результаты зависят от других результатов в Python

Question

У меня есть массив с именем action из 1000 наблюдений; от 0 до 99 означает, что 100 наблюдения были сделаны случайным образом, затем я хочу посчитать значение действия на основе матрицы из 2 столбцов, называемой R, которая зависит от значения действия. Я получил эту ошибку

Traceback (most recent call last)
<ipython-input-21-930470b1de32> in <module>()
     19 print(R)
     20 for i in range(100,999,1):
---> 21     if R[i-1,0]>R[i-1,1]:
     22         action[i]=1
     23     else:
IndexError: index 99 is out of bounds for axis 0 with size 99

Код:

import numpy as np
from numpy import random
import random

action=np.zeros((1000))
def reward(action,i,n):
    R=np.zeros((n,2))
    for i in range (i,n,1):
        if action[i]==1:
            R[i,0]= 1+action[i]/2
        else:
            R[i,1]=1+action[i]/2
    return R
random.seed(771)
for i in range (0,99,1):
        action[i]=random.randint(1,2)
print(action[0:99])
R=reward(action,0,99)
print(R)       
for i in range(100,999,1):
    if R[i-1,0]>R[i-1,1]:
        action[i]=1
    else:
        action[i]=2
    R=reward(action,100,999)

Answer 1

R=reward(action,0,99) создает список из 99 элементов:

>>> len(R)
99

тогда при запуске

for i in range(100,999,1):
    if R[i-1,0]>R[i-1,1]:

, который будет генерировать индексы, начиная с 99, но последний индекс R равен 98 (от 0 до 98, следовательно, длина составляет 99).

Попробуйте: for i in range(99,1000,1):

Answer 2

Я применил тот же принцип к своему набору данных, но он не работал без ошибок, но без результатов

import numpy as np
import matplotlib.pyplot as plt
from datetime import datetime as dt
from numpy import random
import pandas as pd
import random
from sklearn.model_selection import train_test_split
#import excel file
df1 = pd.read_excel('new.xlsx')
df1 = pd.DataFrame(df1) 
df1.head(8)
df1['WMRT'][0]
#discretization of walmart
wal = np.zeros(df1.shape[0])
for i in range(1,df1.shape[0],1):
    if (df1['WMRT'][i] > df1['WMRT'][i-1] ):
        wal[i]=1
    else:
         wal[i]=-1
df1['wal']=wal
print(df1['wal'])
#Discretization Dow Jones
djd = np.zeros(df1.shape[0])
for i in range(1,df1.shape[0],1):
    if (df1['DJ'][i] > df1['DJ'][i-1]  ):
        djd[i]=1
    else:
         djd[i]=-1
df1['DDJ']=djd
print(df1['DDJ'])
#codification of the state based on the Dow Jonesindex and walmart values
state = np.zeros(df1.shape[0])
for i in range (1,df1.shape[0],1):
    if (df1['DDJ'][i]== -1 and df1['wal'][i]== -1):
        state[i]=1
    elif(df1['DDJ'][i]== 1 and df1['wal'][i]== -1):
        state[i]=2
    elif(df1['DDJ'][i]== -1 and df1['wal'][i]== 1):
         state[i]=3
    else:
        state[i]=4
df1['state']=state
print(df1['state'])
#training phase (i=4616)
#generate the same sample each time
random.seed(771)
#intialize randomly the actions for 100 observations
def number_update(action,p0,n):
    N = np.zeros((n,8))
    for i in range(p0,n,1):
        N[i,:]=N[i-1,:]
        k=int(2*(state[i]-1)+action[i])
        N[i,k]=N[i,k]+1
    return N
def Rhoo(action,p0,n):
    RHO = np.zeros((n, 8))
    r = np.zeros((n))
    for i in range(p0,n,1):
        RHO[i,:]=RHO[i-1,:]
        k = int(2*(state[i]-1)+action[i])
        if k in [0,2,4,6]:
            r[i]=df1['WMRT'][i]/df1['WMRT'][i-1]-1
        else:
            r[i]=df1['DJ'][i]/df1['DJ'][i-1]-1   
        RHO[i,k] = RHO[i,k]+r[i]
    return RHO
#Average reward for each action 0 and 1
def reward(N,RHO,p0,n):
    R = np.zeros((n, 2))
    for i in range (p0,n,1):
    #j : even number
    #k : odd number
        k = 0
        for j in range(0,7,2):
            if N[i,j] != 0 :
                  R[i,0] += RHO[i,j]/N[i,j]
            else : 
                 R[i,0] = 0
            k = j+1
            if N[i,k] != 0 :
                R[i,1] += RHO[i,k]/N[i,k]
            else : 
                R[i,1] = 0
    #print(R)
    return R
def reward_max(action,p0,n):
    r = np.zeros((n))
    for i in range(p0,n,1):
        k = int(2*(state[i]-1)+action[i])
        if k in [0,2,4,6]:
            r[i]=df1['WMRT'][i]/df1['WMRT'][i-1]-1
        else:
            r[i]=df1['DJ'][i]/df1['DJ'][i-1]-1   
    return r
#determine the action
def main():

    action=np.zeros((df1.shape[0]))
    train, test = train_test_split(df1, test_size=0.3)
    #Training the model
    #generate the same sample each time
    random.seed(771)
    # take 100 action randomly
    for i in range(0,100,1):
        action[i]=random.randint(0,1)
    print(action)
    print(type(action))
    #N is the number of time the action 1 or 0 was taken from a state i
    print("N")
    N =number_update(action,1,100)
    print(N)
    #RHO represents the cumulative sum of the previous rewards obtained every time the action 0 or 1
    print("RHO")
    RHO = Rhoo(action,1,100)
    print(RHO)
    # represents the average reward for every action 0 or 1
    print("R")
    R = reward(N,RHO,1,100)
    print(R)
    for i in range(100,2265,1):
        if(R[i-1,0]>R[i-1,1]):
            action[i]=1
        else:
            action[i]=0
        N = number_update(action,100,2265)
        RHO = Rhoo(action,100,2265)
        R = reward(N,RHO,100,2265)
    print(action[100:2265])
    print(R.shape)

main()

Answer 3

Конечный индекс для диапазона в Python не включен. Например, первые 100 значений будут заданы как [0, 100). Таким образом, сотый индекс не включен, но ноль включен. Это записано как action[0:100].

Это должно сработать для вас.

import numpy as np
from numpy import random
import random

def reward(action,i,n):
    R=np.zeros((n,2))
    for i in range (i,n,1):
        if action[i]==1:
            R[i,0]= 1+action[i]/2
        else:
            R[i,1]=1+action[i]/2
    return R

random.seed(771)

action=np.zeros((1000))
for i in range (0,100,1):
        action[i]=random.randint(1,2)
print(action[0:100])
R=reward(action,0,100)
print(R)

for i in range(100,999,1):
    if R[i-1,0]>R[i-1,1]:
        action[i]=1
    else:
        action[i]=2
    R=reward(action,100,999)