本文整理匯總了Python中fitter.Fitter.summary方法的典型用法代碼示例。如果您正苦於以下問題：Python Fitter.summary方法的具體用法？Python Fitter.summary怎麽用？Python Fitter.summary使用的例子？那麽, 這裏精選的方法代碼示例或許可以為您提供幫助。您也可以進一步了解該方法所在類fitter.Fitter的用法示例。
在下文中一共展示了Fitter.summary方法的4個代碼示例，這些例子默認根據受歡迎程度排序。您可以為喜歡或者感覺有用的代碼點讚，您的評價將有助於係統推薦出更棒的Python代碼示例。
示例1: test_gamma
# 需要導入模塊: from fitter import Fitter [as 別名]
# 或者: from fitter.Fitter import summary [as 別名]
def test_gamma():
from scipy import stats
data = stats.gamma.rvs(2, loc=1.5, scale=2, size=100000)
f = Fitter(data, bins=100)
f.xmin = -10 #should have no effect
f.xmax = 1000000 # no effet
f.xmin=0.1
f.xmax=10
f.distributions = f.distributions[:3:]
f.fit()
f.summary()
f.plot_pdf(names=['anglit', 'alpha'])示例2: test_fitter
# 需要導入模塊: from fitter import Fitter [as 別名]
# 或者: from fitter.Fitter import summary [as 別名]
def test_fitter():
f = Fitter([1,1,1,2,2,2,2,2,3,3,3,3], distributions=['gamma'], xmin=0, xmax=4)
f.fit()
f.summary()
assert f.xmin == 0
assert f.xmax == 4
# reset the range:
f.xmin = None
f.xmax = None
assert f.xmin == 1
assert f.xmax == 3
f = Fitter([1,1,1,2,2,2,2,2,3,3,3,3], distributions=['gamma'])
f.fit()
f.summary()
assert f.xmin == 1
assert f.xmax == 3示例3: test_gamma
# 需要導入模塊: from fitter import Fitter [as 別名]
# 或者: from fitter.Fitter import summary [as 別名]
def test_gamma():
from scipy import stats
data = stats.gamma.rvs(2, loc=1.5, scale=2, size=10000)
f = Fitter(data, bins=100)
f.xmin = -10 #should have no effect
f.xmax = 1000000 # no effet
f.xmin=0.1
f.xmax=10
f.distributions = ['gamma', "alpha"]
f.fit()
df = f.summary()
assert len(df)
f.plot_pdf(names=["gamma"])
f.plot_pdf(names="gamma")
res = f.get_best()
assert "gamma" in res.keys()示例4: print
# 需要導入模塊: from fitter import Fitter [as 別名]
# 或者: from fitter.Fitter import summary [as 別名]
print(header)
for h in header[1:]:
allele_variants[h] = []
for row in rows[1:]:
for i in range(1,len(row)):
allele_variants[header[i]].append(int(float(row[i])))
#print(allele_variants)
output = {}
for av in allele_variants:
allele_variants[av] = np.array(allele_variants[av])
print("Now working with %s" % (av))
f = Fitter(allele_variants[av],bins=max(allele_variants[av])-min(allele_variants[av]))
#f.distributions = f.distributions[0:10] + ['gamma']
f.fit()
#array = f.fitted_pdf[f.get_best().keys()[0]]
#print(f.get_best())
#generate_histogram(av, allele_variants[av], array, f._get_xmin(), f._get_xmax(), )
f.summary(Nbest=1)
output[av] = f.get_best()
print(output[av])
plt.savefig("img/"+av+".png")
print("Finished!")
a,b,c = output[av]['tukeylambda']
broj = tukeylambda.rvs(a,b,c, size=1)
print('random number %d' % int(broj))
exit()
print(output)