Merging recomm_sys

.devcontainer/devcontainer.json

@@ -1,7 +1,7 @@
 {
-  "name": "TM Graph Devcontainer",
+  "name": "TM Graph Recomm",
   "dockerComposeFile": "docker-compose.yml",
-  "service": "tm-graph-development",
+  "service": "tm-graph-recomm",
   "workspaceFolder": "/app",
   "forwardPorts": [],
   "postCreateCommand": "echo 'Devcontainer is ready'",

.devcontainer/docker-compose.yml

@@ -1,5 +1,5 @@
 services:
-  tm-graph-development:
+  tm-graph-recomm:
     build:
       context: ../
       dockerfile: .devcontainer/Dockerfile
@@ -9,4 +9,5 @@ services:
           devices:
             - driver: nvidia
               capabilities: [gpu]
-              count: 1 # Assign number of GPUs or use 'all' to assign all available GPUs
+              # count: 1 # Assign number of GPUs or use 'all' to assign all available GPUs
+              device_ids: ["6"]

GraphTsetlinMachine/kernels.py

@@ -787,7 +787,6 @@
         unsigned int *local_ta_state = &ta_state[clause * chunks * STATE_BITS];
 
         for (int literal = 0; literal < literals; ++literal) {
-            unsigned int state = 0;
             int chunk_nr = literal / INT_SIZE;
             int chunk_pos = literal % INT_SIZE;
             out[clause * literals + literal] = (local_ta_state[chunk_nr * STATE_BITS + STATE_BITS - 1] & (1 << chunk_pos)) > 0;

GraphTsetlinMachine/tm.py

@@ -75,7 +75,6 @@ def __init__(
 		self.number_of_state_bits = number_of_state_bits
 		self.message_size = message_size
 		self.message_bits = message_bits
-		self.message_literals = message_size*2
 
 		self.double_hashing = double_hashing
 		self.one_hot_encoding = one_hot_encoding
@@ -541,7 +540,7 @@ def _init_fit(self, graphs, encoded_Y, incremental):
 			self.current_clause_node_output_train_gpu = cuda.mem_alloc(int(self.number_of_clauses * graphs.max_number_of_graph_node_chunks) * 4)
 			self.next_clause_node_output_train_gpu = cuda.mem_alloc(int(self.number_of_clauses * graphs.max_number_of_graph_node_chunks) * 4)
 
-			self.clause_X_int_train_gpu = cuda.mem_alloc(int(graphs.max_number_of_graph_nodes * self.message_literals) * 4)
+			self.clause_X_int_train_gpu = cuda.mem_alloc(int(graphs.max_number_of_graph_nodes * self.number_of_message_literals) * 4)
 
 			self.clause_X_train_gpu = []
 			for depth in range(self.depth-1):

LICENSE

@@ -1,6 +1,6 @@
 MIT License
 
-Copyright (c) 2024 Ole-Christoffer Granmo
+Copyright (c) 2025 Ole-Christoffer Granmo and University of Agder
 
 Permission is hereby granted, free of charge, to any person obtaining a copy
 of this software and associated documentation files (the "Software"), to deal

README.md

@@ -48,7 +48,7 @@ pip3 install graphtsetlinmachine
 or
 ```bash
 python ./setup.py sdist
-pip3 install dist/GraphTsetlinMachine-0.3.1.tar.gz
+pip3 install dist/GraphTsetlinMachine-0.3.4.tar.gz
 ```
 
 ## Tutorial 
@@ -339,7 +339,7 @@ tm = MultiClassGraphTsetlinMachine(
 
 ## Licence
 
-Copyright (c) 2024 Ole-Christoffer Granmo
+Copyright (c) 2025 Ole-Christoffer Granmo and University of Agder
 
