"Sleeping is an effect of <span>Depressants, but agitation may cause a person to wake up"</span><span />
Answer: On- demand
Explanation:
On demand CRM system are basically hosted by the external vendor in the vendor data center. CRM is basically stand for customer relationship management.
CRM is a strategy that are used for managing the organisation relationship with the customers.
On demand CRM define as software availability for the customers. It basically connected with the idea of software on demand. The software are modify according to the current requirement and demand.
Yeah, I'm into it. It does show a lot of stereotypical views on drag queens, and it goes a little over the top, but honestly? The LGBT community has spent so long acting like the general population, and we're expected to be a sort of cookie cutter outline of the ideal person in order to fit in. We're not really allowed to be silly and have fun, otherwise we just get labeled as a stereotype, which sucks. When you're queer, you get labeled as that before anything else: your interests are seen as a byproduct of your queerness, not as an interest. So Super Drags, is actually a nice sort of change of pace. It's silly, it shows that queer people are human, and it sorta shows that "Yass bih" look on life, which is hilarious imo. Plus hey, Brazilian LGBT show that doesn't spout homophobic propaganda and supports diversity within all aspects of life? I'll support that.
TLDR; There aren't many silly shows out there that have an LGBT cast. Like, it's always supposed to be grim and sad, and all about heartbreak and coming out, yadda yadda yadda. So, it's cool that we've finally got something lighthearted.
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/
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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
