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// ============================================================
// Autechre風スケッチ
// ============================================================
// ============================================================
// Section 0: Server Boot
// ============================================================
(
s.options.numOutputBusChannels = 2;
s.options.memSize = 8192 * 16;

波形の表示

基本的には{}で括って.plot

  • 音の再生
{LFSaw.ar(4, 3pi/2).range(0, 3000)}.play
  • 波形の表示(引数はx軸の長さ(秒数))
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@kn1kn1
kn1kn1 / r_tz.ipynb
Created December 12, 2022 09:27
r_tz.ipynb
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@kn1kn1
kn1kn1 / undersampling-bagging.ipynb
Created December 5, 2022 05:33
undersampling-bagging.ipynb
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@kn1kn1
kn1kn1 / imbalanced_data.ipynb
Created December 5, 2022 05:00
imbalanced_data.ipynb
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概要

Apache BeamのJavaのパイプラインからPythonの処理を呼び出すことのできるJava multi-language pipelinesの簡単な例( https://beam.apache.org/documentation/sdks/java-multi-language-pipelines/ )を実行したのだが、環境を作るのに少々煩雑であったので、そのメモ。

経緯

Apache Beamの2.40.0からRunInference transformが導入され、Apache Beam PythonではPyTorchとscikit-learnのモデルが直接サポートされるようになった。