-
Notifications
You must be signed in to change notification settings - Fork 0
/
Copy pathcreate_synthetic_data_Nsessions.jl
312 lines (263 loc) · 11.7 KB
/
create_synthetic_data_Nsessions.jl
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
using PyPlot
using Statistics
using Optim
using ForwardDiff
using JLD
using LineSearches
using Pandas
using JSON
println("hola")
auxpath=pwd()
# if occursin("Users",auxpath)
# path_functions="/Users/genis/wm_mice/HMM_wm_mice/functions/"
# path_figures="/Users/genis/wm_mice/figures/"
# path_synthetic_data="/Users/genis/wm_mice/synthetic_data/"
# else
# path_functions="/home/genis/wm_mice/scripts/functions/"
# path_figures="/home/genis/wm_mice/figures/"
# path_synthetic_data="/home/genis/wm_mice/synthetic_data/"
#
# end
if occursin("Users",auxpath)
path_functions="C:\\Users\\Tiffany\\Google Drive\\WORKING_MEMORY\\MODEL\\HMM_wm_mice-main\\functions\\"
path_figures="C:\\Users\\Tiffany\\Google Drive\\WORKING_MEMORY\\MODEL\\HMM_wm_mice-main\\figures\\"
path_synthetic_data="C:\\Users\\Tiffany\\Google Drive\\WORKING_MEMORY\\MODEL\\HMM_wm_mice-main\\synthetic\\"
path_real_data="C:\\Users\\Tiffany\\Google Drive\\WORKING_MEMORY\\MODEL\\HMM_wm_mice-main\\real\\"
else
path_functions="/home/tiffany/HMM_wm_mice-main/functions/"
path_figures="/home/tiffany/HMM_wm_mice-main/figure/"
path_synthetic_data="/home/tiffany/HMM_wm_mice-main/synthetic/"
path_real_data="/home/tiffany/HMM_wm_mice-main/synthetic/"
end
include(path_functions*"functions_wm_mice.jl")
include(path_functions*"function_simulations.jl")
include(path_functions*"functions_mle.jl")
include(path_functions*"behaviour_analysis.jl")
pygui(true)
#Param1
# PDwDw=0.95
# PBiasBias=0.95
# consts=["mu_k","c4","mu_b","tau_w","tau_l","lambda"]
# y=[ 1.0, 1.0, 0.00, 2.5, 2.5, 0.01]
# args=["pi","t11","t22","c2","sigma","x0","beta_w","beta_l","beta_bias"]
# x=[ 1.0,PDwDw, PBiasBias, 4.5, 1.0, 0.0, 5.0, 1.0, -3]
# data_set_num=1
#
PDwDw=0.9
PBiasBias=0.9
consts=["mu_k","c4","mu_b","tau_w","tau_l","lambda"]
y=[ 1.0, 1.0, 0.00, 2.5, 2.5, 0.01]
args=["pi","t11","t22","c2","sigma","x0","beta_w","beta_l","beta_bias"]
x=[ 1.0, PDwDw, PBiasBias, 4.0, 1.0, 0.0, 5.0, 2.0, 3]
data_set_num=7
#
#
# PDwDw=0.9
# PBiasBias=0.4
# consts=["mu_k","c4","mu_b","tau_w","tau_l","lambda"]
# y=[ 0.8, 1.0, 0.00, 2.5, 2.5, 0.01]
# args=["pi","t11","t22","c2","sigma","x0","beta_w","beta_l","beta_bias"]
# x=[ 1.0,PDwDw, PBiasBias, 3.5, 1.0, 0.0, 5.0, -3.0, -3]
# data_set_num=3
# PDwDw=0.9753
# PBiasBias=0.8621
# consts=["sigma","c4","x0","tau_w","tau_l","lambda"]
# y = [1.00, 1.00, 0, 1.8, 1.8, 0]
# args=["pi","t11","t22","mu_k","c2","mu_b","beta_w","beta_l","beta_bias"]
# x=[ 1.0,PDwDw, PBiasBias, 2.35, 4.75, 0.0, 2.73, 1.069, -3.827]
# data_set_num="E22"
# PDwDw=0.9
# PBiasBias=0.9
# consts=["mu_k","c4","mu_b","tau_w","tau_l","lambda"]
# y=[ 1.0, 1.0, 0.3, 2.5, 2.5, 0.01]
# args=["pi","t11","t22","c2","sigma","x0","beta_w","beta_l","beta_bias"]
# x=[ 1.0,PDwDw, PBiasBias, 4., 1.0, 0.0, 5.0, 1.0,0.0]
# data_set_num=4
#
#
# PDwDw=0.9
# PBiasBias=0.9
# consts=["mu_k","c4","mu_b","tau_w","tau_l","lambda"]
# y=[ 1.0, 1.0, 0.00, 2.5, 2.5, 0.01]
# args=["pi","t11","t22","c2","sigma","x0","beta_w","beta_l","beta_bias"]
# x=[ 1.0,PDwDw, PBiasBias, 4., 1.0, 0.0, 3.0, 1.0,1.5]
# data_set_num=5
lower=[ 0.0, 0.05,-1.0,-10.0, -10.0]
