def __init__(self, prob=.5, size=20):
self.n = size
self.p = prob
Distribution.__init__(self, self.calculate_mean(),
self.calculate_stdev())
def __init__(self, prob=.5, size=20):
# TODO: store the probability of the distribution in an instance variable p
# TODO: store the size of the distribution in an instance variable n
# TODO: Now that you know p and n, you can calculate the mean and standard deviation
# Use the calculate_mean() and calculate_stdev() methods to calculate the
# distribution mean and standard deviation
#
# Then use the init function from the Distribution class to initialize the
# mean and the standard deviation of the distribution
#
# Hint: You need to define the calculate_mean() and calculate_stdev() methods
# farther down in the code starting in line 55.
# The init function can get access to these methods via the self
# variable.
self.p = prob
self.n = size
mu = self.calculate_mean()
sigma = self.calculate_stdev()
Distribution.__init__(self, mu, sigma)
def __init__(self, mu=0, sigma=1):
Distribution.__init__(self, mu, sigma)
def __init__(self, p=0.5, n=20, mu=0, sigma=1):
Distribution.__init__(self, mu, sigma)
self.data = []
self.p = p
self.n = n
"""Function to calculate the mean from p and n
def __init__(self, mu=0, sigma=1):
print("Inheriting from Generaldistribution class")
Distribution.__init__(self, mu, sigma)
def __init__(self, mu=0, sigma=1):
#super(Gaussian,self).__init__()
Distribution.__init__(self, mu, sigma) #no need to defaut again
def __init__(self, prob):
self.p = prob
Distribution.__init__(self, self.calculate_mean(),
self.calculate_stdev())
def __init__(self, prob = 0.5, size = 20):
Distribution.__init__(self, self.calculate_mean(), self.calculate_stdev)
self.p = prob
self.n = size
