Refactor the neural net benchmark#3

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Refactor the neural net benchmark #3

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159 changes: 83 additions & 76 deletions time/bench_neural_net.rb
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Open in desktop
Original file line numberDiff line numberDiff line change
@@ -1,131 +1,136 @@
# from bryanbibat's gist: https://gist.github.com/2348802

class Synapse
WEIGHT_RANGE = (-1.0..1.0).freeze

attr_accessor :weight, :prev_weight
attr_accessor :source_neuron, :dest_neuron
attr_reader :source_neuron, :dest_neuron

def initialize(source_neuron, dest_neuron, prng)
self.source_neuron = source_neuron
self.dest_neuron = dest_neuron
self.prev_weight = self.weight = prng.rand(-1.0..1.0)
def initialize(source_neuron:, dest_neuron:)
@source_neuron = source_neuron
@dest_neuron = dest_neuron
@prev_weight = @weight = rand(WEIGHT_RANGE)
end
end

class Neuron

LEARNING_RATE = 1.0
MOMENTUM = 0.3
WEIGHT_RANGE = (-1.0..1.0).freeze

attr_accessor :synapses_in, :synapses_out
attr_accessor :threshold, :prev_threshold, :error
attr_reader :synapses_in, :synapses_out
attr_reader :threshold, :prev_threshold, :error
attr_accessor :output

def initialize(prng)
self.prev_threshold = self.threshold = prng.rand(-1.0..1.0)
self.synapses_in = []
self.synapses_out = []
def initialize
@synapses_in = []
@synapses_out = []
@prev_threshold = @threshold = rand(WEIGHT_RANGE)
end

def calculate_output
# calculate output based on the previous layer
# use logistic function

activation = synapses_in.inject(0.0) do |sum, synapse|
sum + synapse.weight * synapse.source_neuron.output
end
activation -= threshold
activation = synapses_in.sum do |synapse|
synapse.weight * synapse.source_neuron.output
end - @threshold

self.output = 1.0 / (1.0 + Math.exp(-activation))
end

def derivative
output * (1 - output)
@output = 1.fdiv(Math.exp(-activation) + 1)
end

def output_train(rate, target)
self.error = (target - output) * derivative
def output_train(rate:, target:)
@error = (target - @output) * derivative
update_weights(rate)
end

def hidden_train(rate)
self.error = synapses_out.inject(0.0) do |sum, synapse|
sum + synapse.prev_weight * synapse.dest_neuron.error
def hidden_train(rate:)
@error = synapses_out.sum do |synapse|
synapse.prev_weight * synapse.dest_neuron.error
end * derivative
update_weights(rate)
end

private

def derivative
@output * (1 - @output)
end

def update_weights(rate)
synapses_in.each do |synapse|
temp_weight = synapse.weight
synapse.weight += (rate * LEARNING_RATE * error * synapse.source_neuron.output) +
(MOMENTUM * ( synapse.weight - synapse.prev_weight))
synapse.prev_weight = temp_weight
update_synapses_in(rate)
update_thresholds(rate)
end

def update_synapses_in(rate)
@synapses_in.each do |synapse|
prev_weight = synapse.weight
synapse.weight += LEARNING_RATE * rate * @error * synapse.source_neuron.output
synapse.weight += MOMENTUM * (synapse.weight - synapse.prev_weight)
synapse.prev_weight = prev_weight
end
temp_threshold = threshold
self.threshold += (rate * LEARNING_RATE * error * -1) +
(MOMENTUM * (threshold - prev_threshold))
self.prev_threshold = temp_threshold
end

def update_thresholds(rate)
prev_threshold = @threshold
@threshold += LEARNING_RATE * rate * @error * -1
@threshold += MOMENTUM * (@threshold - @prev_threshold)
@prev_threshold = prev_threshold
end
end

class NeuralNetwork
attr_accessor :prng

def initialize(inputs, hidden, outputs)
self.prng = Random.new
def initialize(inputs:, hidden:, outputs:)
@input_layer = Array.new(inputs) { Neuron.new }
@hidden_layer = Array.new(hidden) { Neuron.new }
@output_layer = Array.new(outputs) { Neuron.new }

