def CredIntPlt(sf, kl, kh, ll, lh, house, mk, ml, Title):
"""Given 90 credible values of k and lambda, the mean values of k and lambda,
the survival function, the house color scheme to use, and the plot title, this
function plots the 90 percent credible interval, the best line, and the data
we have"""
listcol = colordict[house]
Dark = listcol[0]
Mid = listcol[1]
Light = listcol[2]
arr = np.linspace(0, 7, num=100)
weibSurv2 = exponweib.cdf(arr, kl, lh) #Lower bound
weibSurv4 = exponweib.cdf(arr, kh, ll) #Upper bound
weibSurv1 = exponweib.cdf(arr, mk, ml) #Best line
p4, = plt.plot(arr, 1 - weibSurv2, color=Dark, linewidth=3)
p1, = plt.plot(arr, 1 - weibSurv2, color=Light, linewidth=4)
p2, = plt.plot(arr, 1 - weibSurv1, color=Mid, linewidth=3, linestyle='--')
p3, = plt.plot(arr, 1 - weibSurv4, color=Light, linewidth=4)
plt.fill_between(arr,
1 - weibSurv2,
1 - weibSurv4,
facecolor=Light,
alpha=.3)
thinkplot.plot(sf, color=Dark)
plt.xlabel('Age in Books')
plt.ylabel('Probability of Survival')
plt.ylim([0, 1])
plt.legend([p1, p2, p4],
['90 Percent Credible Interval', 'Best Estimate', 'Data'])
plt.title(Title)
def CredIntPlt(sf,kl,kh,ll,lh,house,mk,ml,Title):
"""Given 90 credible values of k and lambda, the mean values of k and lambda,
the survival function, the house color scheme to use, and the plot title, this
function plots the 90 percent credible interval, the best line, and the data
we have"""
listcol=colordict[house]
Dark=listcol[0]
Mid=listcol[1]
Light=listcol[2]
arr=np.linspace(0,7,num=100)
weibSurv2 = exponweib.cdf(arr, kl, lh) #Lower bound
weibSurv4 = exponweib.cdf(arr, kh, ll) #Upper bound
weibSurv1 = exponweib.cdf(arr, mk, ml) #Best line
p4,=plt.plot(arr, 1-weibSurv2,color=Dark,linewidth=3)
p1,=plt.plot(arr, 1-weibSurv2,color=Light,linewidth=4)
p2,=plt.plot(arr, 1-weibSurv1,color=Mid,linewidth=3,linestyle='--')
p3,=plt.plot(arr, 1-weibSurv4,color=Light,linewidth=4)
plt.fill_between(arr,1-weibSurv2,1-weibSurv4, facecolor=Light, alpha=.3)
thinkplot.plot(sf,color=Dark)
plt.xlabel('Age in Books')
plt.ylabel('Probability of Survival')
plt.ylim([0,1])
plt.legend([p1,p2,p4],['90 Percent Credible Interval','Best Estimate','Data'])
plt.title(Title)
def CredIntPlt(sf, kl, kh, ll, lh, house, mk, ml, Title):
listcol = colordict[house]
Dark = listcol[0]
Mid = listcol[1]
Light = listcol[2]
arr = np.linspace(0, 7, num=100)
weibSurv2 = exponweib.cdf(arr, kl, lh)
weibSurv4 = exponweib.cdf(arr, kh, ll)
weibSurv1 = exponweib.cdf(arr, mk, ml)
# p4,=plt.plot(arr, 1-weibSurv2,color=Dark,linewidth=3)
p1, = plt.plot(arr, 1 - weibSurv2, color=Light, linewidth=4)
# p2,=plt.plot(arr, 1-weibSurv1,color=Mid,linewidth=3,linestyle='--')
p3, = plt.plot(arr, 1 - weibSurv4, color=Light, linewidth=4)
plt.fill_between(arr,
1 - weibSurv2,
1 - weibSurv4,
facecolor=Light,
alpha=.3)
# thinkplot.plot(sf,color=Dark)
plt.xlabel('Age in Books')
plt.ylabel('Probability of Survival')
plt.ylim([.0, 1])
plt.text(6.3, 0.95, 'Theon', color='Khaki')
plt.text(5.3, 0.4, 'Lord Walder Frey', color='DarkSeaGreen')
# plt.legend([p1,p2,p4],['90 Percent Credible Interval','Best Estimate','Data'])
plt.title(Title)
def weibPredict():
hazard = [claimMth[i]/corrRisk[i] for i in range(len(corrRisk))]
cumulHazard = [0] * len(hazard)
for hzd in hazard:
i = hazard.index(hzd)
cumulHazard[i] = sum(hazard[0:i+1])
reliability = [mt.exp(-x) for x in cumulHazard]
claimCDF = [1-x for x in reliability]
weibY = [mt.log(-mt.log(1-x)) for x in claimCDF[1:]]
weibX = [mt.log(x) for x in range(len(claimCDF))[1:]]
weibPara = weibullFit(weibX, weibY)
weibCDF = [ew.cdf(i, a = 1, c = weibPara[0], scale = weibPara[1]) \
for i in range(predictPeriodUsr)]
print weibCDF
claimPercent = [0] * len(weibCDF)
for cdf in weibCDF:
i = weibCDF.index(cdf)
if i == 0:
claimPercent[i] = cdf
if i > 0:
claimPercent[i] = cdf - weibCDF[i - 1]
## claimPercent = weibPDF
print claimPercent
return claimPercent
def Likelihood(self, data, hypo): age, alive = data k, lam = hypo if alive: prob = 1-exponweib.cdf(age, k, lam) else: prob = exponweib.pdf(age, k, lam) return prob
def Likelihood(self, data, hypo):
age, alive = data
k, lam = hypo
if alive:
prob = 1 - exponweib.cdf(age, k, lam)
else:
prob = exponweib.pdf(age, k, lam)
return prob
