AGAIN 18

packs_sys/logicmoo_agi/prolog/agi_lib/pipeline.pl

@@ -6,23 +6,23 @@
 narrative_to_sim(Narrative,VWorld):- create_empty_sim(VWorld), run_narrative(VWorld, Narrative, VWorld).
 
 run_narrative(VWorldIn, Narrative, VWorldOut):-
-  forall(elementOf(E,Narrative), 
+  forall(elementOf(E,Narrative),
    (resolve_narrative(E,VWorldIn,E2),
     add_narrative(E2,VWorldOut))).
 
 % currently these are the same
 precepts_to_narrative(Precepts, Narrative):- copy_term(Precepts, Narrative).
 narrative_to_precepts(Precepts, Narrative):- copy_term(Narrative, Precepts).
 
-create_empty_sim(VWorld):- 
+create_empty_sim(VWorld):-
   copy_prolog_sim(empty_sim, VWorld).
 
-copy_prolog_sim(World1, World2):- 
+copy_prolog_sim(World1, World2):-
   object_to_props(sim, World1, World1Props),
   copy_term(World1Props,World2Props),
   create_object(sim, World2, World2Props).
 
-create_object(Type, Obj, ObjProps):- 
+create_object(Type, Obj, ObjProps):-
   (\+ ground(Obj)->gen_obj_sym(Type, Obj); true),
   setprops(Obj,type(Type)),
   setprops(Obj,ObjProps).
@@ -58,3 +58,261 @@
 %Increasing and decreasing specificity within the narrative pipelines
 %Can produce both generalized and condensed versions of Internal dialog.
 %Douglas Miles claims this was integral to his solving The Egg Cracking problem
+
+
+% Parameters of the autoencoder
+input_size(4).      % Number of input features
+hidden_size(2).     % Number of hidden units
+output_size(4).     % Number of output features
+learning_rate(0.1). % Learning rate for gradient descent
+
+% Initialize the weights of the encoder and the decoder
+initialize_weights :-
+    input_size(InputSize),
+    hidden_size(HiddenSize),
+    output_size(OutputSize),
+    random_matrix(InputSize, HiddenSize, EncoderWeights),
+    random_matrix(HiddenSize, OutputSize, DecoderWeights),
+    assert(encoder_weights(EncoderWeights)),
+    assert(decoder_weights(DecoderWeights)).
+
+% Encode the input into a lower-dimensional representation
+encode(Input, Hidden) :-
+    encoder_weights(EncoderWeights),
+    dot_product(Input, EncoderWeights, Hidden),
+    sigmoid(Hidden).
+
+% Decode the lower-dimensional representation into the output
+decode(Hidden, Output) :-
+    decoder_weights(DecoderWeights),
+    dot_product(Hidden, DecoderWeights, Output),
+    sigmoid(Output).
+
+% Train the autoencoder using backpropagation
+train(Input, Output) :-
+    encode(Input, Hidden),
+    decode(Hidden, Reconstructed),
+    loss(Input, Reconstructed, Loss),
+    backpropagate(Hidden, Loss, HiddenGradients),
+    backpropagate(Input, HiddenGradients, InputGradients),
+    update_weights(HiddenGradients, InputGradients).
+
+% Compute the loss function
+loss(Input, Reconstructed, Loss) :-
+    subtract(Input, Reconstructed, Error),
+    dot_product(Error, Error, SquaredError),
+    Loss is SquaredError / 2.
+
+% Backpropagate the error gradients through the network
+backpropagate(Input, Gradients, InputGradients) :-
+    encoder_weights(EncoderWeights),
+    transpose(EncoderWeights, TransposedEncoderWeights),
+    dot_product(Gradients, TransposedEncoderWeights, InputGradients),
+    derivative(Input, HiddenGradients, sigmoid),
+    hadamard_product(InputGradients, HiddenGradients, InputGradients).
+
+backpropagate(Hidden, Gradients, HiddenGradients) :-
+    decoder_weights(DecoderWeights),
+    transpose(DecoderWeights, TransposedDecoderWeights),
+    dot_product(Gradients, TransposedDecoderWeights, HiddenGradients),
+    derivative(Hidden, OutputGradients, sigmoid),
+    hadamard_product(HiddenGradients, OutputGradients, HiddenGradients).
+
+% Update the weights using gradient descent
+update_weights(HiddenGradients, InputGradients) :-
+    learning_rate(LearningRate),
