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-rwxr-xr-xdtrain/hstreaming/lplp.rb101
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diff --git a/dtrain/hstreaming/lplp.rb 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.reduce { |sum, i| i.abs }
+end
+
+def l2(feature_column, n=-1)
+ return Math.sqrt feature_column.reduce { |sum, i| i**2 }
+end
+
+def linfty(feature_column, n=-1)
+ return feature_column.map { |i| i.abs }.max
+end
+
+# stats
+def M(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, normfn, n, k=10000)
+ weights.sort{|a,b| normfn.call(b[1], n) <=> normfn.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)
+ weights.each { |k,v|
+ if normfn.call(v).abs > epsilon
+ puts "#{k}\t#{mean(v, n)}"
+ end
+ }
+end
+
+
+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 /\t/
+ if k = shard_count_key
+ shard_count += 1
+ next
+ end
+ if w.has_key? key
+ w[key].push val
+ else
+ w[key] = [val]
+ end
+end
+
+select_k(w, method(:l1), shard_count, 100000)
+
+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
+end
+
+#_test()
+