计算——“你的资产在全世界人民里百分之几？”

你的资产在全世界人民里百分之几.py

# -*- coding: utf-8 -*-
"""
总资产=总资产市场价-总贷款
"""

import matplotlib.pyplot as plt  
import numpy as np  
import scipy as sp  
from scipy.stats import norm  
from sklearn.pipeline import Pipeline  
from sklearn.linear_model import LinearRegression  
from sklearn.preprocessing import PolynomialFeatures  
from sklearn import linear_model  
  
''''' 数据生成 '''  
#x = np.arange(0, 1, 0.002)  
#y = norm.rvs(0, size=500, scale=0.1)  
#y = y + x**2  

x = [10000,100000,1000000,5000000,10000000,50000000]
y = [3474000000,4528000000,4919000000,4950365100,4954902500,4954050700]
x = np.array(x).reshape(-1, 1)
y = np.array(y).reshape(-1, 1)
x_test = np.array(range(1,75000000000,10000)).reshape(-1, 1)


##''''' 均方误差根 '''  
##def rmse(y_test, y):  
##    return sp.sqrt(sp.mean((y_test - y) ** 2))  
##  
##''''' 与均值相比的优秀程度，介于[0~1]。0表示不如均值。1表示完美预测.这个版本的实现是参考scikit-learn官网文档  '''  
##def R2(y_test, y_true):  
##    return 1 - ((y_test - y_true)**2).sum() / ((y_true - y_true.mean())**2).sum()  
##  
##  
##''''' 这是Conway&White《机器学习使用案例解析》里的版本 '''  
##def R22(y_test, y_true):  
##    y_mean = np.array(y_true)  
##    y_mean[:] = y_mean.mean()  
##    return 1 - rmse(y_test, y_true) / rmse(y_mean, y_true)  
##  
#  
#plt.scatter(x, y, s=5)  
#degree = [3,10,40]  
##y_test = []  
##y_test = np.array(y_test)  
#  
#  
#for d in degree:  
#    clf = Pipeline([('poly', PolynomialFeatures(degree=d)),
#                    (LinearRegression())])  
##    clf.fit(x[:, np.newaxis], y)  
#    clf = LinearRegression()
#    clf.fit(x,y) 
#    y_test = clf.predict(x_test)  
#  
#    print(clf.named_steps['linear'].coef_)  
##    print('rmse=%.2f, R2=%.2f, R22=%.2f, clf.score=%.2f' %  
##      (rmse(y_test, y),  
##       R2(y_test, y),  
##       R22(y_test, y),  
##       clf.score(x[:, np.newaxis], y)))      
#      
#    plt.plot(x_test, y_test, linewidth=2)  
#      
#plt.grid()  
#plt.legend(['3','10','40'], loc='upper left')  
#plt.show()  

#import numpy as np
#from sklearn.linear_model import LinearRegression
#from sklearn.preprocessing import PolynomialFeatures
#X_train = x
#y_train = y
#X_test = x_test
#y_test = [[8], [12], [15], [18]]
## 建立线性回归，并用训练的模型绘图
#regressor = LinearRegression()
#regressor.fit(X_train, y_train)
#xx = np.linspace(0, 26, 100)
#yy = regressor.predict(xx.reshape(xx.shape[0], 1))
##plt = runplt()
#plt.plot(X_train, y_train, 'k.')
#plt.plot(xx, yy)
#
#quadratic_featurizer = PolynomialFeatures(degree=2)
#X_train_quadratic = quadratic_featurizer.fit_transform(X_train)
#X_test_quadratic = quadratic_featurizer.transform(X_test)
#regressor_quadratic = LinearRegression()
#regressor_quadratic.fit(X_train_quadratic, y_train)
#xx_quadratic = quadratic_featurizer.transform(xx.reshape(xx.shape[0], 1))
#plt.plot(xx, regressor_quadratic.predict(xx_quadratic), 'r-')
#plt.show()
#print(X_train)
#print(X_train_quadratic)
#print(X_test)
#print(X_test_quadratic)
#y_test = regressor_quadratic.predict(X_test)
#print('1 r-squared', regressor.score(X_test, y_test))
#print('2 r-squared', regressor_quadratic.score(X_test_quadratic, y_test))

from scipy.optimize import curve_fit  
import matplotlib.pyplot as plt  
import numpy as np  
from scipy.integrate import quad,dblquad,nquad
  
def func(x, a, b, c):  
    return a * np.exp(-b * x) + c   

x = [10000,100000,1000000,5000000,10000000,50000000]
y = [3474000000,4528000000,4919000000,4950365100,4954902500,4954050700]
  
x = np.array(x)
y = np.array(y)
 
xdata = x   
ydata = 4954347100-y  
#plt.plot(xdata,ydata,'b-')  
#popt, pcov = curve_fit(func, xdata, ydata)   
##popt数组中，三个值分别是待求参数a,b,c  
#y2 = [func(i, popt[0],popt[1],popt[2]) for i in xdata]  
#plt.plot(xdata,y2,'r--')  
#print (popt)  



def func(x, a, b, c):  
    return a * np.exp(-b * x) + c  
  

plt.plot(xdata,ydata,'b-')  
popt, pcov = curve_fit(func, xdata, ydata)  
#popt数组中，三个值分别是待求参数a,b,c  
y2 = [func(i, popt[0],popt[1],popt[2]) for i in xdata]  
plt.plot(xdata,y2,'r--')  
print (popt)  
print (quad(lambda x:func(x,popt[0],popt[1],popt[2]),0,88888))