+    encoder_weights(EncoderWeights),
+    decoder_weights(DecoderWeights),
+    outer_product(InputGradients, HiddenGradients, EncoderWeightGradients),
+    outer_product(HiddenGradients, InputGradients, DecoderWeightGradients),
+    scalar_multiply(EncoderWeightGradients, -LearningRate, ScaledEncoderWeightGradients),
+    scalar_multiply(DecoderWeightGradients, -LearningRate, ScaledDecoderWeightGradients),
+    add(EncoderWeights, ScaledEncoderWeightGradients, UpdatedEncoderWeights),
+    add(DecoderWeights, ScaledDecoderWeights, ScaledDecoderWeightGradients, UpdatedDecoderWeights),
+    retract(encoder_weights(_)),
+    retract(decoder_weights(_)),
+    assert(encoder_weights(UpdatedEncoderWeights)),
+    assert(decoder_weights(UpdatedDecoderWeights)).
+
+% Helper predicates for matrix operations
+dot_product(Matrix1, Matrix2, Result) :- matrix_multiply(Matrix1, Matrix2, Result).
+
+hadamard_product(Matrix1, Matrix2, Result) :- matrix_elementwise_multiply(Matrix1, Matrix2, Result).
+
+outer_product(Vector1, Vector2, Result) :- matrix_multiply(Vector1, transpose(Vector2), Result).
+
+scalar_multiply(Matrix, Scalar, Result) :- matrix_scalar_multiply(Matrix, Scalar, Result).
+
+add(Matrix1, Matrix2, Result) :- matrix_add(Matrix1, Matrix2, Result).
+
+% Helper predicate for computing the derivative of the activation function
+derivative(Input, Output, sigmoid) :- sigmoid(Input, Output), hadamard_product(Output, subtract(1, Output), Output).
+
+% Helper predicate for computing the logistic sigmoid function
+sigmoid(X, Y) :- Y is 1 / (1 + exp(-X)).
+
+% Helper predicates for matrix manipulation
+random_matrix(Rows, Cols, Matrix) :-
+  length(Matrix, Rows),
+  maplist(random_list(Cols), Matrix).
+
+random_list(Size, List) :-
+  length(List, Size),
+  maplist(random_value, List).
+
+random_value(Value) :- random(Value).
+
+transpose(Matrix, Transposed) :-
+  length(Matrix, Rows),
+  length(Transposed, Cols),
+  maplist(length_list(Rows), Transposed),
+  transpose_helper(Matrix, Transposed).
+
+transpose_helper([], []).
+  transpose_helper([Row|Rows], Transposed) :-
+  maplist(list_head_tail, [Row|Rows], Heads, Tails),
+  maplist(transpose_helper, Tails, TransposedHeads),
+  append(Heads, TransposedHeads, Transposed).
+
+length_list(Len, List) :-
+  length(List, Len).
+
+list_head_tail([Head|Tail], Head, Tail).
+
+matrix_multiply(Matrix1, Matrix2, Result) :-
+  transpose(Matrix2, Transposed),
+  maplist(dot_product_helper(Transposed), Matrix1, Result).
+
+dot_product_helper(Matrix, Vector, Result) :-
+  dot_product(Matrix, Vector, Result).
+
+matrix_elementwise_multiply(Matrix1, Matrix2, Result) :-
+  maplist(elementwise_multiply_helper, Matrix1, Matrix2, Result).
+
+elementwise_multiply_helper(Element1, Element2, Result) :-
+  Result is Element1 * Element2.
+
+matrix_scalar_multiply(Matrix, Scalar, Result) :-
+  maplist(scalar_multiply_helper(Scalar), Matrix, Result).
+
+scalar_multiply_helper(Scalar, Element, Result) :-
+  Result is Scalar * Element.
+
+matrix_add(Matrix1, Matrix2, Result) :-
+  maplist(add_helper, Matrix1, Matrix2, Result).
+
+add_helper(Element1, Element2, Result) :-
+  Result is Element1 + Element2.
+
+  /*
+  
+This implementation uses some helper predicates for matrix manipulation, including matrix multiplication,
+   element-wise multiplication, scalar multiplication, and matrix addition. These predicates are not built-in 
+   Prolog predicates, so they need to be defined explicitly. 
+
+To use this autoencoder, you can call `initialize_weights` to initialize the weights of the encoder and the decoder, 
+ and then call `train` with your input data to train the autoencoder. You can also call `encode` 
