From 51b4f35873a5b5ca7ecf40ce24622ae855a95378 Mon Sep 17 00:00:00 2001 From: Wenjie Du Date: Sat, 6 Apr 2024 19:37:20 +0800 Subject: [PATCH] docs: update README; --- README.md | 11 +++++++---- 1 file changed, 7 insertions(+), 4 deletions(-) diff --git a/README.md b/README.md index b2cf6b3..8e6277c 100644 --- a/README.md +++ b/README.md @@ -6,17 +6,19 @@

- powered by Pytorch - + powered by PyTorch - + + + +

@@ -62,6 +64,7 @@ for easily modeling your partially-observed time-series datasets. 👉 Click here to see the example 👀 ``` python +# pip install pypots>=0.4 import numpy as np from sklearn.preprocessing import StandardScaler from pygrinder import mcar @@ -82,7 +85,7 @@ dataset = {"X": X} # X for model input print(X.shape) # (11988, 48, 37), 11988 samples and each sample has 48 time steps, 37 features # Model training. This is PyPOTS showtime. -saits = SAITS(n_steps=48, n_features=37, n_layers=2, d_model=256, d_inner=128, n_heads=4, d_k=64, d_v=64, dropout=0.1, epochs=10) +saits = SAITS(n_steps=48, n_features=37, n_layers=2, d_model=256, d_ffn=128, n_heads=4, d_k=64, d_v=64, dropout=0.1, epochs=10) # Here I use the whole dataset as the training set because ground truth is not visible to the model, you can also split it into train/val/test sets saits.fit(dataset) imputation = saits.impute(dataset) # impute the originally-missing values and artificially-missing values