upper=[10.0, 10.0,1.0,10.0,10.0]
param=make_dict(args,x,consts,y)
# delays=[0.0,100,200,300,500,800,1000,10000]
delays=[0.0,1000,3000,10000]
#Ntrials=300
Nsessions=50
#choices,state,stim,past_choices,past_rewards,idelays=create_data(Ntrials,delays,args,x)
PiInitialOriginal=[1 0]
initial_state=1
PossibleOutputs=[1,2]
choices,state,stim,past_choices,past_rewards,idelays=create_data_Nsessions(Nsessions,delays,param,initial_state)
choices2=choices[1]
global state2=state[1]
global stim2=stim[1]
global past_choices2=past_choices[1]
global past_rewards2=past_rewards[1]
global idelays2=idelays[1]
for isession in 2:Nsessions
global choices2=vcat(choices2,choices[isession])
global state2=vcat(state2,state[isession])
global stim2=vcat(stim2,stim[isession])
global idelays2=vcat(idelays2,idelays[isession])
global past_choices2=vcat(past_choices2,past_choices[isession])
global past_rewards2=vcat(past_rewards2,past_rewards[isession])
end
# choices2=reshape(transpose(choices),Ntrials*Nsessions)
# state2=reshape(transpose(state),Ntrials*Nsessions)
# stim2=reshape(transpose(stim),Ntrials*Nsessions)
# idelays2=reshape(transpose(idelays),Ntrials*Nsessions)
# past_rewards2=reshape(permutedims(past_rewards,(2,1,3)),(Ntrials*Nsessions,10))
# past_choices2=reshape(permutedims(past_choices,(2,1,3)),(Ntrials*Nsessions,10))
results=standard_analysis(choices2,stim2,state2,delays,idelays2,past_choices2)
POriginal_Nsession=ComputeEmissionProb_Nsessions(stim,delays,idelays,choices,past_choices,past_rewards,args,x,consts,y)
POriginal=ComputeEmissionProb(stim2,delays,idelays2,choices2,past_choices2,past_rewards2,args,x,consts,y)
# PrOriginalDw=ProbRightDw(delays,args,x,consts,y)
T=[param["t11"] 1-param["t11"] ;
1-param["t22"] param["t22"]]
InitialP=[1,0]
LlOriginal=NegativeLoglikelihood_Nsessions(POriginal_Nsession,T,choices,InitialP)
# PyPlot.figure()
# PyPlot.title("Synthetic data vs model")
# PyPlot.plot(delays,PrOriginalDw[2,:],"r-")
# PyPlot.plot(delays,results["PcDwDelay"],"o-")
filename_save=path_synthetic_data*"data_set"*string(data_set_num)*"_50Sessions.jld"
JLD.save(filename_save,"param",param,"consts",consts,"LlOriginal",LlOriginal,
"PiInitialOriginal",PiInitialOriginal,"TOriginal",T,"results",results,
"choices",choices,"state",state,"stim",stim,"past_choices",past_choices,
"past_rewards",past_rewards,"idelays",idelays,"POriginal",POriginal,
"POriginal_Nsession",POriginal_Nsession,"delays",delays, "day", "day")
