + get_distance(graph, current_node, vertex) 
+                if total_distance < distance_from_starting_node: 
+                    set_shortest_distance(table, vertex, total_distance) 
+                    set_previous_node(table, vertex, current_node) 
+        visited_nodes.append(current_node) 
+        #print(visited_nodes)
+        if len(visited_nodes) == len(table.keys()): 
+            break 
+        current_node = get_next_node(table,visited_nodes) 
+   return(table)
+
+graph = dict() 
+graph['A'] = {'B': 5, 'D': 9, 'E': 2} 
+graph['B'] = {'A': 5, 'C': 2} 
+graph['C'] = {'B': 2, 'D': 3} 
+graph['D'] = {'A': 9, 'F': 2, 'C': 3} 
+graph['E'] = {'A': 2, 'F': 3} 
+graph['F'] = {'E': 3, 'D': 2} 
+
+table = dict() 
+table = { 
+        'A': [0, None], 
+        'B': [float("inf"), None], 
+        'C': [float("inf"), None], 
+        'D': [float("inf"), None], 
+        'E': [float("inf"), None], 
+        'F': [float("inf"), None], 
+}
+
+
+DISTANCE = 0 
+PREVIOUS_NODE = 1 
+INFINITY = float('inf')  
+
+shortest_distance_table = find_shortest_path(graph, table, 'A') 
+
+
+for k in sorted(shortest_distance_table): 
+        print("{} - {}".format(k,shortest_distance_table[k])) 

Chapter03/dijkstra.py

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+def dyna_fib(n, lookup):
+    if n == 0:
+        return 0
+    if n <= 2:
+        lookup[n] = 1
+    if lookup[n] is None:
+        lookup[n] = dyna_fib(n-1, lookup) + dyna_fib(n-2, lookup)
+    return lookup[n] 
+
+lookup = [None]*(1000)
+
+for i in range(6): 
+    print(dyna_fib(i, lookup))