Created
October 23, 2017 10:05
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def params | |
[0,0] | |
end | |
def examples | |
[ | |
{ | |
:x => 1, | |
:y => 3 | |
}, | |
{ | |
:x => 2, | |
:y => 8 | |
} | |
] | |
end | |
def hypothesis(x, params) | |
params[0] + params[1] * x | |
end | |
def cost_function(examples, params) | |
examples.inject(0) do |result, example| | |
result + (hypothesis(example[:x], params) - example[:y])**2 | |
end / (2 * examples.count) | |
end | |
def gradient_descent(examples, params, learning_rate) | |
1000.times do | |
temp0 = params[0] - ( learning_rate * summed_error_0(examples, params)) | |
temp1 = params[1] - ( learning_rate * summed_error_1(examples, params)) | |
params[0] = temp0 | |
params[1] = temp1 | |
end | |
params | |
end | |
def summed_error_0(examples, params) | |
examples.inject(0) do |result, example| | |
result + (hypothesis(example[:x], params) - example[:y]) | |
end / examples.count | |
end | |
def summed_error_1(examples, params) | |
examples.inject(0) do |result, example| | |
result + (hypothesis(example[:x], params) - example[:y]) * example[:x] | |
end / examples.count | |
end | |
optimized_params = gradient_descent(examples, params, 0.5) | |
puts optimized_params | |
puts cost_function(examples, optimized_params) |
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