Your python code must provide the following:

• Prompt the user for a region name.
• Read the data from the file selecting only the data for the region.
• Build three dictionaries for the region for the population, GDP, and personal income. The key for each dictionary is the State.
• In the case where the user entered an invalid region, an appropriate error message should be displayed. Note, you will not know this until you've read through the file and found no data.
• Write a function called calc_total_pop is passed the population dictionary and returns the total population. You will obviously need to iterate through the dictionary to produce the total population.
• Write a function called calc_total_gdp that is passed the gdp dictionary and returns the total GDP.
• Write a function called calc_total_pi that is passed the personal income dictionary and returns the total personal income.

The output of your must be as follows:

• A list (not python list) of states in the region.
• The Total population for the region.
• The total GDP for the region.
• The average population of the region.
• The average personal income in the region.

Include any appropriate captions and labels to describe the above date. Below is a suggest output format. Points will be deducted for an untidy report.

Economic statistics for the Great_Lakes region:
States in Region: Illinois, Indiana, Michigan, Ohio, Wisconsin
Total population: 46.436 million
Average population: 9.2872 million
Total GDP: 1776.04 billion
Average PI: 37669.85

You may want to investigate the round function to reduce the number for decimals in floating point numbers. The join function will provide handy for showing all States on one line.

For easier debugging and clarity, use multiple cells to modularize your program. One model would be three cells where:

• The first cell reads the file and builds the data structures.
• The second cell performs the calculations.
• The last cell displays the report

sample.txt

Alabama,Southeast,4.7848,153.84,162.2283,485,98.285,11.019
Alaska,Far_West,0.7141,43.47,32.6496,68,22.956,6.327
Arizona,Southwest,6.4108,221.02,217.7587,763,135.599,16.944
Arkansas,Southeast,2.9228,92.08,93.6831,405,56.79,7.507
California,Far_West,37.3344,1672.5,1579.1,9235,1009.6,133.61
Colorado,Rocky_Mountain,5.0485,230.98,210.6077,868,141,16.786
Connecticut,New_England,3.5766,197.61,197.8393,717,121.063,15.102
Delaware,Mideast,0.8998,55.5,36.9576,249,25.364,3.07
D.O.C,Mideast,0.605,89.97,42.2093,833,75.886,3.668
Florida,Southeast,18.846,650.29,725.4363,2522,399.293,69.981
Georgia,Southeast,9.7147,358.84,333.6327,1215,227.035,25.884
Hawaii,Far_West,1.3643,59.67,56.8272,276,37.876,5.449
Idaho,Rocky_Mountain,1.5708,50.73,50.3852,337,29.356,3.309
Illinois,Great_Lakes,12.8405,571.23,540.2233,2746,362.936,50.556
Indiana,Great_Lakes,6.4899,241.93,223.1581,898,144.024,17.475
Iowa ,Plains,3.0503,124.01,119.0797,1039,72.04,9.097
Kansas,Plains,2.8588,113.32,110.8847,777,71.431,9.03
Kentucky,Southeast,4.3467,141.98,143.211,484,92.351,12.327
Louisiana,Southeast,4.5441,200.94,169.1167,759,105.141,14.309
Maine,New_England,1.3276,45.56,49.3602,175,29.505,4.539
Maryland,Mideast,5.788,264.32,289.6531,1128,175.607,18.693
Massachusetts,New_England,6.5633,340.16,337.9316,1564,229.298,20.998
Michigan,Great_Lakes,9.8777,329.81,346.8182,1159,215.579,29.912
Minnesota,Plains,5.3107,240.42,226.3199,1327,154.014,18.851
Mississippi,Southeast,2.9691,85.36,91.5883,387,52.85,7.368
Missouri,Plains,5.9961,216.68,219.484,911,142.841,14.959
Montana,Rocky_Mountain,0.9907,31.92,34.2688,238,20.06,2.468
Nebraska,Plains,1.8297,80.64,73.0691,313,47.024,5.648
Nevada,Far_West,2.7038,109.61,99.2062,313,62.413,10.268
New_Hampshire,New_England,1.3168,55.24,59.1947,162,35.263,4.486
New_Mexico,Southwest,2.0648,70.79,68.4891,302,42.637,5.554
New_Jersey,Mideast,8.8034,431.41,449.0599,1733,267.585,42.41
New_York,Mideast,19.3992,1013.3,960.8265,6156,638.668,90.878
North_Carolina,Southeast,9.559,380.69,338.9875,1300,218.619,30.474
North_Dakota,Plains,0.6743,31.62,29.1538,603,18.645,2.366
Ohio,Great_Lakes,11.5383,413.99,418.5351,1608,272.716,33.551
Oklahoma,Southwest,3.7595,132.92,135.0626,448,80.058,9.231
Oregon,Far_West,3.8382,174.17,137.6717,703,88.9,8.032
Pennsylvania,Mideast,12.7113,493.53,529.8078,2125,323.795,39.317
Rhode_Island,New_England,1.0528,43.153,45.2676,200,27.112,3.971
South_Carolina,Southeast,4.6358,143.41,151.5368,472,93.025,11.875
South_Dakota,Plains,0.8162,34.37,33.1357,617,18.239,2.733
Tennessee,Southeast,6.3567,227.36,225.2247,730,138.683,19.039
Texas,Southwest,25.2427,1116.27,961.8281,2887,621.098,93.056
Utah,Rocky_Mountain,2.7751,105.2,90.1127,326,62.596,6.559
Vermont,New_England,0.626,23.34,25.1157,105,15.122,2.522
Virginia,Southeast,8.0251,377.47,359.9561,1113,248.951,28.016
Washington,Far_West,6.7436,307.69,286.7438,1526,187.423,28.482
West_Virginia,Southeast,1.854,53.58,58.9501,121,35.19,5.149
Wisconsin,Great_Lakes,5.6896,219.08,220.5023,973,141.446,18.435
Wyoming,Rocky_Mountain,0.5644,32,25.4336,82,15.684,3.662

