class Kmeans

# File lib/kmeans.rb, line 78
def self.argmin arr
  i = 0
  (1..(arr.length-1)).each do |index|
    if arr[i] >= arr[index]
      i = index
    end
  end
  return i
end

# File lib/kmeans.rb, line 51
def self.euclidean_distance p1, p2
  sum_1 = 0
  sum_2 = 0
  sum_12 = 0
  p1.each_with_index do |p, i|
    sum_1 = sum_1 + p**2
    sum_12 = sum_12 + (p1[i].to_f * p2[i].to_f)
  end
  p2.each_with_index do |p|
    sum_2 = sum_2 + p**2
  end
  return 1 - sum_12 / (Math.sqrt(sum_1) * Math.sqrt(sum_2))
end

# File lib/kmeans.rb, line 65
def self.getCenter ps
  p = []
  ps.each do |p1|
    p1.each_with_index do |p2, i|
      p[i] = p[i].to_f+ p2.to_f
    end
  end
  p.each_with_index do |p3, i|
    p[i] = p[i]/ps.count
  end
  return p
end

# File lib/kmeans.rb, line 33
def self.group_data data, centers
  y = []
  (0..(centers.length-1)).each do |i|
    y[i] = []
  end
  list = []
  data.each do |x|
    centers.each do |center|
      list.push(euclidean_distance(x, center))
    end
    min = argmin(list)
    y[min].push(x)
    list = []
  end
  return y
end

# File lib/kmeans.rb, line 4
def self.hi
  puts "K-means clustering ruby!"
end

# File lib/kmeans.rb, line 8
def self.run k, data
  # random first centers
  centers = data.sample(k)
  y = []
  (0..(k-1)).each do |i|
    y[i] = []
  end
  while true
    # save pre-loop groups
    y_old = y
    # grouping
    y = group_data(data, centers)
    # break while loop if groups are not changed
    if y === y_old
      break
    end
    # update centers
    centers = []
    y.each do |ct|
      centers.push(getCenter ct)
    end
  end
  return centers, y
end