added multivariate clf dataset

Author: codeboy5

FastTimeSeries/src/recipes.jl

@@ -36,6 +36,16 @@ const RECIPES = Dict{String,Vector{Datasets.DatasetRecipe}}(
     "ecg5000" => [
         TimeSeriesDatasetRecipe(train_file="ECG5000_TRAIN.ts", test_file="ECG5000_TEST.ts")
     ],
+    "atrial" => [
+        TimeSeriesDatasetRecipe(train_file="AtrialFibrillation_TRAIN.ts", test_file="AtrialFibrillation_TEST.ts")
+    ],
+    "natops" => [
+        TimeSeriesDatasetRecipe(train_file="NATOPS_TEST.ts", test_file="NATOPS_TRAIN.ts")
+    ]
+    #! TODO.
+    # "appliances_energy" => [
+    #     TimeSeriesDatasetRecipe(train_file="AppliancesEnergy_TRAIN.ts", test_file="AppliancesEnergy_TEST.ts")
+    # ]
 )
 
 function _registerrecipes()

src/datasets/fastaidatasets.jl

@@ -46,6 +46,7 @@ const DESCRIPTIONS = Dict(
     "food-101" => "101 food categories, with 101,000 images; 250 test images and 750 training images per class. The training images were not cleaned. All images were rescaled to have a maximum side length of 512 pixels.",
     "ECG5000" => "The original dataset for \"ECG5000\" is a 20-hour long ECG downloaded from Physionet. The name is BIDMC Congestive Heart Failure Database(chfdb) and it is record \"chf07\".",
     "AtrialFibrillation" => "This is a physionet dataset of two-channel ECG recordings has been created from data used in the Computers in Cardiology Challenge 2004, an open competition with the goal of developing automated methods for predicting spontaneous termination of atrial fibrillation (AF).",
+    "NATOPS" => "The data is generated by sensors on the hands, elbows, wrists and thumbs. The data are the x,y,z coordinates for each of the eight locations. "
 )
 
 const DATASETCONFIGS = [
@@ -206,6 +207,7 @@ const DATASETCONFIGS = [
     # timeseries
     TSClassificationDataset("ECG5000", "41f6de20ac895e9ce31753860995518951f1ed42a405d0e51c909d27e3b3c5a4", description = DESCRIPTIONS["ECG5000"] ,datadepname="ecg5000", size="10MB" ),
     TSClassificationDataset("AtrialFibrillation", "218abad67d58190a6daa1a27f4bd58ace6e18f80fb59fb2c7385f0d2d4b411a2", description = DESCRIPTIONS["AtrialFibrillation"], datadepname = "atrial", size = "226KB"),
+    TSClassificationDataset("NATOPS", "57a8debeedadad7764bfa9c87b4300bd64a999ef95a98a6ee07a830c41de4aa1", description = DESCRIPTIONS["NATOPS"], datadepname = "natops", size = "5.1MB"),
 
 ]