state_discriminator3: rename: invalid -> mixed

state_discriminator3.py

@@ -57,40 +57,40 @@ def prune_unreconstructable(sae,data):
 
 def prepare(data_valid, sae):
     gen_batch = 10000 if len(data_valid) < 2000 else None
-    data_invalid = generate_random(data_valid, sae, gen_batch)
+    data_mixed = generate_random(data_valid, sae, gen_batch)
     try:
         p = 0
         pp = 0
         ppp = 0
-        while len(data_invalid) < len(data_valid) and p < len(data_invalid):
+        while len(data_mixed) < len(data_valid) and p < len(data_mixed):
             p = pp
             pp = ppp
-            ppp = len(data_invalid)
-            data_invalid = union(data_invalid, generate_random(data_valid, sae, gen_batch))
+            ppp = len(data_mixed)
+            data_mixed = union(data_mixed, generate_random(data_valid, sae, gen_batch))
             print("valid:",len(data_valid),
-                  "mixed:", len(data_invalid),
+                  "mixed:", len(data_mixed),
                   "## generation stops when it failed to generate new examples three times in a row")
     except KeyboardInterrupt:
         pass
     finally:
         print("generation stopped")
 
-    if len(data_valid) < len(data_invalid):
+    if len(data_valid) < len(data_mixed):
         # downsample
-        data_invalid = data_invalid[:len(data_valid)]
+        data_mixed = data_mixed[:len(data_valid)]
     else:
         # oversample
-        data_invalid = np.repeat(data_invalid, 1+(len(data_valid)//len(data_invalid)), axis=0)
-        data_invalid = data_invalid[:len(data_valid)]
+        data_mixed = np.repeat(data_mixed, 1+(len(data_valid)//len(data_mixed)), axis=0)
+        data_mixed = data_mixed[:len(data_valid)]
 
-    train_in, train_out, test_in, test_out = prepare_binary_classification_data(data_valid, data_invalid)
-    return train_in, train_out, test_in, test_out, data_valid, data_invalid
+    train_in, train_out, test_in, test_out = prepare_binary_classification_data(data_valid, data_mixed)
+    return train_in, train_out, test_in, test_out, data_valid, data_mixed
 
 def prepare_random(data_valid, sae, inflation=1):
     batch = data_valid.shape[0]
-    data_invalid = np.random.randint(0,2,data_valid.shape,dtype=np.int8)
-    train_in, train_out, test_in, test_out = prepare_binary_classification_data(data_valid, data_invalid)
-    return train_in, train_out, test_in, test_out, data_valid, data_invalid
+    data_mixed = np.random.randint(0,2,data_valid.shape,dtype=np.int8)
+    train_in, train_out, test_in, test_out = prepare_binary_classification_data(data_valid, data_mixed)
+    return train_in, train_out, test_in, test_out, data_valid, data_mixed
 
 sae = None
 cae = None
@@ -110,8 +110,8 @@ def learn(method):
         'min_grad'        : 0.0,
     }
     data_valid = np.loadtxt(sae.local("states.csv"),dtype=np.int8)
-    train_in, train_out, test_in, test_out, data_valid, data_invalid = prepare(data_valid,sae)
-    sae.plot_autodecode(data_invalid[:8], "_sd3/fake_samples.png")
+    train_in, train_out, test_in, test_out, data_valid, data_mixed = prepare(data_valid,sae)
+    sae.plot_autodecode(data_mixed[:8], "_sd3/fake_samples.png")
 
     if method == "feature":
         # decode into image, extract features and learn from it
@@ -257,12 +257,12 @@ def test(method):
 
     ################################################################
     # type 2 error
-    _,_,_,_, _, states_invalid = prepare(states_valid[:50000],sae)
-    print(len(states_invalid),"reconstructable states generated.")
+    _,_,_,_, _, states_mixed = prepare(states_valid[:50000],sae)
+    print(len(states_mixed),"reconstructable states generated.")
 
     p = latplan.util.puzzle_module(sae.path)
-    is_valid = p.validate_states(sae.decode_binary(states_invalid))
-    states_invalid = states_invalid[np.logical_not(is_valid)]
+    is_valid = p.validate_states(sae.decode_binary(states_mixed))
+    states_invalid = states_mixed[np.logical_not(is_valid)]
     states_invalid = states_invalid[:30000]
     print(len(states_invalid),"invalid states generated.")