 Permission is hereby granted, free of charge, to any person obtaining a copy
 of this software and associated documentation files (the "Software"), to deal

examples/MNISTConvolutionDemo.py

@@ -63,18 +63,13 @@ def default_args(**kwargs):
     double_hashing = args.double_hashing,
     one_hot_encoding = args.one_hot_encoding
 )
-
 for graph_id in range(X_train.shape[0]):
     graphs_train.set_number_of_graph_nodes(graph_id, number_of_nodes)
-
 graphs_train.prepare_node_configuration()
-
 for graph_id in range(X_train.shape[0]):
     for node_id in range(graphs_train.number_of_graph_nodes[graph_id]):
         graphs_train.add_graph_node(graph_id, node_id, 0)
-
 graphs_train.prepare_edge_configuration()
-
 for graph_id in range(X_train.shape[0]):
     if graph_id % 1000 == 0:
         print(graph_id, X_train.shape[0])
@@ -90,23 +85,17 @@ def default_args(**kwargs):
 
                 graphs_train.add_graph_node_property(graph_id, node_id, "C:%d" % (q))
                 graphs_train.add_graph_node_property(graph_id, node_id, "R:%d" % (r))
-
 graphs_train.encode()
-
 print("Training data produced")
 
 graphs_test = Graphs(X_test.shape[0], init_with=graphs_train)
 for graph_id in range(X_test.shape[0]):
     graphs_test.set_number_of_graph_nodes(graph_id, number_of_nodes)
-
 graphs_test.prepare_node_configuration()
-
 for graph_id in range(X_test.shape[0]):
     for node_id in range(graphs_test.number_of_graph_nodes[graph_id]):
         graphs_test.add_graph_node(graph_id, node_id, 0)
-
 graphs_test.prepare_edge_configuration()
-
 for graph_id in range(X_test.shape[0]):
     if graph_id % 1000 == 0:
         print(graph_id, X_test.shape[0])
@@ -122,9 +111,7 @@ def default_args(**kwargs):
 
                 graphs_test.add_graph_node_property(graph_id, node_id, "C:%d" % (q))
                 graphs_test.add_graph_node_property(graph_id, node_id, "R:%d" % (r))
-
 graphs_test.encode()
-
 print("Testing data produced")
 
 tm = MultiClassGraphTsetlinMachine(

examples/MNISTVanillaDemo.py

@@ -4,7 +4,6 @@
 from GraphTsetlinMachine.tm import MultiClassGraphTsetlinMachine
 from time import time
 import argparse
-from skimage.util import view_as_windows
 from keras.datasets import mnist
 from numba import jit
 
@@ -55,51 +54,36 @@ def default_args(**kwargs):
     double_hashing = args.double_hashing,
     one_hot_encoding = args.one_hot_encoding
 )
-
 for graph_id in range(X_train.shape[0]):
     graphs_train.set_number_of_graph_nodes(graph_id, number_of_nodes)
-
 graphs_train.prepare_node_configuration()
-
 for graph_id in range(X_train.shape[0]):
     number_of_outgoing_edges = 0
     graphs_train.add_graph_node(graph_id, 'Image Node', number_of_outgoing_edges)
-
 graphs_train.prepare_edge_configuration()
-
 for graph_id in range(X_train.shape[0]):
-    if graph_id % 1000 == 0:
-        print(graph_id, X_train.shape[0])
-
+    # if graph_id % 1000 == 0:
+        # print(graph_id, X_train.shape[0])
     for k in X_train[graph_id].nonzero()[0]:
         graphs_train.add_graph_node_property(graph_id, 'Image Node', "W%d,%d" % (k // 28, k % 28))
-
 graphs_train.encode()
-
 print("Training data produced")
 
 graphs_test = Graphs(X_test.shape[0], init_with=graphs_train)
-
 for graph_id in range(X_test.shape[0]):
     graphs_test.set_number_of_graph_nodes(graph_id, number_of_nodes)
-
 graphs_test.prepare_node_configuration()
-
 for graph_id in range(X_test.shape[0]):
     number_of_outgoing_edges = 0
     graphs_test.add_graph_node(graph_id, 'Image Node', number_of_outgoing_edges)
-
 graphs_test.prepare_edge_configuration()
-
 for graph_id in range(X_test.shape[0]):
     if graph_id % 1000 == 0:
         print(graph_id, X_test.shape[0])
 