Part VI: Compare algorithms, and try to speed things up! In this last part of the project, you will write code to facilitate the comparison of the different state-space search algorithms, and you will also attempt to speed things up so that your code can more easily solve puzzles that require many moves. Your tasks In eight_puzzle.py, write a function named process_file(filename, algorithm, param). It should take the following three inputs: a string filename specifying the name of a text file in which each line is a digit string for an eight puzzle. For example, here is a sample file containing 10 digit strings, each of which represents an eight puzzle that can be solved in 10 moves: 10_moves.txt a string algorithm that specifies which state-space search algorithm should be used to solve the puzzles ('random', 'BFS', 'DFS', 'Greedy', or 'A*') a third input param that allows you to specify a parameter for the searcher – either a depth limit (for the uninformed search algorithms) or a choice of heuristic function (for the informed search algorithms). Your function should open the file with the specified filename for reading, and it should use a loop to process the file one line at a time. We discussed line-by-line file processing earlier in the semester. For each line of the file, the function should: obtain the digit string on that line (stripping off the newline at the end of the line) take the steps needed to solve the eight puzzle for that digit string using the algorithm and parameter specified by the second and third inputs to the function report the number of moves in the solution, and the number of states tested during the search for a solution In addition, the function should perform the cumulative computations needed to report the following summary statistics after processing the entire file: number of puzzles solved average number of moves in the solutions average number of states tested For example: >>> process_file('10_moves.txt', 'BFS', -1) 125637084: 10 moves, 661 states tested 432601785: 10 moves, 1172 states tested 025318467: 10 moves, 534 states tested 134602785: 10 moves, 728 states tested 341762085: 10 moves, 578 states tested 125608437: 10 moves, 827 states tested 540132678: 10 moves, 822 states tested 174382650: 10 moves, 692 states tested 154328067: 10 moves, 801 states tested 245108367: 10 moves, 659 states tested solved 10 puzzles averages: 10.0 moves, 747.4 states tested (In this case, because BFS finds optimal solutions, every solution has the same number of moves, but for other algorithms this won’t necessarily be the case.) Notes/hints: You can model your code for solving a given puzzle on the code that we’ve given you in the eight_puzzle driver function. In particular, you can emulate the way that this function: creates Board and State objects for the initial state calls the create_searcher helper function to create the necessary type of searcher object, and handles the possible return value of None from that function Important: Make sure to create a new searcher object for each puzzle (i.e., for each line of the file). Otherwise, the searcher will still have information inside it from previous searches when it starts to solve a new puzzle. When calling the searcher object’s find_solution method, you should do so as follows: soln = None try: soln = searcher.find_solution(s) except KeyboardInterrupt: print('search terminated, ', end='') Making the call to find_solution() in this way will allow you to terminate a search that goes on too long by using Ctrl-C. In such cases, soln will end up with a value of None (meaning that no solution was found), and you should make sure to properly handle such cases. You should not use a timer in this function. This function should not return a value. Testing your function: If you haven’t already done so, download the 10_moves.txt file, putting it in the same folder as the rest of your files for the project. Try to reproduce the results for BFS shown above. Try applying Greedy Search to the same file. You may find that it takes Greedy a very long time to solve some of the puzzles, at least when using h1 (the num-misplaced-tiles heuristic). If this happens, use Ctrl-C as needed to terminate the problematic searches. When we processed 10_moves.txt using our implementation of Greedy, we ended up using Ctrl-C to terminate two of the searches: >>> process_file('10_moves.txt', 'Greedy', h1) 125637084: 204 moves, 658 states tested 432601785: 12 moves, 13 states tested 025318467: search terminated, no solution 134602785: 78 moves, 221 states tested 341762085: 26 moves, 339 states tested 125608437: 162 moves, 560 states tested 540132678: 68 moves, 749 states tested 174382650: search terminated, no solution 154328067: 10 moves, 16 states tested 245108367: 48 moves, 49 states tested solved 8 puzzles averages: 76.0 moves, 325.625 states tested It’s also possible for none of the puzzles to have a solution, and you should handle that situation appropriately. For example, this can happen if you impose a depth limit that is too small: >>> process_file('10_moves.txt', 'DFS', 5) # depth limit of 5 125637084: no solution 432601785: no solution 025318467: no solution 134602785: no solution 341762085: no solution 125608437: no solution 540132678: no solution 174382650: no solution 154328067: no solution 245108367: no solution solved 0 puzzles Note that you can’t compute any averages in this case, so you shouldn’t print the averages line in the results. Create a plain-text file named results.txt, and put your name and email (and those of your partner, if any) at the top of the file. Conduct an initial set of experiments in which you try all of the algorithms on the puzzles in the following files: 5_moves.txt - puzzles that can be solved in 5 moves 10_moves.txt - puzzles that can be solved in 10 moves 15_moves.txt - puzzles that can be solved in 15 moves More specifically, you should run the following algorithms on each file: random BFS DFS with a depth