I have this question that is messing me up because I am not getting that where should I start, which terms should I pick at the beginning? Because this confusing expression does not even let me take the common as it makes no sense. Also, it does not even let me remove compliments (using the Laws) as it also does not make any sense. Please help me in this, at least just guide me what should I do? From where should I start? I would be really grateful.
The Explanation of Symbols I used to write the expression:
! : NOT Gate
+ : OR Gate
. (dot) : AND Gate
Boolean Expression:
A.!B.E + !(B.C).D.!E + !(C.D).E+!A.D.!E + A.!(C.D).E + A.E + A.B.!E + !(A.C) + B.C.!D
I have used an online expression simplifier and that gave me the following answer:
!A + B + !C + D + E
But how the above long expression has been simplified in this short one? I know the Laws and Properties but I am not getting that how should I start simplifying the long one? Which terms should I see first? Kindly anyone please help me.
(This is a direct answer to your comment, and a side-ways answer to your main question. In short, use a different method to get the desired simplified expression.)
You have a complicated expression, but one that uses only 5 logical variables. In this problem it would be much easier to build a truth table, which would have just 2^5 = 32 rows. You could look at the results and use those to build a simplified, equivalent expression. This does not use "the laws and properties" that your original question requires, but it is a standard technique to simplify Boolean expressions.
You should have learned how to build a truth table in just about any Discrete Mathematics class. In short, you make a table where each element in each row is a T for True or F for False. The rows contain all possible combinations of Ts and Fs. For 5 variables this would use 2^5 = 32 rows. For each row, you assign the first value to A, the second to B, etc. You then evaluate the expression for those values and write the result at the end of the line.
This can be done by hand, but your expression is complicated enough that we could avoid that. Here is a Python 3 script that prints the desired table. Note that Python has the product() function which simplifies getting all possible combinations of Ts and Fs. This script used B[] to convert a Boolean value to a single character T or F.
from itertools import product
"""Make a truth table for the Boolean expression
A.!B.E + !(B.C).D.!E + !(C.D).E+!A.D.!E + A.!(C.D).E + A.E + A.B.!E + !(A.C) + B.C.!D
"""
B = ('F', 'T')
print('A B C D E : Result')
print('- - - - - : ------')
for a, b, c, d, e in product((True, False), repeat=5):
print(B[a], B[b], B[c], B[d], B[e], end=' : ')
print(B[
(a and not b and e)
or (not (b and c) and d and not e)
or (not (c and d) and e)
or (not a and d and not e)
or (a and not (c and d) and e)
or (a and e)
or (a and b and not e)
or (not (a and c))
or (b and c and not d)
])
Here are the results:
A B C D E : Result
- - - - - : ------
T T T T T : T
T T T T F : T
T T T F T : T
T T T F F : T
T T F T T : T
T T F T F : T
T T F F T : T
T T F F F : T
T F T T T : T
T F T T F : T
T F T F T : T
T F T F F : F
T F F T T : T
T F F T F : T
T F F F T : T
T F F F F : T
F T T T T : T
F T T T F : T
F T T F T : T
F T T F F : T
F T F T T : T
F T F T F : T
F T F F T : T
F T F F F : T
F F T T T : T
F F T T F : T
F F T F T : T
F F T F F : T
F F F T T : T
F F F T F : T
F F F F T : T
F F F F F : T
We see that the result is always T except for the single line T F T F F. This means your expression is true unless A is True, B is False, C is True, and D and E are False. So we can simplify your expression (using your notation) to
!(A.!B.C.!D.!E)
A simple use of DeMorgan's laws changes this to normal form:
!A + B + !C + D + E
which is what you wanted.
Is it possible to have nested if without else statements. I wrote the following useless program to demonstrate nested ifs. How do I fix this so it's correct in terms of syntax. lines 5 and 6 gives errors.
let rec move_helper b sz r = match b with
[] -> r
|(h :: t) ->
if h = 0 then
if h - 1 = sz then h - 1 ::r
if h + 1 = sz then h + 1 ::r
else move_helper t sz r
;;
let move_pos b =
move_helper b 3 r
;;
let g = move_pos [0;8;7;6;5;4;3;2;1]
You can't have if without else unless the result of the expression is of type unit. This isn't the case for your code, so it's not possible.
