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require 'minitest/autorun' | |
require 'matrix' | |
module LinearRegression | |
def predict(features, theta) | |
features * theta | |
end | |
def cost_function(features, labels, theta) | |
m = features.row_count | |
predictions = predict(features, theta) | |
squared_errors = (predictions - labels).map { |error| error**2 } | |
(1.0 / (2.0 * m)) * squared_errors.reduce { |a, b| a + b } | |
end | |
def normal_equation(features, labels) | |
(features.transpose * features).inverse * features.transpose * labels | |
end | |
end | |
class TestLinearRegression < MiniTest::Test | |
include LinearRegression | |
def setup | |
@features = Matrix[[10, 1000, 10], [5, 800, 2], [12, 2500, 3]] | |
@labels = Vector[900, 600, 1800] | |
@initial_theta = Vector[200, -0.1, -10] | |
end | |
def test_predict_with_inital_theta | |
assert_equal Vector[1800, 900, 2120], predict(@features, @initial_theta) | |
end | |
def test_cost_with_initial_theta | |
assert_equal 167_066.67, cost_function(@features, @labels, @initial_theta).round(2) | |
end | |
def test_normal_equation | |
assert_equal Vector[5.0, 0.675, 17.5], normal_equation(@features, @labels) | |
end | |
def test_cost_function_with_optimal_theta | |
optimal_theta = Vector[5.0, 0.675, 17.5] | |
assert_equal 0, cost_function(@features, @labels, optimal_theta) | |
end | |
def test_predict_with_optimal_theta | |
optimal_theta = normal_equation(@features, @labels) | |
assert_equal Vector[900, 600, 1800], predict(@features, optimal_theta) | |
end | |
end |
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