def gen_sample_ret(period, size):
alpha = wald.rvs(loc=alpha_mean, scale=alpha_conc)
beta = norm.rvs(loc=beta_mean, scale=beta_var)
scale = np.random.exponential(scale=scale_rate)
scale = scale / period
mean = halfnorm.rvs(loc=.1, scale=.1)
mean = mean / period
try:
ret = norminvgauss.rvs(alpha, beta, mean, scale, size=size)
except Exception as e:
ret = norminvgauss.rvs(alpha, beta, mean, scale, size=size)
return ret
def gen_sample_ret():
alpha = wald.rvs(loc=alpha_mean, scale=alpha_conc)
beta = norm.rvs(loc=beta_mean, scale=beta_var)
scale = np.random.exponential(scale=scale_rate)
mean = halfnorm.rvs(loc=.1, scale=.08)
ret = norminvgauss.rvs(alpha, beta, mean, scale)
return ret
def distributions(size):
n = norminvgauss.rvs(1, 0, size=size)
l = laplace.rvs(size=size, scale=1 / m.sqrt(2), loc=0)
p = poisson.rvs(10, size=size)
c = cauchy.rvs(size=size)
u = uniform.rvs(size=size, loc=-m.sqrt(3), scale=2 * m.sqrt(3))
counted_distributions = [n, l, p, c, u]
return counted_distributions
def normalNumbers():
for size in sizes:
fig, ax = plt.subplots(1, 1)
ax.hist(norminvgauss.rvs(1, 0, size=size), histtype='stepfilled', alpha=0.5, color='blue', density=True)
x = np.linspace(norminvgauss(1, 0).ppf(0.01), norminvgauss(1, 0).ppf(0.99), 100)
ax.plot(x, norminvgauss(1, 0).pdf(x), '-')
ax.set_title('NormalNumbers n = ' + str(size))
ax.set_xlabel('NormalNumbers')
ax.set_ylabel('density')
plt.grid()
plt.show()
return
def Gauss():
for s in size:
den = norminvgauss(1, 0)
hist = norminvgauss.rvs(1, 0, size=s)
fig, ax = plt.subplots(1, 1)
ax.hist(hist, density=True, alpha=0.6)
x = np.linspace(den.ppf(0.01), den.ppf(0.99), 100)
ax.plot(x, den.pdf(x), LINE_TYPE, lw=1.5)
ax.set_xlabel("NORMAL")
ax.set_ylabel("DENSITY")
ax.set_title("SIZE: " + str(s))
plt.grid()
plt.show()
def Hist_Normal():
normal_scale = 1 # Параметры
normal_loc = 0
normal_label = "Normal distribution"
normal_color = "green"
for size in selection_size:
fig, ax = plt.subplots(1, 1)
pdf = norminvgauss(normal_scale, normal_loc)
random_values = norminvgauss.rvs(normal_scale, normal_loc, size=size)
ax.hist(random_values,
density=True,
histtype=HIST_TYPE,
alpha=hist_visibility,
color=normal_color)
Create_plot(ax, normal_label, pdf, size)
def norminvgaussFunc():
a, b = 1, 0
for i in range(len(size)):
n = size[i]
fig, ax = plt.subplots(1, 1)
ax.set_title("Нормальное распределение, n = " + str(n))
x = np.linspace(norminvgauss.ppf(0.01, a, b),
norminvgauss.ppf(0.99, a, b), 100)
ax.plot(x, norminvgauss.pdf(x, a, b), 'b-', lw=5, alpha=0.6)
# rv = norminvgauss(a, b)
# ax.plot(x, rv.pdf(x), 'k-', lw = 2, label = "frozen pdf")
r = norminvgauss.rvs(a, b, size=n)
ax.hist(r, density=True, histtype='stepfilled', alpha=0.2)
plt.show()
def work(self, patient):
# slot = int(self.env.now_step // H.SLOT) # how many slots are there opening
# print('Service Begin',self.env.now_step)
if not patient.isRevisit(): # new patient
# For simulator
service_time = norminvgauss.rvs(7.405201799947, 7.189292406621,
0.07906712903266, 1.7043134066555)
patient.time[self.service_type,
1] = self.env.now_step # service start time
patient.time[
self.service_type,
2] = self.env.now_step + service_time # service end time
patient.time[self.service_type, -1] = self.id # service id
## the check items which the patient need to be served
patient.checklist = self.__check_list().copy()
patient.check_list = patient.checklist.copy()
patient.revisit = True # change state
## predict time to revisit based on the checklist and env
# patient.scheduled_revisit_time = self.scheduled_revisit_time(patient)
## for finding next event
self.finish_time = self.env.now_step + service_time # service end time
# For statistics
# self.realization[slot] = 1 if patient.schedule == True else 3 # record the type of patient at this slot
# print('First Come',patient.schedule, patient.id)
self.env.Save[self.service_type].append(patient)
else: # revisit patient
# For simulator
service_time = np.random.normal(7, 2)
patient.time[-1, 1] = self.env.now_step # service start time
patient.time[
-1, 2] = self.env.now_step + service_time # service end time
patient.time[-1, -1] = self.id # service id
## for finding next event
self.finish_time = self.env.now_step + service_time
# For statistics
self.env.Save[self.service_type].append(patient)
# self.realization[slot] = 2 if patient.schedule == True else 4 # record the type of patient at this slot
# print('Revisit',patient.schedule, patient.id, patient.time[0,2])
# print(slot, self.realization[slot])
return patient
def gauss_distribution(select_size, loc=0, dist=1):
return norminvgauss.rvs(dist, loc, size=select_size)
norminvgauss.pdf(x, a, b),
'r-',
lw=5,
alpha=0.6,
label='norminvgauss pdf')
