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Nadya [2.5K]
3 years ago
10

Explain how data structures and algorithms are useful to the use of computer in data management​

Computers and Technology
1 answer:
laiz [17]3 years ago
5 0

Answer:

programmers who are competent  in data  structures and algorithms  can easily perform the tasks related  to data processing ,automated reasoning ,or calculations . data structure and algorithms  is significant  for developers  as it shows their problems solving abilities amongst the prospective employers .

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Dr. Watson has been kidnaped! Sherlock Holmes was contacted by the kidnapper for ransom. Moments later he received a message fro
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please mark me brainlist

Explanation:

This algorithm works for n number of strings in python3

Input:

83217

8213897

683147

Output:

837

from itertools import product

import pdb

import numpy as np

def neigh(index):

N = len(index)

for ri in product((0, -1), repeat=N):

if not all(i == 0 for i in ri):

yield tuple(i + i_rel for i, i_rel in zip(index, ri))

def longestCommonSubSequenceOfN(sqs):

numberOfSequences = len(sqs); # to know number of sequences

lengths = np.array([len(sequence) for sequence in sqs]); # to know length of each sequences placed in # array

incrLengths = lengths + 1; # here we are taking no .of sequences +1

lengths = tuple(lengths); # making lengths into tuple to make it mutable

inverseDistances = np.zeros(incrLengths);

ranges = [tuple(range(1, length+1)) for length in lengths[::-1]]; # finding ranges from 1 to each lengths

for tupleIndex in product(*ranges):

tupleIndex = tupleIndex[::-1];

neighborIndexes = list(neigh(tupleIndex)); # finding neighbours for each tupled index value and # store them in list

operationsWithMisMatch = np.array([]); # creating array which are miss matched

 

for neighborIndex in neighborIndexes:

operationsWithMisMatch = np.append(operationsWithMisMatch, inverseDistances[neighborIndex]);

#appending newly created array with operations miss match and inverseDistances

operationsWithMatch = np.copy(operationsWithMisMatch);

# copying newly generated missmatch indexs

operationsWithMatch[-1] = operationsWithMatch[-1] + 1;

# incrementing last indexed value

chars = [sqs[i][neighborIndexes[-1][i]] for i in range(numberOfSequences)];

# finding a string(chars) with neighbour indexes and checking with other sequences

if(all(elem == chars[0] for elem in chars)):

inverseDistances[tupleIndex] = max(operationsWithMatch);

else:

inverseDistances[tupleIndex] = max(operationsWithMisMatch);

 

subString = ""; # resulted string

mainTupleIndex = lengths; # copying lengths list to mainTupleIndex

while(all(ind > 0 for ind in mainTupleIndex)):

neighborsIndexes = list(neigh(mainTupleIndex));

#generating neighbour indexes with main tuple index in form of list

anyOperation = False;

for tupleIndex in neighborsIndexes:

current = inverseDistances[mainTupleIndex];

if(current == inverseDistances[tupleIndex]): # comparing indexes in main tuple index and inverse #distance tuple index

mainTupleIndex = tupleIndex;

anyOperation = True;

break;

if(not anyOperation): # if anyoperation is False then we are generating sunString

subString += str(sqs[0][mainTupleIndex[0] - 1]);

mainTupleIndex = neighborsIndexes[-1];

return subString[::-1]; # reversing resulted string

sequences = ["83217", "8213897", "683147"]

print(longestCommonSubSequenceOfN(sequences)); #837

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