"s2": s2m2,
# "s3": es3p1,
# "s4": es4p2,
}
},
{
"policies": {
"b1": p1m3,
"b2": p2m3
},
"variables": {
"s1": s1m3,
"s2": s2m3,
# "s3": es3p1,
# "s4": es4p2,
}
}
]
sim_config_dict = {"N": 1, "T": range(5)}
sim_config = config_sim(sim_config_dict)
exp = Experiment()
exp.append_model(model_id='sys_model_1',
sim_configs=sim_config,
initial_state=genesis_states,
env_processes=env_processes,
partial_state_update_blocks=partial_state_update_block,
policy_ops=[lambda a, b: a + b])
}
},
"m3": {
"policies": {
"b1": p1m3,
"b2": p2m3
},
"states": {
"s1": s1m3,
"s2": s2m3
}
}
}
sim_config_dict = {
"N": 3,
"T": range(5),
}
sim_config = config_sim(sim_config_dict)
exp = Experiment()
exp.append_model(
model_id='sys_model_1',
sim_configs=sim_config,
initial_state=genesis_states,
env_processes=env_processes,
partial_state_update_blocks=partial_state_update_block
)
"b2": p2m3
},
"states": {
"s1": s1m3,
"s2": s2m3
}
}
}
sim_config_dict = {
"N": 3,
"T": range(5),
}
sim_config = config_sim(sim_config_dict)
exp = Experiment()
exp.append_model(
# config_list=configs,
user_id='user_b',
sim_configs=sim_config,
initial_state=genesis_states,
env_processes=env_processes,
partial_state_update_blocks=partial_state_update_block)
multi_exp.append_model(model_id='sys_model_2',
sim_configs=sim_config,
initial_state=genesis_states,
env_processes=env_processes,
partial_state_update_blocks=partial_state_update_block)
},
"m2": {
"policies": {
"p1": p1m2,
"p2": p2m2
},
"variables": variables
},
"m3": {
"policies": {
"p1": p1m3,
"p2": p2m3
},
"variables": variables
}
}
sim_config = config_sim({
"N": 1,
"T": range(3),
})
exp = Experiment()
exp.append_model(
sim_configs=sim_config,
initial_state=genesis_states,
partial_state_update_blocks=partial_state_update_block,
policy_ops=[lambda a, b: a + b,
lambda y: y * 2] # Default: lambda a, b: a + b
)
f=sweeped)
# Genesis States
genesis_states = {'alpha': 0, 'beta': 0, 'policies': {}, 'sweeped': {}}
# Environment Process
env_process['sweeped'] = env_timestep_trigger(
trigger_field='timestep',
trigger_vals=[5],
funct_list=[lambda _g, x: _g['beta']])
sim_config = config_sim({
"N": 2,
"T": range(5),
"M": g, # Optional
})
# New Convention
partial_state_update_blocks = psub_list(psu_block, psu_steps)
exp = Experiment()
exp.append_model(sim_configs=sim_config,
initial_state=genesis_states,
env_processes=env_process,
partial_state_update_blocks=partial_state_update_blocks)
experiment.append_model(
sim_configs=sim_config,
initial_state=genesis_states,
env_processes=env_process,
partial_state_update_blocks=partial_state_update_blocks)
from cadCAD.configuration import Experiment
from cadCAD.configuration.utils import config_sim
from .state_variables import genesis_states
from .partial_state_update_block import partial_state_update_block
from .sys_params import sys_params
sim_config = config_sim({
'N': 1, # number of monte carlo runs
'T': range(1000), # number of timesteps
'M': sys_params, # simulation parameters
})
exp = Experiment()
exp.append_model(sim_configs=sim_config,
model_id='sys_model_j',
initial_state=genesis_states,
partial_state_update_blocks=partial_state_update_block)
'3rd_to_last_x': [],
'4th_to_last_x': []
}
PSUB = {"policies": policies, "variables": variables}
psubs = {"PSUB1": PSUB, "PSUB2": PSUB, "PSUB3": PSUB}
sim_config = config_sim({
"N": 1,
"T": range(3),
})
exp = Experiment()
exp.append_model(model_id='hist_state_access',
sim_configs=sim_config,
initial_state=genesis_states,
partial_state_update_blocks=psubs)
exec_mode = ExecutionMode()
local_proc_ctx = ExecutionContext(context=exec_mode.local_mode)
run = Executor(exec_context=local_proc_ctx, configs=exp.configs)
raw_result, tensor_field, sessions = run.execute()
result = pd.DataFrame(raw_result)
cols = [
'run', 'substep', 'timestep', 'x', 'nonexsistant', 'last_x',
'2nd_to_last_x', '3rd_to_last_x', '4th_to_last_x'
]
result = result[cols]
print()
import json
import os
import unittest, pandas as pd
from cadCAD.configuration import Experiment
from cadCAD.engine import ExecutionMode, ExecutionContext, Executor
from testing.models import param_sweep, policy_aggregation
exp = Experiment()
sys_model_A_id = "sys_model_A"
exp.append_model(
model_id=sys_model_A_id,
sim_configs=param_sweep.sim_config,
initial_state=param_sweep.genesis_states,
env_processes=param_sweep.env_process,
partial_state_update_blocks=param_sweep.partial_state_update_blocks
)
sys_model_B_id = "sys_model_B"
exp.append_model(
model_id=sys_model_B_id,
sim_configs=param_sweep.sim_config,
initial_state=param_sweep.genesis_states,
env_processes=param_sweep.env_process,
partial_state_update_blocks=param_sweep.partial_state_update_blocks
)
sys_model_C_id = "sys_model_C"
exp.append_model(
model_id=sys_model_C_id,
sim_configs=policy_aggregation.sim_config,
initial_state=policy_aggregation.genesis_states,
partial_state_update_blocks=policy_aggregation.partial_state_update_block,
policy_ops=[lambda a, b: a + b, lambda y: y * 2] # Default: lambda a, b: a + b
psu_block[m]["variables"]['alpha'] = alpha_plus_gamma
psu_block[m]["variables"]['alpha_plus_gamma'] = alpha
psu_block[m]["variables"]['beta'] = beta
psu_block[m]['variables']['policies'] = policies
psu_block[m]["variables"]['sweeped'] = var_timestep_trigger(y='sweeped', f=sweeped)
psubs = psub_list(psu_block, psu_steps)
print()
pp.pprint(psu_block)
print()
exp = Experiment()
exp.append_model(
model_id='param_sweep',
sim_configs=sim_config,
initial_state=genesis_states,
env_processes=env_process,
partial_state_update_blocks=psubs
)
exec_mode = ExecutionMode()
local_proc_ctx = ExecutionContext(context=exec_mode.local_mode)
run = Executor(exec_context=local_proc_ctx, configs=exp.configs)
raw_result, tensor_field, sessions = run.execute()
result = pd.DataFrame(raw_result)
print()
print("Tensor Field:")
print(tabulate(tensor_field, headers='keys', tablefmt='psql'))
print("Output:")
print(tabulate(result, headers='keys', tablefmt='psql'))