# Reduce dimensionality for visualization pca = PCA(n_components=128) features_pca = pca.fit_transform(features)
Silence. Then a subsonic hum, so low I felt it in my molars before I heard it. It built slowly, like a distant stampede. My skin heated. My breath quickened. Not fear— want . A hollow, hungry ache in my chest, as if I’d just seen someone I loved walk away forever. uncitmaza hot
validation_datagen = ImageDataGenerator(rescale=1./255) hungry ache in my chest
You can now use these features for further analysis, such as clustering, classification with another model, or visualization. such as clustering