def returnDistData(cls, self):
gammaParam = gamma.fit(10**(self.data / 10))
gammaDist = gamma.pdf(self.data, *gammaParam)
rayleighParam = rayleigh.fit(self.data)
rayleighDist = rayleigh.pdf(self.data, *rayleighParam)
normParam = norm.fit(self.data)
normDist = norm.pdf(self.data, *normParam)
logNormParam = lognorm.fit(self.data)
lognormDist = lognorm.pdf(self.data, *logNormParam)
nakagamiParam = nakagami.fit(self.data)
nakagamiDist = nakagami.pdf(self.data, *nakagamiParam)
exponParam = expon.fit(self.data)
exponDist = expon.pdf(self.data, *exponParam)
exponweibParam = exponweib.fit(self.data)
weibDist = exponweib.pdf(self.data, *exponweibParam)
distDF = pd.DataFrame(np.column_stack([
gammaDist, rayleighDist, normDist, lognormDist, nakagamiDist,
exponDist, weibDist
]),
columns=[
'gammaDist', 'rayleighDist', 'normDist',
'lognormDist', 'nakagamiDist', 'exponDist',
'weibDist'
])
self.distDF = distDF
def fit(data, filename):
Pr = 10**(data / 10)
h = np.sqrt(Pr / np.mean(Pr))
paramh = nakagami.fit(h)
x = np.linspace(0, 3, 1000)
fig, ax = plt.subplots(sharey=False)
ax.plot(x, nakagami.pdf(x, nu=paramh[0], loc=paramh[1], scale=paramh[2]))
ax.set_ylabel('Nakagami')
ax2 = ax.twinx()
h.plot(kind='hist', ax=ax2, color='g')
fig.savefig(filename + '_dist' + '.png', bbox_inches='tight', dpi=300)
pass
# Variando a média e plotando os gráficos
#plt.figure(1,[15,5])
#sigma = np.sqrt(vtVar[0]);
#for il in range(0,len(vtMu)):
# mu = vtMu[il]
# plt.subplot(2,2,1)
# plt.plot(x, norm.pdf(x,mu), label='Média = {}'.format(mu))
# plt.subplot(2,2,2)
# plt.plot(x, norm.cdf(x,mu), label='Média = {}'.format(mu))
# Variando a variância e plotando os gráficos
mu = vtMu[0]
for il in range(0, len(vtVar)):
sigma = (vtVar[il])
plt.subplot(2, 1, 1)
plt.plot(x,
nakagami.pdf(x, nu=sigma),
label='$\ni$ = {:01.2f}'.format(sigma))
# plt.xlim([0,10])
plt.subplot(2, 1, 2)
plt.plot(x,
nakagami.cdf(x, nu=sigma),
label='$\ni$ = {:01.2f}'.format(sigma))
# plt.xlim([0,10])
#plt.subplot(2,2,1)
#plt.legend()
#plt.subplot(2,2,2)
#plt.legend()
plt.subplot(2, 1, 1)
plt.legend()
plt.subplot(2, 1, 2)
from scipy.stats import nakagami import matplotlib.pyplot as plt fig, ax = plt.subplots(1, 1) # Calculate a few first moments: nu = 4.97 mean, var, skew, kurt = nakagami.stats(nu, moments='mvsk') # Display the probability density function (``pdf``): x = np.linspace(nakagami.ppf(0.01, nu), nakagami.ppf(0.99, nu), 100) ax.plot(x, nakagami.pdf(x, nu), 'r-', lw=5, alpha=0.6, label='nakagami 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 = nakagami(nu) ax.plot(x, rv.pdf(x), 'k-', lw=2, label='frozen pdf') # Check accuracy of ``cdf`` and ``ppf``: vals = nakagami.ppf([0.001, 0.5, 0.999], nu) np.allclose([0.001, 0.5, 0.999], nakagami.cdf(vals, nu)) # True # Generate random numbers:
def fitDist(self):
n = len(self.data)
# gamma distribution
gammaParam = gamma.fit(self.data)
gammaNumPar = len(gammaParam)
gammaSum = -1 * np.sum(np.log(gamma.pdf(self.data, *gammaParam)))
aicGamma = 2 * gammaNumPar + 2 * gammaSum + (2 * gammaNumPar *
(gammaNumPar + 1) /
(n - gammaNumPar - 1))
# rayleigh distribution
rayleighParam = rayleigh.fit(self.data)
rayleighNumPar = len(rayleighParam)
rayleighSum = -1 * np.sum(
np.log(rayleigh.pdf(self.data, *rayleighParam)))
aicRayleigh = 2 * rayleighNumPar + 2 * rayleighSum + (
2 * rayleighNumPar * (rayleighNumPar + 1) /
(n - rayleighNumPar - 1))
# normal distribution
normParam = norm.fit(self.data)
normNumPar = len(normParam)
normSum = -1 * np.sum(np.log(norm.pdf(self.data, *normParam)))
aicNorm = 2 * normNumPar + 2 * normSum + (2 * normNumPar *
(normNumPar + 1) /
(n - normNumPar - 1))
# LogNormal distribution
logNormParam = lognorm.fit(self.data)
logNormNumPar = len(logNormParam)
logNormSum = -1 * np.sum(np.log(lognorm.pdf(self.data, *logNormParam)))
aicLogNorm = 2 * logNormNumPar + 2 * logNormSum + (
2 * logNormNumPar * (logNormNumPar + 1) / (n - logNormNumPar - 1))
# Nakagami distribution
nakagamiParam = nakagami.fit(self.data)
nakagamiNumPar = len(nakagamiParam)
nakagamiSum = -1 * np.sum(
np.log(nakagami.pdf(self.data, *nakagamiParam)))
aicNakagami = 2 * nakagamiNumPar + 2 * nakagamiSum + (
2 * nakagamiNumPar * (nakagamiNumPar + 1) /
(n - nakagamiNumPar - 1))
# exponential distribution
exponParam = expon.fit(self.data)
exponNumPar = len(exponParam)
exponSum = -1 * np.sum(np.log(expon.pdf(self.data, *exponParam)))
aicExpon = 2 * exponNumPar + 2 * exponSum + (2 * exponNumPar *
(exponNumPar + 1) /
(n - exponNumPar - 1))
# weibul distribution
exponweibParam = exponweib.fit(self.data)
exponweibNumPar = len(exponweibParam)
exponweibSum = -1 * np.sum(
np.log(exponweib.pdf(self.data, *exponweibParam)))
aicExpWeib = 2 * exponweibNumPar + 2 * exponweibSum + (
2 * exponweibNumPar * (exponweibNumPar + 1) /
(n - exponweibNumPar - 1))
return (aicGamma, aicRayleigh, aicNorm, aicLogNorm, aicNakagami,
aicExpon, aicExpWeib)