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import typing

import gym
import matplotlib.pyplot as plt
import numpy as np
from IPython import display


# represent states as arrays and actions as ints
State = np.ndarray

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import pyvirtualdisplay


_display = pyvirtualdisplay.Display(visible=False,  # use False with Xvfb
                                    size=(1400, 900))
_ = _display.start()

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class BinarySearchTree(object):
    
    _length = 0
    
    _root = None
    
    class TreeNode(object):
        
        def __init__(self, value, left_child, parent, right_child):
            """Create an instance of a TreeNode."""

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from collections.abc import Sequence


class DoubleLinkedList(Sequence):
    """Simple implementation of a doubly linked list."""
    
    _length = 0
     
    _head = None
    

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from collections.abc import Sequence


class LinkedList(Sequence):
    """Simple implementation of a linked list."""
    
    _length = 0
     
    _head = None
    

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Creating a Conda development environment

We recommend that you set up a Python environment for developing quantecon that is separate from the "default" Python environment that you use in your own research. This will allow you to contribute to quantecon without worrying about corrupting the Python environment on which your other work depends.

Below are instructions for building a separate development environment for contributing to the quantecon package using the Conda package management system which comes bundled with the Anaconda Python distribution provided by Continuum Analytics.

Step 1:

Fork and clone a copy of the quant-econ repository on to your local machine.

Step 2:

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/////////////// Basic RBC model ///////////////

////////// Declare variables //////////

///// Endogenous variables /////

/* List of variables:
   
   k: capital
   c: consumption