def generate_gamma_rvs(shape):
variance = stats.gamma(shape).var() #get in-built variance for gamma
sigma = sqrt(variance)
margin_error = 0.1
sample_size = gof.get_sample_size(sigma, '99%', margin_error)
rvs = stats.gamma(shape).rvs(size=sample_size)
return rvs
def generate_lcg_rvs(): sigma = 1/float(12) margin_error = 0.01 #chosing 0.01 for mu 0f 0.5 sample_size = gof.get_sample_size(sigma, '99%', margin_error) seed = 345 # just some arbitrary value lcg_data = lcg.generate_univariate_data(seed, sample_size) return lcg_data
def generate_lcg_rvs():
sigma = 1 / float(12)
margin_error = 0.01 #chosing 0.01 for mu 0f 0.5
sample_size = gof.get_sample_size(sigma, '99%', margin_error)
seed = 345 # just some arbitrary value
lcg_data = lcg.generate_univariate_data(seed, sample_size)
return lcg_data
def generate_muller_rvs(variance):
sigma = sqrt(variance)
margin_error = 0.02 #for normal distribution
sample_size = gof.get_sample_size(sigma, '99%', margin_error)
rvs = boxmuller.generate_univariate_data(sample_size, variance)
return rvs
def generate_random_rvs():
sigma = 1 / float(12) #standard deviation ((b-a)^2)/12
margin_error = 0.01 #for mean of 0.5
sample_size = gof.get_sample_size(sigma, '99%', margin_error)
rvs = empty([sample_size, 1])
return [random() for rvs in rvs]
def generate_muller_rvs(variance): sigma = sqrt(variance) margin_error = 0.02 #for normal distribution sample_size = gof.get_sample_size(sigma, '99%', margin_error) rvs = boxmuller.generate_univariate_data(sample_size, variance) return rvs
def generate_random_rvs(): sigma = 1/float(12) #standard deviation ((b-a)^2)/12 margin_error = 0.01 #for mean of 0.5 sample_size = gof.get_sample_size(sigma, '99%', margin_error) rvs = empty([sample_size, 1]) return [random() for rvs in rvs]
