https://github.com/carusel02/sequential-data-processing-and-analysis
Sequential data processing and analysis using linked-list in C
https://github.com/carusel02/sequential-data-processing-and-analysis
data-analysis data-processing linked-list
Last synced: 6 months ago
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Sequential data processing and analysis using linked-list in C
- Host: GitHub
- URL: https://github.com/carusel02/sequential-data-processing-and-analysis
- Owner: Carusel02
- Created: 2022-04-08T16:19:51.000Z (over 3 years ago)
- Default Branch: master
- Last Pushed: 2022-10-17T20:29:02.000Z (almost 3 years ago)
- Last Synced: 2025-02-09T09:15:06.839Z (8 months ago)
- Topics: data-analysis, data-processing, linked-list
- Language: C
- Homepage:
- Size: 777 KB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Sequential data processing and analysis
## Solution method
`main functions`
- as main functions I used adding to the list, deleting
to a node in the list, copying a list to another list, displaying
of a list, freeing the nodes of a list
`task1` (e1)
- the main idea underlying the requirement was the use
a "flag" to mark the nodes to be deleted.
- thus, the list will be scanned once more and they will be deleted
the nodes that have an active flag(1).(could also be used
and changing the timestamp with a negative value for a
highlight this procedure)
- also, I implemented 2 functions that calculate the deviation and
mediate.
`task2` (e2)
- for task2, a simple way to solve it would be to add the window of
5 nodes in an auxiliary list, then sorting it.
- the value in the middle is then added, and for chaining
orders, I had to give free to the previous list and it
update with the new list formed from these extracted values.
`task3` (e3)
- similar to task2, it needed a function thatcalculate the average on a window of 5 nodes and add it to a the new list.
- at the end, the old list is updated.
`task4` (u)
- the equalization of the frequency consisted of a simple operation which
go through the list and modify the nodes that meet the requirements.
- using a smoothing function, I modified the list accordingly
requirement.
`task5` (c)
- completing an interval consists of adding elements after
a knot.
- the timestamp of the node changes by +200 to the last value
and the date of each node is influenced by a parameter C.
- the left and right sums are weighted averages (constant numbers
for an interval in which nodes are added) that take into account
the first 3 terms and the last 3 terms from where the addition is made.
- they are multiplied by some coefficients, the most important (large)
coefficients being multiplied by the terms closest to
the interval in which it is added.
- for this task, I used a single function that calculates
each information of a node and adds it after a node through a
auxiliary function.
`task6` (st)
- the part with the extract from the argument was made using operations with
the ASCII code, then this number was passed as a parameter
in a function that sorts and then counts how many nodes the data has
belonging to the interval.
## Implementation
- to implement the code, I used VScode on Linux
to be able to use the utility valgrind (for memory leaks) and gdb
(for segmentation faults).
- *the hardest part wasn't the requirement
and writing the code, but the debugging part xD*