conormm

class ThompsonSampler:
    """Thompson Sampling using a Beta distribution associated with each option.
    The beta distribution will be updated when rewards associated with each option 
    are observed.
    """

    def __init__(self, env, n_learning=0):
        
        # boilier plate data storage
        self.env = env

conormm

import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
from torch.utils.data import Dataset, DataLoader, SubsetRandomSampler
from torchvision.datasets.folder import ImageFolder, default_loader
from torchvision.datasets.utils import check_integrity
from torchvision import transforms
from torchvision import models
import matplotlib.pyplot as plt

conormm

# usr/bin/env python3

# author: Conor McDonald

# torch==0.4.1
# numpy==1.14.3

import torch
import torch.nn as nn
import torch.nn.functional as F

conormm

class ThompsonSampler(BaseSampler):

    def __init__(self, env):
        super().__init__(env)
        
    def choose_k(self):
        # sample from posterior (this is the thompson sampling approach)
        # this leads to more exploration because machines with > uncertainty can then be selected as the machine
        self.theta = np.random.beta(self.a, self.b)
        # select machine with highest posterior p of payout

conormm

class eGreedy(BaseSampler):

    def __init__(self, env, n_learning, e):
        super().__init__(env, n_learning, e)
        
    def choose_k(self):

        # e% of the time take a random draw from machines
        # random k for n learning trials, then the machine with highest theta
        self.k = np.random.choice(self.variants) if self.i < self.n_learning else np.argmax(self.theta)

conormm

class RandomSampler(BaseSampler):
    def __init__(self, env):
        super().__init__(env)
        
    def choose_k(self):
        
        self.k = np.random.choice(self.variants)
        
        return self.k
    

conormm

import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import pandas as pd
from scipy.stats import beta

sns.set_style("whitegrid")

class Environment:
    def __init__(self, variants, payouts, n_trials, variance=False):

conormm

#%load_ext cythonmagic
#%cython

import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import pandas as pd

sns.set_style("whitegrid")
get_ipython().run_line_magic('matplotlib', 'inline')

conormm

import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import pandas as pd

sns.set_style("whitegrid")
get_ipython().run_line_magic('matplotlib', 'inline')

from IPython.core.display import display, HTML

conormm

# coding: utf-8

# In[150]:


import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns