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How to count the number of unique numbers in sorted array using Binary Search?

I am trying to count the number of unique numbers in a sorted array using binary search. I need to get the edge of the change from one number to the next to count. I was thinking of doing this without using recursion. Is there an iterative approach?
def unique(x):
start = 0
end = len(x)-1
count =0
# This is the current number we are looking for
item = x[start]
while start <= end:
middle = (start + end)//2
if item == x[middle]:
start = middle+1
elif item < x[middle]:
end = middle -1
#when item item greater, change to next number
count+=1
# if the number
return count
unique([1,1,1,1,1,1,1,1,1,1,2,2,2,2,2,2,2,2,2,2,2,5,5,5,5,5,5,5,5,5,5])
Thank you.
Edit: Even if the runtime benefit is negligent from o(n), what is my binary search missing? It's confusing when not looking for an actual item. How can I fix this?

Working code exploiting binary search (returns 3 for given example).
As discussed in comments, complexity is about O(k*log(n)) where k is number of unique items, so this approach works well when k is small compared with n, and might become worse than linear scan in case of k ~ n
def countuniquebs(A):
n = len(A)
t = A[0]
l = 1
count = 0
while l < n - 1:
r = n - 1
while l < r:
m = (r + l) // 2
if A[m] > t:
r = m
else:
l = m + 1
count += 1
if l < n:
t = A[l]
return count
print(countuniquebs([1,1,1,1,1,1,1,1,1,1,2,2,2,2,2,2,2,2,2,2,2,5,5,5,5,5,5,5,5,5,5]))

I wouldn't quite call it "using a binary search", but this binary divide-and-conquer algorithm works in O(k*log(n)/log(k)) time, which is better than a repeated binary search, and never worse than a linear scan:
def countUniques(A, start, end):
len = end-start
if len < 1:
return 0
if A[start] == A[end-1]:
return 1
if len < 3:
return 2
mid = start + len//2
return countUniques(A, start, mid+1) + countUniques(A, mid, end) - 1
A = [1,1,1,1,1,1,1,1,1,1,2,2,2,2,2,2,2,2,2,2,2,3,4,5,5,5,5,5,5,5,5,5,5]
print(countUniques(A,0,len(A)))

Generate sequence of numbers whose k-th digit from the left and from the right sums to 10 for all k

A Python coding exercise asks to make a function f such that f(k) is the k-th number such that its k-th digit from the left and from the right sums to 10 for all k. For example 5, 19, 28, 37 are the first few numbers in the sequence.
I use this function that explicitly checks if the number 'n' satisfies the property:
def check(n):
#even digit length
if len(str(n)) % 2 == 0:
#looping over positions and checking if sum is 10
for i in range(1,int(len(str(n))/2) + 1):
if int(str(n)[i-1]) + int(str(n)[-i]) != 10:
return False
#odd digit length
else:
#checking middle digit first
if int(str(n)[int(len(str(n))/2)])*2 != 10:
return False
else:
#looping over posotions and checking if sum is 10
for i in range(1,int(len(str(n))/2) + 1):
if int(str(n)[i-1]) + int(str(n)[-i]) != 10:
return False
return True
and then I loop over all numbers to generate the sequence:
for i in range(1, 10**9):
if check(i):
print(i)
However the exercise wants a function f(i) that returns the i-th such number in under 10 seconds. Clearly, mine takes a lot longer because it generates the entire sequence prior to number 'i' to calculate it. Is it possible to make a function that doesn't have to calculate all the prior numbers?