     for k in X_test[graph_id].nonzero()[0]:
         graphs_test.add_graph_node_property(graph_id, 'Image Node', "W%d,%d" % (k // 28, k % 28))
-
 graphs_test.encode()
-
 print("Testing data produced")
 
 tm = MultiClassGraphTsetlinMachine(
@@ -128,16 +112,16 @@ def default_args(**kwargs):
 
     print("%d %.2f %.2f %.2f %.2f" % (i, result_train, result_test, stop_training-start_training, stop_testing-start_testing))
 
-weights = tm.get_state()[1].reshape(2, -1)
-for i in range(tm.number_of_clauses):
-        print("Clause #%d Weights:(%d %d)" % (i, weights[0,i], weights[1,i]), end=' ')
-        l = []
-        for k in range(args.hypervector_size * 2):
-            if tm.ta_action(0, i, k):
-                if k < args.hypervector_size:
-                    l.append("x%d" % (k))
-                else:
-                    l.append("NOT x%d" % (k - args.hypervector_size))
-        print(" AND ".join(l))
-
-print(graphs_train.hypervectors)
+# weights = tm.get_state()[1].reshape(2, -1)
+# for i in range(tm.number_of_clauses):
+#         print("Clause #%d Weights:(%d %d)" % (i, weights[0,i], weights[1,i]), end=' ')
+#         l = []
+#         for k in range(args.hypervector_size * 2):
+#             if tm.ta_action(0, i, k):
+#                 if k < args.hypervector_size:
+#                     l.append("x%d" % (k))
+#                 else:
+#                     l.append("NOT x%d" % (k - args.hypervector_size))
+#         print(" AND ".join(l))
+
+# print(graphs_train.hypervectors)

examples/NoisyXORDemo.py

@@ -35,7 +35,6 @@ def default_args(**kwargs):
 print("Creating training data")
 
 # Create train data
-
 graphs_train = Graphs(
     args.number_of_examples,
     symbols=['A', 'B'],
@@ -48,21 +47,16 @@ def default_args(**kwargs):
 
 for graph_id in range(args.number_of_examples):
     graphs_train.set_number_of_graph_nodes(graph_id, 2)
-
 graphs_train.prepare_node_configuration()
-
 for graph_id in range(args.number_of_examples):
     number_of_outgoing_edges = 1
     graphs_train.add_graph_node(graph_id, 'Node 1', number_of_outgoing_edges)
     graphs_train.add_graph_node(graph_id, 'Node 2', number_of_outgoing_edges)
-
-graphs_train.prepare_edge_configuration()
-
+graphs_train.prepar_eedge_configuration()
 for graph_id in range(args.number_of_examples):
     edge_type = "Plain"
     graphs_train.add_graph_node_edge(graph_id, 'Node 1', 'Node 2', edge_type)
     graphs_train.add_graph_node_edge(graph_id, 'Node 2', 'Node 1', edge_type)
-
 Y_train = np.empty(args.number_of_examples, dtype=np.uint32)
 for graph_id in range(args.number_of_examples):
     x1 = random.choice(['A', 'B'])
@@ -78,32 +72,23 @@ def default_args(**kwargs):
 
     if np.random.rand() <= args.noise:
         Y_train[graph_id] = 1 - Y_train[graph_id]
-
 graphs_train.encode()
 
