Answer:
Following are the program in the Python Programming Language.
#define function
def Transfer(S, T):
#set for loop
for i in range(len(S)):
#append in the list
T.append(S.pop())
#return the value of the list
return T
#set list type variable
S = ["a","b","c","d"]
#print the values of the list
print(S)
#set the list empty type variable
T=[]
#call the function
T = Transfer(S, T)
#print the value of T
print(T)
<u>Output:</u>
['a', 'b', 'c', 'd']
['d', 'c', 'b', 'a']
Explanation:
Here, we define the function "Transfer()" in which we pass two list type arguments "S" and "T".
- Set the for loop to append the values in the list.
- Then, we append the value of the variable "S" in the variable "T".
- Return the value of the list variable "T" and close the function.
- Then, set the list data type variable "S" and initialize the elements in it and print that variable.
- Finally, we set the empty list type variable "T" and store the return value of the function "Transfer()" in the variable "T" then, print the value of the variable "T".
Answer:
def typeHistogram(it,n):
d = dict()
for i in it:
n -=1
if n>=0:
if str(type(i).__name__) not in d.keys():
d.setdefault(type(i).__name__,1)
else:
d[str(type(i).__name__)] += 1
else:
break
return list(d.items())
it = iter([1,2,'a','b','c',4,5])
print(typeHistogram(it,7))
Explanation:
- Create a typeHistogram function that has 2 parameters namely "it" and "n" where "it" is an iterator used to represent a sequence of values of different types while "n" is the total number of elements in the sequence.
- Initialize an empty dictionary and loop through the iterator "it".
- Check if n is greater than 0 and current string is not present in the dictionary, then set default type as 1 otherwise increment by 1.
- At the end return the list of items.
- Finally initialize the iterator and display the histogram by calling the typeHistogram.
Answer:
A neuromorphic computer is a machine comprising many simple processors / memory structures (e.g. neurons and synapses) communicating using simple messages (e.g. spikes). ... Neuromorphic computing systems excel at computing complex dynamics using a small set of computational primitives (neurons, synapses, spikes).
Explanation:
The structure of neuromorphic computers makes them much more efficient at training and running neural networks. They can run AI models at a faster speed than equivalent CPUs and GPUs while consuming less power. This is important since power consumption is already one of AI's essential challenges.
