numpy_random.Rmd

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# Random numbers with `np.random`

```{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 has a set of functions 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}
np.random.normal()
```

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

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

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

```{python}
np.random.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}
np.random.normal(size=(8, 5))
```

# 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 `numpy.random` functions.  You can achieve this by giving the random
numbers a *seed*.  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
np.random.seed(42)
# One set of random numbers
first_random_arr = np.random.normal(size=(4, 2))
first_random_arr
```

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

```{python}
# Reset the state of the random number generator
np.random.seed(42)
# The same as "first_random_arr" above.
np.random.normal(size=(4, 2))
```

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