Table Of Contents

Commands model:principal_components/new

Create a ‘new’ instance of a Principal Components model.

POST /v1/commands/

GET /v1/commands/:id

Request

Route

POST /v1/commands/

Body

name:

model:principal_components/new

arguments:

dummy_model_ref : Model

<Missing Description>

name : unicode (default=None)

User supplied name.


Headers

Authorization: test_api_key_1
Content-type: application/json

Description

Principal component analysis [1] is a statistical algorithm that converts possibly correlated features to linearly uncorrelated variables called principal components. The number of principal components is less than or equal to the number of original variables. This implementation of computing Principal Components is done by Singular Value Decomposition [2] of the data, providing the user with an option to mean center the data. The Principal Components model is initialized; trained on specifying the observation columns of the frame and the number of components; used to predict principal components. The MLLib Singular Value Decomposition [3] implementation has been used for this, with additional features to 1) mean center the data during train and predict and 2) compute the t-squared index during prediction.

footnotes

[1]https://en.wikipedia.org/wiki/Principal_component_analysis
[2]https://en.wikipedia.org/wiki/Singular_value_decomposition
[3]https://spark.apache.org/docs/1.5.0/mllib-dimensionality-reduction.html

Response

Status

200 OK

Body

Returns information about the command. See the Response Body for Get Command here below. It is the same.

GET /v1/commands/:id

Request

Route

GET /v1/commands/18

Body

(None)

Headers

Authorization: test_api_key_1
Content-type: application/json

Response

Status

200 OK

Body

Model

A new instance of PrincipalComponentsModel