@input_layer = (1..inputs).map { Neuron.new(prng) }
@hidden_layer = (1..hidden).map { Neuron.new(prng) }
@output_layer = (1..outputs).map { Neuron.new(prng) }

@input_layer.product(@hidden_layer).each do |source, dest|
synapse = Synapse.new(source, dest, prng)
source.synapses_out << synapse
dest.synapses_in << synapse
end
@hidden_layer.product(@output_layer).each do |source, dest|
synapse = Synapse.new(source, dest, prng)
source.synapses_out << synapse
dest.synapses_in << synapse
end
add_synapses(@input_layer, @hidden_layer)
add_synapses(@hidden_layer, @output_layer)
end

def train(inputs, targets)
def train(inputs:, targets:)
feed_forward(inputs)

@output_layer.zip(targets).each do |neuron, target|
neuron.output_train(0.3, target)
neuron.output_train(rate: 0.3, target: target)
end
@hidden_layer.each { |neuron| neuron.hidden_train(0.3) }
@hidden_layer.each { |neuron| neuron.hidden_train(rate: 0.3) }
end

def feed_forward(inputs)
@input_layer.zip(inputs).each do |neuron, input|
neuron.output = input
end
@hidden_layer.each { |neuron| neuron.calculate_output }
@output_layer.each { |neuron| neuron.calculate_output }
@input_layer.zip(inputs) { |neuron, input| neuron.output = input }
@hidden_layer.each(&:calculate_output)
@output_layer.each(&:calculate_output)
end

def current_outputs
@output_layer.map { |neuron| neuron.output }
@output_layer.map(&:output)
end

private

def add_synapses(source_layer, dest_layer)
source_layer.product(dest_layer) do |source_neuron, dest_neuron|
synapse = Synapse.new(source_neuron: source_neuron,
dest_neuron: dest_neuron)

source_neuron.synapses_out << synapse
dest_neuron.synapses_in << synapse
end
end
end

require 'benchmark'

(ARGV[0] || 5).to_i.times do
x = Benchmark.measure do |x|
xor = NeuralNetwork.new(2, 10, 1)
10000.times do
xor.train([0, 0], [0])
xor.train([1, 0], [1])
xor.train([0, 1], [1])
xor.train([1, 1], [0])
ARGV.fetch(0) { 5 }.to_i.times do
results = Benchmark.measure do
xor = NeuralNetwork.new(inputs: 2, hidden: 10, outputs: 1)

10_000.times do
xor.train(inputs: [0, 0], targets: [0])
xor.train(inputs: [1, 0], targets: [1])
xor.train(inputs: [0, 1], targets: [1])
xor.train(inputs: [1, 1], targets: [0])
end

xor.feed_forward([0, 0])
puts xor.current_outputs
xor.feed_forward([0, 1])
Expand All@@ -135,5 +140,7 @@ def current_outputs
xor.feed_forward([1, 1])
puts xor.current_outputs
end
puts x

puts results
end

, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Add copy buttons to all 
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}
} catch(__e) { console.warn('[Userscript:Add Copy Buttons to Code Blocks]', __e); }
})();
(function(){
try {
var __m = "github.com";
var __re = new RegExp('^' + "github\\.com" + '

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Refactor the neural net benchmark#3

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Refactor the neural net benchmark
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159 changes: 83 additions & 76 deletions
time/bench_neural_net.rb