Here's an example where the result is unit:
let f x =
if x land 1 <> 0 then print_string "1";
if x land 2 <> 0 then print_string "2";
if x land 4 <> 0 then print_string "4"
You must understand that if ... then is an expression like any other. If no else is present, it must be understood as if ... then ... else () and thus has type unit. To emphasize the fact that it is an expression, suppose you have two functions f and g of type, say, int → int. You can write
(if test then f else g) 1
You must also understand that x :: r does not change r at all, it constructs a new list putting x in front of r (the tail of this list is shared with the list r). In your case, the logic is not clear: what is the result when h=0 but the two if fail?
let rec move_helper b sz r = match b with
| [] -> r
| h :: t ->
if h = 0 then
if h - 1 = sz then (h - 1) :: r
else if h + 1 = sz then (h + 1) :: r
else (* What do you want to return here? *)
else move_helper t sz r
When you have a if, always put an else. Because when you don't put an else, Java will not know if the case is true or false.
The problem is 2 is non invertible at Integer Mode Ring (6). I would like to divide the result into 2 as an ordinary integer. In another word, I like to escape from integer mode ring's trap and bring the result to ordinary integer and then divide it into 2.
def fast_exponentiation(c, L, q):
Zq = IntegerModRing(q) # create Z_q
g2 = c
result = 1
while True:
y = L % 2
result = Zq(result) * Zq(g2 ** y)
g2 = Zq(g2 * g2)
L = L >> 1
if L == 0:
break
return result
e = fast_exponentiation(2, 4, 6)
print e / 2
If you want to turn e into an Integer again, you have a few options: call Integer (the target object), or ZZ or IntegerRing, the target parent.
sage: e
1
sage: parent(e)
Ring of integers modulo 6
sage: ZZ(e)
1
sage: parent(ZZ(e))
Integer Ring
And so:
sage: e = ZZ(e)
sage: e/2
1/2
sage: e//2
0
or whatever else you'd like.
Exercise 1.11:
A function f is defined by the rule that f(n) = n if n < 3 and f(n) = f(n - 1) + 2f(n - 2) + 3f(n - 3) if n > 3. Write a procedure that computes f by means of a recursive process. Write a procedure that computes f by means of an iterative process.
Implementing it recursively is simple enough. But I couldn't figure out how to do it iteratively. I tried comparing with the Fibonacci example given, but I didn't know how to use it as an analogy. So I gave up (shame on me) and Googled for an explanation, and I found this:
(define (f n)
(if (< n 3)
n
(f-iter 2 1 0 n)))
(define (f-iter a b c count)
(if (< count 3)
a
(f-iter (+ a (* 2 b) (* 3 c))
a
b
(- count 1))))
After reading it, I understand the code and how it works. But what I don't understand is the process needed to get from the recursive definition of the function to this. I don't get how the code could have formed in someone's head.
Could you explain the thought process needed to arrive at the solution?
You need to capture the state in some accumulators and update the state at each iteration.
If you have experience in an imperative language, imagine writing a while loop and tracking information in variables during each iteration of the loop. What variables would you need? How would you update them? That's exactly what you have to do to make an iterative (tail-recursive) set of calls in Scheme.
In other words, it might help to start thinking of this as a while loop instead of a recursive definition. Eventually you'll be fluent enough with recursive -> iterative transformations that you won't need to extra help to get started.
