def getSamples(self, m=None):
"""
Generates samples from the Arcsine distribution.
:param arcsine self:
An instance of Arcsine class.
:param integer m:
Number of random samples. If not provided, a default of 5e05 is assumed.
"""
if m is not None:
number = m
else:
number = 500000
return arcsine.rvs(size=number)
def getSamples(self, m=None):
"""
Generates samples from the Arcsine distribution.
:param arcsine self:
An instance of Arcsine class.
:param integer m:
Number of random samples. If not provided, a default of 5e05 is assumed.
"""
if m is not None:
number = m
else:
number = 500000
return arcsine.rvs(size=number)
x = np.linspace(arcsine.ppf(0.01),
arcsine.ppf(0.99), 100)
ax.plot(x, arcsine.pdf(x),
'r-', lw=5, alpha=0.6, label='arcsine 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 = arcsine()
ax.plot(x, rv.pdf(x), 'k-', lw=2, label='frozen pdf')
# Check accuracy of ``cdf`` and ``ppf``:
vals = arcsine.ppf([0.001, 0.5, 0.999])
np.allclose([0.001, 0.5, 0.999], arcsine.cdf(vals))
# True
# Generate random numbers:
r = arcsine.rvs(size=1000)
# And compare the histogram:
ax.hist(r, density=True, histtype='stepfilled', alpha=0.2)
ax.legend(loc='best', frameon=False)
plt.show()
def arcsin_rvs(N, a):
x = arcsine.rvs(loc=0, scale=a, size=N)
return x