mlts-experiment-data
Version:
Machine learning experiment data downloader.
38 lines • 1.73 kB
JavaScript
;
Object.defineProperty(exports, "__esModule", { value: true });
var tslib_1 = require("tslib");
var path = require("path");
var downloader = require("../utils/downloader");
var Data_1 = require("../Data");
var idx = require("idx-data");
var dataRemoteLocation = 'https://rawgit.com/andnp/ml_data/master/mnist.tar.gz';
function download(location) {
if (location === void 0) { location = '.tmp'; }
return downloader.download(dataRemoteLocation, location);
}
exports.download = download;
function load(location) {
if (location === void 0) { location = '.tmp'; }
return tslib_1.__awaiter(this, void 0, void 0, function () {
var root, _a, dataX, dataY, dataT, dataTY;
return tslib_1.__generator(this, function (_b) {
switch (_b.label) {
case 0: return [4 /*yield*/, download(location)];
case 1:
_b.sent();
root = path.join(location, 'mnist');
return [4 /*yield*/, Promise.all([
idx.loadBits(path.join(root, 'train-images-idx3-ubyte')),
idx.loadBits(path.join(root, 'train-labels-idx1-ubyte')),
idx.loadBits(path.join(root, 't10k-images-idx3-ubyte')),
idx.loadBits(path.join(root, 't10k-labels-idx1-ubyte')),
])];
case 2:
_a = tslib_1.__read.apply(void 0, [_b.sent(), 4]), dataX = _a[0], dataY = _a[1], dataT = _a[2], dataTY = _a[3];
return [2 /*return*/, new Data_1.Dataset(dataX, dataY, dataT, dataTY)];
}
});
});
}
exports.load = load;
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