如何在python中实现列表的四元组？

import scipy.stats as st from scipy.integrate import quad from scipy import stats import numpy as np from matplotlib import pyplot as plt def prod_pdf(x,dists): p_pdf=1 for dist in dists: print('Incoming Array:', dist.pdf(x)) p_pdf=p_pdf*dist.pdf(x) print('final:', p_pdf) return print('Product: ', p_pdf), p_pdf def product_pdf(x,dists): p_pdf=1 for dist in dists: p_pdf=p_pdf*dist.pdf(x) return p_pdf def conflate_pdf(x,dists,lb,ub): print('Input product pdf: ', prod_pdf(x,dists)[1]) denom = quad(product_pdf, lb, ub, args=(dists,))[0] print('Denom: ', denom) conflated_pdf=product_pdf(x,dists)/denom print('Conflated PDF: ', conflated_pdf) return conflated_pdf lb=-10 ub=10 domain=np.arange(lb,ub,.01) dist_1 = st.norm.pdf(domain, 2,1) dist_2 = st.norm.pdf(domain, 2.5,1.5) dist_3 = st.norm.pdf(domain, 2.2,1.6) dist_4 = st.norm.pdf(domain, 2.4,1.3) dist_5 = st.norm.pdf(domain, 2.7,1.5) plt.plot(domain, dist_1, 'r', label='Dist. 1') plt.plot(domain, dist_2, 'g', label='Dist. 2') plt.plot(domain, dist_3, 'b', label='Dist. 3') plt.plot(domain, dist_4, 'y', label='Dist. 4') plt.plot(domain, dist_5, 'c', label='Dist. 5') dists=[stats.norm(2,1), stats.norm(2.5,1.5), stats.norm(2.2,1.6), stats.norm(2.4,1.3), stats.norm(2.7,1.5)] graph=conflate_pdf(domain,dists,lb,ub) plt.plot(domain,graph, 'm', label='Conflated Dist.') plt.xlabel("domain") plt.ylabel("pdf") plt.title("Conflated PDF") plt.legend() plt.show()

import scipy.stats as st from scipy.integrate import quad from scipy import stats import numpy as np from matplotlib import pyplot as plt def product_pdf(x,dists): p_list=[] p_pdf=1 print('Incoming Array:', p_pdf) list_full_size=np.array(dists).shape print('list size: ', list_full_size[0]) for x in range(list_full_size[1]): p_pdf=1 for y in range(list_full_size[0]): p_pdf=p_pdf*dists[y][x] # print('Incoming Distribution Array:', dist) print('Product value: ', p_pdf) print('Product PDF:', p_pdf) p_list.append(p_pdf) print('final Product PDF:', p_pdf) print('Product PDF list: ', p_list) # return p_pdf return p_list # return np.array(p_list) def conflate_pdf(x,dists,lb,ub): denom_list=[] # time.sleep(500) print('\n') print('product pdf: ', product_pdf(x,dists)) prod=product_pdf(x,dists) prod_size=np.array(prod).shape[0] print('Product Size: ', prod_size) # time.sleep(20) for i in range(prod_size): denom = quad(product_pdf, lb, ub, args=(prod[i],))[0] denom_list.append(denom) print('Denom: ', denom) print('Denominator list: ', denom_list) # time.sleep(20) # conflated_pdf=prod_pdf(x,dists)/denom conflated_pdf=product_pdf(x,dists)/denom print('Conflated PDF: ', conflated_pdf) return conflated_pdf lb=-10 ub=10 domain=np.arange(lb,ub,.01) dist_1_pdf = st.norm.pdf(domain, 2,1) dist_2_pdf = st.norm.pdf(domain, 4,2) dist_3_pdf = st.norm.pdf(domain, 7,4) dist_4_pdf = st.norm.pdf(domain, 2.4,1.3) dist_5_pdf = st.norm.pdf(domain, 2.7,1.5) # dist_1 = list(st.norm.pdf(domain, 2,1)) # dist_2 = list(st.norm.pdf(domain, 2.5,1.5)) # dist_3 = list(st.norm.pdf(domain, 2.2,1.6)) # dist_4 = list(st.norm.pdf(domain, 2.4,1.3)) # dist_5 = list(st.norm.pdf(domain, 2.7,1.5)) from matplotlib import pyplot as plt plt.xlabel("domain") plt.ylabel("pdf") plt.title("Conflated PDF") plt.plot(domain, dist_1_pdf, 'r', label='Dist. 1') plt.plot(domain, dist_2_pdf, 'g', label='Dist. 2') plt.plot(domain, dist_3_pdf, 'b', label='Dist. 3') plt.plot(domain, dist_4_pdf, 'y', label='Dist. 4') plt.plot(domain, dist_5_pdf, 'c', label='Dist. 5') dist_1 = st.norm(2,1) dist_2 = st.norm(4,2) dist_3 = st.norm(7,4) dist_4 = st.norm(2.4,1.3) dist_5 = st.norm(2.7,1.5) # dists=[dist_1, dist_2, dist_3, dist_4, dist_5] dists=[dist_1_pdf, dist_2_pdf, dist_3_pdf, dist_4_pdf, dist_5_pdf] graph=conflate_pdf(domain,dists,lb,ub) plt.plot(domain,graph, 'm', label='Conflated PDF') plt.legend() plt.show()

