# 4.3. Random Distributions

## 4.3.1. Continuous Uniform Distribution

• Results are from the "continuous uniform" distribution over the stated interval

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


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

>>> np.random.rand(5)
array([0.5488135 , 0.71518937, 0.60276338, 0.54488318, 0.4236548 ])

>>> np.random.rand(2,3)
array([[0.64589411, 0.43758721, 0.891773  ],
[0.96366276, 0.38344152, 0.79172504]])

>>> np.random.rand(3,2)
array([[0.52889492, 0.56804456],
[0.92559664, 0.07103606],
[0.0871293 , 0.0202184 ]])


## 4.3.2. Normal (Gaussian) Distribution

• Draw pseudorandom samples from a normal (Gaussian) distribution

Defaults:

• μ - loc=0.0

• σ - scale=1.0

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


Draw pseudorandom samples from a normal (Gaussian) distribution:

>>> np.random.normal()
1.764052345967664

>>> np.random.normal(0.0, 1.0)
0.4001572083672233

>>> np.random.normal(loc=0.0, scale=1.0)
0.9787379841057392

>>> np.random.normal(size=5)
array([ 2.2408932 ,  1.86755799, -0.97727788,  0.95008842, -0.15135721])

>>> np.random.normal(loc=0.0, scale=1.0, size=(2,3))
array([[-0.10321885,  0.4105985 ,  0.14404357],
[ 1.45427351,  0.76103773,  0.12167502]])


## 4.3.3. Poisson Distribution

• Draw samples from a Poisson distribution

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


Draw samples from a Poisson distribution:

>>> np.random.poisson(6.0)
11

>>> np.random.poisson(lam=6.0)
4

>>> np.random.poisson(lam=6.0, size=5)
array([9, 7, 8, 5, 5])

>>> np.random.poisson(lam=6.0, size=(2,3))
array([[5, 5, 7],
[3, 5, 6]])


## 4.3.5. Assignments

"""
* 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 = ...


"""
* 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 = ...


"""
* 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 = ...