I'm a newbie in algorithms. I have recently started studying binary search and tryed to implement it on my own. The task is simple: we have an array of integers a and an integer x. If a contains x the result should be its index, otherwise the function should return -1.
Here is the code I have written:
def binary_search(a, x):
l = 0
r = len(a)
while r - l > 0:
m = (l + r) // 2
if a[m] < x:
l = m
else:
r = m
if a[l] == x:
return l
return -1
But this code stucks in infinite cycle on a = [1, 2] and x = 2. I suppose, that I have incorrect cycle condition (probably, should be r - l >= 0), but this solution does not help. Where am I wrong?
Let me do some desk checking. I'll assume a = [1, 2] and we are searching for a 2
So we start with
l = 0
r = 2
Since r - l = 2 > 0, we enter the while-loop.
m = (l + r) / 2 = (0 + 2) / 2 = 1
a[m] = a[1] = 2 == x (hence not less than x)
r = m = 1 (and l remains the same)
Now r - l = 1 - 0 = 1 > 0, so we continue
m = (l + r) / 2 = (0 + 1) / 2 = 0
a[m] = a[0] = 1 < x
l = m = 0 (and r remains the same)
After this iteration both r and l have the same value as before, which then produces an endless loop.
Ashok's answer is a great fix. But I think it'll be educational to do some desk checking on the fixed code and look what improves it.
Basically the problematic situation arises, when l + 1 = r.
Then m will always evaluate to l, a[l] < x and l is set to m again, which doesn't change the situation.
In a larger piece of code it'll make sense to make a table that contains a column for each variable to watch and a column to write down the code line that was evaluated. A column for remarks won't harm either.
As Mani mentioned you are not considering when A[m]==x. Include that case (at that point you've found a so just return m), and once you have that case we can let l=m+1 when we are still below x. Like this:
def binary_search(a, x):
l = 0
r = len(a)
while r - l > 0:
m = (l + r) // 2
if a[m] < x:
l = m + 1
elif a[m]==x:
return m
else:
r = m
if l<len(a) and a[l] == x:
return l
return -1
Related
Given an arbitrary range of 1 to F and a starting point S with an ending point G such that the only directions we could go is L Left steps and R Right Steps (also arbitrary), create a general solution that will return the number of steps it would take to go from S to R if it is possible otherwise return not possible.
You are bound to the range [1, F] which means that you cannot move L or R steps if the next move will be more than F or less than 1
Example:
F = 100
S = 2
G = 1
L = 0
R = 1
Output: not possible
F = 10
S = 1
G = 10
L = 1
R = 2
Output: 6
Explanation: [1 -> 3(R) -> 5(R) -> 7(R) -> 9(R) -> 8(L) -> 10(R)]
I've been given this problem in our class and our current topic is binary search and divide and conquer. Here's my approach but this does not solve one hidden case.
F = int(input())
S = int(input())
G = int(input())
L = int(input())
R = int(input())
count = 0
while S != G:
dist = abs(S - G) # Takes the current distance from S to G
if S > G:
if S-L > 0:
S -= L
count += 1
else:
S += R
count += 1
else:
if S+R <= F:
S += R
count += 1
else:
S -= L
count += 1
if dist == abs(S - G): # If distance doesn't change after trying
print("not possible") # a move, conclude that it is not possible.
break
if S == G: print(count)
Mathematically this problem means that we are looking for
integer solutions (for x and y) of the following equation:
x * R - y * L = G - S
we can start by creating a function to check if there is a solution quickly:
def path(S, G, F, L, R):
x=0
while True:
y = (x * R - G + S) / L
if y>=0 and int(y)==y:
return(x,y)
else:
x+=1
This will work if there are solutions, but not if they are not.
It can be proved mathematically that there is no solution when L devides R but not G-S. Here is the proof:
If
R mod L =0 (L devides R)
(G - S)/L != 0 (L doesn't devide G-S)
then by deviding the whole equation (x * R - y * L = G - S) by L we take:
x * R/L - y = (G - S)/L <=>
y= (x * R/L) - (G - S)/L
Now, we want y mod 1 = 0 (means that y is integer) for x mod 1 =0 (x integers). Using common modulo operations we take:
y mod 1 = [(x * R/L) - (G - S)/L] mod 1 =
[(x * R/L) mod 1 - ((G - S)/L) mod 1] mod 1 =
[(x mod 1 * (R/L) mod 1) mod 1 - ((G - S)/L) mod 1] mod 1 =
[(x mod 1 * 0) mod 1 - ((G - S)/L) mod 1] mod 1 =
[((G - S)/L) mod 1] mod 1
This cannot be 0 if L doesn't devide G-S which eventally means that there are no pair of integers x,y that can satisfy the original condition.
