python未定义的元组的TypeError
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我正在制作我的python程序。下面是我的代码,它返回:
File "relation.py", line 134, in <module>
k = z1[i][0] = sampling(a[l, :], b[l, :], z1_sum[i], alpha[l, :], k_l)
TypeError: list indices must be integers, not tuple
在我的代码(下面这个)中,我没有定义任何元组。所以我不知道这段代码的问题是什么。
import random
import numpy as np
import math
def sampling(a, b, I, alpha, length):
summary = 0
N = len(alpha)
for i in range(N):
A = math.gamma(a[i] + I[i]) * math.gamma(b[i] + length - I[i]) / math.gamma(a[i] + b[i] + I[i])
B = math.gamma(a[i] + b[i]) / (math.gamma(a[i]) * math.gamma(b[i]))
value = alpha[i] * A * B
summary += value
result.append(value)
choose = random.randomrange(summary)
num = N - 1
for i in range(N):
choose -= result[i]
if choose < 0:
num = i
break
return num
def delta(x, y):
if x == y:
return 1
return 0
data = []
print("please input (k, l):")
num = map(int, raw_input().split())
k_l = k = num[0]
l_l = l = num[1]
param_p = [[0 for i in range(k)] for j in range(l)]
param_n = [[0 for i in range(k)] for j in range(l)]
param_k = [0] * k
param_l = [0] * l
count = 1000
print("please input (N1, N2):")
number = map(int, raw_input().split())
N1 = number[0]
N2 = number[1]
random.randint(50, 100)
z1 = []
z2 = []
z1_sum = [2 for i in range(N1)]
z2_sum = [2 for i in range(N2)]
value = 0
alpha = [[2 for i in range(k)] for j in range(l)]
a = np.array([[2 for i in range(k)] for j in range(l)])
b = np.array([[2 for i in range(k)] for j in range(l)])
for i in range(N1):
z1.append([value, i],)
if i % k == k - 1:
value = 0
else:
value += 1
value = 0
for i in range(N2):
z2.append([value, i],)
if i % l == l - 1:
value = 0
else:
value += 1
print(z1)
print(z2)
print("please input relation-data:")
for i in range(N2):
line = map(int, raw_input().split())
data.append(line)
print(data)
param_p[0][0] = -1
print(param_p[1][0])
param_p[0][0] = 0
for i in range(N2):
l = z2[i][0]
for j in range(N1):
k = z1[j][0]
z1_sum[i] += data[i][j]
z2_sum[j] += data[i][j]
param_p[l][k] += data[i][j]
param_n[l][k] += 1 - data[i][j]
param_k[k] += data[i][j]
param_l[l] += data[i][j]
print(param_p)
print(param_n)
print(param_k)
print(param_l)
#sampling(a, b, I, alpha, length)
k = 0
l = 0
for n in range(count):
for i in range(N1):
temp = z1[i][0]
for j in range(l_l):
alpha[j][temp] += param_l[j]
a[j][temp] += param_p[j][temp]
b[j][temp] += param_n[j][temp]
I = delta(z1[i][0], temp)
param_p[j][temp] -= z1_sum[i] * I
param_n[j][temp] -= (k_l - z1_sum[i]) * I
param_l[j] -= I
k = z1[i][0] = sampling(a[l, :], b[l, :], z1_sum[i], alpha[l, :], k_l)
for j in range(l_l):
alpha[j][temp] -= param_l[j]
a[j][temp] -= param_p[j][temp]
b[j][temp] -= param_n[j][temp]
I = delta(z1[i][0], k)
param_p[j][k] += z1_sum[i] * z1_sum[i] * I
param_p[j][k] += (k_l - z1_sum[i]) * z1_sum[i] * I
param_l[j] += z1_sum[i] * I
for i in range(N2):
temp = z2[i][0]
for j in range(k_l):
alpha[temp][j] += param_k[j]
a[temp][j] += param_p[temp][j]
b[temp][j] += param_n[temp][j]
I = delta(z2[i][0], temp)
param_p[temp][j] -= z2_sum[i] * I
param_n[temp][j] -= (l_l - z2_sum[i]) * I
param_k[j] -= I
l = z2[i][0] = sampling(a[:, k], b[:, k], z2_sum[i], alpha[:, k], l_l)
for j in range(k_l):
alpha[temp][j] -= param_k[j]
a[temp][j] -= param_p[temp][j]
b[temp][j] -= param_n[temp][j]
I = delta(z2[i][0], l)
