brainspy.utils.signal

Set of fitness functions for genetic algorithm and loss functions for gradient descent.

Functions

accuracy_fit(output, target[, default_value])	Fitness function for genetic algorithm using accuracy of a perceptron.
corr_fit(output, target[, default_value])	Fitness function for genetic algorithm using Pearson correlation.
corrsig(output, target[, sigmoid_center, ...])	Loss function for gradient descent using a sigmoid function.
corrsig_fit(output, target[, default_value, ...])	Fitness function for genetic algorithm using correlation and a sigmoid function.
fisher(output, target)	Calculate the negative of the Fisher linear discriminant between two datasets.
fisher_fit(output, target[, default_value])	Fitness function for genetic algorithm using the negative of the Fisher linear discriminant.
get_clamped_intervals(output, mode[, boundaries])	Sort and clamp the data, and find the distances between the datapoints.
pearsons_correlation(x, y)	Measure the Pearson correlation between two sets of data (how much the two sets are linearly related).
sigmoid_nn_distance(output[, target, ...])	Sigmoid of nearest neighbour distance: a squashed version of a sum of all internal distances between points.