For this particular example, you have to look closely at the three function calls, because it's not immediately clear how to represent them. However, here's the likely thought process: (in Python pseudo-code to emphasise the imperativeness)
Each recursive step keeps track of three things:
f(n) = f(n - 1) + 2f(n - 2) + 3f(n - 3)
So I need three pieces of state to track the current, the last and the penultimate values of f. (that is, f(n-1), f(n-2) and f(n-3).) Call them a, b, c. I have to update these pieces inside each loop:
for _ in 2..n:
a = NEWVALUE
b = a
c = b
return a
So what's NEWVALUE? Well, now that we have representations of f(n-1), f(n-2) and f(n-3), it's just the recursive equation:
for _ in 2..n:
a = a + 2 * b + 3 * c
b = a
c = b
return a
Now all that's left is to figure out the initial values of a, b and c. But that's easy, since we know that f(n) = n if n < 3.
if n < 3: return n
a = 2 # f(n-1) where n = 3
b = 1 # f(n-2)
c = 0 # f(n-3)
# now start off counting at 3
for _ in 3..n:
a = a + 2 * b + 3 * c
b = a
c = b
return a
That's still a little different from the Scheme iterative version, but I hope you can see the thought process now.
I think you are asking how one might discover the algorithm naturally, outside of a 'design pattern'.
It was helpful for me to look at the expansion of the f(n) at each n value:
f(0) = 0 |
f(1) = 1 | all known values
f(2) = 2 |
f(3) = f(2) + 2f(1) + 3f(0)
f(4) = f(3) + 2f(2) + 3f(1)
f(5) = f(4) + 2f(3) + 3f(2)
f(6) = f(5) + 2f(4) + 3f(3)
Looking closer at f(3), we see that we can calculate it immediately from the known values.
What do we need to calculate f(4)?
We need to at least calculate f(3) + [the rest]. But as we calculate f(3), we calculate f(2) and f(1) as well, which we happen to need for calculating [the rest] of f(4).
f(3) = f(2) + 2f(1) + 3f(0)
↘ ↘
f(4) = f(3) + 2f(2) + 3f(1)
So, for any number n, I can start by calculating f(3), and reuse the values I use to calculate f(3) to calculate f(4)...and the pattern continues...
f(3) = f(2) + 2f(1) + 3f(0)
↘ ↘
f(4) = f(3) + 2f(2) + 3f(1)
↘ ↘
f(5) = f(4) + 2f(3) + 3f(2)
Since we will reuse them, lets give them a name a, b, c. subscripted with the step we are on, and walk through a calculation of f(5):
Step 1: f(3) = f(2) + 2f(1) + 3f(0) or f(3) = a1 + 2b1 +3c1
where
a1 = f(2) = 2,
b1 = f(1) = 1,
c1 = 0
since f(n) = n for n < 3.
Thus:
f(3) = a1 + 2b1 + 3c1 = 4
Step 2: f(4) = f(3) + 2a1 + 3b1
So:
a2 = f(3) = 4 (calculated above in step 1),
b2 = a1 = f(2) = 2,
c2 = b1 = f(1) = 1
Thus:
f(4) = 4 + 2*2 + 3*1 = 11
Step 3: f(5) = f(4) + 2a2 + 3b2
So:
a3 = f(4) = 11 (calculated above in step 2),
b3 = a2 = f(3) = 4,
c3 = b2 = f(2) = 2
Thus:
f(5) = 11 + 2*4 + 3*2 = 25
Throughout the above calculation we capture state in the previous calculation and pass it to the next step,
particularily:
astep = result of step - 1
bstep = astep - 1
cstep = bstep -1
Once I saw this, then coming up with the iterative version was straightforward.
Since the post you linked to describes a lot about the solution, I'll try to only give complementary information.
You're trying to define a tail-recursive function in Scheme here, given a (non-tail) recursive definition.
The base case of the recursion (f(n) = n if n < 3) is handled by both functions. I'm not really sure why the author does this; the first function could simply be:
(define (f n)
(f-iter 2 1 0 n))
The general form would be:
(define (f-iter ... n)
(if (base-case? n)
base-result
(f-iter ...)))
Note I didn't fill in parameters for f-iter yet, because you first need to understand what state needs to be passed from one iteration to another.
