adds feature_numeriche 20190903

dbrepair/numeric_features_20190903.py

+
+import logging; logging.basicConfig(level=logging.INFO)
+
+import numpy as np
+
+import tensorflow as tf
+
+import logictensornetworks_wrapper as ltnw
+
+
+ltnw.constant('a',min_value=[1.,2.,3.,0.],max_value=[1.,2.,3.,100.])
+ltnw.constant('b',min_value=[4.,5.,6.,0.],max_value=[4.,5.,6.,100.])
+ltnw.constant('c',min_value=[4.,5.,6.,0.],max_value=[4.,5.,6.,100.])
+ltnw.constant("ten",[10.])
+ltnw.constant("twenty",[20.])
+print(ltnw.CONSTANTS)
+
+def _close_eta(x,y):
+    return 1-tf.abs(x-y)/100.
+
+
+ltnw.function('eta',4,fun_definition=lambda x:x[:,3])
+ltnw.predicate("close_eta",2,_close_eta)
+
+ltnw.axiom("close_eta(eta(a),ten)")
+ltnw.axiom("close_eta(eta(b),twenty)")
+ltnw.axiom("close_eta(eta(b),eta(c))")
+ltnw.initialize_knowledgebase(optimizer=tf.train.RMSPropOptimizer(learning_rate=.01),
+                              initial_sat_level_threshold=.4)
+ltnw.train(max_epochs=20000)
+print("eta(a)",ltnw.ask('eta(a)'))
+print("eta(b)",ltnw.ask('eta(b)'))
+print("eta(c)",ltnw.ask('eta(c)'))
+