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Author SHA1 Message Date
Zargham 62af34f179 z-saving 2018-11-01 15:41:04 +01:00
Zargham 32367faefe plots 2018-10-18 13:35:39 -07:00
Zargham 4afbdffb8d works on laptop
new experiments;
verified that its an environments issue by simply switching machines and starting from scratch
2018-10-18 13:34:24 -07:00
Michael Zargham 84c02e18ba stoch belief 2018-10-18 13:18:39 -07:00
Michael Zargham c50e8e8c71 it works! 2018-10-18 12:27:59 -07:00
Michael Zargham 261f11e301 typo fix 2018-10-18 12:08:48 -07:00
Michael Zargham 05a760f28d match format in configs 2018-10-18 12:07:14 -07:00
Michael Zargham 2f069a3a2e undeath~
my branch reborn (correctly)
2018-10-18 11:28:14 -07:00
8 changed files with 1077 additions and 2 deletions

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@ -1,4 +1,4 @@
from ui.config import state_dict, mechanisms, exogenous_states, env_processes, sim_config
from ui.simpleBC_Config import state_dict, mechanisms, exogenous_states, env_processes, sim_config
from engine.configProcessor import generate_config
from engine.mechanismExecutor import simulation
from engine.utils import flatten
@ -11,7 +11,7 @@ def main():
configs = generate_config(mechanisms, exogenous_states)
# p = pipeline(states_list, configs, env_processes, range(10))
N = sim_config['N']
r = range(5)
r = range(sim_config["R"])
# Dimensions: N x r x mechs
s = simulation(states_list, configs, env_processes, r, N)
result = pd.DataFrame(flatten(s))

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notebooks/notebook.tex Normal file
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@ -0,0 +1,422 @@
% Default to the notebook output style
% Inherit from the specified cell style.
\documentclass[11pt]{article}
\usepackage[T1]{fontenc}
% Nicer default font (+ math font) than Computer Modern for most use cases
\usepackage{mathpazo}
% Basic figure setup, for now with no caption control since it's done
% automatically by Pandoc (which extracts ![](path) syntax from Markdown).
\usepackage{graphicx}
% We will generate all images so they have a width \maxwidth. This means
% that they will get their normal width if they fit onto the page, but
% are scaled down if they would overflow the margins.
\makeatletter
\def\maxwidth{\ifdim\Gin@nat@width>\linewidth\linewidth
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\makeatother
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% Set max figure width to be 80% of text width, for now hardcoded.
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% Ensure that by default, figures have no caption (until we provide a
% proper Figure object with a Caption API and a way to capture that
% in the conversion process - todo).
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\usepackage{adjustbox} % Used to constrain images to a maximum size
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\usepackage{amsmath} % Equations
\usepackage{amssymb} % Equations
\usepackage{textcomp} % defines textquotesingle
% Hack from http://tex.stackexchange.com/a/47451/13684:
\AtBeginDocument{%
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}
\usepackage{upquote} % Upright quotes for verbatim code
\usepackage{eurosym} % defines \euro
\usepackage[mathletters]{ucs} % Extended unicode (utf-8) support
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% to support a larger range
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% internal cross-reference links, web links for URLs, etc.)