# ------------- Save in json as well
# dict=Dict(:choices=>choices,:stim=>stim,:past_choices=>past_choices,:past_rewards=>past_rewards,:idelays=>idelays)
# PAST_CHOICES=[]
# PAST_REWARDS=[]
# Ntrials=1000
# for itrial in 1:Ntrials
# push!(PAST_CHOICES,past_choices[itrial,:])
# push!(PAST_REWARDS,past_rewards[itrial,:])
# end
#
# Saving the data for the model
dict=Dict(:param=> param, :consts=> consts, :LlOriginal=> LlOriginal, :PiInitialOriginal=> PiInitialOriginal, :TOriginal=> T,
:results=> results,:POriginal=> POriginal, :POriginal_Nsession=> POriginal_Nsession, :delays=> delays)
# pass data as a json string (how it shall be displayed in a file)
stringdata = JSON.json(dict)
# write the file with the stringdata variable information
filename_save=path_synthetic_data*"data_set"*string(data_set_num)*"_model_params.json"
open(filename_save , "w") do f
write(f, stringdata)
end
# Saving data for the useful variables
dict=Dict(:choices=> choices,:state=> state,:stim=> stim, :past_choices=> past_choices,:past_rewards=> past_rewards,
:idelays=> idelays, :day, "day")
df=Pandas.DataFrame(dict)
filename_save=path_synthetic_data*"data_set"*string(data_set_num)*"_behavior_params.json"
Pandas.to_json(df,filename_save)
############## sanity checks data ################################3
#pr,pstate=Compute_negative_LL_hmm_module(PDwDw,PBiasBias,PrDw,PrBias,choices)
#ll=Compute_negative_LL_hmm_module(PDwDw,PBiasBias,PrDw,PrBias,choices)
################ LL vs sigma ###########
# PDwVector=0.05:0.05:0.95
# PBiasVector=0.05:0.05:0.95
#
#
# P=ComputeEmissionProb(stim,delays,idelays,choices,past_choices,past_rewards,args,x,consts,y)
# ll=ComputeNegativeLogLikelihood(P,T,choices,PiInitial)
#
# SIGMA=0.05:0.01:1
# Ll=zeros(length(SIGMA))
# for isigma in 1:length(SIGMA)
# x[1]=SIGMA[isigma]
# P=ComputeEmissionProb(stim,delays,idelays,choices,past_choices,past_rewards,args,x,consts,y)
# Ll[isigma]=ComputeNegativeLogLikelihood(P,T,choices,PiInitial)
# end
#
# figure()
# plot(SIGMA,Ll)
############## fitting ############
PossibleOutputs=[1,2]
#### compute ECLL Original ####
#POriginal=ComputeEmissionProb(stim,delays,idelays,choices,past_choices,past_rewards,args,x,consts,y)
#LlOriginal=ComputeECLL_aux(POriginal,T,choices,PiInitialOriginal)
#Fit using originial parameters as initial parameters
#PNew,TNew,PiNew,Ll,ParamFit,xfit=fitBaumWelchAlgorithm(stim,delays,idelays,choices,past_choices,past_rewards,args,x,lower,upper,T,PiInitialOriginal,PossibleOutputs,consts,y)
#Fit random parameters as initial parameters
# xini=[ 0.4, 0.8, 0.0, 4.0, 1.0]
# PDwDw2=0.5
# PBiasBias2=0.7
# Tx=[PDwDw2 1-PDwDw2; 1-PBiasBias2 PBiasBias2]
# PNew2,TNew2,PiNew2,Ll2,ParamFit2,xfit2=fitBaumWelchAlgorithm(stim,delays,idelays,choices,past_choices,past_rewards,args,xini,lower,upper,Tx,PiInitialOriginal,PossibleOutputs,consts,y)
# PiNew2,PFwdState2,PBackState2,Pstate2,xi=ProbabilityState(PNew,TNew,choices,PiNew)
#Fit using originial parameters as initial parameters other initial param
