如何动态地指定纸浆中的约束条件?
问题描述:
我想检查一下我的数据是否可线性分离。为此,我使用了此处提及的方程式link。下面是我使用的代码:如何动态地指定纸浆中的约束条件?
try:
import os
#import random
import traceback
import datetime
#import numpy as np
import scipy.io as sio
import pulp
os.system('cls')
dicA = sio.loadmat('A1.mat')
A = dicA.get('A1')
var = pulp.LpVariable.dicts("var",range(11),pulp.LpContinuous)
A = A[:,0:10]
model = pulp.LpProblem("Data linearly seaparable", pulp.LpMinimize)
model+= 0
print(datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S'))
for i in range(len(A)):
expr = pulp.LpAffineExpression()
for j in range(len(A[i])):
expr += var[j]*A[i][j]
expr+= var[10] <= -1
model+= expr
print(datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S'))
model.solve()
print(pulp.LpStatus[model.status])
print(datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S'))
except:
print('exception')
tb = traceback.format_exc()
print(tb)
finally:
print('reached finally')
这里是我得到的输出:我添加0指定有作为链接中没有提到的目标函数
C:\Users\puneet\Anaconda3\lib\site-packages\pulp\pulp.py:1348: UserWarning: Overwriting previously set objective.
warnings.warn("Overwriting previously set objective.")
2017-08-29 10:06:21
exception
Traceback (most recent call last):
File "C:/Hackerearth Challenge/Machine Learning #3/LInearlySeaparblePulp.py", line 31, in <module>
model.solve()
File "C:\Users\puneet\Anaconda3\lib\site-packages\pulp\pulp.py", line 1664, in solve
status = solver.actualSolve(self, **kwargs)
File "C:\Users\puneet\Anaconda3\lib\site-packages\pulp\solvers.py", line 1362, in actualSolve
return self.solve_CBC(lp, **kwargs)
File "C:\Users\puneet\Anaconda3\lib\site-packages\pulp\solvers.py", line 1384, in solve_CBC
tmpMps, rename = 1)
File "C:\Users\puneet\Anaconda3\lib\site-packages\pulp\pulp.py", line 1484, in writeMPS
f.write(" LO BND %-8s % .12e\n" % (n, v.lowBound))
TypeError: must be real number, not str
reached finally
。另外,由于A变量中有大约12000行,因此我试图动态地创建约束。但是似乎存在一些问题。因此,我做错了什么?
答
var = pulp.LpVariable.dicts("var",range(11),pulp.LpContinuous)
必须
var = pulp.LpVariable.dicts("var",range(11),cat=pulp.LpContinuous)
为LpVariable.dicts FN看起来像这样
def dicts(self, name, indexs, lowBound = None, upBound = None, cat = LpContinuous, indexStart = []):
+0
谢谢它解决了错误。我也得到了无限的结果。那么,这是否意味着数据不是线性可分的? –
回溯显示您所使用的东西是一个字符串,而不是一个数字。我想知道这是否与你如何导入数据有关 – thomaskeefe