-
Notifications
You must be signed in to change notification settings - Fork 0
/
Development_Regression.py
38 lines (30 loc) · 1.06 KB
/
Development_Regression.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
from sklearn import preprocessing
from sklearn.cluster import KMeans
from sklearn.linear_model import LinearRegression
def data_list(Numpyarray, X):
for i in range(0, len(Numpyarray)):
X.append([Numpyarray[i]])
def main():
development_data=pd.read_csv('DevData.csv')
print("The total number of data:", development_data.size)
fig=plt.figure(figsize=(50,50))
#cleaned.shape[1]
cleaned = np.array(development_data.dropna())
for i in range(0, 5):
for j in range(0, 5):
print(i, j, '\n')
X=[]
Y=[]
data_list(cleaned[:, i], X)
data_list(cleaned[:, j], Y)
ax=fig.add_subplot(12, 12, i*12+j+1)
if i!=j:
ax.scatter(X,Y)
#else:
# ax.annotate("Series"+str(i), xycoords='axes fraction', ha="center", va="center")
plt.savefig('relationship.pdf', format='pdf')
if __name__ == '__main__':
main()