Class: MLAI::SimpleLinearRegression

Inherits:
Object
  • Object
show all
Defined in:
lib/ml_ai/simple_linear_regression.rb

Instance Attribute Summary collapse

Instance Method Summary collapse

Constructor Details

#initialize ⇒ SimpleLinearRegression

Returns a new instance of SimpleLinearRegression.



10
11
12
13
 
# File 'lib/ml_ai/simple_linear_regression.rb', line 10

def initialize
  @slope = nil
  @intercept = nil
end

Instance Attribute Details

#intercept ⇒ Object (readonly)

Returns the value of attribute intercept.



8
9
10
 
# File 'lib/ml_ai/simple_linear_regression.rb', line 8

def intercept
  @intercept
end

#slope ⇒ Object (readonly)

Returns the value of attribute slope.



8
9
10
 
# File 'lib/ml_ai/simple_linear_regression.rb', line 8

def slope
  @slope
end

Instance Method Details

#fit(x_values: nil, y_values: nil, dataset: nil, feature_column: nil, target_column: nil) ⇒ Object

Fit method accepts either x_values and y_values or a Dataset object with specified columns



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
 
# File 'lib/ml_ai/simple_linear_regression.rb', line 16

def fit(x_values: nil, y_values: nil, dataset: nil, feature_column: nil, target_column: nil)
  if dataset
    unless feature_column && target_column
      raise ArgumentError, "When using a Dataset, you must specify feature_column and target_column"
    end

    # Extract indices of the specified columns
    feature_index = dataset.headers.index(feature_column)
    target_index = dataset.headers.index(target_column)

    unless feature_index && target_index
      raise ArgumentError, "Specified feature or target column does not exist in the dataset"
    end

    # Extract x and y values from the dataset
    x_values = dataset.data.map { |row| row[feature_index] }
    y_values = dataset.data.map { |row| row[target_index] }
  elsif x_values && y_values
    # Use x_values and y_values directly
  else
    raise ArgumentError, "You must provide either x_values and y_values or a dataset with feature_column and target_column"
  end

  # Ensure that x and y have the same number of observations
  unless x_values.length == y_values.length
    raise ArgumentError, "Input arrays must have the same length"
  end

  # Calculate means
  mean_x = x_values.sum / x_values.length.to_f
  mean_y = y_values.sum / y_values.length.to_f

  # Calculate the numerator and denominator for the slope
  numerator = 0.0
  denominator = 0.0

  x_values.each_with_index do |x, i|
    numerator += (x - mean_x) * (y_values[i] - mean_y)
    denominator += (x - mean_x) ** 2
  end

  @slope = numerator / denominator
  @intercept = mean_y - @slope * mean_x
end

#mean_squared_error(y_true, y_pred) ⇒ Object

Evaluation Metrics



69
70
71
72
73
74
75
 
# File 'lib/ml_ai/simple_linear_regression.rb', line 69

def mean_squared_error(y_true, y_pred)
  raise "Input arrays must have the same length" unless y_true.length == y_pred.length

  n = y_true.length
  sum_squared_errors = y_true.each_with_index.map { |y, i| (y - y_pred[i]) ** 2 }.sum
  sum_squared_errors / n.to_f
end

#predict(x_values) ⇒ Object

Predict method remains unchanged, accepts an array of x_values



62
63
64
65
66
 
# File 'lib/ml_ai/simple_linear_regression.rb', line 62

def predict(x_values)
  raise "Model has not been fitted yet" if @slope.nil? || @intercept.nil?

  x_values.map { |x| @slope * x + @intercept }
end

#r_squared(y_true, y_pred) ⇒ Object 



77
78
79
80
81
82
83
84
85
 
# File 'lib/ml_ai/simple_linear_regression.rb', line 77

def r_squared(y_true, y_pred)
  raise "Input arrays must have the same length" unless y_true.length == y_pred.length

  mean_y = y_true.sum / y_true.length.to_f
  ss_total = y_true.map { |y| (y - mean_y) ** 2 }.sum
  ss_residual = y_true.each_with_index.map { |y, i| (y - y_pred[i]) ** 2 }.sum

  1 - (ss_residual / ss_total.to_f)
end