why eigenvectors very slow
what is the difference between numpy and sympy when doing matrix calculation
will numpy faster and more accurate?
import csv as csv
from sympy import *
data = []
open1 = []
high1 = []
low1 = []
close1 = []
date1 = []
list_t1 = []
path = r'C:\Users\wilson\Downloads\Downloads\execution.csv'
with open(path, 'rb') as csvfile:
readdata=csv.reader(csvfile)
count = 0
for row in readdata:
date1.append(row[0])
open1.append(row[1])
high1.append(row[2])
low1.append(row[3])
close1.append(row[4])
print(close1[1])
k = 0
inputmatrix1 = []
inputmatrix2 = []
inputmatrix3 = []
eigenvectormatrix1 = []
eigenvectormatrix2 = []
eigenvectormatrix3 = []
for num in range(1, 30):
temp1 =
Matrix([[close1[k+num],close1[k+num+1],close1[k+num+2]],[close1[k+num+1],close1[k+num+2],0],[close1[k+num+2],0,0]])
temp2 =
Matrix([[close1[k+num+1],close1[k+num+2],close1[k+num+3]],[close1[k+num+2],close1[k+num+3],0],[close1[k+num+3],0,0]])
temp3 =
Matrix([[close1[k+num+2],close1[k+num+3],close1[k+num+4]],[close1[k+num+3],close1[k+num+4],0],[close1[k+num+4],0,0]])
print("debug1")
inputmatrix1.append(temp1)
inputmatrix2.append(temp2)
inputmatrix3.append(temp3)
print("debug2")
editedinputmatrix1 = temp1.T*temp1
editedinputmatrix2 = temp2.T*temp2
editedinputmatrix3 = temp3.T*temp3
print("debug3")
print(editedinputmatrix1.eigenvects())
print(editedinputmatrix2.eigenvects())
print(editedinputmatrix3.eigenvects())
eigenvectormatrix1.append(editedinputmatrix1.eigenvects())
eigenvectormatrix2.append(editedinputmatrix2.eigenvects())
eigenvectormatrix3.append(editedinputmatrix3.eigenvects())
print(num)
print(editedinputmatrix3.eigenvects())
print(eigenvectormatrix3)
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