Self Organizing Maps (DL_SOM)
This function creates self organizing maps used for data clustering
Self-organizing maps (SOM), also known as Kohonen maps, are a type of artificial neural network that can be used for unsupervised learning and data visualization. They are typically used for clustering and dimensionality reduction of complex data sets.
SOMs consist of a two-dimensional grid of nodes or neurons, each of which represents a different feature or attribute of the data. During training, the SOM learns to associate similar data points with adjacent neurons on the grid. This results in a topology-preserving mapping of the input space onto the two-dimensional grid.
SOMs are often used in data visualization applications because they can represent high-dimensional data in a two-dimensional map, making it easier to understand and interpret. They have been used in a variety of fields, including image and speech recognition, text mining, and pattern recognition.
Sample Request
Build an SOM for data clustering
Building a Self Organizing Map
SOM Construction
POST https://autogon.ai/api/v1/engine/start
Request Body
project_id*
int
The id of the current project
block_id*
int
The id of the current block
function_code*
string
The function code for current block
parent_id*
int
The id of the previous block
args*
object
Block arguments
x*
int
x dimension
y*
int
y dimension
input_len*
int
Number of the elements of the vectors in input
learning_rate
float
initial learning rate
sigma
float
Spread of the neighborhood function, needs to be adequate to the dimensions of the map
Sample Request
Compile and train the pre-built SOM model, using passed in Hyper Parameters
Training a Sequential Organizing Map
SOM Training
POST https://autogon.ai/api/v1
Request Body
project_id*
int
The id of the current project
hyp_params*
object
hyper parameters for model compilation and training
parent_id*
int
The id of the previous block
block_id*
int
The id of the current block
function_code*
The function code for current block
args*
object
Block arguments
model_name*
String
The name the model would be saved with
num_iterations*
int
number of training iterations
Sample Request
Make predictions with the trained SOM model.
Predicting Cluster with an Self Organizing Map
SOM Predict
POST https://autogon.ai/api/v1/engine/start
Request Body
test_data*
String
Input data for prediction
project_id*
int
ID of the current project
block_id*
int
ID of the current block
parent_id*
int
ID of the previous block
function_code*
String
Function code for the current block
args*
object
Block arguments
row
int
row in the dataset to be used
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