nif.demo

class nif.demo.TravelingWave

Bases: PointWiseData

A class for loading and processing the traveling wave dataset.

Inherits from the PointWiseData class, which is a base class for point-wise data processing.

Variables:

  • mean (ndarray) – Mean values of the normalized data.

  • std (ndarray) – Standard deviation values of the normalized data.

class nif.demo.TravelingWaveHighFreq

Bases: PointWiseData

A class for loading and normalizing the traveling wave high frequency dataset.

Variables:

  • n_p (int) – The number of parameters.

  • n_x (int) – The number of input features.

  • n_o (int) – The number of output targets.

__init__()

Initializes the class and loads the dataset.

standard_normalize(raw_data, area_weighted=False)

Normalizes the given data using standard normalization.

minmax_normalize(raw_data, n_para, n_x, n_target, area_weighted=False)

Normalizes the given data using min-max normalization.

class nif.demo.CylinderFlow

Bases: PointWiseData

A class representing the cylinder flow dataset.

Inherits from the PointWiseData class.

Attributes: - data (numpy.ndarray): A numpy array containing the normalized data. - mean (numpy.ndarray): A numpy array containing the mean values used for normalization. - std (numpy.ndarray): A numpy array containing the standard deviation values used for normalization. - sample_weight (numpy.ndarray): A numpy array containing the weights assigned to each data sample. - n_p (int): The number of parameters in the data. - n_x (int): The number of inputs (features) in the data. - n_o (int): The number of outputs (targets) in the data.