param_p[l][j] += z2_sum[i] * I
param_n[l][j] += (l_l - z2_sum[i]) * I
param_k[j] += I
print("Hello")
print(z1)
print(z2)
正如你所看到的,我只定义了numpy并且[l,:]根本不是元组。保留所有,没有tuple.only列表元素的商务在这里。为什么python返回关于元组的错误。
答案
谢谢abarnert,我终于知道问题是什么了。这是因为当它实际上是一个numpy时我使用的是numpy-array
这是我的代码编辑
import random
import numpy as np
import math
def sampling(a, b, Ip, In, alpha, elements):
#print("sampling")
result = []
print("a:", a)
print("b:", b)
print("alpha:", alpha)
print("elements:", elements)
print("Ip:", Ip)
print("In:", In)
summary = 0
N = len(alpha)
for i in range(N):
A = 0
B = 0
value = 0
'''
if i == elements:
continue
'''
for j in range(elements):
print("A:")
print("1:", a[i][j] + Ip[j], ",2:", b[i][j] + In[j], ",3:", a[i][j] + b[i][j] + Ip[j] + In[j])
A += math.lgamma(a[i][j] + Ip[j]) + math.lgamma(b[i][j] + In[j]) - math.lgamma(a[i][j] + b[i][j] + Ip[j] + In[j])
print("B:")
print("1:", a[i][j] + b[i][j], ",2:", a[i][j], ",3:", b[i][j])
B += math.lgamma(a[i][j] + b[i][j]) - math.lgamma(a[i][j]) - math.lgamma(b[i][j])
print("A:", A)
print("B:", B)
print("value:", value)
value += math.log(alpha[i]) + A + B
print("i:", i, "value:", value)
summary += math.exp(value)
result.append(math.exp(value))
print("percentage:", np.array(result) / summary)
#print("summary:", summary)
choose = random.uniform(0.0, summary)
#print("choose:", choose)
num = N - 1
for i in range(N):
choose -= result[i]
if choose <= 0:
num = i
break
'''
if num >= elements:
num += 1
'''
return num
def delta(x, y):
if x == y:
return 1
return 0
data = []
print("please input (k, l):")
num = map(int, raw_input().split())
k_l = k = num[0]
l_l = l = num[1]
param_p = np.array([[0 for i in range(k)] for j in range(l)])
param_n = np.array([[0 for i in range(k)] for j in range(l)])
param_k = np.array([0] * k)
param_l = np.array([0] * l)
count = 1000
print("please input (N1, N2):")
number = map(int, raw_input().split())
N1 = number[0]
N2 = number[1]
random.randint(50, 100)
z1 = []
z2 = []
z1_sum = np.array([[[0 for i in range(k)] for j in range(l)] for n in range(N1)])
z2_sum = np.array([[[0 for i in range(k)] for j in range(l)] for n in range(N2)])
z1_zero = np.array([[[0 for i in range(k)] for j in range(l)] for n in range(N1)])
z2_zero = np.array([[[0 for i in range(k)] for j in range(l)] for n in range(N2)])
value = 0
valuesL = []
for i in range(N2):
valuesL.append(value)
if i % l == l - 1:
value = 0
else:
value += 1
valuesL = np.sort(np.array(valuesL))
for i in range(N2):
z2.append([valuesL[i], i],)
tempAlpha = 0.5
tempA = 0.5
tempB = 0.5
alpha1 = np.array([tempAlpha for i in range(k)])
alpha2 = np.array([tempAlpha for i in range(l)])
a = np.array([[tempA for i in range(k)] for j in range(l)])
b = np.array([[tempB for i in range(k)] for j in range(l)])
value = 0
valuesK = []
for i in range(N1):
valuesK.append(value)
if i % k == k - 1:
value = 0
else:
value += 1
valuesK = np.sort(np.array(valuesK))
for i in range(N1):
z1.append([valuesK[i], i],)
z1 = np.array(z1)
z2 = np.array(z2)
print(z1)
print(z2)
print("please input relation-data:")
for i in range(N2):
line = map(int, raw_input().split())
data.append(line)
#print(data)
for i in range(N2):
l = z2[i][0]
for j in range(N1):
k = z1[j][0]
z1_sum[j][l][k] += data[i][j]
z2_sum[i][l][k] += data[i][j]
z1_zero[j][l][k] += 1 - data[i][j]
z2_zero[i][l][k] += 1 - data[i][j]
