# minte9 learningjourney

S R Q

### Vector

p01 A vector is an one-dimensional array.

""" Numpy Vectors
One-dimensional arrays
"""

import numpy as np

row = np.array([1, 2, 3])   # row vector
column = np.array([         # column vector
[1],
[4],
[3],
])

print(row) # [1 2 3]
print(column)
# [[1]
#  [4]
#  [3]]


### Matrix

p02 Numpy main data structure is the multidimensional array.

""" Create Matrices
Numpy is the foundation of the Python machine learning stack
The main data structure is the multidimensional array
Arrays are zero-indexed, first element index is 0
Use ':' to select everithing 'up to' or 'after'
"""

import numpy as np

# Matrix
matrix = np.array([ # three rows, two columns
[1, 2],
[1, 2],
[1, 2],
])
print(matrix)   # [[1 2] [1 2] [1 2]]
print()

# Describe
matrix = np.array([
[1, 2, 3, 4],
[5, 6, 7, 8],
[9, 10, 11, 12],
])
print(matrix.shape) # (3, 4)
print(matrix.size)  # 12
print(matrix.ndim)  # 2

# Extract
vector = np.array([1, 2, 3, 4, 5, 6])

print(vector[:])    # [1 2 3 4 5 6]
print(vector[:3])   # [1 2 3]
print(vector[3:])   # [4 5 6]
print(vector[-1])   # 6
print()

assert(vector[:]    == [1, 2, 3, 4, 5, 6])  .all() # passed
assert(vector[:3]   == [1, 2, 3])           .all() # passed
assert(vector[3:]   == [4, 5, 6])           .all() # passed
assert(vector[-1]   == 6)                          # passed

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

print(matrix[1, 1])     # 5
print(matrix[:2, :])    # [[1 2 3] [4 5 6]]     # first two rows
print(matrix[:, 1:2])   # [[2] [5] [8]]         # all rows, second column
print()

assert(matrix[1, 1]     == 5)                              # passed
assert(matrix[:2, :]    == [[1, 2, 3], [4, 5, 6]])  .all() # passed
assert(matrix[:, 1:2]   == [[2], [5], [8]])         .all() # passed


Questions
Last update: 47 days ago