nmf.rb

#!/usr/bin/ruby


require("gsl")



def nmf(v, col, thresh)
   r, c = v.shape

   # w * h = v
   w = GSL::Matrix.alloc(r, col)
   h = GSL::Matrix.alloc(col, c)
   initrand(w, w.max)
   initrand(h, h.max)
   dist = thresh
   iter = 0
   while(dist >= thresh && iter < 1000)
      # multiplicative update rule
      dist, w, h = update(v, w, h)
      puts "#{iter}: #{dist}" #if (iter%10 == 0)
      iter += 1
   end   
   puts "Ended in #{iter} iteration(s) with dist=#{dist}"
   return w, h
end


def initrand(m, max)
   r, c = m.shape
   0.upto(r-1) { |i|
       0.upto(c-1) { |j|
          m[i,j] = rand(max)
       }
   }
end

def update(v, w, h)
   # choose an update rule
   # v, w, h are GSL::Matrix objects
   dist, w, h = update_eu(v, w, h)
   return dist, w, h
end

def update_eu(v, w, h)
   # multiplicative update rule
   # for minimizing euclidean distance
   # v, w, h are GSL::Matrix objects
   dist = dist_eu(v, w*h)
   wt = w.transpose
   ht =  h.transpose
   h = h.mul_elements(wt * v).div_elements(wt * w * h)
   w = w.mul_elements(v * ht).div_elements(w * h * ht)
   return dist, w, h
end


def update_kl(v, w, h)
   # multiplicative update rule
   # for minimizing divergence
   # v, w, h are GSL::Matrix objects
   dist = dist_kl(v, w*h)
   wt = w.transpose
   ht =  h.transpose
   num = v.div_elements(w * h)
   rh, ch = h.shape
   rw, cw = w.shape
   0.upto(rh-1) { |i|
       0.upto(ch-1) { |j|
          h[i, j] *= w.col(i).mul(num.col(j)).sum / w.col(i).sum
       }
   }
   0.upto(rw-1) { |i|
       0.upto(cw-1) { |j|
          w[i, j] *= h.row(j).mul(num.row(i)).sum / h.row(j).sum
       }
   }
   return dist, w, h
end



# Euclidean distance
def dist_eu(a, b)
   # a,b are GSL::Matrix objects
   dist = 0
   (a-b).collect { |d| dist += d * d}
   dist
end

# Kullback-Leibler divergence
def dist_kl(a, b)
   # a, b are GSL::Matrix objects
   dist = 0
   r, c = a.shape
   0.upto(r-1) { |i|
       0.upto(c-1) { |j|
          if (b[i,j] == 0) 
             puts "error"
             dist = 2**32
             break
          end
          dist += a[i,j] * Math::log(a[i,j]/b[i,j]) - a[i,j] + b[i,j]
       }
   }
   dist
end