I know this sounds kind of simple but I'm trying to get the count of "countdown" sequences that exists in my array. Example:
[1,2,3,2,5,4,3,0] - > 2 ([3,2] and [5,4,3])
I just need a little push, please!
Just iterate through the list every time the countdown breaks you increment the counter
Python, also known as pseudo code:
def count_finder(l):
prev = l[0]
counter = 0
inCount = False
for num in l[1:]:
if num == prev-1: #Checks if the previous was 1 greater than this one
inCount = True # if it is then "inCount" is True
elif num+1 != prev and inCount: #Checks if your exiting a countdown
inCount = False
counter += 1 #Increment Counter
prev = num #Change previous number to current number for next loop
if inCount: counter+=1 #If the loop ends while in a count down increment counter
return counter
print(count_finder([9, 8, 7, 6, 5, 4]))
Related
I am coding the Merge sort algorithm but somehow got stuck with a problem. The problem is that I need to use the return value of the merge function as an argument as an previous recursive call of the same merge function. Sorry for not being clear.
Here is my code:
a = [10,5,2,20,-50,30]
def mergeSort(arr):
l = 0
h = len(arr)-1
if h > l:
mid = (l+h) // 2
left = arr[l:mid+1]
right = arr[mid+1:]
mergeSort(left)
mergeSort(right)
merge(left, right)
def merge(l, r):
subarr = []
lc = 0
rc = 0
loop = True
while loop:
if lc > len(l)-1 and rc <= len(r)-1:
for i in range(rc, len(r)):
subarr.append(r[i])
loop = False
elif lc <= len(l)-1 and rc > len(r)-1:
for i in range(lc, len(l)):
subarr.append(l[i])
loop = False
elif l[lc] < r[rc]:
subarr.append(l[lc])
lc += 1
loop = True
elif r[rc] < l[lc]:
subarr.append(r[rc])
rc += 1
loop = True
elif l[lc] == r[rc]:
subarr.append(l[lc])
subarr.append(r[rc])
lc += 1
rc += 1
loop = True
mergeSort(a)
Any help will be appreciated thank you :)
First you need to actually return the result. Right now you return nothing so get None back.
Secondly, just assign to the same variable. left = mergeSort(left) and so on.
UPDATE:
Here is a debugged version.
a=[10,5,2,20,-50,30]
def mergeSort(arr):
l=0
h=len(arr)-1
if h>l:
mid=(l+h)//2
left=arr[l:mid+1]
right=arr[mid+1:]
# Capture the merge into variables here.
left=mergeSort(left)
right=mergeSort(right)
# Need a return of the merge.
return merge(left,right)
# Need to return arr if arr has 0 or 1 elements.
else:
return arr
def merge(l,r):
subarr=[]
lc=0
rc=0
loop=True
while loop:
if lc>len(l)-1 and rc<=len(r)-1:
for i in range(rc,len(r)):
subarr.append(r[i])
loop=False
elif lc<=len(l)-1 and rc>len(r)-1:
for i in range(lc,len(l)):
subarr.append(l[i])
loop=False
elif l[lc]<r[rc]:
subarr.append(l[lc])
lc+=1
loop=True
elif r[rc]<l[lc]:
subarr.append(r[rc])
rc+=1
loop=True
elif l[lc]==r[rc]:
subarr.append(l[lc])
subarr.append(r[rc])
lc+=1
rc+=1
loop=True
# Need to return the results of merge.
return subarr
# Need to actually try calling the function to see the result.
print(mergeSort(a))
I also indented more sanely. Trust me, it matters.
There are multiple problems in our code:
you do not return the sorted slice from mergeSort nor merge. Your implementation does not sort the array in place, so you must return subarr in merge and the return value of merge in mergeSort or arr if the length is less than 2.
your code is too complicated: there are many adjustments such as mid+1, len(l)-1, etc. It is highly recommended to use index values running from 0 to len(arr) excluded. This way you do not have to add error prone +1/-1 adjustments.
the merge function should proceed in 3 phases: merge the left and right arrays as long as both index values are less than the array lengths, then append remaining elements from the left array, finally append remaining elements from the right array.
there is no need to make 3 different tests to determine from which of the left and right array to take the next element, a single test is sufficient.
also use a consistent amount of white space to indent the blocks, 3 or 4 spaces are preferable, tabs are error prone as they expand to different amount of white space on different devices, mixing tabs and spaces, as you did is definitely a problem.
