1 files changed, 0 insertions, 70 deletions
diff --git a/lin_reg b/lin_reg
deleted file mode 100755
index 7a8e614..0000000
--- a/lin_reg
+++ /dev/null
@@ -1,70 +0,0 @@
-#!/usr/bin/env ruby
-
-require 'zipf'
-require 'trollop'
-
-def read_data fn, scale
-  f = ReadFile.new fn
-  data = []
-  while line = f.gets
-    line.strip!
-    a = []
-    a << 1.0
-    tokenize(line).each { |i| a << i.to_f }
-    v = SparseVector.from_a a
-    data << v
-  end
-  if scale
-    data.map { |i| i.keys }.flatten.uniq.each { |k|
-      max = data.map { |i| i[k] }.max
-      data.each { |i| i[k] /= max }
-    }
-  end
-  return data
-end
-
-def main
-  conf = Trollop::options do
-    opt :input,         "input data",         :type => :string, :required => true
-    opt :output,        "output data",        :type => :string, :required => true
-    opt :learning_rate, "learning rate",      :type => :float,  :default => 0.07
-    opt :stop,          "stopping criterion", :type => :int,    :default => 100
-    opt :scale_features,"scale features",     :type => :bool,   :default => false, :short => '-t'
-    opt :show_loss,     "show loss per iter", :type => :bool,   :default => false
-  end
-  data = read_data conf[:input], conf[:scale_features]
-  zeros = [0.0]*data[0].size
-  t = ReadFile.readlines(conf[:output]).map{ |i| i.to_f }
-  model = SparseVector.new zeros
-  stop = 0
-  prev_model = nil
-  i = 0
-  while true
-    i += 1
-    u = SparseVector.new zeros
-    overall_loss = 0.0
-    data.each_with_index { |x,j|
-      loss = model.dot(x) - t[j]
-      overall_loss += loss**2
-      u += x * loss
-    }
-    STDERR.write "#{i} #{overall_loss/data.size}\n" if conf[:show_loss]
-    u *= conf[:learning_rate]*(1.0/t.size)
-    model -= u
-    if model.approx_eql? prev_model
-      stop += 1
-    else
-      stop = 0
-    end
-    break if stop==conf[:stop]
-    prev_model = model
-  end
-  tss = t.map{ |y| (y-t.mean)**2 }.sum
-  j = -1
-  rss = t.map{ |y| j+=1; (y-model.dot(data[j]))**2 }.sum
-  STDERR.write "ran for #{i} iterations\n R^2=#{1-(rss/tss)}\n"
-  puts model.to_s
-end
-
-main
-