Testing every natural number is a bad method. Only a small fraction of the natural numbers have this property, and the fraction decreases quickly as we get into larger numbers. On my machine, the simple Python program below took over 3 seconds to find the 1,000th number (2,195,198), and over 26 seconds to find the 2,000th number (15,519,559).
# Slow algorithm, only shown for illustration purposes
# '1': '9', '2': '8', etc.
compl = {str(i): str(10-i) for i in range(1, 10)}
def is_good(n):
# Does n have the property
s = str(n)
for i in range((len(s)+1)//2):
if s[i] != compl.get(s[-i-1]):
return False
return True
# How many numbers to find before stopping
ct = 2 * 10**3
n = 5
while True:
if is_good(n):
ct -= 1
if not ct:
print(n)
break
n += 1
Clearly, a much more efficient algorithm is needed.
We can loop over the length of the digit string, and within that, generate numbers with the property in numeric order. Sketch of algorithm in pseudocode:
for length in [1 to open-ended]:
if length is even, middle is '', else '5'
half-len = floor(length / 2)
for left in (all 1) to (all 9), half-len, without any 0 digits:
right = 10's complement of left, reversed
whole-number = left + middle + right
Now, note that the count of numbers for each length is easily computed:
Length First Last Count
1 5 5 1
2 19 91 9
3 159 951 9
4 1199 9911 81
5 11599 99511 81
In general, if left-half has n digits, the count is 9**n.
Thus, we can simply iterate through the digit counts, counting how many solutions exist without having to compute them, until we reach the cohort that contains the desired answer. It should then be relatively simple to compute which number we want, again, without having to iterate through every possibility.
The above sketch should generate some ideas. Code to follow once I’ve written it.
Code:
def find_nth_number(n):
# First, skip cohorts until we reach the one with the answer
digits = 1
while True:
half_len = digits // 2
cohort_size = 9 ** half_len
if cohort_size >= n:
break
n -= cohort_size
digits += 1
# Next, find correct number within cohort
# Convert n to base 9, reversed
base9 = []
# Adjust n so first number is zero
n -= 1
while n:
n, r = divmod(n, 9)
base9.append(r)
# Add zeros to get correct length
base9.extend([0] * (half_len - len(base9)))
# Construct number
left = [i+1 for i in base9[::-1]]
mid = [5] * (digits % 2)
right = [9-i for i in base9]
return ''.join(str(n) for n in left + mid + right)
n = 2 * 10**3
print(find_nth_number(n))

This is a function that exploits the pattern where the number of "valid" numbers between adjacent powers of 10 is a power of 9. This allows us to skip over very many numbers.
def get_starting_point(k):
i = 0
while True:
power = (i + 1) // 2
start = 10 ** i
subtract = 9 ** power
if k >= subtract:
k -= subtract
else:
break
i += 1
return k, start
I combined this with the method you've defined. Supposing we are interested in the 45th number,
this illustrates the search starts at 1000, and we only have to find the 26th "valid" number occurring after 1000. It is guaranteed to be less than 10000. Of course, this bound gets worse and worse at scale, and you would want to employ the techniques suggested by the other community members on this post.
k = 45
new_k, start = get_starting_point(k)
print('new_k: {}'.format(new_k))
print('start at: {}'.format(start))
ctr = 0
for i in range(start, 10**9):
if check(i):
ctr += 1
if ctr == new_k:
break
print(i)
Output:
new_k: 26
start at: 1000
3827
It seems the 45th number is 3827.

Trying to write a code that will find the sum of the even numbers of the Fibonacci sequence?

I'm new to programming and I'm trying to write a program in Python that will find the sum of the even numbers of the numbers below 4,000,000 in the Fibonacci sequence. I'm not sure what I'm doing wrong but nothing will print. Thanks for any help.
def fib():
listx = []
for x in range(4000000):
if x == 0:
return 1
elif x == 1:
return 1
else:
listx.append(fib(x - 1) + fib(x - 2))
return listx
def evens(fib):
y = 0
for x in fib():
if x % 2 == 0:
y += x
else:
continue
print (y)

Here's an approach that uses a generator to keep memory usage to a minimum:
def fib_gen(up_to):
n, m = 0, 1
while n <= up_to:
yield n
n, m = m, n + m
total = 0
for f in fib_gen(4000000):
if f % 2 == 0:
total += f
Another option:
def fib_gen(up_to, filter):
n, m = 0, 1
while n <= up_to:
if filter(n):
yield n
n, m = m, n + m
sum(fib_gen(4000000, lambda f: f % 2 == 0)) # sum of evens
sum(fib_gen(4000000, lambda f: f % 2)) # sum of odds