def Likelihood(self, data, hypo): """Determines how well a given k and lam predict the life/death of a character """ age, alive = data k, lam = hypo if alive: prob = 1-exponweib.cdf(age, k, lam) else: prob = exponweib.pdf(age, k, lam) return prob
def Likelihood(self, data, hypo):
"""Determines how well a given k and lam predict the life/death of a character """
age, alive = data
k, lam = hypo
if alive:
prob = 1 - exponweib.cdf(age, k, lam)
else:
prob = exponweib.pdf(age, k, lam)
return prob
def predict(self, next_n):
if not self.params:
pred = [0] * next_n
elif self.fit_model == "Sampling":
pred = self.generate_samples(self.params, next_n, self.time_since_last_spike, self.spike_width_avg, self.spike_max)
elif self.time_since_last_spike== 0:
return [self.last]*next_n
elif self.fit_model == "Weibull":
pred = exponweib.cdf([x for x in range(next_n)], a = self.params[0], c= self.params[1], loc=-self.time_since_last_spike, scale = self.params[3]) * self.spike_avg
else: # self.fit_model == "Expon":
pred = expon.cdf([x for x in range(next_n)], -self.time_since_last_spike, self.params[1]) * self.spike_avg
return self.round_non_negative_int_func(pred)
def CredIntPlt(sf,kl,kh,ll,lh,house,mk,ml,Title):
listcol=colordict[house]
Dark=listcol[0]
Mid=listcol[1]
Light=listcol[2]
arr=np.linspace(0,7,num=100)
weibSurv2 = exponweib.cdf(arr, kl, lh)
weibSurv4 = exponweib.cdf(arr, kh, ll)
weibSurv1 = exponweib.cdf(arr, mk, ml)
# p4,=plt.plot(arr, 1-weibSurv2,color=Dark,linewidth=3)
p1,=plt.plot(arr, 1-weibSurv2,color=Light,linewidth=4)
# p2,=plt.plot(arr, 1-weibSurv1,color=Mid,linewidth=3,linestyle='--')
p3,=plt.plot(arr, 1-weibSurv4,color=Light,linewidth=4)
plt.fill_between(arr,1-weibSurv2,1-weibSurv4, facecolor=Light, alpha=.3)
# thinkplot.plot(sf,color=Dark)
plt.xlabel('Age in Books')
plt.ylabel('Probability of Survival')
plt.ylim([.0,1])
plt.text(6.3,0.95,'Theon',color='Khaki')
plt.text(5.3,0.4,'Lord Walder Frey',color='DarkSeaGreen')
# plt.legend([p1,p2,p4],['90 Percent Credible Interval','Best Estimate','Data'])
plt.title(Title)
def fit_distribution(data, fit_type, x_min, x_max, n_points=1000):
# Initialization of the variables
param, x, cdf, pdf = [-1, -1, -1, -1]
if fit_type == 'exponweib':
x = np.linspace(x_min, x_max, n_points)
# Fit data to the theoretical distribution
param = exponweib.fit(data, 1, 1, scale=02, loc=0)
# param = exponweib.fit(data, fa=1, floc=0)
# param = exponweib.fit(data)
cdf = exponweib.cdf(x, param[0], param[1], param[2], param[3])
pdf = exponweib.pdf(x, param[0], param[1], param[2], param[3])
elif fit_type == 'lognorm':
x = np.linspace(x_min, x_max, n_points)
# Fit data to the theoretical distribution
param = lognorm.fit(data, loc=0)
cdf = lognorm.cdf(x, param[0], param[1], param[2])
pdf = lognorm.pdf(x, param[0], param[1], param[2])
elif fit_type == 'norm':
x = np.linspace(x_min, x_max, n_points)
# Fit data to the theoretical distribution
param = norm.fit(data, loc=0)
cdf = norm.cdf(x, param[0], param[1])
pdf = norm.pdf(x, param[0], param[1])
elif fit_type == 'weibull_min':
x = np.linspace(x_min, x_max, n_points)
# Fit data to the theoretical distribution
param = weibull_min.fit(data, floc=0)
cdf = weibull_min.cdf(x, param[0], param[1], param[2])
pdf = weibull_min.pdf(x, param[0], param[1], param[2])
return param, x, cdf, pdf
from scipy.stats import exponweib print(exponweib.cdf(2,1,3,0,4))
exponweib.pdf(x, a, c),
'r-',
lw=5,
alpha=0.6,
label='exponweib 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 = exponweib(a, c)
ax.plot(x, rv.pdf(x), 'k-', lw=2, label='frozen pdf')
# Check accuracy of ``cdf`` and ``ppf``:
vals = exponweib.ppf([0.001, 0.5, 0.999], a, c)
np.allclose([0.001, 0.5, 0.999], exponweib.cdf(vals, a, c))
# True
# Generate random numbers:
r = exponweib.rvs(a, c, size=1000)
# And compare the histogram:
ax.hist(r, density=True, histtype='stepfilled', alpha=0.2)
ax.legend(loc='best', frameon=False)
plt.show()
# -*- coding: utf-8 -*-
import os
import sys
import numpy
import random
import math
from scipy.stats import exponweib
file1 = open("weibullcdf.txt", "w")
x = 1e-06
num = 1
while x < 0.1:
num += 1
if num % 100 == 0:
file1.write(str(x) + '\t')
file1.write(str(exponweib.cdf(x, 100, 0.4, 0, 0.0001)) + '\n')
x += 1e-06
file1.close()
from scipy.stats import exponweib print(exponweib.cdf(2, 1, 3, 0, 4))
