Merge pull request #19 from mingujo/master

Fixed organize_behavior_data, and did the LOGISTIC REGRESSION ON RESP…

code/utils/logistic_reg.py

@@ -0,0 +1,33 @@
+import pandas as pd
+import statsmodels.api as sm
+import pylab as pl
+import numpy as np
+
+#PREDICT SUBJECT's DECISION TO WHETHER GAMBLE OR NOT GIVEN WITH GAIN AND LOSS AMOUNT using LOGISTIC REGRESSION
+
+project_location="../../"
+data_location=project_location+"data/ds005/"
+
+run1 = pd.read_table(data_location+"sub001/behav/task001_run001/behavdata.txt")
+run2 = pd.read_table(data_location+"sub001/behav/task001_run002/behavdata.txt")
+run3 = pd.read_table(data_location+"sub001/behav/task001_run003/behavdata.txt")
+
+run_1=run1.append(run2)
+run_total=run_1.append(run3) #append all the data frames of run
+
+a = run_total.drop('onset', 1) # drop onset column
+
+run_organized = a.drop('PTval', 1) # drop PTval column
+
+cols = run_organized.columns.tolist() # reorganize the columns
+cols.insert(0, cols.pop(cols.index('respcat'))) # put respcat into front
+run_organized = run_organized.reindex(columns= cols) # reorganize
+
+run_final = run_organized.drop(run_organized[run_organized.respcat == -1].index) # drop error in experiment
+
+train_cols = run_final.columns[1:3] # train columns are gain and loss
+logit = sm.Logit(run_final['respcat'], run_final[train_cols]) # do regression
+result = logit.fit()
+result.summary()
+
+run_final['respcat_pred']=result.predict(run_final[train_cols]) # add prediction on the data frame

code/utils/organize_behavior_data.py

@@ -14,6 +14,6 @@
 behav_total_run=np.concatenate((behav1,behav2,behav3),axis=0)
 
 """delete the rows that contain -1 in respcat (these are errors in experiment so we should take them out"""
-behav_total_run=np.delete(behav_total_run, behav_total_run[:,5]==-1,axis=0)
+behavior=np.delete(behav_total_run, np.where(behav_total_run[:,5]==-1),axis=0)
 
-#your behav_total_run is ready to use!
+print(your "behavior" is ready to use!)