npac_detector.machine_learning.ML
Attributes
Functions
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Converts the hits of a dataframe into a 3D numpy array |
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This functions builds an autoencoder with a convulution network |
Module Contents
- npac_detector.machine_learning.ML.chemin
- npac_detector.machine_learning.ML.folder_path = '/data/pion_1Gev_noShower/'
- npac_detector.machine_learning.ML.file_paths
- npac_detector.machine_learning.ML.event_hits_to_image(df: pandas.DataFrame, n_layers=7, x_size=400, y_size=400) numpy.array
Converts the hits of a dataframe into a 3D numpy array
- Parameters:
df – Input dataframe
n_layers (int) – Number of layers in the detector
x_size (int) – Number of pixels along x
y_size (int) – Number of pixel along y
- Returns:
Array of dimension (n_layers,x_size,y_size) with take the energy as the value
- npac_detector.machine_learning.ML.layer_nb = 0
- npac_detector.machine_learning.ML.X_events = []
- npac_detector.machine_learning.ML.df_event = None
- npac_detector.machine_learning.ML.images
- npac_detector.machine_learning.ML.images
- npac_detector.machine_learning.ML.input_shape
- npac_detector.machine_learning.ML.latent_dim = 64
- npac_detector.machine_learning.ML.build_autoencoder(dim: int)
This functions builds an autoencoder with a convulution network
- Parameters:
dim – latent dimension of the encoder
- Returns:
Autencoder
- npac_detector.machine_learning.ML.images
- npac_detector.machine_learning.ML.images
- npac_detector.machine_learning.ML.batch_size = 32
- npac_detector.machine_learning.ML.epochs = 100
- npac_detector.machine_learning.ML.autoencoder
- npac_detector.machine_learning.ML.image_input
- npac_detector.machine_learning.ML.image_input
- npac_detector.machine_learning.ML.reconstructed
- npac_detector.machine_learning.ML.reconstructed
- npac_detector.machine_learning.ML.im1
- npac_detector.machine_learning.ML.cbar1
- npac_detector.machine_learning.ML.im2
- npac_detector.machine_learning.ML.cbar2