Class: Neuronet::LogNormal

Inherits:

Gaussian

• Object
• Scale
• Gaussian
• Neuronet::LogNormal

Defined in:

lib/neuronet.rb

Overview

Log-Normal Distribution

Instance Attribute Summary

Attributes inherited from Scale

#center, #init, #spread

Instance Method Summary

• #initialize(factor = 1.0, center = nil, spread = nil) ⇒ LogNormal constructor

  A new instance of LogNormal.

• #mapped(inputs) ⇒ Object (also: #mapped_input, #mapped_output)
• #set(inputs) ⇒ Object
• #unmapped(outputs) ⇒ Object (also: #unmapped_input, #unmapped_output)

Methods inherited from Gaussian

#set_center, #set_spread

Methods inherited from Scale

#set_center, #set_init, #set_spread

Constructor Details

#initialize(factor = 1.0, center = nil, spread = nil) ⇒ LogNormal

Returns a new instance of LogNormal.

# File 'lib/neuronet.rb', line 295

def initialize(factor=1.0,center=nil,spread=nil)
  super(factor, center, spread)
end

Instance Method Details

#mapped(inputs) ⇒ Object Also known as: mapped_input, mapped_output

# File 'lib/neuronet.rb', line 303

def mapped(inputs)
  super( inputs.map{|value| Math::log(value)} )
end

#set(inputs) ⇒ Object

# File 'lib/neuronet.rb', line 299

def set(inputs)
  super( inputs.map{|value| Math::log(value)} )
end

#unmapped(outputs) ⇒ Object Also known as: unmapped_input, unmapped_output

# File 'lib/neuronet.rb', line 309

def unmapped(outputs)
  super(outputs).map{|value| Math::exp(value)}
end