-# Create test data
-
+# Create test data 
 print("Creating testing data")
-
 graphs_test = Graphs(args.number_of_examples, init_with=graphs_train)
-
 for graph_id in range(args.number_of_examples):
     graphs_test.set_number_of_graph_nodes(graph_id, 2)
-
 graphs_test.prepare_node_configuration()
-
 for graph_id in range(args.number_of_examples):
     number_of_outgoing_edges = 1
     graphs_test.add_graph_node(graph_id, 'Node 1', number_of_outgoing_edges)
     graphs_test.add_graph_node(graph_id, 'Node 2', number_of_outgoing_edges)
-
 graphs_test.prepare_edge_configuration()
-
 for graph_id in range(args.number_of_examples):
     edge_type = "Plain"
     graphs_test.add_graph_node_edge(graph_id, 'Node 1', 'Node 2', edge_type)
     graphs_test.add_graph_node_edge(graph_id, 'Node 2', 'Node 1', edge_type)
-
 Y_test = np.empty(args.number_of_examples, dtype=np.uint32)
 for graph_id in range(args.number_of_examples):
     x1 = random.choice(['A', 'B'])
@@ -116,7 +101,6 @@ def default_args(**kwargs):
         Y_test[graph_id] = 0
     else:
         Y_test[graph_id] = 1
-
 graphs_test.encode()
 
 tm = MultiClassGraphTsetlinMachine(

examples/NoisyXORMNISTDemo.py

@@ -54,24 +54,18 @@ def default_args(**kwargs):
     hypervector_size=args.hypervector_size,
     hypervector_bits=args.hypervector_bits,
 )
-
 for graph_id in range(args.number_of_examples):
     graphs_train.set_number_of_graph_nodes(graph_id, 2)
-
 graphs_train.prepare_node_configuration()
-
 for graph_id in range(args.number_of_examples):
     number_of_outgoing_edges = 1
     graphs_train.add_graph_node(graph_id, 'Node 1', number_of_outgoing_edges)
     graphs_train.add_graph_node(graph_id, 'Node 2', number_of_outgoing_edges)
-
 graphs_train.prepare_edge_configuration()
-
 for graph_id in range(args.number_of_examples):
     edge_type = "Plain"
     graphs_train.add_graph_node_edge(graph_id, 'Node 1', 'Node 2', edge_type)
     graphs_train.add_graph_node_edge(graph_id, 'Node 2', 'Node 1', edge_type)
-
 Y_train = np.empty(args.number_of_examples, dtype=np.uint32)
 for graph_id in range(args.number_of_examples):
     x1 = random.choice([0, 1])
@@ -91,32 +85,23 @@ def default_args(**kwargs):
 
     if np.random.rand() <= args.noise:
         Y_train[graph_id] = 1 - Y_train[graph_id]
-
 graphs_train.encode()
 
 # Create test data
-
 print("Creating testing data")
-
 graphs_test = Graphs(args.number_of_examples, init_with=graphs_train)
-
 for graph_id in range(args.number_of_examples):
     graphs_test.set_number_of_graph_nodes(graph_id, 2)
-
 graphs_test.prepare_node_configuration()
-
 for graph_id in range(args.number_of_examples):
     number_of_outgoing_edges = 1
     graphs_test.add_graph_node(graph_id, 'Node 1', number_of_outgoing_edges)
     graphs_test.add_graph_node(graph_id, 'Node 2', number_of_outgoing_edges)
-
 graphs_test.prepare_edge_configuration()
-
 for graph_id in range(args.number_of_examples):
     edge_type = "Plain"
     graphs_test.add_graph_node_edge(graph_id, 'Node 1', 'Node 2', edge_type)
     graphs_test.add_graph_node_edge(graph_id, 'Node 2', 'Node 1', edge_type)
-
 Y_test = np.empty(args.number_of_examples, dtype=np.uint32)
 for graph_id in range(args.number_of_examples):
     x1 = random.choice([0, 1])
@@ -133,7 +118,6 @@ def default_args(**kwargs):
         Y_test[graph_id] = 0
     else:
         Y_test[graph_id] = 1
-
 graphs_test.encode()
 
 tm = MultiClassGraphTsetlinMachine(