Class: Callidus::ExponentialRegression

Inherits:
Object
  • Object
show all
Defined in:
lib/src/ExponentialRegression.rb

Instance Attribute Summary collapse

Instance Method Summary collapse

Constructor Details

#initialize(ip = [], op = []) ⇒ ExponentialRegression

Returns a new instance of ExponentialRegression.



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# File 'lib/src/ExponentialRegression.rb', line 15

def initialize(ip = [], op = [])
    @input = ip
    @output = op

    @trained = false
end

Instance Attribute Details

#a ⇒ Object (readonly)

Returns the value of attribute a.



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# File 'lib/src/ExponentialRegression.rb', line 9

def a
  @a
end

#b ⇒ Object (readonly)

Returns the value of attribute b.



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# File 'lib/src/ExponentialRegression.rb', line 10

def b
  @b
end

#correlation ⇒ Object (readonly)

Returns the value of attribute correlation.



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# File 'lib/src/ExponentialRegression.rb', line 12

def correlation
  @correlation
end

#input ⇒ Object

Returns the value of attribute input.



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# File 'lib/src/ExponentialRegression.rb', line 5

def input
  @input
end

#output ⇒ Object

Returns the value of attribute output.



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# File 'lib/src/ExponentialRegression.rb', line 6

def output
  @output
end

#predicted_output ⇒ Object

Returns the value of attribute predicted_output.



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# File 'lib/src/ExponentialRegression.rb', line 7

def predicted_output
  @predicted_output
end

#standard_error ⇒ Object (readonly)

Returns the value of attribute standard_error.



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# File 'lib/src/ExponentialRegression.rb', line 13

def standard_error
  @standard_error
end

Instance Method Details

#find_standard_error ⇒ Object 



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# File 'lib/src/ExponentialRegression.rb', line 67

def find_standard_error
    assert_trained()

    n = @input.size > @output.size ? @output.size : @input.size

    diffs = []

    n.times do |i|
        diffs << (@predicted_output[i] - @output[i]) ** 2
    end

    @standard_error = (Math.sqrt(diffs.sum/(n - 2.0))).round(3)

    self
end

#formatted ⇒ Object 



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# File 'lib/src/ExponentialRegression.rb', line 28

def formatted
    assert_trained()

    "f(x) = #{@a}e^#{@b}x"
end

#predict(x) ⇒ Object 



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# File 'lib/src/ExponentialRegression.rb', line 83

def predict(x)
    assert_trained()

    @a * (2.7182818284590452353602 ** (@b * x))
end

#train ⇒ Object 



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# File 'lib/src/ExponentialRegression.rb', line 34

def train
    mean_x = @input.mean
    mean_lny = @output.inject(0.0) { |s, n|  s + Math.log(n) } / @output.size

    sum_xx = 0.0
    sum_xy = 0.0
    sum_yy = 0.0

    @input.each_index do |i|
        sum_xx += @input[i] * @input[i]
        sum_xy += @input[i] * Math.log(@output[i])
        sum_yy += Math.log(@output[i]) * Math.log(@output[i])
    end

    sum_xx = (sum_xx / @input.size) - (mean_x * mean_x)
    sum_xy = (sum_xy / @input.size) - (mean_x * mean_lny)
    sum_yy = (sum_yy / @output.size) - (mean_lny * mean_lny)

    @b = sum_xy / sum_xx
    @a = 2.7182818284590452353602 ** (mean_lny - b * mean_x)

    @predicted_output = @input.map { |x| (@a * (2.7182818284590452353602 ** (@b * x))).round(3) }

    @correlation = (sum_xy / (Math.sqrt(sum_xx) * Math.sqrt(sum_yy))).round(3)

    @trained = true

    @a = @a.round(3)
    @b = @b.round(3)

    self.find_standard_error
end