limit of 20 DFS with a depth limit of 50 Greedy (using h1 – the num-misplaced-tiles heuristic) A* (using the same heuristic) Note that it may be necessary to use Ctrl-C to terminate searches that take too much time. You might want to pick a given amount of time (e.g., 30 seconds or 1 minute), and use Ctrl-C to terminate any search that doesn’t complete in that time. In your results.txt file, create three tables that summarize the results of these initial experiments. Use a separate table for each file’s results. For example, the results for 5_moves.txt should be put into a table that looks like this: puzzles with 5-move optimal solutions ------------------------------------- algorithm num. solved avg. moves avg. states tested ------------------------------------------------------------------------ random BFS DFS (depth limit 20) DFS (depth limit 50) Greedy Search (using h1) A* (using h1) Below these tables, write a short reflection (one or two paragraphs) in which you summarize the key points that are illustrated by the results. For example, you might discuss whether the algorithms produce reliably optimal results, and how much work they do in finding a solution. There’s no one correct reflection; we just want to see that you have reflected intelligently on the results and drawn some conclusions. Even informed search algorithms like Greedy Search and A* can be slow on problems that require a large number of moves. This is especially true if the heuristic function used by the algorithm doesn’t do a good enough job of estimating the remaining cost to the goal. Our h1 heuristic function–which uses the number of misplaced tiles as the estimate of the remaining cost–is one example of a less than ideal heuristic. For example, consider the following two puzzles: Both of them have 4 misplaced tiles (the ones displayed in red), but the puzzle on the left can be solved in 4 moves, whereas the puzzle on the right requires 24 moves! Clearly, it would be better if our heuristic could do a better job of distinguishing between these two puzzles. Come up with at least one alternate heuristic, and implement it as part of your classes for informed searchers (GreedySearcher and AStarSearcher). To do so, you should take the following steps: As needed, add one or more methods to the Board class that will be used by your new heuristic function. (Adding a new method to the Board class is not required, but it can be helpful to add one so that the heuristic function can obtain the information needed for its estimate.) Add your new heuristic function(s) to searcher.py, and follow these guidelines: Continue the numbering scheme that we established for the earlier heuristic functions. Call your first alternate heuristic function h2, your next heuristic function (if any) h3, etc. Make sure that each heuristic function is a regular function, not a method. In addition, make sure that it takes a single State object and returns an integer. When conducting tests using a new heuristic function, use its name in the same ways that you would use h0 or h1. For example: >>> g = GreedySearcher(h2) >>> eight_puzzle('142358607', 'Greedy', h2) >>> process_file('15_moves.txt', 'A*', h2) You are welcome to design more than one new heuristic function, although only one is required. When testing and refining your heuristic(s), you can use the files that we provided above, as well as the following files: 18_moves.txt - puzzles that can be solved in 18 moves 21_moves.txt - puzzles that can be solved in 21 moves 24_moves.txt - puzzles that can be solved in 24 moves 27_moves.txt - puzzles that can be solved in 27 moves. Compare the performance of Greedy and A* using the h1 heuristic to their performance using your new heuristic(s). Keep revising your heuristic(s) as needed until you are satisfied. Ideally, you should see the following when using your new heuristic(s): Both Greedy and A* are able to solve puzzles more quickly – testing fewer states on average and requiring fewer searches to be terminated. Greedy Search is able to find solutions requiring fewer moves. A* continues to find optimal solutions. (If it starts finding solutions with more than the optimal number of moves, that probably means that your heuristic is overestimating the remaining cost for at least some states.) Although you are welcome to keep more than one new heuristic function, we will ultimately test only one of them. Please adjust your code as needed so that the heuristic function that you want us to test is named h2. Also, please make sure that we will still be able to test the num-misplaced-tiles heuristic if we specify h1 for the heuristic. In your results.txt file, briefly describe how your best new heuristic works: heuristic h2 ------------ This heuristic ... If your code includes other alternate heuristics, you are welcome to describe them as well, although doing so is not required. Conduct a second set of experiments in which you try both your new heuristic function(s) and the h1heuristic function on the puzzles in the four new files provided above (the ones that require 18, 21, 24, and 27 moves). In your results.txt file, create four tables that summarize the results of these experiments. Use a separate table for each file’s results. For example, the results for 18_moves.txt should be put into a table that looks like this: puzzles with 18-move optimal solutions -------------------------------------- algorithm num. solved avg. moves avg. states tested ---------------------------------------------------------------------- Greedy (heuristic h1) Greedy (heuristic h2) # Greedy with any other heuristics A* (heuristic h1) A* (heuristic h2) # Greedy with any other heuristics Below these tables, write a short reflection (one or two paragraphs) in which you summarize the key points that are illustrated by the results. Here again, there is no one correct reflection; we just want to see that you have reflected intelligently on the results and drawn some conclusions.

