4.2. Random Values¶

4.2.1. Generate Integer¶

Random int from low (inclusive) to high (exclusive)

>>> import numpy as np
>>> np.random.seed(0)

Generate pseudorandom int:

>>> np.random.randint(0, 10)
5

>>> np.random.randint(0, 10, size=5)
array([0, 3, 3, 7, 9])

>>> np.random.randint(0, 10, size=(2,3))
array([[3, 5, 2],
       [4, 7, 6]])

4.2.2. Generate Float¶

Random float in the half-open interval [0.0, 1.0)

>>> import numpy as np
>>> np.random.seed(0)

Generate pseudorandom float:

>>> np.random.random()
0.5488135039273248

>>> np.random.random(size=5)
array([0.71518937, 0.60276338, 0.54488318, 0.4236548 , 0.64589411])

>>> np.random.random(size=(2,3))
array([[0.43758721, 0.891773  , 0.96366276],
       [0.38344152, 0.79172504, 0.52889492]])

4.2.3. Assignments¶

Code 4.64. Solution¶

"""
* Assignment: Numpy Random Float
* Complexity: medium
* Lines of code: 1 lines
* Time: 3 min

English:
    1. Set random seed to zero
    2. Define `result: np.ndarray` of 10 random floats
    3. Run doctests - all must succeed

Polish:
    1. Ustaw ziarno losowości na zero
    2. Zdefiniuj `result: np.ndarray` z 10 losowymi liczbami zmiennoprzecinkowymi
    3. Uruchom doctesty - wszystkie muszą się powieść

Tests:
    >>> import sys; sys.tracebacklimit = 0

    >>> assert result is not Ellipsis, \
    'Assign result to variable: `result`'
    >>> assert type(result) is np.ndarray, \
    'Variable `result` has invalid type, expected: np.ndarray'

    >>> result
    array([0.5488135 , 0.71518937, 0.60276338, 0.54488318, 0.4236548 ,
           0.64589411, 0.43758721, 0.891773  , 0.96366276, 0.38344152])
"""

import numpy as np
np.random.seed(0)


result = ...

Code 4.65. Solution¶

"""
* Assignment: Numpy Random Int
* Complexity: easy
* Lines of code: 1 lines
* Time: 3 min

English:
    1. Set random seed to zero
    2. Define `result: np.ndarray` of size 16x16 with random integers `[0;9]` (inclusive)
    3. Run doctests - all must succeed

Polish:
    1. Ustaw ziarno losowości na zero
    2. Zdefiniuj `result: np.ndarray` o rozmiarze 16x16 z losowymi liczbami całkowitymi `<0,9>` (włącznie)
    3. Uruchom doctesty - wszystkie muszą się powieść

Tests:
    >>> import sys; sys.tracebacklimit = 0

    >>> assert result is not Ellipsis, \
    'Assign result to variable: `result`'
    >>> assert type(result) is np.ndarray, \
    'Variable `result` has invalid type, expected: np.ndarray'

    >>> result
    array([[5, 0, 3, 3, 7, 9, 3, 5, 2, 4, 7, 6, 8, 8, 1, 6],
           [7, 7, 8, 1, 5, 9, 8, 9, 4, 3, 0, 3, 5, 0, 2, 3],
           [8, 1, 3, 3, 3, 7, 0, 1, 9, 9, 0, 4, 7, 3, 2, 7],
           [2, 0, 0, 4, 5, 5, 6, 8, 4, 1, 4, 9, 8, 1, 1, 7],
           [9, 9, 3, 6, 7, 2, 0, 3, 5, 9, 4, 4, 6, 4, 4, 3],
           [4, 4, 8, 4, 3, 7, 5, 5, 0, 1, 5, 9, 3, 0, 5, 0],
           [1, 2, 4, 2, 0, 3, 2, 0, 7, 5, 9, 0, 2, 7, 2, 9],
           [2, 3, 3, 2, 3, 4, 1, 2, 9, 1, 4, 6, 8, 2, 3, 0],
           [0, 6, 0, 6, 3, 3, 8, 8, 8, 2, 3, 2, 0, 8, 8, 3],
           [8, 2, 8, 4, 3, 0, 4, 3, 6, 9, 8, 0, 8, 5, 9, 0],
           [9, 6, 5, 3, 1, 8, 0, 4, 9, 6, 5, 7, 8, 8, 9, 2],
           [8, 6, 6, 9, 1, 6, 8, 8, 3, 2, 3, 6, 3, 6, 5, 7],
           [0, 8, 4, 6, 5, 8, 2, 3, 9, 7, 5, 3, 4, 5, 3, 3],
           [7, 9, 9, 9, 7, 3, 2, 3, 9, 7, 7, 5, 1, 2, 2, 8],
           [1, 5, 8, 4, 0, 2, 5, 5, 0, 8, 1, 1, 0, 3, 8, 8],
           [4, 4, 0, 9, 3, 7, 3, 2, 1, 1, 2, 1, 4, 2, 5, 5]])
"""

import numpy as np
np.random.seed(0)


result = ...

Code 4.66. Solution¶

"""
* Assignment: Numpy Random Choice
* Complexity: medium
* Lines of code: 1 lines
* Time: 3 min

English:
    1. Set random seed to zero
    2. Define `result: np.ndarray` with 6 random numbers
       without repetition from `DATA`
    3. Run doctests - all must succeed

Polish:
    1. Ustaw ziarno losowości na zero
    2. Zdefiniuj `result: np.ndarray` z 6 losowymi
       liczbami bez powtórzeń z `DATA`
    3. Uruchom doctesty - wszystkie muszą się powieść

Tests:
    >>> import sys; sys.tracebacklimit = 0

    >>> assert result is not Ellipsis, \
    'Assign result to variable: `result`'
    >>> assert type(result) is np.ndarray, \
    'Variable `result` has invalid type, expected: np.ndarray'

    >>> result
    array([30,  5, 27, 31, 33, 38])
"""

import numpy as np
np.random.seed(0)

DATA = np.arange(1, 50)
result = ...