import scipy.stats as st import numpy as np import scipy.stats as st import matplotlib.pyplot as plt from sklearn.preprocessing import MinMaxScaler, Normalizer, normalize, StandardScaler from scipy.integrate import quad, simps, quad_vec, nquad, cumulative_trapezoid from scipy.integrate import romberg, trapezoid, simpson, romb from scipy.integrate import fixed_quad, quadrature, quad_explain from scipy import stats import time def user_prod_pdf(x,dists): p_list=[] p_pdf=1 print('Incoming Array:', p_pdf) for dist in dists: print('Incoming Distribution Array:', dist.pdf(x)) p_pdf=p_pdf*dist.pdf(x) print('Product PDF:', p_pdf) p_list.append(p_pdf) print('final Product PDF:', p_pdf) print('Product PDF list: ', p_list) return p_pdf def user_conflate_pdf(x,dists,lb,ub): print('Input product pdf: ', user_prod_pdf(x,dists)) denom = quad(user_prod_pdf, lb, ub, args=(dists,))[0] print('Denom: ', denom) conflated_pdf=user_prod_pdf(x,dists)/denom print('Conflated PDF: ', conflated_pdf) return conflated_pdf def my_product_pdf(x,dists): p_list=[] p_pdf=1 print('Incoming Array:', p_pdf) list_full_size=np.array(dists).shape print('Full list size: ', list_full_size) print('list size: ', list_full_size[0]) for x in range(list_full_size[1]): p_pdf=1 for y in range(list_full_size[0]): p_pdf=float(p_pdf)*dists[y][x] print('Product value: ', p_pdf) print('Product PDF:', p_pdf) p_list.append(p_pdf) print('final Product PDF:', p_pdf) print('Product PDF list: ', p_list) # return p_pdf return p_list # return np.array(p_list) def my_conflate_pdf(x,dists,lb,ub): conflated_pdf_list=[] num=my_product_pdf(x,dists) print('\n') # print('product pdf: ', prod_pdf(x,dists)) print('product pdf: ', my_product_pdf(x,dists)) denom = fixed_quad(my_product_pdf, lb, ub, args=(dists,), n=1)[0] print('Denom: ', denom) # conflated_pdf=prod_pdf(x,dists)/denom # conflated_pdf=my_product_pdf(x,dists)/denom # conflated_pdf=[i / j for i,j in zip(my_product_pdf(x,dists), denom)] for i in np.arange(np.array(num).shape[0]): conflated_pdf = num[i] / denom conflated_pdf_list.append(conflated_pdf) print('Conflated PDF: ', conflated_pdf_list) return conflated_pdf_list lb=-10 ub=10 domain=np.arange(lb,ub,.01) # dist_1 = st.norm(2,1) # dist_2 = st.norm(2.5,1.5) # dist_3 = st.norm(2.2,1.6) # dist_4 = st.norm(2.4,1.3) # dist_5 = st.norm(2.7,1.5) # dist_1_pdf = st.norm.pdf(domain, 2,1) # dist_2_pdf = st.norm.pdf(domain, 2.5,1.5) # dist_3_pdf = st.norm.pdf(domain, 2.2,1.6) # dist_4_pdf = st.norm.pdf(domain, 2.4,1.3) # dist_5_pdf = st.norm.pdf(domain, 2.7,1.5) # dist_1_pdf /= dist_1_pdf.sum() # dist_2_pdf /= dist_2_pdf.sum() # dist_3_pdf /= dist_3_pdf.sum() # dist_4_pdf /= dist_4_pdf.sum() # dist_5_pdf /= dist_5_pdf.sum() dist_1 = st.norm(2,1) dist_2 = st.norm(4,2) dist_3 = st.norm(7,4) dist_4 = st.norm(2.4,1.3) dist_5 = st.norm(2.7,1.5) dist_1_pdf = st.norm.pdf(domain, 2,1) dist_2_pdf = st.norm.pdf(domain, 4,2) dist_3_pdf = st.norm.pdf(domain, 7,4) dist_4_pdf = st.norm.pdf(domain, 2.4,1.3) dist_5_pdf = st.norm.pdf(domain, 2.7,1.5) # dist_1_pdf /= dist_1_pdf.sum() # dist_2_pdf /= dist_2_pdf.sum() # dist_3_pdf /= dist_3_pdf.sum() # dist_4_pdf /= dist_4_pdf.sum() # dist_5_pdf /= dist_5_pdf.sum() # User: plt.xlabel("domain") plt.ylabel("pdf") plt.title("User Conflated PDF") plt.plot(domain, dist_1_pdf, 'r', label='Dist. 1') plt.plot(domain, dist_2_pdf, 'g', label='Dist. 2') plt.plot(domain, dist_3_pdf, 'b', label='Dist. 3') plt.plot(domain, dist_4_pdf, 'y', label='Dist. 4') plt.plot(domain, dist_5_pdf, 'c', label='Dist. 5') dists=[dist_1, dist_2, dist_3, dist_4, dist_5] user_graph=user_conflate_pdf(domain,dists,lb,ub) print('Final Conflated PDF: ', user_graph) # user_graph /= user_graph.sum() plt.plot(domain, user_graph, 'm', label='Conflated PDF') plt.legend() plt.show() # My Code: # from matplotlib import pyplot as plt plt.xlabel("domain") plt.ylabel("pdf") plt.title("My Conflated PDF Code") plt.plot(domain, dist_1_pdf, 'r', label='Dist. 1') plt.plot(domain, dist_2_pdf, 'g', label='Dist. 2') plt.plot(domain, dist_3_pdf, 'b', label='Dist. 3') plt.plot(domain, dist_4_pdf, 'y', label='Dist. 4') plt.plot(domain, dist_5_pdf, 'c', label='Dist. 5') dists=[dist_1_pdf, dist_2_pdf, dist_3_pdf, dist_4_pdf, dist_5_pdf] my_graph=my_conflate_pdf(domain,dists,lb,ub) print('Final Conflated PDF: ', my_graph) # my_graph /= np.array(my_graph).sum() # my_graph = inverse_normalise(my_graph) plt.plot(domain, my_graph, 'm', label='Conflated PDF') plt.legend() plt.show() # Conflated PDF: print('User Conflated PDF: ', user_graph) print('My Conflated PDF: ', my_graph)

