R: Multi-dimensional Scaling

hanaml.MDS {hana.ml.r}

R Documentation

Multi-dimensional Scaling

Description

hanaml.MDS is a R wrapper for PAL Multi-dimensional scaling(MDS) algorithm.

Usage

hanaml.MDS(conn.context,
                  data,
                  key,
                  features = NULL,
                  matrix.type,
                  thread.ratio = NULL,
                  dim = NULL,
                  metric = NULL,
                  minkowski.power = NULL)

Arguments

`conn.context`	`ConnectionContext` Database connection object.
`data`	`DataFrame` Dataset used for training the MDS model.
`key`	`character` Specifies ID column of the training data.
`features`	`list, optional` Specifies the feature columns to apply scaling to.
`matrix.type`	`integer` The type of the input table: `"observation_feature"`: Observation-feature matrixc `"dissimilarity"`: Dissimilarity matrix
`thread.ratio`	`double, optional` Specifies the ratio of total number of threads that can be used by this function. The value range is from 0 to 1, where 0 means only using 1 thread, and 1 means using at most all the currently available threads. Values outside the range will be ignored and this function heuristically determines the number of threads to use.
`dim`	`integer, optional` The number of dimension that the input dataset is to be reduced to.
`metric`	`chracter, optional` The type of distance during the calculation of dissimilarity matrix: `"manhattan"`: Manhattan distance `"euclidean"`: Euclidean distance `"minkowski"`: Minkowski distance
`minkowski.power`	`double, optional` When you use the Minkowski distance, this parameter controls the value of power.

Format

R6Class object.

Value

DataFrame
Sampling results, structured as follows:
- DATA_ID: name as shown in input DataFrame. - DIMENSION: dimension. - VALUE: value n_clusters - 1.
DataFrame
Statistic results, structured as follows: - STAT_NAME: statistic name. - STAT_VALUE: statistic value.

Examples

## Not run: 
 Input DataFrame for Multidimensional Scaling:

  > data$collect()
  ID        X1        X2        X3        X4
1  1 0.0000000 0.9047814 0.9085961 0.9103063
2  2 0.9047814 0.0000000 0.2514457 0.5975016
3  3 0.9085961 0.2514457 0.0000000 0.4403572
4  4 0.9103063 0.5975016 0.4403572 0.0000000

Training the model:
> mds <- hanaml.MDS(conn, data, matrix.type = "dissimilarity",
                    thread.ratio = 0.5)

expected output:
> mds$labels$Collect()
    ID   DIMENSION   VALUE
  1  1         1  0.65191741
  2  1         2 -0.01585861
  3  2         1 -0.21773716
  4  2         2 -0.25319456
  5  3         1 -0.24990695
  6  3         2 -0.07294968
  7  4         1 -0.18427330
  8  4         2  0.34200285

## End(Not run)

[Package hana.ml.r version 1.0.8 Index]