我有以下代码：
当前代码：
import math
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
from scipy.stats import linregress
c1_high = 98
c1_low = 75
c2_high = 15
c2_low = 6
c3_high = 8
c3_low = 2
def mix_gen(number):
flag = 0
container = []
y_array = [1,2,3,4,5,6,7,8,9,10,11]
while flag < number:
c1 = np.random.uniform(c1_low, c1_high)
c2 = np.random.uniform(c2_low, c2_high)
c3 = np.random.uniform(c3_low, c3_high)
tot = c1+c2+c3
if 99.99 <= tot <= 100.01:
flag += 1
container.append([c1,c2,c3])
return container
def average(x):
assert len(x) > 0
return float(sum(x)) / len(x)
def pearson_def(x, y):
assert len(x) == len(y)
n = len(x)
assert n > 0
avg_x = average(x)
avg_y = average(y)
diffprod = 0
xdiff2 = 0
ydiff2 = 0
for idx in range(n):
xdiff = x[idx] - avg_x
ydiff = y[idx] - avg_y
diffprod += xdiff * ydiff
xdiff2 += xdiff * xdiff
ydiff2 += ydiff * ydiff
return diffprod / math.sqrt(xdiff2 * ydiff2)
def corr_check():
while True:
mixes = mix_gen(5)
mixes_C1 =[item[0] for item in mixes]
mixes_C2 =[item[1] for item in mixes]
mixes_C3 =[item[2] for item in mixes]
mylen = [1,2,3,4,5]
c1_r = pearson_def(mixes_C1, mylen)
c2_r = pearson_def(mixes_C2, mylen)
c3_r = pearson_def(mixes_C3, mylen)
if c1_r >0.99 and c2_r >0.99 and c3_r>0.99:
print(mixes)
print (c1_r)
else:
continue
corr = corr_check()
print(corr)
这段代码为我提供了有效的（当转换为数据帧时）以下输出：
C1 C2 C3 sum range
1 70 20 10 100 ^
2 .. |
3 .. |
4 .. |
5 .. |
6 .. |
7 .. |
8 .. |
9 .. |
10 .. |
11 90 _
我要求每行的总和等于100，并且每列具有r ^ 2值（Pearson Corr。）为＆gt; 0.99。
然而，所需的复杂性和迭代次数使问题几乎无法解决。是否有更好的方法来实现这一目标，而不是试图依赖所有三个组件C1，C2和C3的初始随机数生成？