My life career in computer science
Answer:
enables you to make changes to one part of an image without accidentally changing other parts
Explanation:
Computer aided designs incorporate the use of multiple layers in drawings. The first layer is known as the layer 0, while the present layer the designer is working on is known as the current layer. The advantage of the incorporation of layers in designs include the following
1. It helps objects to be altered, grouped, hidden and moved as the designer wishes.
2. Layers can be grouped and worked on separated and common properties like color and line weight assigned to them.
3. Layers can be manipulated as the user wishes. They can be locked, frozen, turned off, etc. Locking prevents accidental changes being made on objects.
Answer:
harris_poll_ranking = int(input("Enter team's Harris Poll ranking [1 - 2,850]: "))
coaches_poll_ranking = int(input("Enter team's Coaches Poll ranking [1 - 1,475]: "))
computer_ranking = float(input("Enter team's computer ranking [0 - 1]: "))
harris_poll_score = harris_poll_ranking / 2850
coaches_poll_score = coaches_poll_ranking / 1475
bcs_score = harris_poll_score / 3 + coaches_poll_score / 3 + computer_ranking / 3
print(bcs_score)
Explanation:
*The code is in Python.
Ask the user to enter the harris_poll_ranking as int, coaches_poll_ranking as int and computer_ranking as float
Calculate the harris_poll_score, divide the harris_poll_ranking by 2850
Calculate the coaches_poll_score, divide the coaches_poll_ranking by 1475
Calculate the bcs_score, harris_poll_score, coaches_poll_score and computer_ranking by 3 and sum them
Print the bcs_score
Implement the simulation of a biased 6-sided die which takes the values 1,2,3,4,5,6 with probabilities 1/8,1/12,1/8,1/12,1/12,1/
hjlf
Answer:
see explaination
Explanation:
import numpy as np
import matplotlib.pyplot as plt
a = [1, 2, 3, 4, 5, 6]
prob = [1.0/8.0, 1.0/12.0, 1.0/8.0, 1.0/12.0, 1.0/12.0, 1.0/2.0]
smls = 1000000
rolls = list(np.random.choice(a, smls, p=prob))
counts = [rolls.count(i) for i in a]
prob_exper = [float(counts[i])/1000000.0 for i in range(6)]
print("\nProbabilities from experiment : \n\n", prob_exper, end = "\n\n")
plt.hist(rolls)
plt.title("Histogram with counts")
plt.show()
check attachment output and histogram
