versions <= 0.1.0 had a stupid bug in the Freedman-Diaconis method for finding bins. So, if you weren't specifying your own number of bins or bin sizes, then you may not have been getting the optimal bin size by default.
require 'histogram/array' # enables Array#histogram data = [0,1,2,2,2,2,2,3,3,3,3,3,3,3,3,3,5,5,9,9,10] # by default, uses Scott's method to calculate optimal number of bins # and the bin values are midpoints between the bin edges (bins, freqs) = data.histogram # equivalent to: data.histogram(:scott, :bin_boundary => :avg)
# :scott, :fd, :sturges, or :middle data.histogram(:fd) # use Freedman-Diaconis method to calc num bins data.histogram(:middle) # (median value between the three methods) (bins, freqs) = data.histogram(20) # use 20 bins (bins, freqs) = data.histogram([-3,-1,4,5,6]) # custom bins (bins, freqs) = data.histogram(10, :min => 2, :max => 12) # 10 bins with set min and max # bins are midpoints, but can be set as minima (bins, freqs) = data.histogram([-3,-1,4,5,6], :bin_boundary => :min) # custom bins with :min # can also set the bin_width (which interpolates between the min and max of the set) (bins, freqs) = data.histogram(:bin_width => 0.5)
Sometimes, we want to create histograms where the bins are calculated based on all the data sets. That way, the resulting frequencies will all line up:
# returns [bins, freq1, freq2 ...] (bins, *freqs) = set1.histogram(30, :other_sets => [[3,3,4,4,5], [-1,0,0,3,3,6]])
# histogramming with weights data.histogram(20, :weights => [3,3,8,8,9,9,3,3,3,3])
require 'histogram/narray' # enables NArray#histogram # if the calling object is an NArray, the output is two NArrays: (bins, freqs) = NArray.float(20).random!(3).histogram(20) # bins and freqs are both NArray.float objects
gem install histogram