First things first, there appears to be some contention between your requirements and the code you've delivered :-) The text of your question (presumably taken from an assignment, or Euler #2) requests the ...
sum of the even numbers of the numbers below 4,000,000 in the Fibonacci sequence.
Your code is summing the even numbers from the first four million Fibonacci numbers which is vastly different. The four millionth Fibonacci number has, according to Binet's formula, north of 800,000 digits in it (as opposed to the seven digits in the highest one below four million).
So, assuming the text to be more correct than the code, you don't actually need to construct a list and then evaluate every item in it, that's rather wasteful on memory.
The Fibonacci numbers can be generated on the fly and then simply accumulated if they're even. It's also far more useful to be able to use an arbitrary method to accumulate the numbers, something like the following:
def sumFibWithCond(limit, callback):
# Set up initial conditions.
grandparent, parent, child = 0, 0, 1
accum = 0
# Loop until number is at or beyond limit.
while child < limit:
# Add any suitable number to the accumulator.
accum = accum + callback(child)
# Set up next Fibonacci cycle.
grandparent, parent = parent, child
child = grandparent + child
# Return accumulator when done.
return accum
def accumulateEvens(num):
# Return even numbers as-is, zero for odd numbers.
if num % 2 == 0:
return num
return 0
sumEvensBelowFourMillion = sumFibWithCond(4000000, accumulateEvens)
Of special note is the initial conditions. The numbers are initialised to 0, 0, 1 since we want to ensure we check every Fibonacci number (in child) for the accumulating condition. This means the initial value of child should be one assuming, as per the question, that's the first number you want.
This doesn't make any difference in the current scenario since one is not even but, were you to change the accumulating condition to "odd numbers" (or any other condition that allowed for one), it would make a difference.
And, if you'd prefer to subscribe to the Fibonacci sequence starting with zero, the starting values should be 0, 1, 0 instead.

Maybe this will help you.
def sumOfEvenFibs():
# a,b,c in the Fibonacci sequence
a = 1
b = 1
result = 0
while b < 4000000:
if b % 2 == 0:
result += b
c = a + b
a = b
b = c
return result

How can I get the length of repeating decimal?

I got an interview and the question is how to get the length of repeating decimal?
For example
1/3=0.3333..., it returns 1,
5/7=0.7142857142857143, it returns 6, since 714285 is the repeating decimal.
1/15=0.066666666666666, it returns 1.
17/150=0.11333333333333333, it returns 1. since 3 is the repeating decimal.
And I have tried to write a code
def solution(a, b):
n = a % b
if n == 0:
return 0
mem = []
n *= 10
while True:
n = n % b
if n == 0:
return 0
if n in mem:
i = mem.index(n)
return len(mem[i:])
else:
mem.append(n)
n *= 10
However, my code can't pass all tests. And it's time complexity is O(n*logn). How can I improve that and make its time complexity O(n)?

Probably the proper way is to follow the math stack exchange link suggested by #Henry. But concerning your code here's my optimized version of it. The key point here is to use dictionary instead of array - the in operation is much faster in this case.
def solution(a, b):
n = a % b
if n == 0:
return 0
mem = {}
n *= 10
pos = 0
while True:
pos += 1
n = n % b
if n == 0:
return 0
if n in mem:
i = mem[n]
return pos - i
else:
mem[n] = pos
n *= 10
On my computer for 29/39916801 this code finishes calculations in several seconds.

Project Euler 14 code efficiency

l = [[i, i, 1] for i in range(1,1000000)]
def collatz(li):
for el in li:
if el[1] == 1:
li.remove(el)
elif el[1] % 2 == 0:
el[1] = el[1] / 2
el[2] += 1
elif el[1] % 2 == 1:
el[1] = 3*el[1] + 1
el[2] += 1
return li
while len(collatz(l)) >= 2:
l = collatz(l)
print l
Hi, this is a (partial) solution to Euler problem 14, written in Python.
Longest Collatz sequence
Problem 14
The following iterative sequence is defined for the set of positive integers:
n → n/2 (n is even)
n → 3n + 1 (n is odd)
Using the rule above and starting with 13, we generate the following sequence:
13 → 40 → 20 → 10 → 5 → 16 → 8 → 4 → 2 → 1
It can be seen that this sequence (starting at 13 and finishing at 1) contains 10 terms. Although it has not been proved yet (Collatz Problem), it is thought that all starting numbers finish at 1.
Which starting number, under one million, produces the longest chain?
NOTE: Once the chain starts the terms are allowed to go above one million.
I wrote partial because it does not really output the solution since I can't really run it in the whole 1 - 1000000 range. It's way too slow - taking more than 20 minutes the last time I killed the process. I have barely just started with python and programming in general (about 2 weeks) and I am looking to understand what's the obvious mistake I am making in terms of efficiency. I googled some solutions and even the average ones are orders of magnitude faster than mine. So what am I missing here? Any pointers to literature to avoid making the same mistakes in the future?

