dataset


        require(R);
                               

#' the machine learning dataset toolkit
imports "dataset" from "MLkit";

the machine learning dataset toolkit

Datasets are collections of raw data gathered during the research process usually in the form of numerical data. Many organizations, e.g. government agencies, universities or research institutions make the data they have collected freely available on the web for other researchers to use.

.NET clr type export
data_matrix: UnionMatrix	A matrix for the sparse numeric (double) data.
data_sample: SampleData	the in-memory sample data object

.NET clr function exports
as.data.frame.sampledata	SampleData: the in-memory sample data object
as.data.frame.unionmatrix	UnionMatrix: A matrix for the sparse numeric (double) data.
as.data.frame.sequencegraphtransform	SequenceGraphTransform: Sequence Graph Transform (SGT) — Sequence Embedding for Clustering, Classification, and Search Sequence Graph Transform (SGT) is a sequence embedding function. SGT extracts the short- and long-term sequence features and embeds them in a finite-dimensional feature space. The long and short term patterns embedded in SGT can be tuned without any increase in the computation. > https://github.com/cran2367/sgt/blob/25bf28097788fbbf9727abad91ec6e59873947cc/python/sgt-package/sgt/sgt.py
as.data.frame.dataframe	DataFrame: R language liked dataframe object
plot.idataembedding	IDataEmbedding:
SGT	Sequence Graph Transform (SGT) — Sequence Embedding for Clustering, Classification, and Search Sequence Graph Transform (SGT) is a sequence embedding function. SGT extracts the short- and long-term sequence features and embeds them in a finite-dimensional feature space. The long and short term patterns embedded in SGT can be tuned without any increase in the computation. > https://github.com/cran2367/sgt/blob/25bf28097788fbbf9727abad91ec6e59873947cc/python/sgt-package/sgt/sgt.py
fit_embedding	Make sequence graph embedding as a vector
split_training_test
estimate_alphabets	A helper function for estimates the char set for SGT algorithm features input
sample_id
get_feature	get feature vector by a given feature column index
project_features	Makes the feature projection
sort_samples	sort the sample dataset
add_sample	Add a data sample into the target sparse sample matrix object
toFeatureSet	helper function for cast the R# dataframe runtime object as the clr dataframe object
as.MLdataset	Convert the sciBASIC general dataframe as the Machine learning general dataset
description	get summary and descriptions about the given dataset
normalize_matrix	get the normalization matrix from a given machine learning training dataset.
as.tabular	convert machine learning dataset to dataframe table.
as.sampleSet	make data set convert
read.ML_model	read the dataset for training the machine learning model
write.ML_model	write the data model to file
create_single_sampledata
write.sample_set
read.sample_set
MNIST.dims
read.MNIST	read mnist dataset file as R# dataframe object
gaussian	create demo matrix for run test
q_factors	encode a given numeric sequence as factors by quantile levels
encoding	do feature encoding
to_bins
to_factors
to_ints