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authorPatrick Simianer <p@simianer.de>2013-03-15 10:29:13 +0100
committerPatrick Simianer <p@simianer.de>2013-03-15 10:29:13 +0100
commit2b4b3adc764085bccc6ddbde96b8cc7ba4287a9f (patch)
treebbe72e5e3f425d2389b1f037a83aefd2c40269eb /training/dtrain/hstreaming/lplp.rb
parent08d5de939f85075fc1569ddfa545b5d815231c3f (diff)
removed hadoop/hstreaming mode
Diffstat (limited to 'training/dtrain/hstreaming/lplp.rb')
-rwxr-xr-xtraining/dtrain/hstreaming/lplp.rb131
1 files changed, 0 insertions, 131 deletions
diff --git a/training/dtrain/hstreaming/lplp.rb b/training/dtrain/hstreaming/lplp.rb
deleted file mode 100755
index f0cd58c5..00000000
--- a/training/dtrain/hstreaming/lplp.rb
+++ /dev/null
@@ -1,131 +0,0 @@
-# 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
-