a little improvement upon sara's answer
import time
start = time.time()
def collatz(n):
k = n
length = 1
nList = []
nList.append(n)
while n != 1:
if n not in dic:
n = collatzRule(n)
nList.append(n)
length += 1
else:
# we dont need the values but we do need the real length for the for-loop
nList.extend([None for _ in range(dic[n] - 1)])
length = (length - 1) + dic[n]
break
for seq in nList:
if seq not in dic:
dic[seq] = len(nList) - nList.index(seq)
return length
def collatzRule(n):
if n % 2 == 0:
return n // 2
else:
return 3 * n + 1
longestLen = 0
longestNum = 0
dic = {}
for n in range(2, 1000001):
prsntLen = collatz(n)
if prsntLen > longestLen:
longestLen = prsntLen
longestNum = n
# print(f'{n}: {prsntLen}')
print(f'The starting num is: {longestNum} with the longest chain having: {longestLen} terms.')
print(f'time taken: {time.time() - start}')

Sara's answer is great, but can be more efficient.
If the value we return from the function is len(seq), why not just counting the number of iterations instead of conducting a list first?
I have changed the code slightly, and the performance improvement is significant
def collatz(x):
count = 1
temp = x
while temp > 1:
if temp % 2 == 0:
temp = int(temp/2)
if temp in has2: # calculate temp and check if in cache
count += has2[temp]
break
else:
count += 1
else:
temp = 3*temp + 1
if temp in has2:
count += has2[temp]
break
else:
count += 1
has2[x] = count
return count
837799 has 525 elements. calculation time =1.97099995613 seconds.
Compared to the original version
837799 has 525 elements. calculation time =11.3389999866 seconds.
Using list of int rather than building the whole list is ~80% faster.

the problem is you use brute force algorithm that is inefficient.this is my solution to problem 14 from project Euler. it takes a few second to run. the key is you should save previous results in a dictionary so you don't have to compute those results again.:
#problem 14 project euler
import time
start=time.time()
has2={}
def collatz(x):
seq=[]
seq.append(x)
temp=x
while(temp>1):
if temp%2==0:
temp=int(temp/2)
if temp in has2:
seq+=has2[temp]
break
else:
seq.append(temp)
else:
temp=3*temp+1
if temp in has2:
seq+=has2[temp]
break
else:
seq.append(temp)
has2[x]=seq
return len(seq)
num=0
greatest=0
for i in range(1000000):
c=collatz(i)
if num<c:
num=c
greatest=i
print('{0} has {1} elements. calculation time ={2} seconds.'.format(greatest,num,time.time()-start))

As #Sara says you could use dictionary to save previous results and then look them up for making program run faster. But I don't quite understand your results, taking more than 20 mins sounds like you have some problem.
By using bruteforce i get code to run about at 16 sec.
#!/bin/python3
########################
# Collatz Conjecture #
# Written by jeb 2015 #
########################
import time
current = 0
high = 0
# While number is not one, either divide it by 2
# or multiply with 3 and add one
# Returns number of iterations
def NonRecursiveCollatz(i):
counter = 1
while i != 1:
counter = counter + 1
if i%2 == 0:
i = i / 2
else:
i = 3*i + 1
return counter
time_start = time.time()
# Test all numbers between 1 and 1.000.000
# If number returned is higher than last one, store it nd remember
# what number we used as input to the function
for i in range(1,1000000):
current = NonRecursiveCollatz(i)
if current > high:
high = current
number = i
elapsed_time = time.time() - time_start
print "Highest chain"
print high
print "From number "
print number
print "Time taken "
print elapsed_time
With the output:
Highest chain
525
From number
837799
Time taken
16.730340004

//Longest Colletz Sequence
public class Problem14 {
static long getLength(long numb) {
long length = 0;
for(long i=numb; i>=1;) {
length++;
if(i==1)
break;
if(i%2==0)
i = i/2;
else
i = (3*i)+1;
}
return length;
}
static void solution(long numb) {
long number = numb;
long maxLength = getLength(number);
for(long i=numb; i>=1; i--) {
if(getLength(i)>=maxLength) {
maxLength = getLength(i);
number = i;
}
}
System.out.println("`enter code here`Length of "+number+" is : "+maxLength);
}
public static void main(String args[]) {
long begin = System.currentTimeMillis();
solution(1000000);
long end = System.currentTimeMillis();
System.out.println("Time : "+(end-begin));
}
}
output :
Length of 837799 is : 525
Time : 502