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5.4.1.1 Alphie's method will need to create a list of N numbers first and then add each of them one by one. This method will run in linear time, meaning as you increase the input size N it will proportionally increase the running time. Betsy's method on the other hand will always take the same amount of time regardless of the input size, so it runs in constant time. Betsy's methd is more efficient.5.4.1.3: On a slow computer threre may be a significant speed difference for 25. But the difference isn't /unbearable/ even for 1000.

Like many games, this is coded as a FOREVER loop, with a STOP THIS BLOCK to escape when we win or detect an inconsistency. It uses two global variables, one to count the number of guesses and the other for the buttons to tell us when they are pressed. There are more elegant ways to accomplish the latter, but they'd result in multiple WHEN I RECEIVE scripts, and we'd have to make LOW and HIGH global for those scripts to use. Also, we change the values of our input variables, which some people would consider bad style. But since those variables are local to this procedure, it really doesn't hurt anyone outside this script. Students will organize their code in many different ways. It might be useful to have them flag with comments where each of the five steps (or however many steps /their/ algorithm has) is carried out.

110i

cont

catchtag

cont

catchtag

Sprite

Not In ListThis will be the most common student solution. There are many other ways it could be done. For example, here's a recursive solution:EMPTY? is in the Tools library.1

1The last line says that the position of VALUE is one more than its position in the list without its first item. For example, if the DATA list is {3,4,5,6} and we want to know the position of 5, first find its position in {4,5,6} (namely, 2) and add 1 to get 3, which is the position of 5 in {3,4,5,6}.

guess22This is essentially the game program, but with the user interface parts removed. I also replaced the FOREVER loop with a REPEAT UNTIL. I think FOREVER is okay for games, but in a serious program the stopping condition for the loop should be self-documenting. (There's still an early stop if the error condition LOW>HIGH occurs. In this case it means that the supposedly sorted list given as input isn't sorted, or if the desired item isn't in the list.)

guess2This is exactly the code from the previous problem, but with REPORT instead of SAY.

Each item of DATA is a list of two numbers, the x and y coordinates of a point. The ITEM blocks extract the two numbers. An item from DATA is substituted into the empty list slots. (If you prefer you could drag the ITEM orange block from the FOR EACH into those slots -- the meaning is the same.)

We go to the first point with the pen up, so we won't see an extra line from wherever the sprite happened to be. After that, we put the pen down for each point if the value of PENDOWN is TRUE.

This is straightforward apart from the two points brought out on the lab page: 1. How to declare an input of type Reporter. 2. How to call that function and give it an input. Otherwise it's just figuring out the WAIT UNTIL / REPEAT UNTIL structure, and remembering from two pages back to put the PEN DOWN /after/ moving to the first point.

The above is probably how most students will write this. It can also be done recursively:

i11

truedatatrueThis so much calls out for a recursive solution that I couldn't help putting that first. CONTAINS is a primitive block. Students who remember that will have a much easier time writing this procedure than those who don't remember it. The other thing that will complicate the solution is if the students have programmed before in one of those other languages and therefore insist on using index variables to find their way through the list instead of ALL BUT FIRST OF. Here are a few examples of possible correct solutions:datatrue

This has the same structure as ARE THERE ANY DUPLICATES (5.3.2.1), but with lists as the reported values instead of Booleans. Similarly, the alternate solutions to 5.3.2.1 work here too:

i11

datadataThe timing experiment in the previous problem would be easier with this procedure because it has to examine every pair of items even if a duplicate is found early on.

The versions of DUPLICATES IN that use CONTAINS are easy to turn into a remove-duplicates (REMDUP) function: Just put a NOT around the CONTAINS in the script. (The recursive version could be written without NOT by swapping the then part and else part of the IF/ELSE block, but I did it this way to make the relationship clearer.) The versions of DUPLICATES IN that essentially reimplement CONTAINS with a FOR loop are harder, because you don't want to keep an item in the result just because /one/ other item is different from it. You have to test /all/ the items. One way is with a flag variable:

i11

data1dataAnother solution uses a feature we haven't taught yet: nonlocal exit, like BREAK in some other languages. The CATCH and THROW blocks are in the Tools library. Don't teach this to the class; it'll be a distraction. But if a student asks "is there something like BREAK?" you can show it to them:But neither of these last two solutions is exactly elegant. If I had to do this problem and I'd forgotten about CONTAINS, I'd start by writing my own CONTAINS as a helper procedure for REMDUP.

This isn't the most efficient solution, because it makes that list of numbers, only to have a list of length TIMES so that we can run EXPRESSION that many times! But it took me way less thinking time and coding time than making a loop. "Use what you have to get what you need." -- Võ Nguyên Giáp

LABEL will stamp text on the stage at the given font size. The direction of the text is the direction the sprite is facing, and color will match the pen color.

Hello!12

1datamapmany

data lists

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