Now, let's look at the dependencies of the recursive form of f(n). It references f(n - 1), f(n - 2), and f(n - 3), so we need to keep around these values. And of course we need the value of n itself, so we can stop iterating over it.
So that's how you come up with the tail-recursive call: we compute f(n) to use as f(n - 1), rotate f(n - 1) to f(n - 2) and f(n - 2) to f(n - 3), and decrement count.
If this still doesn't help, please try to ask a more specific question — it's really hard to answer when you write "I don't understand" given a relatively thorough explanation already.
I'm going to come at this in a slightly different approach to the other answers here, focused on how coding style can make the thought process behind an algorithm like this easier to comprehend.
The trouble with Bill's approach, quoted in your question, is that it's not immediately clear what meaning is conveyed by the state variables, a, b, and c. Their names convey no information, and Bill's post does not describe any invariant or other rule that they obey. I find it easier both to formulate and to understand iterative algorithms if the state variables obey some documented rules describing their relationships to each other.
With this in mind, consider this alternative formulation of the exact same algorithm, which differs from Bill's only in having more meaningful variable names for a, b and c and an incrementing counter variable instead of a decrementing one:
(define (f n)
(if (< n 3)
n
(f-iter n 2 0 1 2)))
(define (f-iter n
i
f-of-i-minus-2
f-of-i-minus-1
f-of-i)
(if (= i n)
f-of-i
(f-iter n
(+ i 1)
f-of-i-minus-1
f-of-i
(+ f-of-i
(* 2 f-of-i-minus-1)
(* 3 f-of-i-minus-2)))))
Suddenly the correctness of the algorithm - and the thought process behind its creation - is simple to see and describe. To calculate f(n):
We have a counter variable i that starts at 2 and climbs to n, incrementing by 1 on each call to f-iter.
At each step along the way, we keep track of f(i), f(i-1) and f(i-2), which is sufficient to allow us to calculate f(i+1).
Once i=n, we are done.
What did help me was running the process manually using a pencil and using hint author gave for the fibonacci example
a <- a + b
b <- a
Translating this to new problem is how you push state forward in the process
a <- a + (b * 2) + (c * 3)
b <- a
c <- b
So you need a function with an interface to accept 3 variables: a, b, c. And it needs to call itself using process above.
(define (f-iter a b c)
(f-iter (+ a (* b 2) (* c 3)) a b))
If you run and print each variable for each iteration starting with (f-iter 1 0 0), you'll get something like this (it will run forever of course):
a b c
=========
1 0 0
1 1 0
3 1 1
8 3 1
17 8 3
42 17 8
100 42 17
235 100 42
...
Can you see the answer? You get it by summing columns b and c for each iteration. I must admit I found it by doing some trail and error. Only thing left is having a counter to know when to stop, here is the whole thing:
(define (f n)
(f-iter 1 0 0 n))
(define (f-iter a b c count)
(if (= count 0)
(+ b c)
(f-iter (+ a (* b 2) (* c 3)) a b (- count 1))))
A function f is defined by the rule that f(n) = n, if n<3 and f(n) = f(n - 1) + 2f(n - 2) + 3f(n - 3), if n > 3. Write a procedure that computes f by means of a recursive process.
It is already written:
f(n) = n, (* if *) n < 3
= f(n - 1) + 2f(n - 2) + 3f(n - 3), (* if *) n > 3
Believe it or not, there was once such a language. To write this down in another language is just a matter of syntax. And by the way, the definition as you (mis)quote it has a bug, which is now very apparent and clear.
Write a procedure that computes f by means of an iterative process.
Iteration means going forward (there's your explanation!) as opposed to the recursion's going backwards at first, to the very lowest level, and then going forward calculating the result on the way back up:
f(0) = 0
f(1) = 1
f(2) = 2
f(n) = f(n - 1) + 2f(n - 2) + 3f(n - 3)
= a + 2b + 3c
f(n+1) = f(n ) + 2f(n - 1) + 3f(n - 2)
= a' + 2b' + 3c' where
a' = f(n) = a+2b+3c,
b' = f(n-1) = a,
c' = f(n-2) = b
......
