149 lines
3.2 KiB
Python
149 lines
3.2 KiB
Python
import numpy as np
|
|
from datetime import timedelta
|
|
|
|
from cadCAD.configuration import append_configs
|
|
from cadCAD.configuration.utils import env_proc_trigger, bound_norm_random, ep_time_step, config_sim, env_trigger, \
|
|
time_step
|
|
|
|
seeds = {
|
|
'z': np.random.RandomState(1),
|
|
'a': np.random.RandomState(2),
|
|
'b': np.random.RandomState(3),
|
|
'c': np.random.RandomState(4)
|
|
}
|
|
|
|
|
|
# Policies per Mechanism
|
|
def p1m1(_g, step, sL, s):
|
|
return {'param1': 1}
|
|
def p2m1(_g, step, sL, s):
|
|
return {'param2': 4}
|
|
|
|
def p1m2(_g, step, sL, s):
|
|
return {'param1': 'a', 'param2': 2}
|
|
def p2m2(_g, step, sL, s):
|
|
return {'param1': 'b', 'param2': 4}
|
|
|
|
def p1m3(_g, step, sL, s):
|
|
return {'param1': ['c'], 'param2': np.array([10, 100])}
|
|
def p2m3(_g, step, sL, s):
|
|
return {'param1': ['d'], 'param2': np.array([20, 200])}
|
|
|
|
|
|
# Internal States per Mechanism
|
|
def s1m1(_g, step, sL, s, _input):
|
|
y = 's1'
|
|
x = _input['param1']
|
|
return (y, x)
|
|
def s2m1(_g, step, sL, s, _input):
|
|
y = 's2'
|
|
x = _input['param2']
|
|
return (y, x)
|
|
|
|
def s1m2(_g, step, sL, s, _input):
|
|
y = 's1'
|
|
x = _input['param1']
|
|
return (y, x)
|
|
def s2m2(_g, step, sL, s, _input):
|
|
y = 's2'
|
|
x = _input['param2']
|
|
return (y, x)
|
|
|
|
def s1m3(_g, step, sL, s, _input):
|
|
y = 's1'
|
|
x = _input['param1']
|
|
return (y, x)
|
|
def s2m3(_g, step, sL, s, _input):
|
|
y = 's2'
|
|
x = _input['param2']
|
|
return (y, x)
|
|
|
|
|
|
# Exogenous States
|
|
proc_one_coef_A = 0.7
|
|
proc_one_coef_B = 1.3
|
|
|
|
def es3(_g, step, sL, s, _input):
|
|
y = 's3'
|
|
x = s['s3'] * bound_norm_random(seeds['a'], proc_one_coef_A, proc_one_coef_B)
|
|
return (y, x)
|
|
|
|
def es4(_g, step, sL, s, _input):
|
|
y = 's4'
|
|
x = s['s4'] * bound_norm_random(seeds['b'], proc_one_coef_A, proc_one_coef_B)
|
|
return (y, x)
|
|
|
|
def update_timestamp(_g, step, sL, s, _input):
|
|
y = 'timestamp'
|
|
return y, time_step(dt_str=s[y], dt_format='%Y-%m-%d %H:%M:%S', _timedelta=timedelta(days=0, minutes=0, seconds=1))
|
|
|
|
|
|
# Genesis States
|
|
genesis_states = {
|
|
's1': 0,
|
|
's2': 0,
|
|
's3': 1,
|
|
's4': 1,
|
|
'timestamp': '2018-10-01 15:16:24'
|
|
}
|
|
|
|
|
|
# Environment Process
|
|
# ToDo: Depreciation Waring for env_proc_trigger convention
|
|
env_processes = {
|
|
"s3": [lambda _g, x: 5],
|
|
"s4": env_trigger(3)(trigger_field='timestep', trigger_vals=[2], funct_list=[lambda _g, x: 10])
|
|
}
|
|
|
|
partial_state_update_block = {
|
|
"m1": {
|
|
"policies": {
|
|
"b1": p1m1,
|
|
# "b2": p2m1
|
|
},
|
|
"states": {
|
|
"s1": s1m1,
|
|
# "s2": s2m1
|
|
"s3": es3,
|
|
"s4": es4,
|
|
"timestep": update_timestamp
|
|
}
|
|
},
|
|
"m2": {
|
|
"policies": {
|
|
"b1": p1m2,
|
|
# "b2": p2m2
|
|
},
|
|
"states": {
|
|
"s1": s1m2,
|
|
# "s2": s2m2
|
|
}
|
|
},
|
|
"m3": {
|
|
"policies": {
|
|
"b1": p1m3,
|
|
"b2": p2m3
|
|
},
|
|
"states": {
|
|
"s1": s1m3,
|
|
"s2": s2m3
|
|
}
|
|
}
|
|
}
|
|
|
|
|
|
sim_config = config_sim(
|
|
{
|
|
"N": 2,
|
|
"T": range(5),
|
|
}
|
|
)
|
|
|
|
|
|
append_configs(
|
|
sim_configs=sim_config,
|
|
initial_state=genesis_states,
|
|
seeds=seeds,
|
|
env_processes=env_processes,
|
|
partial_state_update_blocks=partial_state_update_block
|
|
) |