View file


Open in desktop

Original file line numberDiff line numberDiff line change
@@ -1,131 +1,136 @@
# from bryanbibat's gist: https://gist.github.com/2348802

class Synapse
 WEIGHT_RANGE = (-1.0..1.0).freeze

 attr_accessor :weight, :prev_weight
attr_accessor :source_neuron, :dest_neuron
attr_reader :source_neuron, :dest_neuron

 def initialize(source_neuron, dest_neuron, prng)
self.source_neuron = source_neuron
self.dest_neuron = dest_neuron
self.prev_weight = self.weight = prng.rand(-1.0..1.0)
 def initialize(source_neuron:, dest_neuron:)
@source_neuron = source_neuron
@dest_neuron = dest_neuron
@prev_weight = @weight = rand(WEIGHT_RANGE)
 end
end

class Neuron

 LEARNING_RATE = 1.0
 MOMENTUM = 0.3
 WEIGHT_RANGE = (-1.0..1.0).freeze

attr_accessor :synapses_in, :synapses_out
attr_accessor :threshold, :prev_threshold, :error
attr_reader :synapses_in, :synapses_out
attr_reader :threshold, :prev_threshold, :error
 attr_accessor :output

 def initialize(prng)
self.prev_threshold = self.threshold = prng.rand(-1.0..1.0)
self.synapses_in = []
self.synapses_out = []
 def initialize
@synapses_in = []
@synapses_out = []
@prev_threshold = @threshold = rand(WEIGHT_RANGE)
 end

 def calculate_output
 # calculate output based on the previous layer
 # use logistic function

 activation = synapses_in.inject(0.0) do |sum, synapse| 
 sum + synapse.weight * synapse.source_neuron.output 
 end
 activation -= threshold
 activation = synapses_in.sum do |synapse|
 synapse.weight * synapse.source_neuron.output
 end - @threshold

 self.output = 1.0 / (1.0 + Math.exp(-activation))
 end

 def derivative
 output * (1 - output)
 @output = 1.fdiv(Math.exp(-activation) + 1)
 end

 def output_train(rate, target)
self.error = (target - output) * derivative
 def output_train(rate:, target:)
@error = (target - @output) * derivative
 update_weights(rate)
 end

 def hidden_train(rate)
self.error = synapses_out.inject(0.0) do |sum, synapse|
sum + synapse.prev_weight * synapse.dest_neuron.error
 def hidden_train(rate:)
@error = synapses_out.sum do |synapse|
 synapse.prev_weight * synapse.dest_neuron.error
 end * derivative
 update_weights(rate)
 end

 private

 def derivative
 @output * (1 - @output)
 end

 def update_weights(rate)
 synapses_in.each do |synapse|
 temp_weight = synapse.weight
 synapse.weight += (rate * LEARNING_RATE * error * synapse.source_neuron.output) +
 (MOMENTUM * ( synapse.weight - synapse.prev_weight))
 synapse.prev_weight = temp_weight
 update_synapses_in(rate)
 update_thresholds(rate)
 end

 def update_synapses_in(rate)
 @synapses_in.each do |synapse|
 prev_weight = synapse.weight
 synapse.weight += LEARNING_RATE * rate * @error * synapse.source_neuron.output
 synapse.weight += MOMENTUM * (synapse.weight - synapse.prev_weight)
 synapse.prev_weight = prev_weight
 end
 temp_threshold = threshold
 self.threshold += (rate * LEARNING_RATE * error * -1) + 
 (MOMENTUM * (threshold - prev_threshold))
 self.prev_threshold = temp_threshold
 end

 def update_thresholds(rate)
 prev_threshold = @threshold
 @threshold += LEARNING_RATE * rate * @error * -1
 @threshold += MOMENTUM * (@threshold - @prev_threshold)
 @prev_threshold = prev_threshold
 end
end

class NeuralNetwork
attr_accessor :prng

def initialize(inputs, hidden, outputs)
self.prng = Random.new
def initialize(inputs:, hidden:, outputs:)
 @input_layer = Array.new(inputs) { Neuron.new }
 @hidden_layer = Array.new(hidden) { Neuron.new }
@output_layer = Array.new(outputs) { Neuron.new }

 @input_layer = (1..inputs).map { Neuron.new(prng) }
 @hidden_layer = (1..hidden).map { Neuron.new(prng) }
 @output_layer = (1..outputs).map { Neuron.new(prng) }

