Algebra lineare

di
Numpy
  1. np.linalg.eig(a), determinante
  2. np.linalg.eig(a), autovalori e autovettori
  3. np.linalg.norm(a)
  4. np.linalg.solve(a,b)
  5. …

Inoltre

  1. np.dot(a,b)
  2. np.inner(a,b)
  3. np.outer(a,b)
  4. np.trace(a)
  5. …

Prova le operazioni più frequenti dell’algebra lineare

import numpy as np

a = np.array([[1.0, 2.0], [3.0, 4.0]]) # [[ 1.  2.] [ 3.  4.]]
a.transpose()                          # array([[ 1.,  3.], [ 2.,  4.]])
np.linalg.inv(a)                       # array([[-2. ,  1. ], [ 1.5, -0.5]])
u = np.eye(2)                          # array([[ 1.,  0.], [ 0.,  1.]])
j = np.array([[0.0, -1.0], [1.0, 0.0]])
j @ j                                  # array([[-1.,  0.], [ 0., -1.]])
np.trace(u)                            # 2.0
y = np.array([[5.], [7.]]) 
np.linalg.solve(a, y)                  # array([[-3.], [ 4.]])
np.linalg.eig(j)                       # (array([ 0.+1.j,  0.-1.j]), array([[ 0.70710678+0.j,  0.70710678-0.j ], 
                                       # [ 0.00000000-0.70710678j,  0.00000000+0.70710678j]]))

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