minor

get_bayesian_gp_means_std.py

@@ -3,7 +3,7 @@
 from tqdm import tqdm
 from scipy.special import logsumexp
 from scipy.stats import norm
-from get_noise_nmll import get_noise_nmll
+from get_kalman import get_noise_nmll
 
 
 def get_means_stdev(x_mu_rbf, x_sd_rbf, x_mu_lin, x_sd_lin, x_mu_kal, x_sd_kal,
@@ -95,8 +95,7 @@ def get_means_stdev(x_mu_rbf, x_sd_rbf, x_mu_lin, x_sd_lin, x_mu_kal, x_sd_kal,
 
     return pd.concat(all_subj)
 
-
-## N.B. each experiment needs a seperate function to prepare it's own data
+# N.B. each experiment needs a seperate function to prepare it's own data
 
 def exp_lin():
     lin_gp_data = pd.read_csv('Data/exp_linear/linpred.csv')
@@ -106,7 +105,7 @@ def exp_lin():
     rbf_gp_data.index = range(len(rbf_gp_data))
 
     raw_data = pd.read_csv('Data/exp_linear/lindata.csv')
-    rewards = raw_data[ 'out'].values
+    rewards = raw_data['out'].values
 
     noise_nmll = get_noise_nmll(raw_data_path='Data/exp_linear/lindata.csv')
 
@@ -120,7 +119,6 @@ def exp_lin():
         lin_gp_data = lin_gp_data[lin_gp_data.id != s].copy()
         noise_nmll = noise_nmll[noise_nmll.Subject != s].copy()
 
-
     x_mu_rbf = np.array([rbf_gp_data.loc[:, 'mu_ %d' % ii].values for ii in range(8)]).T
     x_sd_rbf = np.array([rbf_gp_data.loc[:, 'sig_ %d' % ii].values for ii in range(8)]).T
 
@@ -293,9 +291,10 @@ def exp_scrambled():
         )
     all_subjs.to_pickle('Data/exp_scrambled/bayes_gp_exp_scram.pkl')
 
+
 if __name__ == "__main__":
-    # exp_lin()
+    exp_lin()
     exp_shifted()
-    # exp_cp()
-    # exp_srs()
-    # exp_scrambled()
+    exp_cp()
+    exp_srs()
+    exp_scrambled()