paperagent.py

"""
CVR COLLEGE OF ENGINEERING AND TECHNOLOGY
DEPARTMENT OF ARTIFICIAL INTELLIGENCE
PROGRAM: A PAPER BUYING AGENT
BY: MIR HABEEBULLAH SHAH QUADRI
ROLL NO: 18B81DA914
CLASS: MTECH - I YEAR (AI)
UNDER THE GUIDANCE AND SUPERVISION OF: DR.PONNUSAMY

"""

class Displayable(object):
    max_display_level = 1 # can be overriden in subclasses
    
    def display(self,level,*args,**nargs):
        """
        print the arguments if level is less than or equal to the
        current max_display_level. level is an integer.
        the other arguments are whatever arguments print can take.

        """
        if level <= self.max_display_level:
            print(*args, **nargs)
            ##if error you are using Python2 not Python3

import random

class Agent(object):
    def __init__(self, env):
        """ set up the agent """
        self.env = env

    def go(self, n):
        """ acts for n time steps """
        raise NotImplementedError("go") #abstract method

class Environment(Displayable):
    def initial_percepts(self):
        """ returns the initial percepts of the agent """
        raise NotImplementedError("initial_percepts") #abstract method

    def do(self, action):
        """ does the action in the environment
            and returns the next percept """
        raise NotImplementedError("do") #abstract method

class TP_env(Environment):
    prices = [234, 234, 234, 234, 255, 255, 275, 275, 211, 211, 211,
              234, 234, 234, 234, 199, 199, 275, 275, 234, 234, 234,
              234, 255, 255, 260, 260, 265, 265, 265, 265, 270, 270,
              255, 255, 260, 260, 265, 265, 150, 150, 265, 265, 270,
              270, 255, 255, 260, 260, 265, 265, 265, 265, 270, 270,
              211, 211, 255, 255, 260, 260, 265, 265, 260, 265, 270,
              270, 205, 255, 255, 260, 260, 265, 265, 265, 265, 270,
              270]
    max_price_addon = 20 # maximum random value added to get price

    def __init__(self):
        """ paper buying agent """
        self.time = 0
        self.stock = 20
        self.stock_history = [] # memory of the stock history
        self.price_history = [] # memory of the price history

    def initial_percepts(self):
        """ return initial percepts """
        self.stock_history.append(self.stock)
        price = self.prices[0]+random.randrange(self.max_price_addon)
        self.price_history.append(price)
        return {'price': price, 'instock': self.stock}

    def do(self, action):
        """ does action (buy) and returns percepts price and instock """
        used = pick_from_dist({6:0.1, 5:0.1, 4:0.2, 3:0.3, 2:0.2, 1:0.1})
        bought = action['buy']
        self.stock = self.stock+bought-used
        self.stock_history.append(self.stock)
        self.time += 1
        price = (self.prices[self.time%len(self.prices)]
                 +random.randrange(self.max_price_addon)
                 +self.time//2)
        self.price_history.append(price)
        return {'price': price, 'instock': self.stock}

def pick_from_dist(item_prob_dist):
    """ returns a value from a distribution 
    item_prob_dist is an item:probability dictionary
    where the probabilities sum to 1.
    returns an item chosen in proportion to its probability
    """

    ranreal = random.random()
    for (it, prob) in item_prob_dist.items():
        if ranreal < prob:
            return it
        else:
            ranreal -= prob
    raise RuntimeError(str(item_prob_dist)+ " is not a probability distribution")

class TP_agent(Agent):
    def __init__(self, env):
        self.env = env

        self.spent = 0
        percepts = env.initial_percepts()
        self.ave = self.last_price = percepts['price']
        self.instock = percepts['instock']

    def go(self, n):
        """ go for n time steps """

        for i in range(n):
            if self.last_price < 0.9 * self.ave and self.instock < 60:
                tobuy = 48
            elif self.instock < 12:
                tobuy = 12
            else:
                tobuy = 0

            self.spent += tobuy * self.last_price
            percepts = env.do({'buy':tobuy})
            self.last_price = percepts['price']
            self.ave = self.ave + (self.last_price - self.ave) * 0.5
            self.instock = percepts['instock']
        
env = TP_env()
ag = TP_agent(env)

import matplotlib.pyplot as plt

class Plot_prices(object):
    """ Set up the plot for history of price and number in stock """
    def __init__(self, ag, env):
        self.ag = ag
        self.env = env
        plt.ion()
        plt.xlabel = ("Time")
        plt.ylabel = ("Number in stock                                    Price")

    def plot_run(self):
        """ plot history of price and instock """
        num = len(env.stock_history)
        plt.plot(range(num), env.stock_history, label= "In stock")
        plt.plot(range(num), env.price_history, label = "Price")
        plt.draw()

pl = Plot_prices(ag, env)
ag.go(90)
pl.plot_run()