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series.name = label
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{\color{outcolor}Out[{\color{outcolor}13}]:} <matplotlib.axes.\_subplots.AxesSubplot at 0x1132f1e80>
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\begin{Verbatim}[commandchars=\\\{\}]
{\color{outcolor}Out[{\color{outcolor}9}]:} count 2001.0
mean 2.0
std 0.0
min 2.0
25\% 2.0
50\% 2.0
75\% 2.0
max 2.0
Name: invar, dtype: float64
\end{Verbatim}
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{\color{outcolor}Out[{\color{outcolor}10}]:} Belief Pool Price Supply mech\_step time\_step \textbackslash{}
1996 1.479643 6.525178e+08 2.394507 3.262589e+08 2 998
1997 1.604208 6.525178e+08 2.464774 3.262589e+08 1 999
1998 1.806759 5.766994e+08 2.381050 2.883497e+08 2 999
1999 1.988029 5.766994e+08 2.394131 2.883497e+08 1 1000
2000 2.061324 5.198756e+08 2.332448 2.599378e+08 2 1000
timestamp invar
1996 2018-10-01 15:33:02 2.0
1997 2018-10-01 15:33:03 2.0
1998 2018-10-01 15:33:03 2.0
1999 2018-10-01 15:33:04 2.0
2000 2018-10-01 15:33:04 2.0
\end{Verbatim}
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series.name = label
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from engine.utils import bound_norm_random, ep_time_step, env_proc
import numpy as np
from decimal import Decimal
alpha = Decimal('.95') #forgetting param
theta = Decimal('.75') #weight param for rational price
beta = Decimal('0.5') #agant response gain
gamma = Decimal('.03') #action friction param
delta = Decimal('.3') #bounds on price change
omega = Decimal('.5') #bound on burn frac per period
seed = {
'z': np.random.RandomState(1),
'a': np.random.RandomState(2),
'b': np.random.RandomState(3),
'c': np.random.RandomState(3)
}
# Behaviors per Mechanism
#arbit X Bond
def b1m1(step, sL, s):
#returns "delta p"
if s['Price']< s['Pool']/s['Supply']-gamma:
#print('arbit bond')
#print((s['Pool']/s['Supply']-s['Price'])/s['Price']*s['Pool']*beta)
return s['Pool']*(s['Pool']/s['Supply']-s['Price'])/s['Price']*beta
else :
return 0
#invest X Bond
def b2m1(step, sL, s):
#returns "delta p"
if s['Price']< s['Belief']:
#print('invest bond')
#print((s['Belief']-s['Price'])/s['Price']*s['Pool']*beta)
return s['Pool']*(s['Belief']-s['Price'])/s['Price']*beta
else :
return 0
#arbit X Burn
def b1m2(step, sL, s):
#returns "delta s"
if Decimal('1')/s['Price']< s['Supply']/s['Pool']-gamma:
#print('arbit burn')
#print((s['Supply']/s['Pool']-Decimal('1')/s['Price'])*s['Price']*s['Supply']*beta)
return s['Supply']*s['Price']*(s['Supply']/s['Pool']-Decimal('1')/s['Price'])*beta
else :
return 0
#invest X Burn
def b2m2(step, sL, s):
#returns "delta s"
if Decimal('1')/s['Belief']< Decimal('1')/s['Price']:
#print('invest burn')
#print(np.min([ s['Pool']*(Decimal('1')/s['Price']-Decimal('1')/s['Belief'])*beta, omega*s['Supply']]))
return np.min([ s['Pool']*(Decimal('1')/s['Price']-Decimal('1')/s['Belief'])*beta, omega*s['Supply']])
else :
return 0
#
#def b1m3(step, sL, s):
# return s['s1']
#def b2m3(step, sL, s):
# return s['s2']
# Internal States per Mechanism
#Pool X Bond
def s1m1(step, sL, s, _input):
#_input = "delta p"
return ('Pool',s['Pool']+_input)
#print("Pool="+str(s['Pool']))
#Supply X Bond
def s2m1(step, sL, s, _input):
#_input = "delta p"
return ('Supply', s['Supply']+ _input*s['Supply']/s['Pool'] )
#print("Supply="+str(s['Supply']))
# Pool X Burn
def s1m2(step, sL, s, _input):
