Principal Component Analysis (DP_PCA)

This function reduces the dimensionality using PCA

Linear dimensionality reduction using Singular Value Decomposition of the data to project it to a lower dimensional space. The input data is centered but not scaled for each feature before applying the SVD

Sample Request

{
    "project_id": 41,
    "block_id": 10,
    "parent_id": 9,
    "function_code": "DP_PCA",
    "args": {
        "n_components": 3,
        "dataset": false,
        "xtrain": true,
        "xtest": true,
        "x": true,
        "ytrain": false,
        "ytest": false,
        "y": false
    }
}

Parameter Details

Principal Component Analysis

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

Request Body

Name	Type	Description
project_id*	int	current project ID
parent_id*	int	parent block ID
block_id*	int	current block ID
function_code*	String	block's function code
args*	object	block arguments
n_components	float	int, float or 'mle' Number of components to keep. If n_components is not set, all components are kept Defaults to 'null'
dataset/x/y/xtrain/ytrain/xtest/ytest	bool	variables to apply function

200: OK Data Encode Successful

{
    "status": "true",
    "message": {
        "id": 3,
        "project": 1,
        "block_id": 7,
        "parent_id": 6,
        "dataset_url": "",
        "x_value_url": "",
        "y_value_url": ""
    }
}

Python

// Some code

Node

projectId = 1
parentId = 6
blockId = 7

client.array_reshaping(projectId, parentId, blockId, {