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Andrews [41]
3 years ago
8

Curriculum Exam

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

Answer:

4,6

Explanation:

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It identifies and removes viruses in computers
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Write a program to print "I love to program" to the screen 2 times. Edhesive​
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It matters what programming language you are using. It would be something like this:

print("I love to program");

print("I love to program");

(written in modified javascript)

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A company is looking for an employee to install new computers and integrate them into its existing network. Which computer field
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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
adelina 88 [10]

Answer:

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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3 years ago
A personal directory of e-mail addresses stored and maintained with one’s e-mail program
iris [78.8K]

Answer:

It’s a address book

8 0
4 years ago
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