param_p[l][k] += data[i][j]
param_n[l][k] += 1 - data[i][j]
param_k[k] += data[i][j]
param_l[l] += data[i][j]
#print("param_p:", param_p)
#print("param_n:", param_n)
#print("param_k:", param_k)
#print("param_l:", param_l)
#print("z1-sum:")
#print(z1_sum)
#print("z2-sum:")
#print(z2_sum)
#sampling(a, b, I, alpha, length, elements)
k = 0
l = 0
pastK = np.array([0 for i in range(N1)])
pastL = np.array([0 for i in range(N2)])
for n in range(count):
pastK[:] = z1[:, 0]
for i in range(N1): #sampling z1 objects
t_k = z1[i][0]
k = z1[i][0] = sampling((a[:, :] + param_p[:, :] - z1_sum[i, :, :]).T, (b[:, :] + param_n[:, :] - z1_zero[i, :, :]).T, np.sum(z1_sum[i, :, :], axis=1), np.sum(z1_zero[i, :, :], axis=1), alpha1[:] + param_k[:] - np.sum(z1_sum[i, :, :], axis=0), l_l)
#print("t_k:", t_k, "k:", k)
for i in range(N1):
t_k = pastK[i]
k = z1[i][0]
print("t_k:", t_k, "k:", k)
if t_k != k:
#change param_p param_n param_k
#print("old-z1:", z1)
print("param_p(past):", param_p)
print("param_n(past):", param_n)
print("param_k(past):", param_k)
z1_sum_temp = z1_sum[i, :, t_k] #reduce order
z1_zero_temp = z1_zero[i, :, t_k]
summary = np.sum(z1_sum_temp)
param_p[:, t_k] -= z1_sum_temp #minus old value
param_n[:, t_k] -= z1_zero_temp
param_k[t_k] -= summary
param_p[:, k] += z1_sum_temp #plus new value
param_n[:, k] += z1_zero_temp
param_k[k] += summary
print("param_p(now):", param_p)
print("param_n(now):", param_n)
print("param_k(now):", param_k)
#print("past-z2-sum:", z2_sum)
for j in range(N2):
l_j = z2[j][0]
z2_sum[j][l_j][t_k] -= data[j][i]
z2_sum[j][l_j][k] += data[j][i]
z2_zero[j][l_j][t_k] -= 1 - data[j][i]
z2_zero[j][l_j][k] += 1 - data[j][i]
#print("now-z2-sum:", z2_sum)
#print("past-z1-sum:", z1_sum)
#change z1_sum
z1_sum[i, :, k] = z1_sum[i, :, t_k]
z1_sum[i, :, t_k] = 0
z1_zero[i, :, k] = z1_zero[i, :, t_k]
z1_zero[i, :, t_k] = 0
#print("now-z1-sum:", z1_sum)
#print("new-z1:", z1)
pastL[:] = z2[:, 0]
for i in range(N2): #sampling z2 objects
t_l = z2[i][0]
l = z2[i][0] = sampling(a[:, :] + param_p[:, :] - z2_sum[i, :, :], b[:, :] + param_n[:, :] - z2_zero[i, :, :], np.sum(z2_sum[i, :, :], axis=0), np.sum(z2_zero[i, :, :], axis=0), alpha2[:] + param_l[:] - np.sum(z2_sum[i, :, :], axis=1), k_l)
print("t_l:", t_l, "l:", l)
for i in range(N2):
t_l = pastL[i]
l = z2[i][0]
print("t_l:", t_l, "l:", l)
if t_l != l:
#change param_p param_n param_l
#print("old-z2:", z2)
z2_sum_temp = z2_sum[i, t_l, :] #reduce order
z2_zero_temp = z2_zero[i, t_l, :]
summary = np.sum(z2_sum_temp)
param_p[t_l, :] -= z2_sum_temp #minus old value
param_n[t_l, :] -= z2_zero_temp
param_l[t_l] -= summary
param_p[l, :] += z2_sum_temp #plus new value
param_n[l, :] += z2_zero_temp
param_l[l] += summary
#print("param_p(past):", param_p)
#print("param_n(past):", param_n)
#print("param_l(past):", param_l)
print("param_p(now):", param_p)
print("param_n(now):", param_n)
print("param_l(now):", param_l)
#print("past-z1-sum(past):", z1_sum)
#print("past-z1-zero(past):", z1_zero)
#change z1_sum
for j in range(N1):
k_j = z1[j][0]
z1_sum[j][t_l][k_j] -= data[i][j]
z1_sum[j][l][k_j] += data[i][j]
z1_zero[j][t_l][k_j] -= 1 - data[i][j]
z1_zero[j][l][k_j] += 1 - data[i][j]
#print("now-z1-sum(now):", z1_sum)
#print("past-z1-zero(now):", z1_zero)
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