lore_sa.util

Functions

best_fit_distribution(data[, bins, ax])

Model data by finding best fit distribution to data

calculate_feature_values(X, ...[, ...])

estimate_nbr_bins(x)

freedman_diaconis(x)

get_distr_values(x[, size])

mixed_distance_idx(x, y, idx[, ddist, cdist])

multi_dt_predict(X, dt_list)

multilabel2str(y, class_name)

neuclidean(x, y)

nmeandev(x, y)

record2str(x, feature_names, numeric_columns)

sigmoid(x[, x0, k, L])

A logistic function or logistic curve is a common "S" shape (sigmoid curve

struges(x)

vector2dict(x, feature_names)

Given an array ```x''' and an ordered list of feature names, returns a dictionary where each entry has the form ```feature_name : value''' for each feature in x. :param x: list of values :param feature_names: ordered feature list as the x instance :return:.
lore_sa.util.best_fit_distribution(data, bins=200, ax=None)[source]

Model data by finding best fit distribution to data

lore_sa.util.sigmoid(x, x0=0.5, k=10.0, L=1.0)[source]

A logistic function or logistic curve is a common “S” shape (sigmoid curve

Parameters:

  • x – value to transform

  • x0 – the x-value of the sigmoid’s midpoint

  • k – the curve’s maximum value

  • L – the steepness of the curve

Returns:

sigmoid of x

lore_sa.util.vector2dict(x, feature_names)[source]

Given an array ```x’’’ and an ordered list of feature names, returns a dictionary where each entry has the form ```feature_name : value’’’ for each feature in x. :param x: list of values :param feature_names: ordered feature list as the x instance :return: