Ki R Hobe
কী পেয়েছো তুমি, কী চেয়েছিলে তুমি.? হিসেবটা তোমার বড়ই গড়মিল...!!
Saturday, August 15, 2020
Tuesday, October 1, 2019
Sunday, September 15, 2019
Wednesday, November 21, 2018
Monday, October 22, 2018
Thursday, March 1, 2018
Random Restart Hill Climbing For N Queen
Hill climbing is an optimization technique which is a local
search algorithm. It is an algorithm that starts with a random solution to a
problem. By changing a single element of the solution it attempts to find a
better solution. Hill climbing is sometimes called greedy local search because
it grabs a good neighbor state without thinking ahead about where to go next.
To find a global maxima random restart hill climbing is
used. Random restart hill climbing is a series of hill climbing searches. From
randomly generated initial state until a goal is found.
board setup: n queens, one queen per column
successors : The successors of a state are all possible states generated by moving a single queen to another square in the same column. Meaning, there are n queens on the board and each queen can move to n-1 defined positions. So, each state has n*(n-1) successors.
heuristic cost function h : h is the number of pairs of queens that are attacking each other
Basic hill climbing creates a random state of the board. Then it finds the highest value successor of the current state(for n queen the lowest valued successor), which is called the neighbor. If the heuristic cost value of the neighbor is less than the heuristic cost value of the current state, then the neighbor becomes the current state. If not then the current solution is the minima of the current hill.
What random restart does is, it runs the hill climbing until the global minima is found. In each hill climbing, it creates a new initial state of the board then runs the hill climbing algorithm to find the minima of the hill. It stops when the global minima is reached.
h(board)
1 returns the number of attacking pair on board
highest_valued_successor_of_current(current)
1 best_h ← ∞
2 best_successor ← NULL
3 while(new successor can be created)
4 successor ← new_successor(current)
5 if h(successor) < best_h
6 best_h ← h(successor)
7 best_successor ← successor
8 return successor
hill_climbing()
1 current ← A random state of the board
2 while(1)
3 neighbor ← highest_valued_successor_of_current(current)
4 if h(neighbor) >= h(current)
5 return current
6 neighbor ← current
random_restart_hill_climbing()
1 final_h ← ∞
2 best_board ← NULL
3 while(final_h != 0)
4 result ← hill_climbing()
5 now_h ← h(result)
6 if final_h > now_h
7 final_h ← now_h
8 best_board ← result
9 return best_board
Friday, February 16, 2018
Football Player Transfer Prediction
Football Player Transfer Prediction Using Different Classifiers
Project Report : Football Player Transfer Prediction Report
DataSet : LINK
Saturday, January 27, 2018
Use of CFGs for Parsing OPEN
We can think of using CFGs to detect various language constructs in the token streams freed from simple
syntactic and semantic errors, as it is easier to describe the constructs with CFGs. But CFGs are hard to
apply practically. In this session we first exercise on simpler implementations of CFGs, and then
implement the FIRST and FOLLOW functions that facilitate efficient parsing algorithms for practical
problems.
syntactic and semantic errors, as it is easier to describe the constructs with CFGs. But CFGs are hard to
apply practically. In this session we first exercise on simpler implementations of CFGs, and then
implement the FIRST and FOLLOW functions that facilitate efficient parsing algorithms for practical
problems.
Session 5 by Nafis Islam on Scribd
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Football Player Transfer Prediction
Football Player Transfer Prediction Using Different Classifiers Project Report : Football Player Transfer Prediction Report ...
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This Project has been designed and developed for fulfillment of my 2nd year,1st semester Project Work. Video:
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A 2D game on basic c and c++ for fulfillment of my 1st year,2nd semester Project Work. Screenshot: