diff --git a/data_viz.py b/data_viz.py
index 52fd3095cda563f91c9a055a39ad2ef1c8c4740b..3d0f590b2006daec7cc598eb6a84494fe004d2d4 100644
--- a/data_viz.py
+++ b/data_viz.py
@@ -234,5 +234,5 @@ df = pd.read_csv('output/out_ISA.csv')
 add_length(df)
 df['abs_error'] =  np.abs(df['rt pred']-df['true rt'])
 # histo_abs_error(df, display=False, save=True, path='temp.png')
-# scatter_rt(df, display=False, save=True, path='temp.png')
-histo_length_by_error(df, 10, save=True, path='temp.png')
\ No newline at end of file
+scatter_rt(df, display=False, save=True, path='temp.png')
+# histo_length_by_error(df, 10, save=True, path='temp.png')
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diff --git a/main_custom.py b/main_custom.py
index fcc907494f8c2419cfd47376f0c5a9f4fac3a94c..db984b4baa652a0b7f364dd7c1ffacd5d81c3f87 100644
--- a/main_custom.py
+++ b/main_custom.py
@@ -207,7 +207,7 @@ def main(args):
 
     print('\nData loaded')
 
-    model = Model_Common_Transformer_TAPE(encoder_ff=args.encoder_ff, decoder_rt_ff=args.decoder_rt_ff,
+    model = Model_Common_Transformer(encoder_ff=args.encoder_ff, decoder_rt_ff=args.decoder_rt_ff,
                                      decoder_int_ff=args.decoder_int_ff
                                      , n_head=args.n_head, encoder_num_layer=args.encoder_num_layer,
                                      decoder_int_num_layer=args.decoder_int_num_layer,
diff --git a/vector_sim_test.py b/vector_sim_test.py
new file mode 100644
index 0000000000000000000000000000000000000000..1adb08082125225f3b2ca5d09bac1ab274b9f57c
--- /dev/null
+++ b/vector_sim_test.py
@@ -0,0 +1,27 @@
+import numpy as np
+import matplotlib.pyplot as plt
+
+def cos_sim(a,b):
+    return (a.dot(b))/(np.linalg.norm(a)*np.linalg.norm(b))
+
+l10=[]
+l100=[]
+for _ in range(1000):
+    vec10 = np.random.random(10)
+    vec10b = vec10 + np.random.random(10)
+
+    l10.append(cos_sim(vec10,vec10b))
+
+    vec100 = np.random.random(100)
+    vec100b = vec100 + np.random.random(100)
+
+    l100.append(cos_sim(vec100,vec100b))
+
+
+fig, ax = plt.subplots()
+
+ax.plot(l10,c='b',ls='',marker='.')
+ax.plot(l100,c='y',ls='',marker='.')
+plt.savefig('temp.png')
+print(np.mean(l10))
+print(np.mean(l100))
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