nsl-kdd

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Meeting a problem in Gaussian Mixture clustering part

5

`# Gaussian Mixture clustering from pyspark.ml.clustering import GaussianMixture t0 = time() gm = GaussianMixture(k=8, maxIter=150, seed=seed, featuresCol="pca_features", predictionCol="cluster", probabilityCol="gm_prob") gm_pipeline = Pipeline(stages=[pca_slicer, pca, gm]) gm_model = gm_pipeline.fit(scaled_train_df) gm_train_df = gm_model.transform(scaled_train_df).cache()...

lockbro

Epoch [1/30], Loss: 0.0000 Accuracy on test set: 81.23% 0.009939057285945933 Epoch [2/30], Loss: 0.0245 Accuracy on test set: 76.59% 0.009757729755661013 Epoch [3/30], Loss: 0.0623 Accuracy on test set: 77.08% 0.009460482294732423...

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