module Enumerable

# File lib/floatstats.rb, line 59
def self.distance(x,y)
  x.zip(y).map{|i,j| i-j}
end

# File lib/floatstats.rb, line 65
def self.pearson_correlation(x, y)
  xda = x.diffavg
  yda = y.diffavg
  num = self.product(xda, yda).sum
  den = Math.sqrt(xda.sum_of_squares * yda.sum_of_squares)
  num / den
end

# File lib/floatstats.rb, line 56
def self.product(x,y)
  x.zip(y).map{|i,j| i*j}
end

# File lib/floatstats.rb, line 62
def self.ratio(x,y)
  x.zip(y).map{|i,j| i.to_f / j.to_f}
end

# File lib/floatstats.rb, line 72
def self.spearman_rank(x, y)
  n = x.size
  num = 6 * self.distance(x.rank, y.rank).sum_of_squares
  den = n * (n**2 - 1)
  1.0 - num.to_f/den.to_f 
end

# File lib/floatstats.rb, line 78
def self.spearman_rank2(x, y)
  self.pearson_correlation(x.rank, y.rank)
end

# File lib/floatstats.rb, line 18
def average
  self.sum / self.length.to_f
end

# File lib/floatstats.rb, line 21
def diffavg
  avg = self.average
  self.map{|v| v - avg}
end

# File lib/floatstats.rb, line 40
def mad # median absolute deviation
  med = self.median
  deviation_set = (self.map{|n| (n-med).abs }).sort.delete_if{|x| x == 0.0 }
  1.4826 * deviation_set.median # scale for consistency with std dev
end

# File lib/floatstats.rb, line 31
def median
  n = (self.length - 1) / 2
  n2 = (self.length) / 2
  if self.length % 2 == 0 # even case
    (self[n] + self[n2]) / 2
  else
    self[n]
  end
end

# File lib/floatstats.rb, line 45
def rank
  ranked = []
  order = (0...self.size).to_a
  self.zip(order).sort{|a,b| b[0]<=>a[0]}.each_with_index do |elem, i|
    ranked[elem[1]] = i + 1 
  end
  ranked
end

# File lib/floatstats.rb, line 25
def sample_variance
  self.diffavg.sum_of_squares / self.length.to_f
end

# File lib/floatstats.rb, line 28
def standard_deviation
  Math.sqrt(self.sample_variance)
end

# File lib/floatstats.rb, line 12
def sum
  self.inject(0){|acc,i|acc + i}
end

# File lib/floatstats.rb, line 15
def sum_of_squares
  self.inject(0){|acc,i| acc + i**2}
end