fig fixture

code/setups/experiments/Known-Fixpoint-Variation/log.txt

@@ -1,30 +0,0 @@
-variation 10e-0
-avg time to vergence 3.63
-avg time as fixpoint 0
-variation 10e-1
-avg time to vergence 5.02
-avg time as fixpoint 0
-variation 10e-2
-avg time to vergence 6.46
-avg time as fixpoint 0
-variation 10e-3
-avg time to vergence 8.04
-avg time as fixpoint 0
-variation 10e-4
-avg time to vergence 9.61
-avg time as fixpoint 0.04
-variation 10e-5
-avg time to vergence 11.23
-avg time as fixpoint 1.38
-variation 10e-6
-avg time to vergence 12.99
-avg time as fixpoint 3.23
-variation 10e-7
-avg time to vergence 14.58
-avg time as fixpoint 4.84
-variation 10e-8
-avg time to vergence 21.95
-avg time as fixpoint 11.91
-variation 10e-9
-avg time to vergence 26.45
-avg time as fixpoint 16.47

code/setups/known-fixpoint-variation.py

@@ -28,7 +28,8 @@ def generate_fixpoint_weights():
 def generate_fixpoint_net():
     #NOTE: Weightwise only is all we can do right now IMO
     net = WeightwiseNeuralNetwork(width=2, depth=2).with_keras_params(activation='sigmoid')
-    # net = AggregatingNeuralNetwork(aggregates=4, width=2, depth=2).with_keras_params(activation='sigmoid') # I don't know if this work for aggregaeting. We don't actually need it, though.
+    # I don't know if this work for aggregaeting. We don't actually need it, though.
+    # net = AggregatingNeuralNetwork(aggregates=4, width=2, depth=2).with_keras_params(activation='sigmoid')
     net.set_weights(generate_fixpoint_weights())
     return net