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Implementation of the Lock in Feedback algorithm in the Python Tornado based StreamingBandit web server
# Implementation of Lock in Feedback.
# -*- coding: utf-8 -*-
from libs.base import *
import numpy as np
import json
class Lif:
def __init__(self, theta, x0=1.0, A=1.4, T=100, gamma=.004, omega=0.8, lifversion=2):
self._set_parameters(x0, A, T, gamma, omega, lifversion)
self._set_dict(theta)
def _set_parameters(self, x0, A, T, gamma, omega, lifversion):
self.x0 = x0
self.A = A
self.T = T
self.gamma = gamma
self.omega = omega
self.lifversion = lifversion
def _set_dict(self,theta):
if theta == {}:
self.theta = {'Yw': self._np_nan_fill(self.T, 3), 't':0, 'x0':self.x0}
else:
self.theta = theta.copy()
self.theta['Yw'] = np.array(json.loads(str(self.theta['Yw'])))
self.theta['t'] = int(self.theta['t'])
self.theta['x0'] = float(self.theta['x0'])
def get_dict(self):
theta_dict = {'Yw': json.dumps(self.theta['Yw'].tolist()), 't':self.theta['t'], 'x0':self.theta['x0']}
return theta_dict
def suggest(self):
if np.all(np.isfinite(self.theta['Yw'][:,0])):
self.theta['x0'] = np.mean(self.theta['Yw'][:,1])
self.theta['x0'] = self.theta['x0'] + self.gamma * sum( self.theta['Yw'][:,2] )
if self.lifversion==1: self.theta['Yw'].fill(np.nan)
self.theta['t'] = self.theta['t'] + 1
x = self.theta['x0'] + self.A*np.cos(self.omega * self.theta['t'])
suggestion = {'x': x, 't':self.theta['t'], 'x0': self.theta['x0']}
return suggestion
def update(self, t, x, y):
y = self.A*np.cos(self.omega * t)*y
row_to_add = np.array([t,x,y])
self.theta['Yw'] = self._matrixpush(self.theta['Yw'], row_to_add)
return True
def _matrixpush(self, m, row):
if not np.all(np.isfinite(self.theta['Yw'][:,0])):
i = np.count_nonzero(np.logical_not(np.isnan(self.theta['Yw'][:,0])))
m[i,] = row
else:
m = np.vstack([m,row])
m = m[1:,]
return(m)
def _np_nan_fill(self,rows,columns):
nan_values = np.zeros((rows,columns))
nan_values.fill(np.nan)
return nan_values
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