diff options
| -rwxr-xr-x | linreg.rb | 18 |
1 files changed, 15 insertions, 3 deletions
@@ -4,7 +4,7 @@ require 'nlp_ruby' require 'trollop' -def read_data fn +def read_data fn, scale f = ReadFile.new fn data = [] while line = f.gets @@ -16,6 +16,12 @@ def read_data fn v.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 @@ -25,8 +31,10 @@ def main 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 cfg[:input] + data = read_data cfg[:input], cfg[:scale_features] zeros = [0.0]*data[0].size t = ReadFile.new(cfg[:output]).readlines.map{ |i| i.to_f } model = SparseVector.new zeros @@ -36,9 +44,13 @@ def main while true i += 1 u = SparseVector.new zeros + overall_loss = 0.0 data.each_with_index { |d,j| - u += d * ((model.dot(d) - t[j])*(1.0/t.size)) + loss = model.dot(d) - t[j] + overall_loss += loss**2 + u += d * loss *(1.0/t.size) } + STDERR.write "#{i} #{overall_loss/data.size}\n" if cfg[:show_loss] u *= cfg[:learning_rate] model -= u if model.approx_eql? prev_model |