+ to encode your input data into a lower-dimensional representation, and `decode` to decode the representation back into the original data. 
+
+Note that this implementation is a simple proof-of-concept and is not optimized for performance. 
+ There are many ways to improve this
+
+  */
+
+% Define the n-gram size
+ngram_size(2).
+
+% Read in the corpus and tokenize it
+read_corpus(File, Tokens) :-
+    read_file_to_string(File, String, []),
+    split_string(String, "\s\t\n", "\s\t\n", Tokens).
+
+% Compute the n-grams of the corpus
+compute_ngrams(Tokens, Ngrams) :-
+    ngram_size(N),
+    length(Tokens, Len),
+    MaxIndex is Len - N + 1,
+    findall(Ngram, (between(1, MaxIndex, Index),
+                    nth1(Index, Tokens, First),
+                    length(Ngram, N),
+                    append([First], Rest, Ngram),
+                    nth1(Index2, Tokens, Rest),
+                    succ(Index, Index2)), Ngrams).
+
+% Train the language model on the corpus
+train_model(File) :-
+    read_corpus(File, Tokens),
+    compute_ngrams(Tokens, Ngrams),
+    assert_ngrams(Ngrams).
+
+% Store the n-grams in the knowledge base
+assert_ngrams([]).
+assert_ngrams([Ngram|Ngrams]) :-
+    increment_count(Ngram),
+    assert_ngrams(Ngrams).
+
+% Increment the count of an n-gram in the knowledge base
+increment_count(Ngram) :-
+    ngram_count(Ngram, Count),
+    NewCount is Count + 1,
+    retract(ngram_count(Ngram, Count)),
+    assert(ngram_count(Ngram, NewCount)),
+    !.
+increment_count(Ngram) :-
+    assert(ngram_count(Ngram, 1)).
+
+% Predict the next word given a sequence of words
+predict_next(Sequence, Next) :-
+    ngram_size(N),
+    length(Sequence, Len),
+    PrefixSize is N - 1,
+    (Len >= PrefixSize ->
+        append(Prefix, [Last], Sequence),
+        findall(Count-Word, (ngram_count(Ngram, Count),
+                             append(Prefix, [Word], Ngram)), Pairs),
+        keysort(Pairs, SortedPairs),
+        reverse(SortedPairs, [MaxCount-Next|_])
+    ;
+        Next = ""
+    ).
+
+% Reset the knowledge base
+reset_model :-
+    retractall(ngram_count(_, _)).
+
+% Export the model as a file
+export_model(File) :-
+    tell(File),
+    listing(ngram_count),
+    told.
+
+
+% Here's an example usage of the language model:
+
+% Train the model on a corpus file
+?- train_model('corpus.txt').
+
+% Predict the next word given a sequence of words
+?- predict_next(["the", "quick"], Next). % returns "brown"
+
+% Reset the knowledge base
+?- reset_model().
+
+% Export the model to a file
+?- export_model('model.pl').
+
+%implementation is a simple proof-of-concept and can be extended to handle larger n-grams, handle unseen words, and use smoothing techniques to handle zero counts.
+
+%In this example, we train the language model on a corpus file using the train_model/1 predicate. We then use the predict_next/2 predicate to predict the next word given a sequence of words ("the" and "quick"). The predicted next word is "brown". We can also reset the knowledge base using the reset_model/0 predicate and export the model to a file using the export_model/1 predicate.
+
+%Note that this implementation is just a basic example of a language model in Prolog, and there are many ways to improve and extend it. For example, we can use higher-order n-grams, handle out-of-vocabulary words, use more sophisticated smoothing techniques, and integrate the language model into a larger natural language processing pipeline.
+/*
+ngram_count(["the", "quick"], 5).
+ngram_count(["quick", "brown"], 3).
+ngram_count(["brown", "fox"], 2).
+ngram_count(["fox", "jumps"], 4).
+ngram_count(["jumps", "over"], 1).
+ngram_count(["over", "the"], 3).
+ngram_count(["the", "lazy"], 2).
+ngram_count(["lazy", "dog"], 1).
+*/