# #x=[0.8,0.9, -2]
# PDwDw=0.8
# PBiasBias=0.8
# Tx=[PDwDw 1-PDwDw; 1-PBiasBias PBiasBias]
#
# PNew,TNew,PiNew,Ll,ParamFit,xfit=fitBaumWelchAlgorithm(stim,delays,idelays,choices,past_choices,past_rewards,args,x,lower,upper,Tx,PiInitialOriginal,PossibleOutputs,consts,y)
# Nconditions=2
# Nstates=2
# XInitial=zeros(Nconditions,length(lower))
# TInitialAll=zeros(Nconditions,Nstates,Nstates)
# ConfideceIntervals=zeros(Nconditions,length(lower)+Nstates)
# Ll=zeros(Nconditions)
# ParamFit=zeros(Nconditions,length(lower)+Nstates)
# PiInitial=zeros(Nconditions,Nstates)
#
#
# for icondition in 1:Nconditions
# println("icondition:", icondition)
# #random initial conditions
# for iparam in 1:length(lower)
# XInitial[icondition,iparam]=lower[iparam]+ (upper[iparam]-lower[iparam])*rand()
# end
#
# pdwdw=rand()
# pbiasbias=rand()
# TInitial=[pdwdw 1-pdwdw ; 1-pbiasbias pbiasbias]
# TInitialAll[icondition,:,:]=TInitial
# aux=rand()
# PiInitial[icondition,1]=aux
# PiInitial[icondition,2]=1-aux
#
# PNew,TNew,PiNew,Ll[icondition],ParamFit[icondition,:],xfit=fitBaumWelchAlgorithm(stim,delays,idelays,choices,past_choices,past_rewards,args,XInitial[icondition,:],lower,upper,TInitial,PiInitial[icondition,:],PossibleOutputs,consts,y)
# ConfideceIntervals[icondition,:]=ComputeConfidenceIntervals(stim,delays,idelays,choices,past_choices,past_rewards,args,xfit,lower,upper,TNew,PiNew,PossibleOutputs,consts,y)
#
# end
# LL=zeros(length(PDwVector),length(PBiasVector))
# for idw in 1:length(PDwVector)
# for ibias in 1:length(PBiasVector)
# LL[idw,ibias]=Compute_negative_LL_hmm_module(PDwVector[idw],PBiasVector[ibias],PrDw,PrBias,choices)
# #LL[idw,ibias]=Compute_negative_LL_hmm_module(PDwDw,PBiasBias,PDwVector[idw],PBiasVector[ibias],choices)
#
# end
# end
# figure()
# imshow(LL,origin="lower",extent=[PBiasVector[1],PBiasVector[end],PDwVector[1],PDwVector[end]],aspect="auto",cmap="hot")
# xlabel("PbiasBias")
# ylabel("PDwDw")
# plot([ PBiasBias],[PDwDw],"bo")
#
# #plot( [ PrBias],[PrDw],"bo")
#
#
# a=findall(x->x==minimum(LL),LL)
# plot([ PBiasVector[a[1][2]]],[PDwVector[a[1][1]]],"bs")
#
# colorbar()
# show()
# filename_save="/home/genis/wm_mice/synthetic_data/minimize_sigma_c2_Ntrials"*string(Ntrials)*".jld"
# filename_save="/home/genis/wm_mice/synthetic_data/minimize_sigma_c2_Ntrials"*string(Ntrials)*"_NDataSets"*string(NDataSets)*".jld"
# filename_save="/home/genis/wm_mice/synthetic_data/minimize_betaw_betal_only_history_bias_Ntrials"*string(Ntrials)*".jld"
# filename_save="/home/genis/wm_mice/synthetic_data/minimize_betaw_betal_only_history_bias_Ntrials"*string(Ntrials)*"_NDataSets"*string(NDataSets)*".jld"
# filename_save="/home/genis/wm_mice/synthetic_data/minimize_sigma_c2_wm_only_Ntrials"*string(Ntrials)*"_NDataSets"*string(NDataSets)*".jld"
# filename_save="/home/genis/wm_mice/synthetic_data/minimize_sigma_x0_betal_pdwdw_pbiasbias_Ntrials"*string(Ntrials)*".jld"
#
# save(filename_save,"x",x,"args",args,"y",y,"consts",consts,"XInitial",XInitial,"Ll",Ll,
# "PiInitial",PiInitial,"TInitialAll",TInitialAll,"ConfideceIntervals",ConfideceIntervals,
# "LlOriginal",LlOriginal)