def uniform_demand_distribution(C, t, low=0, high=25):
n = int(C.shape[0])
U = np.linalg.cholesky(C)
raw_demand = np.random.normal(size=(t, n))
shifted_demand = np.dot(raw_demand, U.T)
flat_demand = flatten_matrix(shifted_demand)
true_std = np.std(flat_demand)
true_mean = np.mean(flat_demand)
normalized_demand = (shifted_demand - true_mean) / true_std
new_demand = randint.ppf(norm.cdf(normalized_demand), low, high)
return new_demand.T
def generate_graph_data(self):
ageGroup = self.tableModel.data[self.selected_item_index.row()][0]
parameter = self.tableModel.data[self.selected_item_index.row()][1]
p1 = self.temporaryParametersDict[ageGroup][parameter]["p1"]
p2 = self.temporaryParametersDict[ageGroup][parameter]["p2"]
distributionType = self.temporaryParametersDict[ageGroup][parameter][
"distributionType"]
xyDict = {"x": [], "y": []}
try:
if distributionType == 'Binomial':
xyDict["x"] = np.arange(binom.ppf(0.01, int(p1), p2 / 100),
binom.ppf(0.99, int(p1), p2 / 100))
xyDict["y"] = binom.pmf(xyDict["x"], int(p1), p2 / 100)
elif distributionType == 'Geometric':
xyDict["x"] = np.arange(geom.ppf(0.01, p1 / 100),
geom.ppf(0.99, p1 / 100))
xyDict["y"] = geom.pmf(xyDict["x"], p1 / 100)
if p2 != 0:
self.tableModel.setData(
self.selected_item_index.sibling(
self.selected_item_index.row(), 3), 0, Qt.EditRole)
elif distributionType == 'Laplacian':
xyDict["x"] = np.arange(dlaplace.ppf(0.01, p1 / 100),
dlaplace.ppf(0.99, p1 / 100))
xyDict["y"] = dlaplace.pmf(xyDict["x"], p1 / 100)
if p2 != 0:
self.tableModel.setData(
self.selected_item_index.sibling(
self.selected_item_index.row(), 3), 0, Qt.EditRole)
elif distributionType == 'Logarithmic':
xyDict["x"] = np.arange(logser.ppf(0.01, p1 / 100),
logser.ppf(0.99, p1 / 100))
xyDict["y"] = logser.pmf(xyDict["x"], p1 / 100)
if p2 != 0:
self.tableModel.setData(
self.selected_item_index.sibling(
self.selected_item_index.row(), 3), 0, Qt.EditRole)
elif distributionType == 'Neg. binomial':
xyDict["x"] = np.arange(nbinom.ppf(0.01, p1, p2 / 100),
nbinom.ppf(0.99, p1, p2 / 100))
xyDict["y"] = nbinom.pmf(xyDict["x"], p1, p2 / 100)
elif distributionType == 'Planck':
xyDict["x"] = np.arange(planck.ppf(0.01, p1 / 100),
planck.ppf(0.99, p1 / 100))
xyDict["y"] = planck.pmf(xyDict["x"], p1 / 100)
if p2 != 0:
self.tableModel.setData(
self.selected_item_index.sibling(
self.selected_item_index.row(), 3), 0, Qt.EditRole)
elif distributionType == 'Poisson':
xyDict["x"] = np.arange(poisson.ppf(0.01, p1),
poisson.ppf(0.99, p1))
xyDict["y"] = poisson.pmf(xyDict["x"], p1)
if p2 != 0:
self.tableModel.setData(
self.selected_item_index.sibling(
self.selected_item_index.row(), 3), 0, Qt.EditRole)
elif distributionType == 'Uniform':
if p1 - 0.5 * p2 < 0:
p2 = p1
min = p1 - 0.5 * p2
max = p1 + 0.5 * p2
xyDict["x"] = np.arange(randint.ppf(0.01, min, max),
randint.ppf(0.99, min, max))
xyDict["y"] = randint.pmf(xyDict["x"], min, max)
elif distributionType == 'Zipf (Zeta)':
xyDict["x"] = np.arange(zipf.ppf(0.01, p1), zipf.ppf(0.99, p1))
xyDict["y"] = zipf.pmf(xyDict["x"], p1)
if p2 != 0:
self.tableModel.setData(
self.selected_item_index.sibling(
self.selected_item_index.row(), 3), 0, Qt.EditRole)
self.update_graph(xyDict)
except Exception as E:
log.error(E)
