def __init__(self):
self.hoursOpen = D.HOURS_OPEN
self.nExamRooms = D.N_EXAM_ROOMS
self.arrivalTimeDist = RVGs.Exponential(scale=D.MEAN_ARRIVAL_TIME)
self.examTimeDist = RVGs.Exponential(scale=D.MEAN_EXAM_DURATION)
self.probDepression = D.PROB_DEPRESSION
self.mentalHealthConsultDist = RVGs.Exponential(
scale=D.MEAN_MH_CONSULT)
def test_exponential(rnd, scale, loc=0):
# exponential random variate generator
exp_dist = RVGs.Exponential(scale, loc)
# obtain samples
samples = get_samples(exp_dist, rnd)
# report mean and variance
print_test_results('Exponential',
samples,
expectation=scale + loc,
variance=scale**2)
def __init__(self, rows, list_x_min, list_x_max, list_x_delta):
"""
:param rows: (list of list) the table above
:param list_x_min: list of minimum value of x (in example above: [0, 0])
:param list_x_max: list of maximum value of x (in example above: [10, 1])
:param list_x_delta: list of interval between break points of x
if set to 'int', x is treated as categorical variable
(in example above: [5, 'int'])
"""
DataFrameOfObjects.__init__(self,
list_x_min=list_x_min,
list_x_max=list_x_max,
list_x_delta=list_x_delta)
for row in rows:
self.set_obj(x_value=row[0:-1],
obj=RVGs.Exponential(scale=1 / row[-1]))
def __init__(self, y_values, x_min=0, x_max=1, x_delta=1):
"""
:param y_values: (list) of rates (in example above: [0.1, 0.2, 0.3]
:param x_min: minimum value of x (in example above: 0)
:param x_max: maximum value of x (in example above: 10)
:param x_delta: interval between break points of x (in example above: 5)
if set to 'int', x is treated as categorical variable
"""
# create the list of exponential distributions
y_objects = []
for v in y_values:
if v <= 0:
raise ValueError(
'All y_values (rates of exponential distributions) should be greater than 0.'
)
y_objects.append(RVGs.Exponential(scale=1 / v))
OneDimDataFrame.__init__(self,
y_objects=y_objects,
x_min=x_min,
x_max=x_max,
x_delta=x_delta)
def test_fitting_exponential():
print("\nTesting Exponential with scale=0.5, loc=2")
dist = RVGs.Exponential(scale=0.5, loc=2)
print(' percentile interval: ', dist.get_percentile_interval(alpha=0.05))
data = np.array(get_samples(dist, np.random))
# method of moment
dict_mm_results = RVGs.Exponential.fit_mm(mean=np.average(data),
fixed_location=2)
# maximum likelihood
dict_ml_results = RVGs.Exponential.fit_ml(data=data, fixed_location=2)
print(" Fit:")
print(" MM:", dict_mm_results)
print(" ML:", dict_ml_results)
# plot the fitted distributions
Plot.plot_exponential_fit(data=data,
fit_results=dict_mm_results,
title='Method of Moment')
Plot.plot_exponential_fit(data=data,
fit_results=dict_ml_results,
title='Maximum Likelihood')