#_input is "delta s"
return ('Pool',s['Pool']- _input*s['Pool']/s['Supply'])
#print("Pool="+str(s['Pool']))
# Supply X Burn
def s2m2(step, sL, s, _input):
return ('Supply',s['Supply'] - _input)
#print("Supply="+str(s['Supply']))
#def s1m3(step, sL, s, _input):
# s['s1'] = s['s1']+Decimal(.25)*(s['s2']-s['s1']) + Decimal(.25)*(_input-s['s1'])
#
#def s2m3(step, sL, s, _input):
# s['s2'] = s['s2']+Decimal(.25)*(s['s1']-s['s2']) + Decimal(.25)*(_input-s['s2'])
# Exogenous States
proc_one_coef_A = -delta
proc_one_coef_B = delta
def es3p1(step, sL, s, _input):
rv = bound_norm_random(seed['a'], proc_one_coef_A, proc_one_coef_B)
return ('Price', theta*s['Price'] * (Decimal('1')+rv) +(Decimal('1')-theta)*s['Pool']/s['Supply'] )
def es4p2(step, sL, s, _input):
rv = bound_norm_random(seed['b'], proc_one_coef_A, proc_one_coef_B)
return ('Belief', alpha*s['Belief']*(Decimal('1')+rv )+(Decimal('1')-alpha)*s['Pool']/s['Supply'])
def es5p2(step, sL, s, _input): # accept timedelta instead of timedelta params
return ('timestamp', ep_time_step(s, s['timestamp'], seconds=1))
# Environment States
#from numpy.random import randn as rn
def env_a(x):
return 3
def env_b(x):
return 7
# def what_ever(x):
# return x + 1
# Genesis States
state_dict = {
'Pool': Decimal(10.0),
'Supply': Decimal(5.0),
'Price': Decimal(.01),
'Belief': Decimal(3.0),
'timestamp': '2018-10-01 15:16:24'
}
exogenous_states = {
"Price": es3p1,
"Belief": es4p2,
"timestamp": es5p2
}
env_processes = {
"Price": env_proc('2018-10-01 15:16:25', env_a),
"Belief": env_proc('2018-10-01 15:16:25', env_b)
}
# test return vs. non-return functions as lambdas
# test fully defined functions
mechanisms = {
"bond": {
"behaviors": {
"arbit": b1m1, # lambda step, sL, s: s['s1'] + 1,
"invest": b2m1
},
"states": {
"Pool": s1m1,
"Supply": s2m1,
}
},
"burn": {
"behaviors": {
"arbit": b1m2,
"invest": b2m2
},
"states": {
"Pool": s1m2,
"Supply": s2m2,
}
},
# "m3": {
# "behaviors": {
# "b1": b1m3,
# "b2": b2m3
# },
# "states": {
# "s1": s1m3,
# "s2": s2m3,
# }
# }
}
sim_config = {
"N": 1,
"R": 1000
}

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from engine.utils import bound_norm_random, ep_time_step, env_proc
import numpy as np
from decimal import Decimal
alpha = Decimal('.7') #forgetting param
theta = Decimal('.75') #weight param for rational price
beta = Decimal('0.5') #agant response gain
gamma = Decimal('.03') #action friction param
delta = Decimal('.3') #bounds on price change
omega = Decimal('.5') #bound on burn frac per period
seed = {
'z': np.random.RandomState(1),
'a': np.random.RandomState(2),
'b': np.random.RandomState(3),
'c': np.random.RandomState(3)
}
# Behaviors per Mechanism
#arbit X Bond
def b1m1(step, sL, s):
#returns "delta p"
if s['Price']< s['Pool']/s['Supply']-gamma:
print('arbit bond')
print((s['Pool']/s['Supply']-s['Price'])/s['Price']*s['Pool']*beta)
return (s['Pool']/s['Supply']-s['Price'])/s['Price']*s['Pool']*beta
else :
return 0
#invest X Bond
def b2m1(step, sL, s):
#returns "delta p"
if s['Price']< s['Belief']:
print('invest bond')
print(s['Pool']*(s['Belief']-s['Price'])/s['Price']*beta)
return s['Pool']*(s['Belief']-s['Price'])/s['Price']*beta
else :
return 0
#arbit X Burn
def b1m2(step, sL, s):
#returns "delta s"
if Decimal('1')/s['Price']< s['Supply']/s['Pool']-gamma:
print('arbit burn')
print((s['Supply']/s['Pool']-Decimal('1')/s['Price'])*s['Price']*s['Supply']*beta)
