def get_bernoulli(p, size):
rv = bernoulli(p)
x = np.arange(0, np.minimum(rv.dist.b, 3))
h = plt.vlines(x, 0, rv.pmf(x), lw=2)
prb = bernoulli.cdf(x, p)
h = plt.semilogy(np.abs(x - bernoulli.ppf(prb, p)) + 1e-20)
labels = bernoulli.rvs(p, size=size).tolist()
return labels
def test_bernoulli_draw_list(self, n, mu):
array_c = covid19.intArray(n)
covid19.bernoulli_draw_list(array_c, n, mu)
array_c = c_array_as_python_list(array_c, n)
a = int(np.floor(mu))
array_np = a + bernoulli.ppf(
(np.arange(n) + 1) / (n + 1), mu - a, loc=0)
np.testing.assert_equal(np.sort(array_c), array_np)
def test_bernoulli(self):
fig, ax = plt.subplots(1, 1)
p = 0.3
mean, var, skew, kurt = bernoulli.stats(p, moments='mvsk')
x = np.arange(bernoulli.ppf(0.01, p), bernoulli.ppf(0.99, p))
ax.plot(x, bernoulli.pmf(x, p), 'bo', ms=8, label='bernoulli pmf')
ax.vlines(x, 0, bernoulli.pmf(x, p), colors='b', lw=5, alpha=0.5)
rv = bernoulli(p)
ax.vlines(x,
0,
rv.pmf(x),
colors='k',
linestyles='-',
lw=1,
label='frozen pmf')
ax.legend(loc='best', frameon=False)
self.assertEqual("AxesSubplot(0.125,0.11;0.775x0.77)", str(ax))
from scipy.stats import bernoulli
import matplotlib.pyplot as plt
import numpy as np
fig, ax = plt.subplots(1, 1)
# Calculate a few first moments:
p = 0.3
mean, var, skew, kurt = bernoulli.stats(p, moments='mvsk')
# Display the probability mass function (pmf):
x = np.arange(bernoulli.ppf(0.01, p),
bernoulli.ppf(0.99, p))
ax.plot(x, bernoulli.pmf(x, p), 'bo', ms=8, label='bernoulli pmf')
ax.vlines(x, 0, bernoulli.pmf(x, p), colors='b', lw=5, alpha=0.5)
# Freeze the distribution and display the frozen pmf:
rv = bernoulli(p)
ax.vlines(x, 0, rv.pmf(x), colors='k', linestyles='-', lw=1,
label='frozen pmf')
ax.legend(loc='best', frameon=False)
plt.show()
# Check accuracy of cdf and ppf:
prob = bernoulli.cdf(x, p)
np.allclose(x, bernoulli.ppf(prob, p))
# Generate random numbers:
r = bernoulli.rvs(p, size=1000)
ax.hist(r, normed=True, histtype='stepfilled', alpha=0.2) ax.legend(loc='best', frameon=False) # ============================================= # # ================== BERNOULLI ================ # # ============================================= # fig, ax = plt.subplots(1, 1) p = 0.3 mean, var, skew, kurt = bernoulli.stats(p, moments='mvsk') x = np.arange(bernoulli.ppf(0.01, p), bernoulli.ppf(0.99, p)) ax.plot(x, bernoulli.pmf(x, p), 'bo', ms=8, label='bernoulli pmf') ax.vlines(x, 0, bernoulli.pmf(x, p), colors='b', lw=5, alpha=0.5) rv = bernoulli(p) ax.vlines(x, 0, rv.pmf(x), colors='k', linestyles='-', lw=1, label='frozen pmf') ax.legend(loc='best', frameon=False) # ============================================= # # ================== BINOMIALE ================ # # ============================================= # fig, ax = plt.subplots(1, 1)
# In[1]:
#Import Common Libraries
import numpy as np
from matplotlib import pyplot as plt
plt.style.use('seaborn')
import seaborn as sns
# ### Bernoulli Distribution
# In[2]:
#Bernoulli Distribution
from scipy.stats import bernoulli
p = 0.7
x = np.arange(bernoulli.ppf(0.01, p), bernoulli.ppf(
0.99, p)) #Percent Point Function (inverse of cdf — percentiles)
print("Mean : ", bernoulli.stats(p, moments='m'))
print("Variance : ", bernoulli.stats(p, moments='v'))
print("Prob. Mass Func. : ", bernoulli.pmf(x, p).item())
print("Cum. Density Func.: ", bernoulli.cdf(x, p).item())
fig = plt.figure(figsize=(20, 10))
plt.subplot(221)
plt.plot(x, bernoulli.pmf(x, p), 'ro', ms=8, label='PMF=(1-p)')
plt.plot(1 - x, 1 - bernoulli.pmf(x, p), 'go', ms=8, label='PMF=p')
plt.vlines(x, 0, bernoulli.pmf(x, p), colors='r', lw=5, alpha=0.5)
plt.vlines(1 - x, 0, 1 - bernoulli.pmf(x, p), colors='g', lw=5, alpha=0.5)
plt.xlabel("Sample Space of Bernoulli Distribution", fontsize=14)
plt.ylabel("PMF", fontsize=14)