packs_sys/logicmoo_agi/prolog/kaggle_arc/data/training/makesboxsq.json

@@ -1 +1,5 @@
-{"train":[{"id":0,"input":[[0,0,0,0,0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,8,8,0,0,0,0,0],[0,0,0,0,0,0,8,0,0,0,0,0,0],[0,2,2,0,0,0,0,0,0,0,0,0,0],[0,0,2,0,0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,1,0,0,0,0],[0,0,0,0,0,0,0,1,1,0,0,0,0],[0,0,0,3,0,0,0,0,0,0,0,0,0],[0,0,0,3,3,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0,0,0,0,0]],"output":[[8,8,2,2],[8,0,0,2],[3,0,0,1],[3,3,1,1]]},{"id":1,"input":[[0,0,0,0,0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,8,8,0,0,0],[0,0,0,0,0,0,0,0,0,8,0,0,0],[0,0,1,1,0,0,0,0,0,0,0,0,0],[0,0,1,0,0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,2,0,0,0,0],[0,0,0,0,0,0,0,2,2,0,0,0,0],[0,0,0,0,0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0,0,0,0,0],[0,0,0,0,4,0,0,0,0,0,0,0,0],[0,0,0,0,4,4,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0,0,0,0,0]],"output":[[1,1,8,8],[1,0,0,8],[4,0,0,2],[4,4,2,2]]},{"id":2,"input":[[0,0,0,0,0,0,0,0,0,0,0,0,0],[0,0,0,6,0,0,0,0,0,0,0,0,0],[0,0,0,6,0,0,0,0,0,8,8,8,0],[0,0,6,6,0,0,0,0,0,0,0,8,0],[0,0,0,0,0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,1,0,0,0,0,0,0],[0,0,0,0,0,0,1,1,1,0,0,0,0],[0,0,0,0,0,0,0,0,0,0,0,0,0],[0,0,3,3,0,0,0,0,0,0,0,0,0],[0,0,3,0,0,0,0,0,0,0,0,0,0],[0,0,3,0,0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0,0,0,0,0]],"output":[[3,3,8,8,8],[3,0,0,0,8],[3,0,0,0,6],[1,0,0,0,6],[1,1,1,6,6]]}],"test":[{"id":3,"input":[[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],[0,0,0,3,9,0,0,0,0,0,0,0,0,0,0,0],[0,0,0,3,9,0,0,0,0,0,0,0,0,0,0,0],[3,3,3,3,9,9,0,0,0,0,0,0,0,0,0,0],[0,4,4,4,3,3,3,0,0,0,0,0,0,0,0,0],[0,0,0,4,3,0,0,0,0,0,0,0,0,0,0,0],[0,0,0,4,3,0,0,0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]],"output":[[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]]}],"name":"makesboxsq","description":""}
+{"train":[
+ {"id":0,"input":[[0,0,0,0,0,0,0,0,0,0,0,0,0],[0,0,0,6,0,0,0,0,0,0,0,0,0],[0,0,0,6,0,0,0,0,0,8,8,8,0],[0,0,6,6,0,0,0,0,0,0,0,8,0],[0,0,0,0,0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,1,0,0,0,0,0,0],[0,0,0,0,0,0,1,1,1,0,0,0,0],[0,0,0,0,0,0,0,0,0,0,0,0,0],[0,0,3,3,0,0,0,0,0,0,0,0,0],[0,0,3,0,0,0,0,0,0,0,0,0,0],[0,0,3,0,0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0,0,0,0,0]],"output":[[3,3,8,8,8],[3,0,0,0,8],[3,0,0,0,6],[1,0,0,0,6],[1,1,1,6,6]]},
+ {"id":2,"input":[[0,0,0,0,0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,8,8,0,0,0,0,0],[0,0,0,0,0,0,8,0,0,0,0,0,0],[0,2,2,0,0,0,0,0,0,0,0,0,0],[0,0,2,0,0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,1,0,0,0,0],[0,0,0,0,0,0,0,1,1,0,0,0,0],[0,0,0,3,0,0,0,0,0,0,0,0,0],[0,0,0,3,3,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0,0,0,0,0]],"output":[[8,8,2,2],[8,0,0,2],[3,0,0,1],[3,3,1,1]]},
+ {"id":1,"input":[[0,0,0,0,0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,8,8,0,0,0],[0,0,0,0,0,0,0,0,0,8,0,0,0],[0,0,1,1,0,0,0,0,0,0,0,0,0],[0,0,1,0,0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,2,0,0,0,0],[0,0,0,0,0,0,0,2,2,0,0,0,0],[0,0,0,0,0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0,0,0,0,0],[0,0,0,0,4,0,0,0,0,0,0,0,0],[0,0,0,0,4,4,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0,0,0,0,0]],"output":[[1,1,8,8],[1,0,0,8],[4,0,0,2],[4,4,2,2]]} 
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packs_sys/logicmoo_agi/prolog/kaggle_arc/kaggle_arc.pl

@@ -766,6 +766,7 @@
 :- load_json_files(eval400,v,'./data/devaluation/*.json').
 :- scan_uses_test_id.
 :- store_grid_size_predictions.
+%:- test_grid_size_predictions.
 :- make_grid_cache.
 :- initialization(gen_gids).