From 0e2289344a34cc490beaeb80e2d9e3272afec4fd Mon Sep 17 00:00:00 2001
From: steffen-illium <steffen.illium@ifi.lmu.de>
Date: Fri, 11 Jun 2021 14:38:22 +0200
Subject: [PATCH] robustness

---
 README.md               | 25 +++++++++++++++----------
 functionalities_test.py |  8 +++++---
 journal_robustness.py   |  2 +-
 3 files changed, 21 insertions(+), 14 deletions(-)

diff --git a/README.md b/README.md
index 5b2f39d..99d049e 100644
--- a/README.md
+++ b/README.md
@@ -12,31 +12,36 @@
     - there is also a distance matrix for all-to-all particle comparisons (with distance parameter one of: `MSE`, `MAE` (mean absolute error = mean manhattan) and `MIM` (mean position invariant manhattan))
 
 
-- [ ] Same Thing with Soup interactionWe would expect the same behaviour...Influence of interaction with near and far away particles.
-
-- [ ] How are basins / "attractor areas" shaped?
-      - Weired.... tbc...
-
+- [ ] Same Thing with Soup interaction. We would expect the same behaviour...Influence of interaction with near and far away particles.
+      - 
+      - 
+  
 - [x] Robustness test with a trained NetworkTraining for high quality fixpoints, compare with the "perfect" fixpoint. Average Loss per application step
-    
+
     - see `journal_robustness.py` for robustness test modeled after cristians robustness-exp (with the exeption that we put noise on the weights). Has `synthetic` bool to switch to hand-modeled perfect fixpoint instead of naturally trained ones. 
 
     - Also added two difference between the "time-as-fixpoint" and "time-to-verge" (i.e. to divergence / zero).
 
     - We might need to consult about the "average loss per application step", as I think application loss get gradually higher the worse the weights get. So the average might not tell us much here.
 
-- [ ] Adjust Self Training so that it favors second order fixpoints-> Second order test implementation (?)
+- [x] Adjust Self Training so that it favors second order fixpoints-> Second order test implementation (?)
 
 - [x] Barplot over clones -> how many become a fixpoint cs how many diverge per noise level
 
-- [ ] Box-Plot of Avg. Distance of clones from parent
+- [x] Box-Plot of Avg. Distance of clones from parent
+
+- [ ] Search subspace between two fixpoints by linage(10**-5), check were they end up
+
+- [ ] How are basins / "attractor areas" shaped?
+      - Weired.... tbc...
+      - 
+
 
 # Future Todos:
 
 - [ ] Find a statistik over weight space that provides a better init function
 - [ ] Test this init function on a mnist classifier - just for the lolz
-- [ ] 
- 
+
 ---
 ## Notes: 
 
diff --git a/functionalities_test.py b/functionalities_test.py
index fe672de..374811b 100644
--- a/functionalities_test.py
+++ b/functionalities_test.py
@@ -24,9 +24,11 @@ def is_identity_function(network: Net, epsilon=pow(10, -5)) -> bool:
                        rtol=0, atol=epsilon)
 
 
-def is_zero_fixpoint(network: Net) -> bool:    
-    result = bool(len(np.nonzero(network.create_target_weights(network.input_weight_matrix()))))
-    return not result
+def is_zero_fixpoint(network: Net, epsilon=pow(10, -5)) -> bool:
+    target_data = network.create_target_weights(network.input_weight_matrix().detach().numpy())
+    result = np.allclose(target_data, np.zeros_like(target_data), rtol=0, atol=epsilon)
+    # result = bool(len(np.nonzero(network.create_target_weights(network.input_weight_matrix()))))
+    return result
 
 
 def is_secondary_fixpoint(network: Net, epsilon: float = pow(10, -5)) -> bool:
diff --git a/journal_robustness.py b/journal_robustness.py
index 4330614..4d44686 100644
--- a/journal_robustness.py
+++ b/journal_robustness.py
@@ -225,7 +225,7 @@ if __name__ == "__main__":
     ST_net_hidden_size = 2
     ST_net_learning_rate = 0.04
     ST_name_hash = random.getrandbits(32)
-    ST_synthetic = True
+    ST_synthetic = False
 
     print(f"Running the robustness comparison experiment:")
     RobustnessComparisonExperiment(