Programatically this means for our code, the following additions:
def path(S, G, F, L, R):
if R%L==0 and (G-S)%L != 0 :
return 'No solutions'
x=0
while True:
y = (x * R - G + S) / L
if y>=0 and int(y)==y:
return(x,y)
else:
x+=1
I don;t know if mathematically we can prove that the above if is the only exception, it can probably be proved with some more modulo operations. Programatically we can add some clock in our code so that if there are no solutions, which means that it will go to infitive loop, we can return False after some time. Here is how we can do this:
How would I stop a while loop after n amount of time?
If the distance didn't change that doesn't mean it's impossible, you could have just jumped over the point and made it to the other side with the same distance, imagine L=2, R=1, S=3, G=2, you start distance 1 from goal, jump left (still distance 1) then jump right and win. What you need to check is whether you have gone in a loop and ended up at a location you have already tried before. You can either keep track of these locations (say in a set) or do some math ahead of time and figure out how many Ls and Rs it takes before you have definitely looped (probably not intended to figure this out).
F=int(input())
S=int(input())
G=int(input())
L=int(input())
R=int(input())
L*=-1
Fl=1-L
Fr=F-R
h0=set()
n=0
while True:
if S<G:
S+= R if S<=Fr else L
elif G<S:
S+= L if Fl<=S else R
else:
print(n)
break
if S in h0:
print('not possible')
break
h0.add(S)
n+=1
I have written a code to find Pythagorean triplets but it is not optimized
it took 5-6 minutes for the algorithm to find answer for big numbers...
my teacher said it should take less than 3 secs...
num = int(input())
def main(n):
for x in range(1, n):
for y in range(1, x):
for z in range(1, y):
if x + y + z == n:
if x * x == y * y + z * z or y * y == x * x + z * z or z * z == x * x + y * y:
a = f'{z} {y} {x}'
print(a)
return
else:
print('Impossible')
for example if you enter 12, you'll get 3,4,5
if you enter 30 , the answer will be 5,12,13
The sum of these three numbers must be equal to the number you entered.
can anyone please help me ?
Note the proof for the parametric representation of primitive pythagorean triples. In the proof, the author states:
We can use this proof to write an optimized algorithm:
def p(num):
a, b, c = 1, 1, 0
n = 0
while c < num:
for m in range(1, n):
a = 2 * m * n
b = n ** 2 - m ** 2
c = n ** 2 + m ** 2
if c >= num:
return "Impossible!"
elif a + b + c == num:
return b, a, c
n = n + 1
print(p(12)) # >>> (3, 4, 5)
print(p(30)) # >>> (5, 12, 13)
print(p(31)) # >>> Impossible!
You're doing a lot of repeated and unnecessary work. You know that A^2 + B^2 = C^2 and you know that C > B > A. It doesn't matter if you want to say C > A > B because any solution you find with that would be satisfied with C > B > A. For instance take 12 and solution 3, 4, 5. It doesn't actually matter if you say that A=3 and B=4 or A=4 and B=3. Knowing this we can adjust the loops of each for loop.
A can go from 1 to num, that's fine. Technically it can go to a bit less since you are adding another value to it that has to be at least 1 as well.
B then can go from A+1 to num since it needs to be greater than it.
So what about C? Well it doesnt' need to go from 1 since that's not possible. In fact we only care about A + B + C = num, so solve for C and you get C = num - A - B. That means you don't need to use a loop to find C since you can just solve for it. Knowing this you can do something like so:
In [142]: def find_triplet(num):
...: for a in range(1, num-1):
...: for b in range(a+1, num):
...: # A^2 + B^2 = C^2
...: # And A+B+C = N
...: c = num - a - b
...: if c > 0:
...: if a*a + b*b == c*c:
...: print(f'{a} {b} {c}')
...: else:
...: break
...:
In [143]: find_triplet(30)
5 12 13
So why check to see if C > 0 and break otherwise? Well, if you know C = num - A - B and you are incrementing B, then once B becomes too large, C is going to continue to get more and more negative. Because of that you can check if C > 0 and if it's not, break out of that inner loop to have A increment and B reset.
