Answer: Triangle
Explanation: Triangle has Tri
Answer:
- def getCharacterForward(char, key):
- charList = "ABCDEFGHIJKLMNOPQRSTUVWXYZ"
-
- if(len(char) > 1):
- return None
- elif(not isinstance(key, int)):
- return -1
- else:
- index = charList.find(char)
- if(index + key <= 25):
- return charList[index + key]
- else:
- return charList[(index + key)% 26]
-
- print(getCharacterForward("C", 4))
- print(getCharacterForward("X", 4))
Explanation:
Firstly, define a charList that includes all uppercase alphabets (Line 2). We presume this program will only handle uppercase characters.
Follow the question requirement and define necessary input validation such as checking if the char is a single character (Line 4). We can do the validation by checking if the length of the char is more than 1, if so, this is not a single character and should return None (Line 5). Next, validate the key by using isinstance function to see if this is an integer. If this is not an integer return -1 (Line 6 - 7).
Otherwise, the program will proceed to find the index of char in the charList using find method (Line 9). Next, we can add the key to index and use the result value to get forwarded character from the charList and return it as output (Line 11).
However, we need to deal a situation that the char is found at close end of the charList and the forward key steps will be out of range of alphabet list. For example the char is X and the key is 4, the four steps forward will result in out of range error. To handle this situation, we can move the last two forward steps from the starting point of the charList. So X move forward 4 will become B. We can implement this logic by having index + key modulus by 26 (Line 13).
We can test the function will passing two sample set of arguments (Line 15 - 16) and we shall get the output as follows:
G
B
The two different uses or applications of data that is biases in word embeddings and may cause significant ethical harms are:
- Class immobility
- Systemic racism
<h3>What are the
biases in word embedding?</h3>
Word embeddings is known to be made up of a high level bias such as group stereotypes and prejudice.
The two different uses or applications of data that is biases in word embeddings and may cause significant ethical harms are:
Class immobility
Learn more about biases from
brainly.com/question/24491228
#SPJ1
Answer:
Explanation:
The following code is written in Python and is a function that loops three times asking for the last name and first name. Then it uses this information to create a username. Finally, each of the names and usernames is printed on the screen.
def userName():
for x in range(3):
last_name = input("What is your last name: ")
first_name = input("What is your first name: ")
username = first_name[:3] + "#" + last_name[-3:]
print(last_name + ", " + first_name)
print(username)
