uploaded my code (not yet 100% finished)

functionalities_test.py

@@ -0,0 +1,103 @@
+import copy
+from typing import Dict, List
+import numpy as np
+from torch import Tensor
+from network import Net
+
+
+def overall_fixpoint_test(network: Net, epsilon: float, input_data) -> bool:
+    predicted_values = network(input_data)
+
+    check_smaller_epsilon = all(epsilon > predicted_values)
+    check_greater_epsilon = all(-epsilon < predicted_values)
+
+    if check_smaller_epsilon and check_greater_epsilon:
+        return True
+    else:
+        return False
+
+
+def is_divergent(network: Net) -> bool:
+    for i in network.input_weight_matrix():
+        weight_value = i[0].item()
+
+        if np.isnan(weight_value) or np.isinf(weight_value):
+            return True
+
+    return False
+
+
+def is_identity_function(network: Net, input_data: Tensor, target_data: Tensor, epsilon=pow(10, -5)) -> bool:
+    predicted_values = network(input_data)
+
+    return np.allclose(target_data.detach().numpy(), predicted_values.detach().numpy(), 0, epsilon)
+
+
+def is_zero_fixpoint(network: Net, input_data: Tensor, epsilon=pow(10, -5)) -> bool:
+    result = overall_fixpoint_test(network, epsilon, input_data)
+
+    return result
+
+
+def is_secondary_fixpoint(network: Net, input_data: Tensor, epsilon: float) -> bool:
+    """ Secondary fixpoint check is done like this: compare first INPUT with second OUTPUT.
+    If they are within the boundaries, then is secondary fixpoint. """
+
+    # Calculating first output
+    first_output = network(input_data)
+
+    # Getting the second output by initializing a new net with the weights of the original net.
+    net_copy = copy.deepcopy(network)
+    net_copy.apply_weights(net_copy, first_output)
+    input_data_2 = net_copy.input_weight_matrix()
+
+    # Calculating second output
+    second_output = network(input_data_2)
+
+    check_smaller_epsilon = all(epsilon > second_output)
+    check_greater_epsilon = all(-epsilon < second_output)
+
+    if check_smaller_epsilon and check_greater_epsilon:
+        return True
+    else:
+        return False
+
+
+def is_weak_fixpoint(network: Net, input_data: Tensor, epsilon: float) -> bool:
+    result = overall_fixpoint_test(network, epsilon, input_data)
+
+    return result
+
+
+def test_for_fixpoints(fixpoint_counter: Dict, nets: List, id_functions=[]):
+    zero_epsilon = pow(10, -5)
+    epsilon = pow(10, -3)
+
+    for i in range(len(nets)):
+        net = nets[i]
+        input_data = net.input_weight_matrix()
+        target_data = net.create_target_weights(input_data)
+
+        if is_divergent(nets[i]):
+            fixpoint_counter["divergent"] += 1
+            nets[i].is_fixpoint = "divergent"
+        elif is_identity_function(nets[i], input_data, target_data, zero_epsilon):
+            fixpoint_counter["identity_func"] += 1
+            nets[i].is_fixpoint = "identity_func"
+            id_functions.append(nets[i])
+        elif is_zero_fixpoint(nets[i], input_data, zero_epsilon):
+            fixpoint_counter["fix_zero"] += 1
+            nets[i].is_fixpoint = "fix_zero"
+        elif is_weak_fixpoint(nets[i], input_data, epsilon):
+            fixpoint_counter["fix_weak"] += 1
+            nets[i].is_fixpoint = "fix_weak"
+        elif is_secondary_fixpoint(nets[i], input_data, zero_epsilon):
+            fixpoint_counter["fix_sec"] += 1
+            nets[i].is_fixpoint = "fix_sec"
+        else:
+            fixpoint_counter["other_func"] += 1
+            nets[i].is_fixpoint = "other_func"
+
+
+def changing_rate(x_new, x_old):
+    return x_new - x_old