Random Sampling in NumPy: Beyond the Non-existent void random_standard_uniform_fill()

1. Random Sampling
   This refers to generating random numbers according to a specific probability distribution. In NumPy, the random module provides functions for various distributions.

2. Uniform Distribution
   This is a common distribution where values are equally likely to fall within a specified range.

3. NumPy Arrays
   NumPy excels at working with multidimensional arrays. Filling an array with random numbers is a common use case.

Using numpy.random.rand()

This function generates an array of random floats between 0 (inclusive) and 1 (exclusive).

import numpy as np

# Create an empty array of any shape
my_array = np.empty((3, 4))

# Fill the array with random floats between 0 and 1
np.random.rand(my_array.shape, out=my_array)

print(my_array)

Using numpy.random.uniform()

This function allows you to specify the lower and upper bound of the uniform distribution.

import numpy as np

# Create an empty array
my_array = np.empty((2, 2))

# Fill with random values between 5 and 10 (inclusive)
np.random.uniform(low=5, high=10, size=my_array.shape, out=my_array)

print(my_array)

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Generating random integers within a range

This code uses numpy.random.randint to generate random integers between a specified low (inclusive) and high (exclusive) bound.

import numpy as np

# Generate 10 random integers between 1 and 100 (inclusive)
random_ints = np.random.randint(low=1, high=101, size=10)

print(random_ints)

Specifying the size and shape of the random array

This code demonstrates controlling the size and shape of the output array with size and shape arguments.

import numpy as np

# Generate a 3x2 array of random floats between 0 and 1
random_array = np.random.rand(3, 2)

print(random_array)

Scaling the random values

This code shows how to scale the generated random values to a different range.

import numpy as np

# Generate 5 random floats between 0 and 1, then scale them to a range of 10 to 20
random_values = np.random.rand(5) * (20 - 10) + 10

print(random_values)

Seeding the random number generator (optional)

By default, NumPy's random number generator uses an unpredictable seed. To ensure reproducibility of your results, you can set a specific seed.

import numpy as np

# Set a seed for reproducibility
np.random.seed(10)

# Generate random values (results will be the same if you run this again)
random_values = np.random.rand(3)

print(random_values)

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1. Using numpy.random.rand()
   

This function directly generates an array of random floats between 0 (inclusive) and 1 (exclusive). You can then assign this array to your desired variable.

import numpy as np

# Create an empty array (optional)
my_array = np.empty((3, 4))

# Fill the array with random floats
my_array[:] = np.random.rand(my_array.shape)  # Fills the entire array

print(my_array)

1. Using numpy.random.uniform() with pre-allocation
   

Similar to the previous example, you can pre-allocate an array and then fill it with random values from a specific uniform distribution.

import numpy as np

# Create an empty array
my_array = np.empty((2, 2))

# Generate random values and assign them to the array
random_data = np.random.uniform(low=5, high=10, size=my_array.shape)
my_array[:] = random_data

print(my_array)

1. Using vectorized operations (for advanced users)
   

For simple uniform distribution, you can leverage vectorized operations. This approach might be more concise but requires a bit more NumPy knowledge.

import numpy as np

# Create an empty array
my_array = np.empty((3, 4))

# Fill with ones and scale to the range (0, 1) (assuming float type)
my_array[:] = np.random.rand(1)  # Generate a single random float

print(my_array)

Remember, these approaches don't modify an existing array in-place. They create a new array filled with random values.