The approach you discussed takes O(n^3) complexity.
An efficient solution is to run two loops, where first loop runs from x = 1 to n/3, second loop runs from y = x+1 to n/2. In second loop, we check if (n – x – y) is equal to (x * x + y * y):
def pythagoreanTriplet(n):
# Considering triplets in
# sorted order. The value
# of first element in sorted
# triplet can be at-most n/3.
for x in range(1, int(n / 3) + 1):
# The value of second element
# must be less than equal to n/2
for y in range(x + 1,
int(n / 2) + 1):
z = n - x - y
if (x * x + y * y == z * z):
print(x, " ", y, " ", z)
return
print("Impossible")
# Driver Code
n = int(input())
pythagoreanTriplet(n)
PS: Time complexity = O(n^2)
I want to find the number of ways, a given integer X can be decomposed into sums of numbers which are N-th powers and every summand must be unique. For example if X = 10 and N=3, I can decompose this number like that:
10 = 2^3+1^3+1^3 ,but this is not a valid decomposition, because the number 1 appears twice. A valid decomposition for X = 10 and N = 2 would be 10 = 3^2+1^2, since no summand is repeating here.
Now I tried it to use recursion and created the following Python Code
st = set(range(1,int(pow(X,1/float(N))))) # generate set of unique numbers
print(str(ps(X, N, st)))
def ps(x, n, s):
res = 0
for c in s:
chk = x-pow(c,n) # test validity
if chk > 0:
ns = s-set([c])
res += ps(chk,n,ns)
elif chk == 0:
res += 1 # one result is found
else:
res += 0 # no valid result
return res
I used a set called st and then I recursively called the function ps that includes the base case "decomposition found" and "decomposition not found". Moreover it reduces a larger number to a smaller one by considering only the ways how to decompose a given number into only two summands.
Unfortunately, I get completely wrong results, e.g.
X = 100, N = 3: Outputs 0, Expected 1
X = 100, N = 2: Outputs 122, Expected 3
X = 10, N = 2: Outputs 0, Expected 1
My thoughts are correct, but I think the Problem is anywhere in the recursion. Does anybody see what I make wrong? Any help would be greatly appreciated.
Hint:
>>> X = 100
>>> N = 3
>>> int(pow(X, 1/float(N)))
4
>>> list(range(1, 4))
[1, 2, 3]
The output is indeed correct for the input you are feeding it.
The problem is line res += 1 # one result is found in conjuction with res += ps(chk,n,ns) will make the algorithm add twice.
E.g X = 10, N = 2: Outputs 0, Expected 1 because:
c=1:
10 - 1^2 > 0 -> res += ps(chk,n,ns)
c=3:
9 - 3^2 == 0 -> res += 1 # one result is found ... return res
So, in c=3 res=1 is returned to the c=1 call, which will
res += ps(chk,n,ns), and ps(chk,n,ns) = 1, making res = 2 and doubling the result expected.
E.g. X = 29, N = 2.
29 = 2^2 + 3^2 + 4^2
Solving from bottom to top (the algorithm flow):
c=4 -> res += 1... return res
c=3 -> res += ps() -> res += 1 -> res = 2 ... return res
c=2 -> res += ps() -> res += 2 -> res = 4 ... return res
But res is supposed to be 1.
Solution: You cannot add res to res. And you must remove the previous iterated objects to avoid path repetition. Check the solution below (with prints for better understanding):
def ps(x, n, s):
print(s)
print("")
return ps_aux(x, n, s, 0) # level
def ps_aux(x, n, s, level):
sum = 0
for idx, c in enumerate(s):
print("----> " * level + "C = {}".format(c))
chk = x - pow(c,n) # test validity
if chk > 0:
ns = s[idx + 1:]
sum += ps_aux(chk,n,ns, level + 1)
elif chk == 0:
print("OK!")
sum += 1 # one result is found
else:
sum += 0 # no valid result
return sum
Try with:
X=10 # 1 solution
N=2
st = list(range(1,int(pow(X,1/float(N))) + 1 )) # generate set of unique numbers
print(str(ps(X, N, st)))
X=25 # 2 solutions [3,4], [5]
N=2
st = list(range(1,int(pow(X,1/float(N))) + 1 )) # generate set of unique numbers
print(str(ps(X, N, st)))
I must write a recursive function that gets a number(n) in the 10 base and converts it to a given number base(k) and return a list which its components are the final number digits,for example f(5, 3) must result [1, 2] which 5 in base 3 is 12, or f(22, 3) must result [2, 1, 1].
