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)

# 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