Input range of numerical values to be binned.
The number of bins to divide the data into.
result: A tuple containing: - bins_normalized: An array of counts representing the number of values falling into each bin. - bins: An array representing the bin edges.
Notes: - The function determines the minimum and maximum values of numbers to set the bin intervals. - Bins are spaced evenly between minimum_value and maximum_value. - Assumes numbers are normalized before binning. - The function enforces that values are within [0, 1] after normalization and maps them into bins_length intervals. - Uses reduce!min and reduce!max to find the data range. - If numbers contains outliers, the histogram bins may be unevenly populated. Consider using logarithmic or adaptive binning if necessary. - Useful for visualizing distributions, density estimation, and statistical analysis.
Summary: Computes a histogram by binning numerical values into a specified number of intervals (bins_length). The function normalizes input values and counts occurrences within each bin.