Using LC-3 Programming Exercise 1 Use .BLKW to set up a remote array of 10 values, starting at memory location x4000, as in lab 3. Now programmatically populate the array with the numbers 0 through 9 (note - NOT the characters '0' through '9'!) – i.e. hard-code just the first value (0), then calculate the rest one at a time, storing them in the array as you go. Remember the proper way of setting a register to 0! After you've stored them all, grab the seventh value (i.e. 6) and store it in R2. Clearly, you can't access this location via a label, so you'll need its actual address. How will you obtain that? And how will you use that address to get the value stored there? As always, step through your program and examine the values as they are stored in the array, and examine the final value stored in R2, to make sure your program works as expected. Exercise 2 You'll notice that Exercise 1 didn't ask you to output to console the array you built. Why not? Because as you know by now, numbers are not characters! So now copy your exercise 1 code into lab4_ex2.asm, and add an output loop, making it output the characters corresponding to the numbers stored in your array. Exercise 3 Let's try another modification of our well-used array program from Exercise 1: This time, instead of calculating and storing the numbers from 0 to 9 in the array, calculate and store the first ten powers of 2, starting with 20 = 1 in array index 0. Finally, grab the seventh value (26) from the array, and store it in R2. In order to do this, you will have to figure out how to calculate powers of 2. Some hints: Mathematically speaking: How do I obtain 2n+1 if I know 2n? What LC-3 operation could I use to multiply a number by 2? As always, place a breakpoint in your program, and step through it, examining the values as they are being stored in the array, and examine the final value stored in R2,  to make sure your program works as expected. Pay attention to both the decimal and hex representations of the values being stored. You already understand that you can't simply output the values in the array to the console “as is”, so we have to manipulate them somehow to turn them into characters. But this time all but the first four are multi-digit numbers when represented as decimal values, so our trick from the last exercise won't work – it can only convert the numbers from 0 to 9 into the single-digit ascii characters '0' through '9'.  Exercise 4 For now, we will instead observe what happens if we attempt to do what we just said can't be done - output the contents of the array "as is", i.e. as if they were ascii codes (which they are not!). Copy your exercise 3 code into your lab4_ex4.asm file, and add a second loop that loads the content of each element of the array into R0 and outputs it to the console using OUT. Now place a breakpoint at the start of your display loop and step through it: You will need to keep an eye on the Console to understand what is going on!! Please add comments!!!

