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// C# Program for Boyer Moore String Matching Algorithm | ||
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using System; | ||
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public class Algorithm | ||
{ | ||
static int CHARACTERS = 256; | ||
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// Getting maximum of two integers | ||
static int max (int a, int b) { return (a > b)? a: b; } | ||
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// Bad Character Pre-Processing Function | ||
static void badChar( char []str, int size,int []badCharacter) | ||
{ | ||
int i; | ||
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// Initializing all occurences to -1 | ||
for (i = 0; i < CHARACTERS; i++) | ||
badCharacter[i] = -1; | ||
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// Filling the Actual Value | ||
for (i = 0; i < size; i++) | ||
badCharacter[(int) str[i]] = i; | ||
} | ||
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// Pattern Searching Function | ||
static void search( char []txt, char []pat) | ||
{ | ||
int m = pat.Length; | ||
int n = txt.Length; | ||
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int []Character = new int[CHARACTERS]; | ||
badChar(pat, m, Character); | ||
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/* | ||
s is used to keep track of | ||
pattern shifting with respect to text | ||
*/ | ||
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int s = 0; | ||
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while(s <= (n - m)) | ||
{ | ||
int j = m - 1; | ||
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while(j >= 0 && pat[j] == txt[s+j]) | ||
j--; | ||
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/* | ||
If the pattern is present at current | ||
shift, then index j will become -1 after | ||
the above loop | ||
*/ | ||
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if (j < 0) | ||
{ | ||
Console.WriteLine("Pattern occurs at index: " + s); | ||
s += (s+m < n)? m-Character[txt[s+m]] : 1; | ||
} | ||
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else | ||
s += max(1, j - Character[txt[s+j]]); | ||
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} | ||
} | ||
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public static void Main() | ||
{ | ||
Console.WriteLine("Enter The String Value: "); | ||
String valueEntered = Console.ReadLine(); | ||
Console.WriteLine("Enter The Pattern To Search: "); | ||
String pattern = Console.ReadLine(); | ||
Console.WriteLine(); | ||
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char []txt = valueEntered.ToCharArray(); | ||
char []pat = pattern.ToCharArray(); | ||
search(txt, pat); | ||
} | ||
} | ||
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/** | ||
Enter The String Value: | ||
ABAAABCD | ||
Enter The Pattern To Search: | ||
ABC | ||
Pattern occurs at index: 4 | ||
-------------------------------------------------- | ||
Enter The String Value: | ||
AABAACAADAABAABA | ||
Enter The Pattern To Search: | ||
AABA | ||
Pattern occurs at index: 0 | ||
Pattern occurs at index: 9 | ||
Pattern occurs at index: 12 | ||
-------------------------------------------------- | ||
Enter The String Value: | ||
THIS IS A TEST TEXT | ||
Enter The Pattern To Search: | ||
TEST | ||
Pattern occurs at index: 10 | ||
*/ |
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/* | ||
Johnson’s algorithm for All-pairs shortest paths | ||
Given a weighted Directed Graph where the weights may be negative, | ||
find the shortest path between every pair of vertices in the Graph using | ||
Johnson’s Algorithm. | ||
*/ | ||
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int MAX_INT = 9223372036854775807; | ||
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int minDistance(distance, visited) | ||
{ | ||
var minimum = MAX_INT; | ||
var minVertex = 0; | ||
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var v = distance.length; | ||
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for(var vertex = 0; vertex < v; vertex++) | ||
{ | ||
if( (minimum > distance[vertex]) && (visited[vertex] == false) ) | ||
{ | ||
minimum = distance[vertex]; | ||
minVertex = vertex; | ||
} | ||
} | ||
return minVertex; | ||
} | ||
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void Dijkstra(graph, modified, src) | ||
{ | ||
var num_vertices = graph.length; | ||
var distance = new List(num_vertices); | ||