Here's the code I tried:
def cb(n, k):
b = []
if n == 0:
b.append(0)
if n < k:
b.append(n)
if n == k:
b.append(10)
else:
a = n // k
b.append(n - ((n // k) * k))
if a < k:
b.append(a)
else:
cb(a, k)
return b
print(cb(22, 3))
Actually I thought a lot on it but since I'm not so good at writing codes I couldn't go any further. I appreciate your help and modifications to my code .
If you think in terms of recursion the base case is when n < k for which the answer is n and to get the last digit of the n you do n%k so the recusive case is cb(n//k,k)+[n%k].
The code will look like this :
def cb(n, k):
if n < k:
return [n]
else:
return cb(n//k, k) + [n%k]
print(cb(22, 3))
you were very close, the only thing that you needed to do was change:
else:
cb(a, k)
to:
else:
b.extend(cb(a, k))
however, your output is going to be:
>>> cb(22, 3)
[1, 1, 2]
which is the reverse of what you want, since 22 in base 3 is 211. you can fix this by either reversing the list [1,1,2][::-1] == [2,1,1] or replace all your calls to append and your new call to extend to instead add at the beginning of the list like: b.insert(0,element) or b = cb(a,k) + b
The biggest problem is that you aren't doing anything with the results of the recursive call, so they never go in your list. I think you are also complicating this too much. Something like this should work:
def cb(n,k):
if n > 0:
q = n // k
r = n - q * k
b = cb(q, k)
b.append(r)
else:
b = [0]
if b[0] == 0 and len(b) > 1:
b = b[1:]
return b
I think if you don't do the last part, then you always get a 0 on the front? You could also simplify it further by just testing to see if it is less than the radix, which gives an even simpler solution
def cb(n,k):
if n < k:
return [n]
else:
q = n // k
r = n - q * k
b = cb(q, k)
b.append(r)
return b
Imagine you're trying to allocate some fixed resources (e.g. n=10) over some number of territories (e.g. t=5). I am trying to find out efficiently how to get all the combinations where the sum is n or below.
E.g. 10,0,0,0,0 is good, as well as 0,0,5,5,0 etc., while 3,3,3,3,3,3 is obviously wrong.
I got this far:
import itertools
t = 5
n = 10
r = [range(n+1)] * t
for x in itertools.product(*r):
if sum(x) <= n:
print x
This brute force approach is incredibly slow though; there must be a better way?
Timings (1000 iterations):
Default (itertools.product) --- time: 40.90 s
falsetru recursion --- time: 3.63 s
Aaron Williams Algorithm (impl, Tony) --- time: 0.37 s
Possible approach follows. Definitely would use with caution (hardly tested at all, but the results on n=10 and t=5 look reasonable).
The approach involves no recursion. The algorithm to generate partitions of a number n (10 in your example) having m elements (5 in your example) comes from Knuth's 4th volume. Each partition is then zero-extended if necessary, and all the distinct permutations are generated using an algorithm from Aaron Williams which I have seen referred to elsewhere. Both algorithms had to be translated to Python, and that increases the chance that errors have crept in. The Williams algorithm wanted a linked list, which I had to fake with a 2D array to avoid writing a linked-list class.
There goes an afternoon!
Code (note your n is my maxn and your t is my p):
import itertools
def visit(a, m):
""" Utility function to add partition to the list"""
x.append(a[1:m+1])
def parts(a, n, m):
""" Knuth Algorithm H, Combinatorial Algorithms, Pre-Fascicle 3B
Finds all partitions of n having exactly m elements.
An upper bound on running time is (3 x number of
partitions found) + m. Not recursive!
"""
while (1):
visit(a, m)
while a[2] < a[1]-1:
a[1] -= 1
a[2] += 1
visit(a, m)
j=3
s = a[1]+a[2]-1
while a[j] >= a[1]-1:
s += a[j]
j += 1
if j > m:
break
x = a[j] + 1
a[j] = x
j -= 1
while j>1:
a[j] = x
s -= x
j -= 1
a[1] = s
def distinct_perms(partition):
""" Aaron Williams Algorithm 1, "Loopless Generation of Multiset
Permutations by Prefix Shifts". Finds all distinct permutations
of a list with repeated items. I don't follow the paper all that
well, but it _possibly_ has a running time which is proportional
to the number of permutations (with 3 shift operations for each
permutation on average). Not recursive!
