sigma = 0.04 # Perceptual sensitivity # Initialize task and agent objects # --------------------------------- # Task parameters in TaskVars object task_vars = TaskVars() task_vars.T = T task_vars.B = 1 task = Task(task_vars) # Agent parameters in AgentVars object agent_vars = AgentVars() agent_vars.agent = 1 agent_vars.sigma = sigma agent_vars.task_agent_analysis = False agent = Agent(agent_vars) agent.d_t = 0 # Fix perceptual decision to d_t = 0 agent.a_t = 0 # Fix action to a_t = 0 # Sampling-based approximation object p_hat = PHat(n_bins) # Number of variables x number of samples x number of time points sample matrix S = np.full([5, n_samples, T], np.nan) # Simulation scenarios / observed random variable values # ------------------------------------------------------ O_list = list() R_list = list()
def gb_simulation(n_trials,
n_blocks,
sigma,
agent,
beta,
task_agent_analysis=False,
eval_ana=True):
""" This function runs the simulations for model demonstration
:param n_trials: Number of trials
:param n_blocks: Number of blocks
:param sigma: Perceptual sensitivity
:param agent: Agent-based computational model
:param beta: Inverse temperature parameter
:param task_agent_analysis: Indicates if use for data analysis or not
:param eval_ana: Evaluate agent analytically
:return: df_subj: Data frame containing simulated data
"""
# Parameter definition
# --------------------
# Task parameters in GbTaskVars.py object
task_vars = TaskVars()
task_vars.T = n_trials
task_vars.B = n_blocks
task_vars.experiment = 3
# Agent parameters in GbAgentVars.py object
agent_vars = AgentVars()
agent_vars.sigma = sigma
agent_vars.beta = beta
agent_vars.c0 = np.array([1])
agent_vars.kappa = task_vars.kappa
agent_vars.agent = agent
agent_vars.lambda_param = np.nan
agent_vars.task_agent_analysis = task_agent_analysis
agent_vars.eval_ana = eval_ana
# Simulation parameters in GbSimVars.py object
sim_vars = SimVars()
# Initialize data frame for simulation
df_subj = pd.DataFrame()
sleep(0.1)
print('\nSimulating data for agent demonstration:')
sleep(0.1)
pbar = tqdm(total=task_vars.B)
# Cycle over task blocks
for b in range(0, task_vars.B):
# Update progress bar
pbar.update(1)
# Block number definition
sim_vars.block = b
sim_vars.take_pd = 0
# Single block task-agent-interaction simulation
df_block = gb_task_agent_int(task_vars, agent_vars, sim_vars)
# Add data to data frame
df_subj = df_subj.append(df_block, ignore_index=True)
# Close progress bar
pbar.close()
return df_subj