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'])