avg.rb lplp.rb

1 files changed, 69 insertions, 43 deletions

diff --git a/dtrain/hstreaming/lplp.rb b/dtrain/hstreaming/lplp.rb
index edb93e77..0ec21a46 100755
--- a/dtrain/hstreaming/lplp.rb
+++ b/dtrain/hstreaming/lplp.rb
@@ -2,15 +2,15 @@
 
 # norms
 def l0(feature_column, n)
-  if feature_column.size == n then return 1 else return 0 end
+  if feature_column.size >= n then return 1 else return 0 end
 end
 
 def l1(feature_column, n=-1)
-  return feature_column.reduce { |sum, i| i.abs }
+  return feature_column.map { |i| i.abs }.reduce { |sum,i| sum+i }
 end
 
 def l2(feature_column, n=-1)
-  return Math.sqrt feature_column.reduce { |sum, i| i**2 }
+  return Math.sqrt feature_column.map { |i| i.abs2 }.reduce { |sum,i| sum+i }
 end
 
 def linfty(feature_column, n=-1)
@@ -18,7 +18,7 @@ def linfty(feature_column, n=-1)
 end
 
 # stats
-def M(feature_column, n)
+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
 
@@ -27,22 +27,73 @@ def mean(feature_column, n)
 end
 
 # selection
-def select_k(weights, normfn, n, k=10000)
-  weights.sort{|a,b| normfn.call(b[1], n) <=> normfn.call(a[1], n)}.each { |p|
+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, normfn, n, epsilon=0.0001)
+def cut(weights, norm_fun, n, epsilon=0.0001)
   weights.each { |k,v|
-    if normfn.call(v).abs > epsilon
+    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>"
+  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__"
 
@@ -53,49 +104,24 @@ w = {}
 shard_count = 0
 while line = STDIN.gets
   key, val = line.split /\t/
-  if k = shard_count_key
+  if key == shard_count_key
     shard_count += 1
     next
   end
   if w.has_key? key
-    w[key].push val
+    w[key].push val.to_f
   else
-    w[key] = [val]
+    w[key] = [val.to_f]
   end
 end
 
-select_k(w, method(:l1), shard_count, 100000)
+if ARGV.size == 4 then shard_count = ARGV[3].to_f end
 
-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 c"
-select_k(w, method(:l1), n, 1)
-puts "l2 expect d"
-select_k(w, method(:l2), n, 1)
-puts
-puts "cut"
-puts "l1 expect cd"
-cut(w, method(:l1), n, 7)
-puts
-puts "M"
-a = [1,3,4,5,6]
-puts a.to_s
-puts M(a, 7)
-puts "that's because we add missing 0s"
-puts a.concat(0.step(7-a.size-1).map{|i|0}).to_s
-puts
+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
 
-#_test()
-