Here is a modified version:
def mergeSort(arr):
# no need to use l and h, use len(arr) directly
if len(arr) > 1:
# locate the middle point
mid = len(arr) // 2
# left has the elements before mid
left = arr[:mid]
# right has the elements from mid to the end
right = arr[mid:]
# sort the slices
left = mergeSort(left)
right = mergeSort(right)
# merge the slices into a new array and return it
return merge(left, right)
else:
# return the original array (should actually return a copy)
return arr
def merge(l, r):
subarr = []
lc = 0
rc = 0
# phase1: merge the arrays
while lc < len(l) and rc < len(r):
if l[lc] <= r[rc]:
subarr.append(l[lc])
lc += 1
else:
subarr.append(r[rc])
rc += 1
# phase2: copy remaining elements from l
while lc < len(l):
subarr.append(l[lc])
lc += 1
# phase3: copy remaining elements from r
while rc < len(r):
subarr.append(r[rc])
rc += 1
# return the merged array
return subarr
a = [10, 5, 2, 20, -50, 30]
print(mergeSort(a))
I am working on the QuickSort - Median Three Algorithm.
I have no problem with the first and last element sorting. But, when comes to the Median-three, I am slightly confused. I hope someone could help me on this.
Would be appreciate if someone could provide me some pseudocode?
My understanding is to get the middle index by doing this. (start + end) / 2 , then swap the middle pivot value to the first value, after all these done it should goes well with the normal quick sort ( partitioning and sorting).
Somehow, I couldn't get it works. Please help!
#Array Swap function
def swap(A,i,k):
temp=A[i]
A[i]=A[k]
A[k]=temp
# Get Middle pivot function
def middle(lista):
if len(lista) % 2 == 0:
result= len(lista) // 2 - 1
else:
result = len(lista) // 2
return result
def median(lista):
if len(lista) % 2 == 0:
return sorted(lista)[len(lista) // 2 - 1]
else:
return sorted(lista)[len(lista) // 2]
# Create partition function
def partition(A,start,end):
m = middle(A[start:end+1])
medianThree = [ A[start], A[m], A[end] ]
if A[start] == median(medianThree):
pivot_pos = start
elif A[m] == median(medianThree):
tempList = A[start:end+1]
pivot_pos = middle(A[start:end+1])
swap(A,start,pivot_pos+start)
elif A[end] == median(medianThree):
pivot_pos = end
#pivot = A[pivot_pos]
pivot = pivot_pos
# swap(A,start,end) // This line of code is to switch the first and last element pivot
swap(A,pivot,end)
p = A[pivot]
i = pivot + 1
for j in range(pivot+1,end+1):
if A[j] < p:
swap(A,i,j)
i+=1
swap(A,start,i-1)
return i-1
count = 0
#Quick sort algorithm
def quickSort(A,start,end):
global tot_comparisons
if start < end:
# This to create the partition based on the
pivot_pos = partition(A,start,end)
tot_comparisons += len(A[start:pivot_pos-1]) + len(A[pivot_pos+1:end])
# This to sort the the left partition
quickSort(A,start,pivot_pos -1)
#This to sort the right partition
quickSort(A,pivot_pos+1,end)
I have a code block like this:
hash["value"].delete_if{|cell| cell["id"]["name"] == ('foo')}
There is a simple way to count how many times the delete_if has actually acted ?
counter
Since every integer is truthy, you could write :
count = 0
hash["value"].delete_if{|cell| cell["id"]["name"] == ('foo') && count += 1}
cell will be deleted every time count is incremented, and count is incremented every time cell["id"]["name"] == ('foo').
