Fundamentos de Álgebra Lineal

!pip install --upgrade pip !pip install scipy pillow imageio matplotlib seaborn scikit-learn;

Requirement already satisfied: pip in /home/mazzaroli/anaconda3/lib/python3.9/site-packages (22.3.1)
Requirement already satisfied: scipy in /home/mazzaroli/anaconda3/lib/python3.9/site-packages (1.7.1)
Requirement already satisfied: pillow in /home/mazzaroli/anaconda3/lib/python3.9/site-packages (8.4.0)
Requirement already satisfied: imageio in /home/mazzaroli/anaconda3/lib/python3.9/site-packages (2.9.0)
Requirement already satisfied: matplotlib in /home/mazzaroli/anaconda3/lib/python3.9/site-packages (3.5.1)
Requirement already satisfied: seaborn in /home/mazzaroli/anaconda3/lib/python3.9/site-packages (0.11.2)
Requirement already satisfied: scikit-learn in /home/mazzaroli/anaconda3/lib/python3.9/site-packages (1.1.2)
ERROR: Could not find a version that satisfies the requirement mpl_toolkits (from versions: none)
ERROR: No matching distribution found for mpl_toolkits

import seaborn as sns vuelos = sns.load_dataset("flights") vuelos = vuelos.pivot("month", "year", "passengers") ax = sns.heatmap(vuelos)

escalar = 5.678 print(escalar)

5.678

escalar_python = True print(escalar_python) print(type(escalar_python))

True
<class 'bool'>

import numpy as np

vector = np.array([1,2,3,4]) print(vector)

[1 2 3 4]

matriz = np.array([[1,2,3],[4,5,6],[7,8,9]]) print(matriz)

[[1 2 3]
 [4 5 6]
 [7 8 9]]

tensor = np.array( [ [[1,2,3],[4,5,6],[7,8,9]], [[11,12,13],[14,15,16],[17,18,19]], [[21,22,23],[24,25,26],[27,28,29]], ]) print(tensor)

[[[ 1  2  3]
  [ 4  5  6]
  [ 7  8  9]]

 [[11 12 13]
  [14 15 16]
  [17 18 19]]

 [[21 22 23]
  [24 25 26]
  [27 28 29]]]

%matplotlib inline import matplotlib.pyplot as plt

plt.imshow(tensor,interpolation='nearest') plt.show()

tensor = np.array( [ [[0,0,0],[0,0,0],[0,0,0]], [[128,128,128],[128,128,128],[128,128,128]], [[255,255,255],[255,255,255],[255,255,255]], ] ) plt.imshow(tensor,interpolation='nearest') plt.show()

escalar = 5.678 vector = np.array([1,2,3]) matriz = np.array([[1,2,3],[4,5,6],[7,8,9]]) tensor = np.array([ [[1,2,3],[4,5,6],[7,8,9]], [[11,12,13],[14,15,16],[17,18,19]], [[21,22,23],[24,25,26],[27,28,29]], ])

print( '\nContenido del escalar:\n',escalar, )


Contenido del escalar:
 5.678

print( '\nContenido del vector:\n',vector, '\nDimensiones del vector:\n',vector.shape, '\nLargo de la primera columna el vector:\n',len(vector), )


Contenido del vector:
 [1 2 3] 
Dimensiones del vector:
 (3,) 
Largo de la primera columna el vector:
 3

print( '\nContenido de la matriz:\n',matriz, '\nDimensiones de la matriz:\n',matriz.shape, '\nLargo de la primera columna de la matriz:\n',len(matriz), '\nNumero de contenido dentro de la matriz:\n',matriz.size, )


Contenido de la matriz:
 [[1 2 3]
 [4 5 6]
 [7 8 9]] 
Dimensiones de la matriz:
 (3, 3) 
Largo de la primera columna de la matriz:
 3 
Numero de contenido dentro de la matriz:
 9

print( '\nContenido de la tensor:\n',tensor, '\nDimensiones de la tensor:\n',tensor.shape, '\nLargo de la primera columna de la tensor:\n',len(tensor), '\nNumero de contenido dentro de la tensor:\n',tensor.size, )


Contenido de la tensor:
 [[[ 1  2  3]
  [ 4  5  6]
  [ 7  8  9]]

 [[11 12 13]
  [14 15 16]
  [17 18 19]]

 [[21 22 23]
  [24 25 26]
  [27 28 29]]] 
Dimensiones de la tensor:
 (3, 3, 3) 
Largo de la primera columna de la tensor:
 3 
Numero de contenido dentro de la tensor:
 27

import numpy as np

escalar = np.array(5.678) vector = np.array([1,2,3]) matriz = np.array([[1,2,3],[4,5,6],[7,8,9]]) tensor = np.array([ [[1,2,3],[4,5,6],[7,8,9]], [[11,12,13],[14,15,16],[17,18,19]], [[21,22,23],[24,25,26],[27,28,29]], ])

escalar_t = escalar.T print(escalar_t)

5.678

vector_t = vector.T print(vector_t)

[1 2 3]

vector_t = vector_t.reshape(-1,1) print(vector_t)

[[1]
 [2]
 [3]]

matriz_t = matriz.T print( 'Matriz A:',matriz,'', 'Matriz A^t:',matriz_t, sep='\n')

Matriz A:
[[1 2 3]
 [4 5 6]
 [7 8 9]]

Matriz A^t:
[[1 4 7]
 [2 5 8]
 [3 6 9]]

tensor_t = tensor.T print('Tensor A^t:\n',tensor_t)

Tensor A^t:
 [[[ 1 11 21]
  [ 4 14 24]
  [ 7 17 27]]

 [[ 2 12 22]
  [ 5 15 25]
  [ 8 18 28]]

 [[ 3 13 23]
  [ 6 16 26]
  [ 9 19 29]]]

print('Tensor A:\n',tensor)

Tensor A:
 [[[ 1  2  3]
  [ 4  5  6]
  [ 7  8  9]]

 [[11 12 13]
  [14 15 16]
  [17 18 19]]

 [[21 22 23]
  [24 25 26]
  [27 28 29]]]

A = matriz[0:2] B = matriz_t[0:3,0:2] print(f''' Matriz A:\n{A} Matriz B:\n{B} ''')


Matriz A:
[[1 2 3]
 [4 5 6]]
Matriz B:
[[1 4]
 [2 5]
 [3 6]]

C = A+B C

There’s an error in this block

Try running the app again, or contact the app’s creator

A = np.array([[1,2],[3,4],[5,6]]) B = np.array([[6,5],[4,3],[2,1]]) print(f''' Matriz A: \n{A} Matriz B:\n{B} ''')