Write an assembly code that performs the following four tasks: enable the interrupt number 31, and sets its priority bits to 2, and clears its pending bit disable the interrupt 31 (Hints: Each interrupt has one enable bit, one pending bit, and one byte of priority.)

Describe the Main Points about the given following: a.) Program Optimization Techniques b.) Disk Characteristics c.) Cache Memory Stack d.) Static and Dynamic Linking

Complete MS Project Exercise 2 on Appendix A: Brief Guide to Microsoft Project Professional 2016 2. Continue performing the steps in this appendix, starting with the section called Developing the Schedule. Print out the following screens or send them to your instructor, as directed: a. Figure A-32. All task durations and recurring task entered b. Figure A-39. Network diagram view c. Figure A-46. Changing Work hours for tasks d. Figure A-56. Earned value report e. Continue performing the steps, or at least read them. Write a one-to-two page paper describing the capabilities of Project Professional 2016 and your opinion of this software. What do you like and dislike about it?

Please DO NOT respond to this question by copy/pasting the code provided elsewhere on the site, none of those work. Thanks. Virtual Memory Lab This lab project addresses the implementation of page-replacement algorithms in a demand-paging system. Each process in a demand-paging system has a page table that contains a list of entries. For each logical page of the process, there is an entry in the table that indicates if the page is in memory. If the page is in memory, the memory frame number that page is resident in is indicated. Also, for each page, the time at which the page has arrived in memory, the time at which it has been last referenced, and the number of times the page has been referenced since the page arrived in memory are maintained. The page table data structure is a simple array of page-table entries (PTEs). Each PTE contains five fields as defined below: struct PTE { int is_valid; int frame_number; int arrival_timestamp; int last_access_timestamp; int reference_count; } Each process in the system has a page table that is simply an array of PTEs. Each process also has a pool of frames that is allocated. The frame pool for a process is represented as an array of integers, where each Integer represents the frame number of a frame that is free and is available for use by the process. Note that in order to get the frame number in the pool, you first need to access the integer in the array. This lab project aims to exercise the various policies for page replacement. In particular, we study the following three page-replacement policies: First-In-First-Out (FIFO) Least-Recently-Used (LRU) Least-Frequently-Used (LFU) In order to implement the above policies, we need to develop corresponding functions that process page accesses for a process. That is, for each process, given its page table, a logical page number being referenced and the free frame pool of the process, the functions should determine the memory frame number for the logical page. Also, the functions should modify the page table and the free frame pool appropriately. The details of the functions with respect to the different policies are described below. You need to develop code for these functions that implement the specifications. Place the code in a file called virtual.c. You should include the oslabs.h file.