1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
|
#!/usr/bin/env ruby
require 'zipf'
require 'optimist'
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 = Optimist::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
|