 @input_layer.product(@hidden_layer).each do |source, dest|
 synapse = Synapse.new(source, dest, prng)
 source.synapses_out << synapse
 dest.synapses_in << synapse
 end
 @hidden_layer.product(@output_layer).each do |source, dest|
 synapse = Synapse.new(source, dest, prng)
 source.synapses_out << synapse
 dest.synapses_in << synapse
 end
 add_synapses(@input_layer, @hidden_layer)
 add_synapses(@hidden_layer, @output_layer)
 end

 def train(inputs, targets)
 def train(inputs:, targets:)
 feed_forward(inputs)

 @output_layer.zip(targets).each do |neuron, target|
 neuron.output_train(0.3, target)
 neuron.output_train(rate: 0.3, target: target)
 end
 @hidden_layer.each { |neuron| neuron.hidden_train(0.3) }
 @hidden_layer.each { |neuron| neuron.hidden_train(rate: 0.3) }
 end

 def feed_forward(inputs)
 @input_layer.zip(inputs).each do |neuron, input|
 neuron.output = input
 end
 @hidden_layer.each { |neuron| neuron.calculate_output }
 @output_layer.each { |neuron| neuron.calculate_output }
 @input_layer.zip(inputs) { |neuron, input| neuron.output = input }
 @hidden_layer.each(&:calculate_output)
 @output_layer.each(&:calculate_output)
 end

 def current_outputs
 @output_layer.map { |neuron| neuron.output }
 @output_layer.map(&:output)
 end

 private

 def add_synapses(source_layer, dest_layer)
 source_layer.product(dest_layer) do |source_neuron, dest_neuron|
 synapse = Synapse.new(source_neuron: source_neuron,
 dest_neuron: dest_neuron)

 source_neuron.synapses_out << synapse
 dest_neuron.synapses_in << synapse
 end
 end
end

require 'benchmark'

(ARGV[0] || 5).to_i.times do
x = Benchmark.measure do |x|
 xor = NeuralNetwork.new(2, 10, 1)
10000.times do
 xor.train([0, 0], [0])
 xor.train([1, 0], [1])
 xor.train([0, 1], [1])
 xor.train([1, 1], [0])
ARGV.fetch(0) { 5 }.to_i.times do
results = Benchmark.measure do
 xor = NeuralNetwork.new(inputs: 2, hidden: 10, outputs: 1)

10_000.times do
 xor.train(inputs: [0, 0], targets: [0])
 xor.train(inputs: [1, 0], targets: [1])
 xor.train(inputs: [0, 1], targets: [1])
 xor.train(inputs: [1, 1], targets: [0])
 end

 xor.feed_forward([0, 0])
 puts xor.current_outputs
 xor.feed_forward([0, 1])
Expand All@@ -135,5 +140,7 @@ def current_outputs
 xor.feed_forward([1, 1])
 puts xor.current_outputs
 end
 puts x

 puts results
end


, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Force GitHub README to respect dark mode\n(function() {\n var style = document.createElement('style');\n style.textContent = '\n .markdown-body {\n color-scheme: dark light;\n }\n .markdown-body pre { background: #161b22 !important; }\n .markdown-body code { background: rgba(110, 118, 129, 0.4) !important; }\n .markdown-body table th, .markdown-body table td { border-color: #30363d !important; }\n .markdown-body img { background: #0d1117; }\n .markdown-body blockquote { border-left-color: #8b949e; }\n .markdown-body hr { border-color: #30363d; }\n ';\n document.head.appendChild(style);\n})();", "GitHub Dark Mode README Fix");
}
} catch(__e) { console.warn('[Userscript:GitHub Dark Mode README Fix]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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Refactor the neural net benchmark#3

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Refactor the neural net benchmark
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159 changes: 83 additions & 76 deletions
time/bench_neural_net.rb

