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True [87]
4 years ago
11

What was Leonardo da Vinci an expert in

Computers and Technology
1 answer:
Ipatiy [6.2K]4 years ago
5 0

Leonardo da Vinci was a very famous painter. I would assume that he is an expert in painting.

Hope this helped

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Human services organizations seek to make changes and help people in need to improve their .
bixtya [17]

Answer:

quality of life

Explanation:

7 0
3 years ago
HI! THIS IS THE PERSON FROM EARLIER WHO HAD PROBLEMS WITH THEIR PASSWORD
photoshop1234 [79]

Answer:

I dont know anything abt that

Explanation:

7 0
3 years ago
How many bits per pixel does a black and white image require?
allochka39001 [22]
Depends on the size of the image

Hope this is helpful
8 0
3 years ago
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

8 0
3 years ago
A certain social media Web site allows users to post messages and to comment on other messages that have been posted. When a use
vagabundo [1.1K]

D) To determine which posts from a particular user have received the greatest number of comments

<u>Explanation:</u>

Since social media excepting comments from end user website or social media should keep track of end user information such as name with email address and phone extra.

If we do so we can end user information in cans, end user post, wrong post information can be blocked or given end user information for further analysis.  Moreover web site can receive positive or negative posts.

For me all four will be my answer. If we don’t track any information about end user it is against to law.

4 0
3 years ago
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