This thus describes the problem's state transitions as
(n, a, b, c) -> (n+1, a+2*b+3*c, a, b)
We could code it as
g (n, a, b, c) = g (n+1, a+2*b+3*c, a, b)
but of course it wouldn't ever stop. So we must instead have
f n = g (2, 2, 1, 0)
where
g (k, a, b, c) = g (k+1, a+2*b+3*c, a, b), (* if *) k < n
g (k, a, b, c) = a, otherwise
and this is already exactly like the code you asked about, up to syntax.
Counting up to n is more natural here, following our paradigm of "going forward", but counting down to 0 as the code you quote does is of course entirely equivalent.
The corner cases and possible off-by-one errors are left out as exercise non-interesting technicalities.
I have this complex iterations program I wrote in TI Basic to perform a basic iteration on a complex number and then give the magnitude of the result:
INPUT “SEED?”, C
INPUT “ITERATIONS?”, N
C→Z
For (I,1,N)
Z^2 + C → Z
DISP Z
DISP “MAGNITUDE”, sqrt ((real(Z)^2 + imag(Z)^2))
PAUSE
END
What I would like to do is make a Haskell version of this to wow my teacher in an assignment. I am still only learning and got this far:
fractal ::(RealFloat a) =>
(Complex a) -> (Integer a) -> [Complex a]
fractal c n | n == a = z : fractal (z^2 + c)
| otherwise = error "Finished"
What I don't know how to do is how to make it only iterate n times, so I wanted to have it count up a and then compare it to n to see if it had finished.
How would I go about this?
Newacct's answer shows the way:
fractal c n = take n $ iterate (\z -> z^2 + c) c
Iterate generates the infinite list of repeated applications.
Ex:
iterate (2*) 1 == [1, 2, 4, 8, 16, 32, ...]
Regarding the IO, you'll have to do some monadic computations.
import Data.Complex
import Control.Monad
fractal c n = take n $ iterate (\z -> z^2 + c) c
main :: IO ()
main = do
-- Print and read (you could even omit the type signatures here)
putStr "Seed: "
c <- readLn :: IO (Complex Double)
putStr "Number of iterations: "
n <- readLn :: IO Int
-- Working with each element the result list
forM_ (fractal c n) $ \current -> do
putStrLn $ show current
putStrLn $ "Magnitude: " ++ (show $ magnitude current)
Since Complex is convertible from and to strings by default, you can use readLn to read them from the console (format is Re :+ Im).
Edit: Just for fun, one could desugar the monadic syntax and type signatures which would compress the whole programm to this:
main =
(putStr "Seed: ") >> readLn >>= \c ->
(putStr "Number of iterations: ") >> readLn >>= \n ->
forM_ (take n $ iterate (\z -> z^2 + c) c) $ \current ->
putStrLn $ show current ++ "\nMagnitude: " ++ (show $ magnitude current)
Edit #2: Some Links related to plotting and Mandelbrot's sets.
Fractal plotter
Plotting with
Graphics.UI
Simplest solution
(ASCII-ART)
Well you can always generate an infinite list of results of repeated applications and take the first n of them using take. And the iterate function is useful for generating an infinite list of results of repeated applications.
If you'd like a list of values:
fractalList c n = fractalListHelper c c n
where
fractalListHelper z c 0 = []
fractalListHelper z c n = z : fractalListHelper (z^2 + c) c (n-1)
If you only care about the last result:
fractal c n = fractalHelper c c n
where
fractalHelper z c 0 = z
fractalHelper z c n = fractalHelper (z^2 + c) c (n-1)
Basically, in both cases you need a helper function to the counting and accumulation. Now I'm sure there's a better/less verbose way to do this, but I'm pretty much a Haskell newbie myself.
Edit: just for kicks, a foldr one-liner:
fractalFold c n = foldr (\c z -> z^2 + c) c (take n (repeat c))
(although, the (take n (repeat c)) thing seems kind of unnecessary, there has to be an even better way)