ruby implementation of gradient descent linear regression

line.rb

require 'generator'

samples = [
  { :xs => [ 1.0, 0.25], :y => 0.98},
  { :xs => [ 1.0, 0.49], :y => 0.82},
  { :xs => [ 1.0, 0.60], :y => 0.41},
  { :xs => [ 1.0, 0.89], :y => 0.31}
]

# line is the sum of the dot product of the weight (thetas)
# an input (xs) vectors.
def line( thetas, xs)
  thetas.zip( xs).map { |t, x| t * x}.inject( :+)
end

# the error for a function, f, is the difference between the
# expected value, y, and f with a certain parameterization, thetas,
# applied to the inputs, xs.
#
# f needs to be a symbol, :function.
def error( f, thetas, in_and_out)
  xs, y = in_and_out.values_at( :xs, :y)
  y_hat = method( f).call( thetas, xs)
  return y_hat - y
end

# because we want an overall sense of error, we need to sum up
# the errors for all samples for a given parameterization; however,
# simply adding errors would lead to no error if the first were
# -10 and the second were 10.
#
# Therefore, we square the error. Additionally, we take the average
# squared error (because the sample size is fixed, this doesn't affect
# the outcome. Finally, we take 1/2 of the value (because it makes the
# derivative nicer. Because this is a constant, it doesn't affect the
# outcome either.
def squared_error( f, thetas, data)
  data.map { |datum| error( f, thetas, datum) ** 2}.inject( :+)
end

def mean_squared_error( f, thetas, data)
  count = data.length()
  return 0.5 * (1.0 / count) * squared_error( f, thetas, data)
end

# we want to generate a grid of potential parameter
# values and then plot out the MSE for each
# set of values.
def plot_mse_for_thetas( step, samples)
  range = Generator.new do |g|
    start = -3.0
    stop = 3.0
    current = start
    while current <= stop do
      g.yield current
      current += step
    end
  end

  domain = []
  while range.next?
    domain << range.next
  end

  puts "\t#{domain.join( "\t")}"
  domain.each do |t0|
    print "#{t0}"
    domain.each do |t1|
      mse = mean_squared_error( :line, [t0, t1], samples)
      print "\t#{ '%.3f' % mse}"
    end
    puts ""
  end
end

plot_mse_for_thetas( 0.40, samples)

# view LaTex here: http://www.codecogs.com/latex/eqneditor.php
# according to Andrew Ng's notes, in gradient descent, each theta should be updated
# by the following rule:
# \theta_j := \theta_j - \alpha \frac{\partial}{\partial \theta_j}MSE(\theta)
# where every $\theta_j$ should be updated simultaneously.
# the derivative of MSE with respect to \theta_j is:
# \frac{1}{m} \sum_i (f(x_i) - y_i)x_{i,j}
def calculate_gradient_mse( f, thetas, samples)
  averager = 1.0 / samples.length()
  gradients = []
  thetas.each_with_index do |theta, index|
    accum = 0.0
    samples.each do |sample|
      xs = *sample.values_at( :xs)
      accum += error( f, thetas, sample) * xs[ index]
    end
    gradients << averager * accum
  end
  gradients
end

puts calculate_gradient_mse( :line, [-3.0, -3.0], samples).inspect

def gradient_descent( f, samples, thetas, alpha)
  mse = mean_squared_error( f, thetas, samples)
  diff = 1.0
  while diff.abs > 1.0e-12
    puts "current MSE is #{mse} with thetas #{thetas.inspect}"
    gradients = calculate_gradient_mse( f, thetas, samples)
    changes = gradients.map {|g| - alpha * g}
    thetas = thetas.zip( changes).map {|a, b| a + b}
    prev, mse = mse, mean_squared_error( f, thetas, samples)
    diff = prev - mse
  end
  thetas
end

# the true global minimum is around [1.4, -1.4]
# puts gradient_descent( :line, samples, [1.4, -1.8], 0.01).inspect()
# puts gradient_descent( :line, samples, [-3.0, 3.0], 0.01).inspect() # doesn't find global.
# puts gradient_descent( :line, samples, [3.0, -3.0], 0.01).inspect() # does find global.