moor tidy up!

Author: monkstone

nature-of-code/animation/Exercise_10_05_LayeredNetworkAnimation.rb

@@ -6,9 +6,7 @@
 require 'propane'
 # An animated drawing of a Neural Network
 class LayeredNetwork < Propane::App
-
   load_libraries :neural_network
-
   attr_reader :network, :output
 
   def setup
@@ -29,7 +27,7 @@ def setup
             network.connect(prev, n, rand)
           end
         end
-        network.connect(n, output, rand) if (i == layers - 1)
+        network.connect(n, output, rand) if i == (layers - 1)
         network.add_neuron(n)
       end
     end

nature-of-code/animation/library/neural_network/lib/connection.rb

@@ -24,7 +24,7 @@ def feedforward(val)
 
   # Update traveling sender
   def update
-    return unless sending       # favour a guard clause in ruby
+    return unless sending # favour a guard clause in ruby
     # Use a simple interpolation
     sender.lerp!(b.location, 0.1)
     d = sender.dist(b.location)

nature-of-code/animation/library/neural_network/lib/neuron.rb

@@ -30,12 +30,12 @@ def feedforward(input)
     # Activate it?
     return sum unless sum > 1
     fire
-    @sum = 0  # Reset the sum to 0 if it fires
+    @sum = 0 # Reset the sum to 0 if it fires
   end
 
   # The Neuron fires
   def fire
-    @r = 64   # It suddenly is bigger
+    @r = 64 # It suddenly is bigger
     # We send the output through all connections
     connections.each { |c| c.feedforward(sum) }
   end
@@ -45,7 +45,7 @@ def display
     stroke(0)
     stroke_weight(1)
     # Brightness is mapped to sum
-    b = map1d(sum, (0 .. 1), (255 .. 0))
+    b = map1d(sum, (0..1), (255..0))
     fill(b)
     ellipse(location.x, location.y, r, r)
     # Size shrinks down back to original dimensions

nature-of-code/animation/library/neural_network/lib/static_network.rb

@@ -7,13 +7,13 @@ class StaticNetwork
   attr_reader :neurons, :location
 
   def initialize(layers, inputs, _outputs)
-    @location = Vec2D.new($app.width / 2, $app.height / 2)
+    @location = Vec2D.new(width / 2, height / 2)
     @neurons = []
     output = Neuron.new(250, 0)
     layers.times do |i|
       inputs.times do |j|
-        x = map1d(i, (0 .. layers), (-200 .. 200))
-        y = map1d(j, (0 .. inputs-1), (-100 .. 100))
+        x = map1d(i, (0..layers), (-200..200))
+        y = map1d(j, (0..inputs - 1), (-100..100))
         puts "#{j} #{y}"
         n = Neuron.new(x, y)
         if i > 0
@@ -22,7 +22,7 @@ def initialize(layers, inputs, _outputs)
             prev.join(n)
           end
         end
-        n.join(output) if (i == layers - 1)
+        n.join(output) if i == (layers - 1)
         neurons << n
       end
     end