import numpy as np # for numeric calculations
import time #calculate runtime
import Agents as ag
import func as f
#%%
agent_list = [
ag.RLWM_noneFree(),
ag.RLWM_allFree(),
ag.RLWM_noise(),
ag.RLWM_modulation(),
ag.RLWM_actionSoftmax()
]
start_time = time.time()
AICs, BICs = f.modRec(agent_list, initSample=22, endSample=33)
print("--- %s seconds ---" % (time.time() - start_time))
np.savez('Model_Recovery', AICs=AICs,
BICs=BICs) # save the file as "outfile_name.npy"
#%%
# import necessary packages
import pandas as pd # for python data frame
import time#calculate runtime
import numpy as np # for numeric calculations
import Agents as ag
import func as f
m = np.load('../Model_Recovery/Model_Recovery_Sub1.npz') # save the file as "outfile_name.npy"
print(m['AICs'][:,:,1])
#%%
agent_list = [ag.RLWM_modulation(), ag.RLWM_actionSoftmax()]
#%%
'''
for agent in agent_list:
agent.Sim_agent()
data = f.actBias(pd.read_csv("../Learning_Validation/"+agent.name+".csv"))
data.to_csv(r'../Action_Bias/'+agent.name+'.csv', index=False, header=True)
data = f.actBias(pd.read_csv("../Subject_Data/DT-RLWM-version4.csv"))
data.to_csv(r'../Action_Bias/DT-RLWM-version4.csv',index=False, header=True)
'''
#%%
# import necessary packages
import numpy as np # for numeric calculations
import pandas as pd
import func as f
import Agents as ag
agent_list = [ag.RLWM_noneFree(),ag.RLWM_allFree(),ag.RLWM_noise(),ag.RLWM_modulation(), ag.RLWM_actionSoftmax()]
numMod, numBlocks = len(agent_list), len(agent_list[0].Sub)
ModOrder = iter(range(numMod))
AICs, BICs, Mods = np.zeros((numBlocks, numMod)), np.zeros((numBlocks, numMod)), np.empty(numMod, dtype="object")
AICs_te, BICs_te = np.zeros((numBlocks, numMod)), np.zeros((numBlocks, numMod))
#%%
for i in agent_list:
Order = next(ModOrder)
agent = agent_list[Order]
ModFit = np.load('../ModelFit/ModFit'+agent.name+'.npz',allow_pickle=True) # loads your saved array into variable a.
te_ModFit = np.load('../Testing_ModelFit/Testing_ModelFit' + agent.name + '.npz', allow_pickle=True)
f.ParamBar(agent.name,agent.pname, ModFit['Params'], ModFit['Ks'])
AICs[:, Order], BICs[:, Order], Mods[Order] = ModFit['AICs'], ModFit['BICs'], ModFit['agtName']
AICs_te[:, Order], BICs_te[:, Order] = te_ModFit['AICs'], te_ModFit['BICs']
#%%
Mods = ['noneFree', 'allFree', 'noise', 'modulation', 'action confusion']
f.ICBar(AICs, BICs, Mods, degree = 10)
import numpy as np # for numeric calculations
import Agents as ag
import func as f
#%%
model = ag.RLWM_actionSoftmax()
#genrec, optimcurve, bounds = f.genRec(model)
#np.savez('genRec'+model.name, genrec = genrec, optimcurve = optimcurve, bounds = bounds)
#%%
agent = ag.RLWM_actionSoftmax()
genRec = np.load('genRec'+model.name+'.npz')
#genrec = np.log(genRec['genrec'])
f.recPlot(['\u03B1', '\u03C6', '\u03C1', '\u03B5', '\u03B3', '\u03BB TS', '\u03BB DT' ], genRec['bounds'], genRec['genrec'])