1条回答

网友

1楼 · 发布于 2024-10-02 22:35:33

第一个大抱怨-你没有显示完整的回溯！到现在为止，您应该知道，检查回溯以准确了解问题的原因是非常重要的

从你所展示的一点，我推断问题在于：

def product_pdf(x,dists):
    p_list=[]
    p_pdf=1
    print('Incoming Array:', p_pdf)
    list_full_size=np.array(dists).shape
    print('list size: ', list_full_size[0])
    ...

你为什么不突出那个地方？阅读代码需要花费不必要的时间才能找到打印行

在conflate_pdf(x,dists,lb,ub)中多次调用该函数。至少打印一次。那张照片有用吗？您的代码有一堆打印，但您没有显示其中任何一个：（

从

dists=[dist_1_pdf, dist_2_pdf, dist_3_pdf, dist_4_pdf, dist_5_pdf]
graph=conflate_pdf(domain,dists,lb,ub)

dists是一个由5个对象组成的列表。因此，我希望np.array(dists)至少是一个1d数组，如果那些pdf对象是具有相同形状的数组，可能会更多。这意味着list_full_size[0]应该工作，并且匹配len(dists)（即不需要将列表转换为数组）

这种array转换有助于

for x in range(list_full_size[1]):

虽然那也可以

for x in range(len(dists[0])):

因为x和y循环从dists开始工作，而不是从np.array(dists)开始工作

无论如何，看起来product_pdf(x,dists)调用应该可以工作

但是，您也可以在中使用product_pdf

denom = quad(product_pdf, lb, ub, args=(prod[i],))[0]

那里的dists参数现在是prod[i]，而不是原来的dists

什么是prod

prod=product_pdf(x,dists)

等等，product_pdf不是应该为quad生成一个数字吗？你在用prod[i]做什么，并把它作为dist参数传递给product_pdf

product_pdf返回plist，它看起来像一个数字列表。因此prod[i]是一个数字，它的形状作为数组是（）。因此出现了错误。现在我们回到上次帮助您的错误-quad需要标量值函数。

相关问题更多 >

编程相关推荐

热门问题

热门文章