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journal_basins.py debugged II

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Questions for functionalities_test.py
corrected some fixes
Redo and implementation of everything path related now using pathlib.Path
This commit is contained in:
steffen-illium
2021-05-16 13:35:38 +02:00
parent 042188f15a
commit b1472479cb
4 changed files with 54 additions and 53 deletions
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19
functionalities_test.py
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@ -37,6 +37,8 @@ def is_identity_function(network: Net, epsilon=pow(10, -5)) -> bool:
def is_zero_fixpoint(network: Net, input_data: Tensor, epsilon=pow(10, -5)) -> bool:
# FIXME: Is the the correct test?
raise NotImplementedError
result = overall_fixpoint_test(network, epsilon, input_data)
return result
@ -50,6 +52,7 @@ def is_secondary_fixpoint(network: Net, input_data: Tensor, epsilon: float) -> b
first_output = network(input_data)
# Getting the second output by initializing a new net with the weights of the original net.
# FixMe: Is this correct? I Think it should be the same function thus the same network
net_copy = copy.deepcopy(network)
net_copy.apply_weights(first_output)
input_data_2 = net_copy.input_weight_matrix()
@ -57,18 +60,18 @@ def is_secondary_fixpoint(network: Net, input_data: Tensor, epsilon: float) -> b
# Calculating second output
second_output = network(input_data_2)
check_smaller_epsilon = all(epsilon > second_output)
check_greater_epsilon = all(-epsilon < second_output)
# Perform the Check:
check_abs_within_epsilon = all(epsilon > abs(input_data - second_output))
if check_smaller_epsilon and check_greater_epsilon:
return True
else:
return False
# FIXME: This is wrong, is it?
# check_smaller_epsilon = all(epsilon > second_output)
# check_greater_epsilon = all(-epsilon < second_output)
return True if check_abs_within_epsilon else False
def is_weak_fixpoint(network: Net, input_data: Tensor, epsilon: float) -> bool:
result = overall_fixpoint_test(network, epsilon, input_data)
return result
@ -80,7 +83,6 @@ def test_for_fixpoints(fixpoint_counter: Dict, nets: List, id_functions=None):
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
@ -104,5 +106,6 @@ def test_for_fixpoints(fixpoint_counter: Dict, nets: List, id_functions=None):
return id_functions
def changing_rate(x_new, x_old):
return x_new - x_old