amount_nodes = 400
percent_edges = 0.5
# +
# using graph class to generate matrix
graph1 = Graph()
# +
# matrix genaration
matrix1 = graph1.create_matrix(amount_nodes)
edges1 = graph1.generate_edges(amount_nodes, percent_edges)
matrix1 = graph1.add_edges(matrix1, edges1)
list_edges = graph1.make_list_edges_distances(matrix1)
netxgraph1 = graph1.build_networkx_graph(list_edges)
# +
# matrix1
# -
# This initializes regular SOM grid matrix, it needs to be passes instead of matrix1 for it to work
# Also one needs to experiment with sigma to achieve good results learning rate
matrix2 = graph1.standard_som_distance_matrix(length, width)
matrix2
map1 = MapClass(data, length, width, learning_rate, number_epochs, matrix2,
sigma, data_lables, batch_size, shuffle, netxgraph1)
# +
# ### Drawing configuration
# matrix1 = graph1.create_matrix(amount_nodes)
# edges1 = graph1.generate_edges(amount_nodes, percent_edges)
# matrix1 = graph1.add_edges(matrix1, edges1)
# list_edges = graph1.make_list_edges_distances(matrix1)
# netxgraph1 = graph1.build_networkx_graph(list_edges)
# +
# matrix1
# -
# This initializes regular SOM grid matrix, it needs to be passes instead of matrix1 for it to work
# Also one needs to experiment with sigma to achieve good results learning rate
graph2 = Graph()
matrix2 = graph2.standard_som_distance_matrix(length, width)
list_edges = graph2.make_list_edges_distances(matrix2)
netxgraph2 = graph2.build_networkx_graph(list_edges)
map1 = MapClass(data, length, width, learning_rate, number_epochs, matrix2,
sigma, data_lables, batch_size, shuffle, netxgraph2,
special_shape_map)
# +
# ### Drawing configuration
# drawtype="rbg" tries to draw colors on map - needs an input data with 3 dimensions or columns (colors)
# drawtype="black-white" draws black-white - needs an input data with one dimension or column (gray_colors)
# drawtype="networkx" graph drawing using the networkx library
# drawtype="None" - default draws empty space
# Also there is networkx graph drawing