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var visited = new List(num_vertices); | ||
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for (var i = 0; i < num_vertices; i++) | ||
{ | ||
distance[i] = MAX_INT; | ||
visited[i] = false; | ||
} | ||
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distance[src] = 0; | ||
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for(var count = 0; count < num_vertices; count++) | ||
{ | ||
var curVertex = minDistance(distance, visited); | ||
visited[curVertex] = true; | ||
for(var vertex = 0; vertex < num_vertices; vertex++) | ||
{ | ||
if ((visited[vertex] == false) && (distance[vertex] > (distance[curVertex] + | ||
modified[curVertex][vertex])) && (graph[curVertex][vertex] != 0)) | ||
{ | ||
distance[vertex] = (distance[curVertex] + modified[curVertex][vertex]); | ||
} | ||
} | ||
} | ||
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for(var vertex = 0; vertex < num_vertices; vertex++) | ||
{ | ||
print('Vertex ${vertex} : ${distance[vertex]}'); | ||
} | ||
} | ||
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List BellmanFord(edges, graph, num_vertices) | ||
{ | ||
var distance = new List(num_vertices+1); | ||
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for(var i = 0; i <= num_vertices; i++) | ||
{ | ||
distance[i]=MAX_INT; | ||
} | ||
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distance[num_vertices] = 0; | ||
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for(var i = 0; i < num_vertices; i++) | ||
{ | ||
edges.add([num_vertices, i, 0]); | ||
} | ||
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for(var i = 0; i < num_vertices; i++) | ||
{ | ||
for(var j in edges) | ||
{ | ||
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if((distance[j[0]] != MAX_INT) && (distance[j[0]] + j[2] < distance[j[1]]) ) | ||
{ | ||
distance[j[1]] = distance[j[0]] + j[2]; | ||
} | ||
} | ||
} | ||
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return distance; | ||
} | ||
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void JohnsonAlgorithm(graph) | ||
{ | ||
var edges = new List(); | ||
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for(var i = 0; i < graph.length ; i++) | ||
{ | ||
for(var j = 0; j < graph[i].length; j++) | ||
{ | ||
if(graph[i][j] != 0) | ||
{ | ||
edges.add([i, j, graph[i][j]]); | ||
} | ||
} | ||
} | ||
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var modifiedwei = BellmanFord(edges, graph, graph.length); | ||
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var modified = [[0,0,0,0], | ||
[0,0,0,0], | ||
[0,0,0,0], | ||
[0,0,0,0]]; | ||
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for(var i = 0; i < graph.length; i++) | ||
{ | ||
for(var j = 0; j < graph[i].length; j++) | ||
{ | ||
if(graph[i][j] != 0) | ||
{ | ||
modified[i][j] = (graph[i][j] + | ||
modifiedwei[i] - modifiedwei[j]); | ||
} | ||
} | ||
} | ||
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print ('Modified Graph: ${modified}'); | ||
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for(var src = 0; src < graph.length; src++) | ||
{ | ||
print ('\nShortest Distance with vertex ${src} as the source:\n'); | ||
Dijkstra(graph, modified, src); | ||
} | ||
} | ||
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void main() { | ||
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var graph = [[0, -8, 2, 4], | ||
[0, 0, 2, 6], | ||
[0, 0, 0, 2], | ||
[0, 0, 0, 0]]; | ||
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JohnsonAlgorithm(graph); | ||
} | ||
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/* | ||
Modified Graph: [[0, 0, 8, 8], [0, 0, 0, 2], [0, 0, 0, 0], [0, 0, 0, 0]] | ||
Shortest Distance with vertex 0 as the source: | ||
Vertex 0 : 0 | ||
Vertex 1 : 0 | ||
Vertex 2 : 0 | ||
Vertex 3 : 0 | ||
Shortest Distance with vertex 1 as the source: | ||
Vertex 0 : 9223372036854775807 | ||
Vertex 1 : 0 | ||
Vertex 2 : 0 | ||
Vertex 3 : 0 | ||
Shortest Distance with vertex 2 as the source: | ||
Vertex 0 : 9223372036854775807 | ||
Vertex 1 : 9223372036854775807 | ||
Vertex 2 : 0 | ||
Vertex 3 : 0 | ||
Shortest Distance with vertex 3 as the source: | ||
Vertex 0 : 9223372036854775807 | ||
Vertex 1 : 9223372036854775807 | ||
Vertex 2 : -9223372036854775801 | ||
Vertex 3 : 0 | ||
*/ |
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Machine_Learning/Logistic_Regression/Logistic_Regression.ipynb
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