---
description: 2.3.5
---

# Functionality

In this chapter, the author mainly talks about the importance of balancing the pros and cons of using abstraction in Python, highlighting the need for a trade-off. However, considering that I'm just a coding newbie, I'm way off that level. For now, learning the Python mentioned in the chapter is sufficient for me.

```python
samples = []
for _ in range(100):
    samples += [distribution.sample()]
```

This Python code is designed to draw 100 samples from a distribution. Here's how it works:

1. `samples = []`: This line of code creates an empty list named `samples`. This list will be used to store the samples that we draw from the distribution.
2. `for _ in range(100):`: This line of code starts a **loop** that will run 100 times. In Python, `_` is a commonly used placeholder for a variable that we don't plan to use within the loop body.
3. `samples += [distribution.sample()]`: This line of code is executed in each iteration of the loop. `distribution.sample()` draws a sample from an object named `distribution`. This `distribution` object should be a distribution object, which has a `sample` method that can be used to draw samples. `[distribution.sample()]` creates a list containing one element, and then `+=` adds this list to the `samples` list.

After the loop has run its course, the `samples` list will contain 100 samples, all drawn from the `distribution` object.

It's important to note that this code assumes that the `distribution` object already exists and has a `sample` method. If `distribution` is not defined before this code is run, or if `distribution` does not have a `sample` method, then this code will raise an error.

<pre class="language-python"><code class="lang-python">class Distribution(ABC, Generic[A]):
    ...
    
<strong>    def sample_n(self, n: int) -> Sequence[A]:
</strong><strong>        return [self.sample() for _ in range(n)]
</strong></code></pre>

* please teach me the code like I'm a fool(to gpt4)
* This Python code is defining a class called `Distribution`. A class in Python is like a blueprint for creating objects. In this case, the `Distribution` class is a blueprint for creating distribution objects.

1. `class Distribution(ABC, Generic[A]):` This line is declaring the class and its inheritance. The `Distribution` class is inheriting from two parent classes: `ABC` and `Generic[A]`.
   * `ABC` stands for Abstract Base Class. An abstract base class is a kind of class that is meant to be inherited by other classes, and it can't be used to create objects directly. It's a way of enforcing certain methods and structure for the classes that inherit from it.
   * `Generic[A]` is a way of making the class "generic" so it can work with any data type. The `A` is a placeholder for whatever data type you want to use when you create an object from this class.
2. `...` This is a placeholder indicating that there might be more code in the `Distribution` class that isn't shown here.
3. `def sample_n(self, n: int) -> Sequence[A]:` This line is defining a method within the `Distribution` class. A method is a function that belongs to an object.
   * `sample_n` is the name of the method. This method takes two arguments: `self` and `n`. `self` is a special argument that refers to the object itself. `n` is an integer that specifies how many samples you want to take.
   * `-> Sequence[A]` is a type hint that tells you what kind of value the method returns. In this case, it returns a sequence (like a list or a tuple) of items of type `A`.
4. `return [self.sample() for _ in range(n)]` This line is the body of the `sample_n` method. It's using a **list comprehension** to create a list of samples. It does this by calling the `sample` method (which isn't shown here, but we can assume it's defined elsewhere in the `Distribution` class) `n` times.

So, to sum up, this `Distribution` class is a blueprint for creating distribution objects. These objects can work with any data type (`A`), and they have a method (`sample_n`) that generates a list of `n` samples from the distribution.

<pre class="language-python"><code class="lang-python">
import numpy as np

@dataclass
class Gaussian(Distribution[float]):
    μ: float
<strong>    σ: float
</strong><strong>    
</strong>    def sample(self) -> float:
        return np.random.normal(loc=self.μ, scale=self.σ)
    
<strong>    def sample_n(self, n: int) -> Sequence[float]:
</strong>        return np.random.normal(loc=self.μ, scale=self.σ, size=n)
</code></pre>

* please teach me the code like I'm a fool(to gpt4)
*   Absolutely, let's break this down!

    This Python code is defining a class called `Gaussian` that represents a Gaussian (or normal) distribution. Here's what each part of the code does:

    1. `import numpy as np`: This line imports the numpy library, which is a powerful library for numerical computations in Python. The `as np` part means that we can refer to numpy as `np` in the rest of the code.
    2. `@dataclass`: This is a **decorator** in Python that **automatically adds special methods** to the class, including the `__init__` and `__repr__` methods, which are used for creating and representing the class objects respectively. This saves us from having to write these methods ourselves.
    3. `class Gaussian(Distribution[float]):` This line defines the `Gaussian` class, which inherits from the `Distribution` class. The `[float]` part means that this class works with data of type `float`.
    4. `μ: float` and `σ: float`: These lines define two instance variables for the class: **`μ` (mu, representing the mean of the distribution) and `σ` (sigma, representing the standard deviation of the distribution)**. Both are of type `float`.
    5. `def sample(self) -> float:`: This line defines a method called `sample` that returns a single sample from the Gaussian distribution. The `-> float` part is a type hint that indicates the method returns a float.
    6. `return np.random.normal(loc=self.μ, scale=self.σ)`: This line is the body of the `sample` method. It uses the **`np.random.normal`** function from numpy to generate a random number from a normal distribution with mean `self.μ` and standard deviation `self.σ`.
    7. `def sample_n(self, n: int) -> Sequence[float]:`: This line defines a method called `sample_n` that returns `n` samples from the Gaussian distribution. The `n: int` part means the method takes an integer argument `n`, and the `-> Sequence[float]` part is a type hint that indicates the method returns a sequence of floats.
    8. `return np.random.normal(loc=self.μ, scale=self.σ, size=n)`: This line is the body of the `sample_n` method. It's similar to the `sample` method, but it uses the `size` parameter of the `np.random.normal` function to generate `n` numbers instead of just one.

    So, to sum up, this `Gaussian` class represents a Gaussian distribution with a given mean and standard deviation. It has two methods for generating random samples from the distribution: one that generates a single sample, and one that generates `n` samples.

The code for Distribution and several concrete classes implementing the Distribution interface (including Gaussian) is in the file [rl/distribution.py](https://github.com/TikhonJelvis/RL-book/blob/master/rl/distribution.py).
