Answer:
Function sparse code is
def sparse(a):
rl = []
cl = []
for i in range(0,len(a)):
rl.append(a[i][1])
cl.append(a[i][2])
r = max(rl)
c = max(cl)
s = []
k = 0
for i in range(0,r+1):
s.append([])
for j in range(0,c+1):
if (i==a[k][1]) & (j == a[k][2]):
s[i].append(a[k][0])
k = k+1
else:
s[i].append(0)
return s
def main():
k = sparse([[3,1,2],[4,5,3]])
print(k)
if __name__ == '__main__':
main()
Explanation:
Please see attachment for output
Answer:
See the attached pictures.
Explanation:
See the attached pictures for explanation.
Answer: Point-to-point topology
Explanation:Point-to-point topology is the connection between the nodes in a simple/regular manner by establishing only one path for the communication with each other.The flow that occurs in this type of topology are of two types - bidirectional(two-way) and unidirectional(one-way) .
Thus the topology for the communication of the routers through a single communication path is done with the help of point-to-point pattern , considering each router as a point/node.
Answer:
c. Using the Data Refinery tool
Explanation:
Data wrangling and tidying in Data Science is the process whereby data to be analysed is obtained, cleaned and arranged before it is analysed in the environment.
Since Watson Studio happens to be an IBM premier integrated development environment for data science and artificial intelligence practitioners, there is need for them to have data softwares to make data scientists practitioners' works easier.
<em>In this scenario, the best tools to aid in tidying data in the Watson studio would be the use of </em><u><em>Data Refinery Tool.</em></u>
I think it'd be terminal concentrator. A(n) terminal concentrator is a front - end processor that multiplexes the traffic from hundreds of remote terminals into one port on a large computer.