return (s['Supply']/s['Pool']-Decimal('1')/s['Price'])*s['Price']*s['Supply']*beta
else :
return 0
#invest X Burn
def b2m2(step, sL, s):
#returns "delta s"
if Decimal('1')/s['Belief']< Decimal('1')/s['Price']:
print('invest burn')
print(np.min([ s['Pool']*(Decimal('1')/s['Price']-Decimal('1')/s['Belief'])*beta, omega*s['Supply']]))
return np.min([ s['Pool']*(Decimal('1')/s['Price']-Decimal('1')/s['Belief'])*beta, omega*s['Supply']])
else :
return 0
#
#def b1m3(step, sL, s):
# return s['s1']
#def b2m3(step, sL, s):
# return s['s2']
# Internal States per Mechanism
#Pool X Bond
def s1m1(step, sL, s, _input):
#_input = "delta p"
print("Bond Called")
print("update Pool")
print("Pool="+str(s['Pool']))
print(str(_input)+" Bonded")
return ('Pool',s['Pool']+_input)
print("Pool="+str(s['Pool']))
#Supply X Bond
def s2m1(step, sL, s, _input):
#_input = "delta p"
print("update Supply")
print("Supply="+str(s['Supply']))
print("Pool="+str(s['Pool']))
print(str(s['Supply']/s['Pool'])+"minted token per Bonded")
return ('Supply', s['Supply']+ _input*s['Supply']/s['Pool'] )
print("Supply="+str(s['Supply']))
print('')
# Pool X Burn
def s1m2(step, sL, s, _input):
#_input is "delta s"
print("Burn Called")
print("update Pool")
print("Supply="+str(s['Supply']))
print(str(_input)+" Burned")
print("Pool="+str(s['Pool']))
return ('Pool',s['Pool']- _input*s['Pool']/s['Supply'])
print(str(s['Pool']/s['Supply'])+"withdrawn token per Burned")
print("Pool="+str(s['Pool']))
# Supply X Burn
def s2m2(step, sL, s, _input):
print("update Supply")
print("Supply="+str(s['Supply']))
print(str(_input)+" Burned")
return ('Supply',s['Supply'] - _input)
print("Supply="+str(s['Supply']))
#def s1m3(step, sL, s, _input):
# s['s1'] = s['s1']+Decimal(.25)*(s['s2']-s['s1']) + Decimal(.25)*(_input-s['s1'])
#
#def s2m3(step, sL, s, _input):
# s['s2'] = s['s2']+Decimal(.25)*(s['s1']-s['s2']) + Decimal(.25)*(_input-s['s2'])
# Exogenous States
proc_one_coef_A = -delta
proc_one_coef_B = delta
def es3p1(step, sL, s, _input):
rv = bound_norm_random(seed['a'], proc_one_coef_A, proc_one_coef_B)
return ('Price', theta*s['Price'] * (Decimal('1')+rv) +(Decimal('1')-theta)*s['Pool']/s['Supply'] )
def es4p2(step, sL, s, _input):
return ('Belief', alpha*s['Belief']+s['Pool']/s['Supply']*(Decimal('1')-alpha))
def es5p2(step, sL, s, _input): # accept timedelta instead of timedelta params
return ('timestamp', ep_time_step(s, s['timestamp'], seconds=1))
# Environment States
#from numpy.random import randn as rn
def env_a(x):
return 3
def env_b(x):
return 7
# def what_ever(x):
# return x + 1
# Genesis States
state_dict = {
'Pool': Decimal(10.0),
'Supply': Decimal(5.0),
'Price': Decimal(.01),
'Belief': Decimal(10.0),
'timestamp': '2018-10-01 15:16:24'
}
exogenous_states = {
"Price": es3p1,
"Belief": es4p2,
"timestamp": es5p2
}
env_processes = {
"Price": env_proc('2018-10-01 15:16:25', env_a),
"Belief": env_proc('2018-10-01 15:16:25', env_b)
}
# test return vs. non-return functions as lambdas
# test fully defined functions
mechanisms = {
"bond": {
"behaviors": {
"arbit": b1m1, # lambda step, sL, s: s['s1'] + 1,
"invest": b2m1
},
"states": {
"Pool": s1m1,
"Supply": s2m1,
}
},
"burn": {
"behaviors": {
"arbit": b1m2,
"invest": b2m2
},
"states": {
"Pool": s1m2,
"Supply": s2m2,
}
},
# "m3": {
# "behaviors": {
# "b1": b1m3,
# "b2": b2m3
# },
# "states": {
# "s1": s1m3,
# "s2": s2m3,
# }
# }
}
sim_config = {
"N": 1,
"R": 2
}