"""
perms = []
val = 0
nxt = 1
l1 = [[partition[i],i+1] for i in range(len(partition))]
l1[-1][nxt] = None
#print(l1)
head = 0
i = len(l1)-2
afteri = i+1
tmp = []
tmp += [l1[head][val]]
c = head
while l1[c][nxt] != None:
tmp += [l1[l1[c][nxt]][val]]
c = l1[c][nxt]
perms.extend([tmp])
while (l1[afteri][nxt] != None) or (l1[afteri][val] < l1[head][val]):
if (l1[afteri][nxt] != None) and (l1[i][val]>=l1[l1[afteri][nxt]][val]):
beforek = afteri
else:
beforek = i
k = l1[beforek][nxt]
l1[beforek][nxt] = l1[k][nxt]
l1[k][nxt] = head
if l1[k][val] < l1[head][val]:
i = k
afteri = l1[i][nxt]
head = k
tmp = []
tmp += [l1[head][val]]
c = head
while l1[c][nxt] != None:
tmp += [l1[l1[c][nxt]][val]]
c = l1[c][nxt]
perms.extend([tmp])
return perms
maxn = 10 # max integer to find partitions of
p = 5 # max number of items in each partition
# Find all partitions of length p or less adding up
# to maxn or less
# Special cases (Knuth's algorithm requires n and m >= 2)
x = [[i] for i in range(maxn+1)]
# Main cases: runs parts fn (maxn^2+maxn)/2 times
for i in range(2, maxn+1):
for j in range(2, min(p+1, i+1)):
m = j
n = i
a = [0, n-m+1] + [1] * (m-1) + [-1] + [0] * (n-m-1)
parts(a, n, m)
y = []
# For each partition, add zeros if necessary and then find
# distinct permutations. Runs distinct_perms function once
# for each partition.
for part in x:
if len(part) < p:
y += distinct_perms(part + [0] * (p - len(part)))
else:
y += distinct_perms(part)
print(y)
print(len(y))
Make your own recursive function which do not recurse with an element unless it's possible to make a sum <= 10.
def f(r, n, t, acc=[]):
if t == 0:
if n >= 0:
yield acc
return
for x in r:
if x > n: # <---- do not recurse if sum is larger than `n`
break
for lst in f(r, n-x, t-1, acc + [x]):
yield lst
t = 5
n = 10
for xs in f(range(n+1), n, 5):
print xs
You can create all the permutations with itertools, and parse the results with numpy.
>>> import numpy as np
>>> from itertools import product
>>> t = 5
>>> n = 10
>>> r = range(n+1)
# Create the product numpy array
>>> prod = np.fromiter(product(r, repeat=t), np.dtype('u1,' * t))
>>> prod = prod.view('u1').reshape(-1, t)
# Extract only permutations that satisfy a condition
>>> prod[prod.sum(axis=1) < n]
Timeit:
>>> %%timeit
prod = np.fromiter(product(r, repeat=t), np.dtype('u1,' * t))
prod = prod.view('u1').reshape(-1, t)
prod[prod.sum(axis=1) < n]
10 loops, best of 3: 41.6 ms per loop
You could even speed up the product computation by populating combinations directly in numpy.
You could optimize the algorithm using Dynamic Programming.
Basically, have an array a, where a[i][j] means "Can I get a sum of j with the elements up to the j-th element (and using the jth element, assuming you have your elements in an array t (not the number you mentioned)).
Then you can fill the array doing
a[0][t[0]] = True
for i in range(1, len(t)):
a[i][t[i]] = True
for j in range(t[i]+1, n+1):
for k in range(0, i):
if a[k][j-t[i]]:
a[i][j] = True
Then, using this info, you could backtrack the solution :)
def backtrack(j = len(t)-1, goal = n):
print j, goal
all_solutions = []
if j == -1:
return []
if goal == t[j]:
all_solutions.append([j])
for i in range(j-1, -1, -1):
if a[i][goal-t[j]]:
r = backtrack(i, goal - t[j])
for l in r:
print l
l.append(j)
all_solutions.append(l)
all_solutions.extend(backtrack(j-1, goal))
return all_solutions
backtrack() # is the answer