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This sample demonstrates how a model is generated out of a data set using the k-means algorithm. The sample uses a data set to generate a model, which is divided into two sets for training and testing.

Prerequisites

Follow the steps below to set up the prerequisites before you start.

1. Download WSO2 Machine Learner, and start the server.

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1. For information on setting up and running WSO2 ML, see Getting Started.

Building the sample

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1. Download and install jq (CLI JSON processor). For instructions, see jq Documentation.
2. If you are using Mac OS X, download and install GNU stream editor (sed). For instructions, see GNU sed Documentation.

Executing the sample

Once the sample is successfully executed, you obtain the following output.

Follow the steps below

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• Execute the following command to remove the databases created: rm -rf repository/database/WSO2ML_DB.*
• Navigate to <ML_HOME>/bin/ directory using the CLI.
• Restart the WSO2 ML server by executing the following command: ./wso2server.sh -Dsetup 

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to execute the sample.

1. Navigate to <ML_HOME>/samples/rest-apidefault/k-means/ directory using the CLI.

   Infotip
   <ML_HOME> refers to the downloaded product-ml directory with the source code of the product.

2. Execute the following command to execute the sample: sh ./model-generation.sh

Output of the sample

Once the sample is successfully executed, you can obtain the following output.view the summary and the prediction of the model as described below.

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{
"type" : "file",
"location" : "/tmp"
}

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Viewing the model 

You can view the summary of the built model using the ML UI as follows.

1. Log in to the ML UI from your Web browser using admin/admin credentials and the following URL: https://<ML_HOST>:<ML_PORT>/ml

2. Click the Projects button as shown below.
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3. Click MODELS button of the new analysis which you created by executing the sample as shown below.
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   You view the built new model as shown below.
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