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

{
    "project_id": 1,
    "parent_id": 7,
    "block_id": 8,
    "function_code": "DL_SOM_I",
    "args": {
    "hyp_params":{
            "x": 10,
            "y": 10,
            "input_len": 14,
            "sigma": 1.0,
            "learning_rate": 0.1,
        }
    }
}

Building a Self Organizing Map

SOM Construction

POST https://autogon.ai/api/v1/engine/start

Request Body

{
    "status": "true",
    "message": {
        "id": 8,
        "project": 1,
        "block_id": 8,
        "parent_id": 7,
        "dataset_url": "",
        "x_value_url": "",
        "y_value_url": "",
        "x_train_url": "",
        "y_train_url": "",
        "x_test_url": "",
        "y_test_url": "",
        "output": {}
    }
}

Sample Request

Compile and train the pre-built SOM model, using passed in Hyper Parameters

{
    "project_id": 1,
    "parent_id": 8,
    "block_id": 9,
    "function_code": "DL_SOM_T",
    "args": {
        "model_name": "titanic_model",
        "hyp_params":{
            "num_iterations": 100
        }
    }
}

Training a Sequential Organizing Map

SOM Training

POST https://autogon.ai/api/v1

Request Body

{
    "status": "true",
    "message": {
        "id": 9,
        "project": 1,
        "block_id": 9,
        "parent_id": 8,
        "dataset_url": "",
        "x_value_url": "",
        "y_value_url": "",
        "x_train_url": "",
        "y_train_url": "",
        "x_test_url": "",
        "y_test_url": "",
        "output": {}
    }
}

Sample Request

Make predictions with the trained SOM model.

{
    "project_id": 1,
    "parent_id": 8,
    "block_id": 9,
    "function_code": "DL_SOM_P",
    "args": {
        "test_data": "",
        "row": 12,
    }
}

Predicting Cluster with an Self Organizing Map

SOM Predict

POST https://autogon.ai/api/v1/engine/start

Request Body

{
    "status": "true",
    "message": {
        "id": 9,
        "project": 1,
        "block_id": 9,
        "parent_id": 8,
        "dataset_url": "",
        "x_value_url": "",
        "y_value_url": "",
        "x_train_url": "",
        "y_train_url": "",
        "x_test_url": "",
        "y_test_url": "",
        "output": {}
    }
}

// Some code

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