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diff --git a/dtrain/hstreaming/lplp.rb b/dtrain/hstreaming/lplp.rb
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+++ b/dtrain/hstreaming/lplp.rb
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+# lplp.rb
+
+# norms
+def l0(feature_column, n)
+  if feature_column.size >= n then return 1 else return 0 end
+end
+
+def l1(feature_column, n=-1)
+  return feature_column.map { |i| i.abs }.reduce { |sum,i| sum+i }
+end
+
+def l2(feature_column, n=-1)
+  return Math.sqrt feature_column.map { |i| i.abs2 }.reduce { |sum,i| sum+i }
+end
+
+def linfty(feature_column, n=-1)
+  return feature_column.map { |i| i.abs }.max
+end
+
+# stats
+def median(feature_column, n)
+  return feature_column.concat(0.step(n-feature_column.size-1).map{|i|0}).sort[feature_column.size/2]
+end
+
+def mean(feature_column, n)
+  return feature_column.reduce { |sum, i| sum+i } / n
+end
+
+# selection
+def select_k(weights, norm_fun, n, k=10000)
+  weights.sort{|a,b| norm_fun.call(b[1], n) <=> norm_fun.call(a[1], n)}.each { |p|
+    puts "#{p[0]}\t#{mean(p[1], n)}"
+    k -= 1
+    if k == 0 then break end
+  }
+end
+
+def cut(weights, norm_fun, n, epsilon=0.0001)
+  weights.each { |k,v|
+    if norm_fun.call(v, n).abs >= epsilon
+      puts "#{k}\t#{mean(v, n)}"
+    end
+  }
+end
+
+# test
+def _test()
+  puts
+  w = {}
+  w["a"] = [1, 2, 3]
+  w["b"] = [1, 2]
+  w["c"] = [66]
+  w["d"] = [10, 20, 30]
+  n = 3
+  puts w.to_s
+  puts
+  puts "select_k"
+  puts "l0 expect ad"
+  select_k(w, method(:l0), n, 2)
+  puts "l1 expect cd"
+  select_k(w, method(:l1), n, 2)
+  puts "l2 expect c"
+  select_k(w, method(:l2), n, 1)
+  puts
+  puts "cut"
+  puts "l1 expect cd"
+  cut(w, method(:l1), n, 7)
+  puts
+  puts "median"
+  a = [1,2,3,4,5]
+  puts a.to_s
+  puts median(a, 5)
+  puts
+  puts "#{median(a, 7)} <- that's because we add missing 0s:"
+  puts a.concat(0.step(7-a.size-1).map{|i|0}).to_s
+  puts
+  puts "mean expect bc"
+  w.clear
+  w["a"] = [2]
+  w["b"] = [2.1]
+  w["c"] = [2.2]
+  cut(w, method(:mean), 1, 2.05)
+ exit
+end
+#_test()
+
+# actually do something
+def usage()
+  puts "lplp.rb <l0,l1,l2,linfty,mean,median> <cut|select_k> <k|threshold> [n] < <input>"
+  puts "   l0...: norms for selection"
+  puts "select_k: only output top k (according to the norm of their column vector) features"
+  puts "     cut: output features with weight >= threshold"
+  puts "       n: if we do not have a shard count use this number for averaging"
+  exit
+end
+
+if ARGV.size < 3 then usage end
+norm_fun = method(ARGV[0].to_sym)
+type = ARGV[1]
+x = ARGV[2].to_f
+
+shard_count_key = "__SHARD_COUNT__"
+
+STDIN.set_encoding 'utf-8'
+STDOUT.set_encoding 'utf-8'
+
+w = {}
+shard_count = 0
+while line = STDIN.gets
+  key, val = line.split /\s+/
+  if key == shard_count_key
+    shard_count += 1
+    next
+  end
+  if w.has_key? key
+    w[key].push val.to_f
+  else
+    w[key] = [val.to_f]
+  end
+end
+
+if ARGV.size == 4 then shard_count = ARGV[3].to_f end
+
+if type == 'cut'
+  cut(w, norm_fun, shard_count, x)
+elsif type == 'select_k'
+  select_k(w, norm_fun, shard_count, x)
+else
+  puts "oh oh"
+end
+