Having trouble with finding standard distribution of this matrix. Trying to make sense of my egienvectors and...
so I have this matrix
I wanted to normalize my eigenvector, using np.linalg.norm() and I got a value say xxx. However, when I divided each index of my eigenvector by xxx and I took the sum, I got a value greater than 1??? Here's what happened
here's my array P
array([[0.756, 0.113, 0.129, 0.002, 0. , 0. , 0. ],
[0.174, 0.821, 0.004, 0.001, 0. , 0. , 0. ],
[0.141, 0.001, 0.776, 0.082, 0. , 0. , 0. ],
[0.003, 0. , 0.192, 0.753, 0.052, 0. , 0. ],
[0. , 0. , 0.002, 0.227, 0.735, 0.036, 0. ],
[0. , 0. , 0. , 0.007, 0.367, 0.604, 0.022],
[0. , 0. , 0. , 0. , 0.053, 0.158, 0.789]])
PT =P.T #To transpose the matrix into column vectors
a=np.linalg.eig(PT)
c=np.linalg.norm(a[1][0])
for i in range(len(a[1][0])):
b[i]=a[1][0][i]/c
print(b)
print(sum(b))
[ 0.48084502 0.32410593 0.53032645 -0.4925481 0.33841701 -0.15920675
0.00455786]
1.026497437739756
python statistics
asked Nov 20 at 11:04
Kevin Vo
11
add a comment |
so I have this matrix
I wanted to normalize my eigenvector, using np.linalg.norm() and I got a value say xxx. However, when I divided each index of my eigenvector by xxx and I took the sum, I got a value greater than 1??? Here's what happened
here's my array P
array([[0.756, 0.113, 0.129, 0.002, 0. , 0. , 0. ],
[0.174, 0.821, 0.004, 0.001, 0. , 0. , 0. ],
[0.141, 0.001, 0.776, 0.082, 0. , 0. , 0. ],
[0.003, 0. , 0.192, 0.753, 0.052, 0. , 0. ],
[0. , 0. , 0.002, 0.227, 0.735, 0.036, 0. ],
[0. , 0. , 0. , 0.007, 0.367, 0.604, 0.022],
[0. , 0. , 0. , 0. , 0.053, 0.158, 0.789]])
PT =P.T #To transpose the matrix into column vectors
a=np.linalg.eig(PT)
c=np.linalg.norm(a[1][0])
for i in range(len(a[1][0])):
b[i]=a[1][0][i]/c
print(b)
print(sum(b))
[ 0.48084502 0.32410593 0.53032645 -0.4925481 0.33841701 -0.15920675
0.00455786]
1.026497437739756
python statistics
asked Nov 20 at 11:04
Kevin Vo
11
add a comment |
so I have this matrix
I wanted to normalize my eigenvector, using np.linalg.norm() and I got a value say xxx. However, when I divided each index of my eigenvector by xxx and I took the sum, I got a value greater than 1??? Here's what happened
here's my array P
array([[0.756, 0.113, 0.129, 0.002, 0. , 0. , 0. ],
[0.174, 0.821, 0.004, 0.001, 0. , 0. , 0. ],
[0.141, 0.001, 0.776, 0.082, 0. , 0. , 0. ],
[0.003, 0. , 0.192, 0.753, 0.052, 0. , 0. ],
[0. , 0. , 0.002, 0.227, 0.735, 0.036, 0. ],
[0. , 0. , 0. , 0.007, 0.367, 0.604, 0.022],
[0. , 0. , 0. , 0. , 0.053, 0.158, 0.789]])
PT =P.T #To transpose the matrix into column vectors
a=np.linalg.eig(PT)
c=np.linalg.norm(a[1][0])
for i in range(len(a[1][0])):
b[i]=a[1][0][i]/c
print(b)
print(sum(b))
[ 0.48084502 0.32410593 0.53032645 -0.4925481 0.33841701 -0.15920675
0.00455786]
1.026497437739756
python statistics
asked Nov 20 at 11:04
Kevin Vo
11
so I have this matrix
I wanted to normalize my eigenvector, using np.linalg.norm() and I got a value say xxx. However, when I divided each index of my eigenvector by xxx and I took the sum, I got a value greater than 1??? Here's what happened
here's my array P
array([[0.756, 0.113, 0.129, 0.002, 0. , 0. , 0. ],
[0.174, 0.821, 0.004, 0.001, 0. , 0. , 0. ],
[0.141, 0.001, 0.776, 0.082, 0. , 0. , 0. ],
[0.003, 0. , 0.192, 0.753, 0.052, 0. , 0. ],
[0. , 0. , 0.002, 0.227, 0.735, 0.036, 0. ],
[0. , 0. , 0. , 0.007, 0.367, 0.604, 0.022],
[0. , 0. , 0. , 0. , 0.053, 0.158, 0.789]])
PT =P.T #To transpose the matrix into column vectors
a=np.linalg.eig(PT)
c=np.linalg.norm(a[1][0])
for i in range(len(a[1][0])):
b[i]=a[1][0][i]/c
print(b)
print(sum(b))
[ 0.48084502 0.32410593 0.53032645 -0.4925481 0.33841701 -0.15920675
0.00455786]
1.026497437739756
python statistics
python statistics
asked Nov 20 at 11:04
Kevin Vo
11
asked Nov 20 at 11:04
Kevin Vo
11
asked Nov 20 at 11:04
Kevin Vo
11
asked Nov 20 at 11:04
Kevin Vo
11
asked Nov 20 at 11:04
Kevin Vo
11
11
add a comment |
add a comment |
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