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import numpy as np
class Data_generator(object):
def __init__(self,K,d,reward_type='binary'):
self.d = d # dimension of the feature vector
self.K = K # number of bandits
self.reward_type = reward_type
self.means = np.random.normal(size=self.K)
self.stds = 1 + 2*np.random.rand(self.K)
# generate the weight vectors
self.generate_weight_vectors()
def generate_weight_vectors(self,loc=0.0,scale=1.0):
self.W = np.random.normal(loc=loc,scale=scale,size=(self.K,self.d))
def generate_samples(self,n=1000):
# the X are only binary
X = np.random.randint(0,2,size=(n,self.d))
# the rewards are functions of the inner products with self.W
IP = np.dot(X,self.W.T)
# now get the rewards
if self.reward_type == 'binary':
R = (np.sign(np.random.normal(self.means + IP,self.stds)) + 1) / 2
elif self.reward_type == 'positive':
R = np.random.lognormal(self.means + IP,self.stds)
elif self.reward_type == 'mixed':
R = (np.sign(np.random.normal(self.means + IP,self.stds)) + 1) / 2
R *= np.random.lognormal(self.means + IP,self.stds)
return X,R

lemondy commented May 12, 2016

hi sergeyf, I have read your three post whoes title is Personalization with Contextual Bandits. I am very curious about how you implement your simulation, can you open it? thanks.

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