As an example :
count = 0
true && count += 1
false && count += 1
true && count += 1
false && count += 1
true && count += 1
count
# 3
difference
Since delete_if is destructive, you can simply compare the size difference before and after the method call.
original_size = hash["value"].size
hash["value"].delete_if{|cell| cell["id"]["name"] == ('foo') && count += 1}
original_size - hash["value"].size
partition
If you need not only the number of times delete_if has acted, but for which elements, you could use partition :
deleted, hash["value"] = hash["value"].partition{|cell| cell["id"]["name"] == ('foo')}
deleted.size
# deleted contains the deleted cells from `hash['value']`
The code below returns "Arithmetic", "Geometric" if the input array is an arithmetic and geometric series respectively and -1 if it is neither.
Although the code works fine, when I change
if s = arr.length - 1
to
if s == arr.length - 1
in the while loop, the code is not working properly anymore.
I do not understand why. Shouldn't == work instead of =?
def ArithGeo(arr)
# code goes here
len = arr.length
difference = arr[len-1] - arr[len-2]
ratio = arr[len-1]/arr[len-2]
k = 0
s = k + 1
while (arr[s] - arr[k]) == difference && s < arr.length
if s = arr.length - 1
return "Arithmetic"
end
k += 1
end
k = 0
while arr[s] / arr[k] == ratio && s < arr.length
if s = arr.length - 1
return "Geometric"
end
k += 1
end
return -1
end
You're never changing the value of s which I think you want to do. You should do that at the point that you increment k
k += 1
s = k + 1
Also, at the point where you reinitialize k for the geometric test, you want to reset s as well...
k = 0
s = k + 1
You could also get rid of the variable s completely and make it a method... add these three lines at the top of the code
def s(k)
k + 1
end
And remove all the lines where you assign a value to s and use s(k)... s(k) will be a method that always returns the next higher value to k
The difference between those two statements is that variable s is set for the first statement but not for the second. The first if statement has thus a side effect of setting s to arr.length - 1
if s = arr.length - 1 # s => arr.length - 1
if s == arr.length - 1 # s => undefined
Because the if statement is inside a while loop which uses s in its expression the change of the statement changes the behavior of the programm.
If you put == the statement will try to check if they are equals , with just = the statement work properly because your are only setting the value to a value , so this is always true.
If it's different compare something to equals than just set a variable , that can be always true.
I am learning ruby and the way I am going about this is by learning and implementing sort algorithms. While working on selection sort, I tried to modify it as follows:
In every pass, instead of finding the smallest and moving it to the top or beginning of the array, find the smallest and the largest and move them to both ends
For every pass, increment the beginning and decrease the ending positions of the array that has to be looped through
While swapping, if the identified min and max are in positions that get swapped with each other, do the swap once (otherwise, two swaps will be done, 1 for the min and 1 for the max)
This doesn't seem to work in all cases. Am I missing something in the logic? If the logic is correct, I will revisit my implementation but for now I haven't been able to figure out what is wrong.
Please help.
Update: This is my code for the method doing this sort:
def mss(array)
start = 0;
stop = array.length - 1;
num_of_pass = 0
num_of_swap = 0
while (start <= stop) do
num_of_pass += 1
min_val = array[start]
max_val = array[stop]
min_pos = start
max_pos = stop
(start..stop).each do
|i|
if (min_val > array[i])
min_pos = i
min_val = array[i]
end
if (max_val < array[i])
max_pos = i
max_val = array[i]
end
end
if (min_pos > start)
array[start], array[min_pos] = array[min_pos], array[start]
num_of_swap += 1
end
if ((max_pos < stop) && (max_pos != start))
array[stop], array[max_pos] = array[max_pos], array[stop]
num_of_swap += 1
end
start += 1
stop -= 1
end
puts "length of array = #{array.length}"
puts "Number of passes = #{num_of_pass}"
puts "Number of swaps = #{num_of_swap}"
return array
end
The problem can be demonstrated with this input array
7 5 4 2 6
After searching the array the first time, we have
start = 0
stop = 4
min_pos = 3
min_val = 2
max_pos = 0 note: max_pos == start
max_val = 7
The first if statement will swap the 2 and 7, changing the array to
2 5 4 7 6
The second if statement does not move the 7 because max_pos == start. As a result, the 6 stays at the end of the array, which is not what you want.