# Alternatively, the distribution object can be called (as a function)
# to fix the shape, location and scale parameters. This returns a "frozen"
# RV object holding the given parameters fixed.
# Freeze the distribution and display the frozen ``pdf``:
rv = norminvgauss(a, b)
ax.plot(x, rv.pdf(x), 'k-', lw=2, label='frozen pdf')
# Check accuracy of ``cdf`` and ``ppf``:
vals = norminvgauss.ppf([0.001, 0.5, 0.999], a, b)
np.allclose([0.001, 0.5, 0.999], norminvgauss.cdf(vals, a, b))
# True
# Generate random numbers:
r = norminvgauss.rvs(a, b, size=1000)
# And compare the histogram:
ax.hist(r, density=True, histtype='stepfilled', alpha=0.2)
ax.legend(loc='best', frameon=False)
plt.show()
k1['Date'] = [i.date() for i in k1.index]
k1['VIX_Close'] = k1.VIX_Close.shift(1)
k1['VIX_Close_diff'] = ((k1.VIX_Close.shift(1) - k1.VIX_Close.shift(2)) /
k1.VIX_Close.shift(2)) * 100
k1 = k1.dropna()
k1 = feats.feattrans(k1)
k1['close_diff'] = (k1.Close.shift(1).diff()) / k1.Close.shift(2) * 100
#k1['close_diff5'] = ((k1.Close-k1.Close.shift(5))/k1.Close.shift(5))*100
k1['rsi20_diff'] = k1.rsi20.diff()
k1['rsi14_diff'] = k1.rsi14.diff()
k1['stoch20_diff'] = k1.stoch20.diff()
k1['dayret'] = ((k1.Close - k1.Open) / k1.Open) * 100
k1['gap'] = (k1.Open - k1.Close.shift(1)) / k1.Close.shift(1)
k1 = k1[k1.gap > 0]
nig = norminvgauss.fit(k1.dayret)
l = norminvgauss.rvs(nig[0], nig[1], nig[2], nig[3], size=100)
l = k1.dayret
a1 = []
a2 = []
s = 2000
for i in l:
if (i < -2):
r = 2
elif (i > 1):
r = -1
else:
r = -i
s = s * (1 + r / 100)
a2.append(r)
a1.append(s)
plt.plot(a1)
count = 1000
names = ["Normal", "Laplace", "Poisson", "Cauchy", "Uniform"]
characteristics = [[[], [], [], [], []], [[], [], [], [], []],
[[], [], [], [], []]]
mean_characteristics = [[[], [], [], [], []], [[], [], [], [], []],
[[], [], [], [], []]]
mean_characteristics_sq = [[[], [], [], [], []], [[], [], [], [], []],
[[], [], [], [], []]]
functions = [mean, median, z_r, z_q, z_tr]
for i in range(len(sizes)):
size = sizes[i]
for k in range(count + 1):
n = norminvgauss.rvs(1, 0, size=size)
l = laplace.rvs(size=size, scale=1 / m.sqrt(2), loc=0)
p = poisson.rvs(10, size=size)
c = cauchy.rvs(size=size)
u = uniform.rvs(size=size, loc=-m.sqrt(3), scale=2 * m.sqrt(3))
distributions = [n, l, p, c, u]
for num in range(len(distributions)):
d = distributions[num]
for s in range(len(functions)):
if k == 0:
characteristics[i][num].append([])
f = functions[s]
value = f(d, size)
characteristics[i][num][s].append(value)
for num in range(len(distributions)):
for s in range(len(functions)):