numpy_random.Rmd

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# Numpy random number generators

```{python}
#: standard imports
import numpy as np
# print arrays to 4 decimal places
np.set_printoptions(precision=4, suppress=True)
```

We often need random numbers, for tests and for taking random samples, and for
other things. `np.random` is a submodule within numpy:

```{python}
type(np.random)
```

It contains function that will create a *random number generator*.

```{python}
# Make a random number generator.
rng = np.random.default_rng()
type(rng)
```

This generator is an object that has a set of methods for returning random
numbers of various sorts.  For example, to return a single random number from
the default normal distribution (mean 0, variance 1):

```{python}
rng.normal()
```

You can set the mean and variance with the first two input parameters:

```{python}
# Random number from distribution with mean 15, variance 2
rng.normal(15, 2)
```

To return a 8 by 5 array of random numbers from the same distribution:

```{python}
rng.normal(15, 2, size=(8, 5))
```

A 5 by 3 array of random numbers from the standard normal distribution with
mean 1 and variance 1:

```{python}
rng.normal(size=(5, 3))
```

## Making random numbers predictable


Sometimes you want to make sure that the random numbers are predictable, in
that you will always get the same set of random numbers from a series of calls
to the `rng` methods.  You can achieve this by giving the random number
generator a *seed* when you create it.  This is an integer that sets the
random number generator into a predictable state, such that it will always
return the same sequence of random numbers from this point:

```{python}
# Set the state of the random number generator on creation.
new_rng = np.random.default_rng(seed=42)
# One set of random numbers
first_random_arr = new_rng.normal(size=(4, 2))
first_random_arr
```

```{python}
# Another set
second_random_arr = new_rng.normal(size=(4, 2))
second_random_arr
```

```{python}
# Make another random number generator with the same seed.
new_rng2 = np.random.default_rng(seed=42)
# The same as "first_random_arr" above.
new_rng2.normal(size=(4, 2))
```

```{python}
# The same as "second_random_arr" above.
new_rng2.normal(size=(4, 2))
```