Thomascountz

Commons Labs Code of Conduct

1. Purpose

A primary goal of Commons Labs is to be inclusive to the largest number of contributors, with the most varied and diverse backgrounds possible. As such, we are committed to providing a friendly, safe and welcoming environment for all, regardless of gender, sexual orientation, ability, ethnicity, socioeconomic status, and religion (or lack thereof).

This code of conduct outlines our expectations for all those who participate in our community, as well as the consequences for unacceptable behavior.

We invite all those who participate in Commons Labs to help us create safe and positive experiences for everyone.

Thomascountz

Thomascountz

Keybase proof

I hereby claim:

• I am thomascountz on github.
• I am thomascountz (https://keybase.io/thomascountz) on keybase.
• I have a public key ASC8Ou3Vfx6fn6eZBfQaeqzDbC6ob2qkyJi9Qjxa6P7IPgo

To claim this, I am signing this object:

Thomascountz

class LinearModel
  attr_reader :slope, :y_intercept

  def initialize(learning_rate = 0.1, epoch = 100000)
      # Initialize y_intercept & slope
      @y_intercept = 0
      @slope = 1

      # How much will the error affect the change in parameters
      @learning_rate = learning_rate

Thomascountz

errors = dataset.map do |data_point|
    prediction = string_length_predictor.predict(data_point[:input])
    prediction - data_point[:output]
end
#=> [4.50, -0.75, 1.35, 2.50]

square_errors = errors.map { |error| error ** 2 }
#=> [20.25, 0.56, 1.82, 6.25]

mean_square_error = square_errors.inject(&:+) / errors.length

Thomascountz

predict(4)
#=> 8

Thomascountz

class LinearModel
    def initialize(y_intercept, slope)
        @y_intercept = y_intercept
        @slope = slope
    end
    
    def predict(x)
        return @slope * x + @y_intercept
    end
end

Thomascountz

dataset = [
    {input: 0.50, output: 56}, 
    {input: 0.85, output: 119},
    {input: 0.99, output: 140}, 
    {input: 1.30, output: 190}
]

Thomascountz

def predict(x)
    return x * 2
end

Thomascountz

dataset = [
    {input: 1, output: 2}, 
    {input: 2, output: 4},
    {input: 3, output: 6}, 
]