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View Code? Open in Web Editor NEWA topology and parameter evolving universal learning network.
License: Apache License 2.0
A topology and parameter evolving universal learning network.
License: Apache License 2.0
apxr comes with "examples" complex enough to do Forex (foreign exchange) calculus, could you add virus analysis? Developer of Clamscan asks for pull request of artificial CNS for virus analysis
The input is vectors of floats/integers, so just convert sources/texts to integers/hex, plus convert descriptions to integers/hex, plus convert outputs from integers/hex to texts
As both projects use Apache 2
licenses, can use Erlang functions from C/C++ (see ClassCns.cxx
),
but for lots of applications (such as https://github.com/SwuduSusuwu/SubStack/tree/trunk/cxx/ClassCns.cxx-based VirusAnalysis.cxx and ConversationCns.cxx) the clients do not have enough resources to load the databases (yet alone setup the synapses,)
but have enough resources to import synapses to use this.
Can you backup synapses to SQLite
, csv
, or some other storages for future uses?
https://github.com/Rober-t/apxr_run/blob/master/src/agent_mgr/signal_aggregator.erl
0001-The-are-many-other-There-are-many-other.patch
Above Git patch, or below diff fix
diff --git a/src/agent_mgr/signal_aggregator.erl b/src/agent_mgr/signal_aggregator.erl
index c903663..2e73abc 100644
--- a/src/agent_mgr/signal_aggregator.erl
+++ b/src/agent_mgr/signal_aggregator.erl
@@ -34,9 +34,9 @@
%%% composes the scalar value by aggregating the input vectors, and then
%%% calculating the dot product of the input vectors and the synaptic
%%% weights. Another way to calculate a scalar value from the input and
-%%% weight vectors is by multiplying the corresponding input signals by
+%o%% weight vectors is by multiplying the corresponding input signals by
%%% their weights, but instead of adding the resulting multiplied values,
-%%% we multiply them. The are many other types of aggregation functions
+%%% we multiply them. There are many other types of aggregation functions
%%% that could be created. We can also add normalizer functions, which
%%% could normalize the input signals. The normalizers could be
%%% implemented as part of the aggregator functions, although it
@@ -145,4 +145,4 @@ mult_product([], [], Acc) ->
mult([I | Input], [{W, _LPs} | WeightsP], Acc) ->
mult(Input, WeightsP, I * W * Acc);
mult([], [], Acc) ->
- Acc.
\ No newline at end of file
+ Acc.
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