ax.plot([], [], "g", label=str_esp_var_emp) # Histogramme ax.hist(r, normed=True, histtype='stepfilled', alpha=0.2) ax.legend(loc='best', frameon=False) # ============================================= # # ============= UNIFORME DISCRETE ============= # # ============================================= # fig, ax = plt.subplots(1, 1) low, high = 7, 31 mean, var, skew, kurt = randint.stats(low, high, moments='mvsk') x = np.arange(randint.ppf(0.01, low, high), randint.ppf(0.99, low, high)) ax.plot(x, randint.pmf(x, low, high), 'bo', ms=8, label='randint pmf') ax.vlines(x, 0, randint.pmf(x, low, high), colors='b', lw=5, alpha=0.5) rv = randint(low, high) ax.vlines(x, 0, rv.pmf(x), colors='k', linestyles='-', lw=1, label='frozen pmf') ax.legend(loc='best', frameon=False) # ============================================= # # ================== NORMALE ================== # # ============================================= # fig, ax = plt.subplots(1, 1) mean, var, skew, kurt = norm.stats(moments='mvsk')
from scipy.stats import randint
import matplotlib.pyplot as plt
fig, ax = plt.subplots(1, 1)
# Calculate a few first moments:
low, high = 7, 31
mean, var, skew, kurt = randint.stats(low, high, moments='mvsk')
# Display the probability mass function (``pmf``):
x = np.arange(randint.ppf(0.01, low, high), randint.ppf(0.99, low, high))
ax.plot(x, randint.pmf(x, low, high), 'bo', ms=8, label='randint pmf')
ax.vlines(x, 0, randint.pmf(x, low, high), colors='b', lw=5, alpha=0.5)
# Alternatively, the distribution object can be called (as a function)
# to fix the shape and location. This returns a "frozen" RV object holding
# the given parameters fixed.
# Freeze the distribution and display the frozen ``pmf``:
rv = randint(low, high)
ax.vlines(x,
0,
rv.pmf(x),
colors='k',
linestyles='-',
lw=1,
label='frozen pmf')
ax.legend(loc='best', frameon=False)
plt.show()
def ppf(self, dist, p):
return randint.ppf(p, *self._get_params(dist))
fontsize=16)
plt.xticks(np.arange(-1, 20, 1))
plt.yticks(np.arange(0, 1.1, 0.1))
plt.legend(loc='upper left', shadow=True)
plt.show()
# ### Uniform (Discrete) Distribution
# In[6]:
#Uniform (Discrete) Distribution
from scipy.stats import randint
low, high = 1, 10
x = np.arange(randint.ppf(0.01, low, high), randint.ppf(
0.99, low, high)) #Percent Point Function (inverse of cdf — percentiles)
print("Mean : ", randint.stats(low, high, moments='m'))
print("Variance : ", randint.stats(low, high, moments='v'))
print("Prob. Mass Func. : ", randint.pmf(x, low, high))
print("Cum. Density Func.: ", randint.cdf(x, low, high))
CDF = randint.cdf(x, low, high)
fig = plt.figure(figsize=(20, 10))
plt.subplot(221)
plt.plot(x, randint.pmf(x, low, high), 'go', ms=8, label='PMF')
plt.vlines(x, 0, randint.pmf(x, low, high), colors='g', lw=5, alpha=0.5)
plt.xlabel("Sample Space of Discrete Uniform Distribution", fontsize=14)
plt.ylabel("PMF", fontsize=14)
#!/usr/bin/env python # -*- coding: utf-8 -*- """ lois de distribution uniforme discrète """ __author__ = "Dominique Lefebvre" __copyright__ = "Copyright 2019 - TangenteX.com" __version__ = "1.0" __date__ = "20 septembre 2019" __email__ = "*****@*****.**" from scipy.stats import randint from scipy import arange from matplotlib.pylab import figure, show, plot, grid # loi uniform - variable aléatoire discrète a = 1 b = 1 x = arange(randint.ppf(1.0,a,b) fig1 = figure(figsize=(14,8)) ax = fig1.add_subplot(111,autoscale_on=False, xlim=(0,1), ylim=(0,1)) grid(True) plot(x) show()