View file


Open in desktop

Original file line numberDiff line numberDiff line change
@@ -1,131 +1,136 @@
# from bryanbibat's gist: https://gist.github.com/2348802

class Synapse
 WEIGHT_RANGE = (-1.0..1.0).freeze

 attr_accessor :weight, :prev_weight
attr_accessor :source_neuron, :dest_neuron
attr_reader :source_neuron, :dest_neuron

 def initialize(source_neuron, dest_neuron, prng)
self.source_neuron = source_neuron
self.dest_neuron = dest_neuron
self.prev_weight = self.weight = prng.rand(-1.0..1.0)
 def initialize(source_neuron:, dest_neuron:)
@source_neuron = source_neuron
@dest_neuron = dest_neuron
@prev_weight = @weight = rand(WEIGHT_RANGE)
 end
end

class Neuron

 LEARNING_RATE = 1.0
 MOMENTUM = 0.3
 WEIGHT_RANGE = (-1.0..1.0).freeze

attr_accessor :synapses_in, :synapses_out
attr_accessor :threshold, :prev_threshold, :error
attr_reader :synapses_in, :synapses_out
attr_reader :threshold, :prev_threshold, :error
 attr_accessor :output

 def initialize(prng)
self.prev_threshold = self.threshold = prng.rand(-1.0..1.0)
self.synapses_in = []
self.synapses_out = []
 def initialize
@synapses_in = []
@synapses_out = []
@prev_threshold = @threshold = rand(WEIGHT_RANGE)
 end

 def calculate_output
 # calculate output based on the previous layer
 # use logistic function

 activation = synapses_in.inject(0.0) do |sum, synapse| 
 sum + synapse.weight * synapse.source_neuron.output 
 end
 activation -= threshold
 activation = synapses_in.sum do |synapse|
 synapse.weight * synapse.source_neuron.output
 end - @threshold

 self.output = 1.0 / (1.0 + Math.exp(-activation))
 end

 def derivative
 output * (1 - output)
 @output = 1.fdiv(Math.exp(-activation) + 1)
 end

 def output_train(rate, target)
self.error = (target - output) * derivative
 def output_train(rate:, target:)
@error = (target - @output) * derivative
 update_weights(rate)
 end

 def hidden_train(rate)
self.error = synapses_out.inject(0.0) do |sum, synapse|
sum + synapse.prev_weight * synapse.dest_neuron.error
 def hidden_train(rate:)
@error = synapses_out.sum do |synapse|
 synapse.prev_weight * synapse.dest_neuron.error
 end * derivative
 update_weights(rate)
 end

 private

 def derivative
 @output * (1 - @output)
 end

 def update_weights(rate)
 synapses_in.each do |synapse|
 temp_weight = synapse.weight
 synapse.weight += (rate * LEARNING_RATE * error * synapse.source_neuron.output) +
 (MOMENTUM * ( synapse.weight - synapse.prev_weight))
 synapse.prev_weight = temp_weight
 update_synapses_in(rate)
 update_thresholds(rate)
 end

 def update_synapses_in(rate)
 @synapses_in.each do |synapse|
 prev_weight = synapse.weight
 synapse.weight += LEARNING_RATE * rate * @error * synapse.source_neuron.output
 synapse.weight += MOMENTUM * (synapse.weight - synapse.prev_weight)
 synapse.prev_weight = prev_weight
 end
 temp_threshold = threshold
 self.threshold += (rate * LEARNING_RATE * error * -1) + 
 (MOMENTUM * (threshold - prev_threshold))
 self.prev_threshold = temp_threshold
 end

 def update_thresholds(rate)
 prev_threshold = @threshold
 @threshold += LEARNING_RATE * rate * @error * -1
 @threshold += MOMENTUM * (@threshold - @prev_threshold)
 @prev_threshold = prev_threshold
 end
end

class NeuralNetwork
attr_accessor :prng

def initialize(inputs, hidden, outputs)
self.prng = Random.new
def initialize(inputs:, hidden:, outputs:)
 @input_layer = Array.new(inputs) { Neuron.new }
 @hidden_layer = Array.new(hidden) { Neuron.new }
@output_layer = Array.new(outputs) { Neuron.new }

 @input_layer = (1..inputs).map { Neuron.new(prng) }
 @hidden_layer = (1..hidden).map { Neuron.new(prng) }
 @output_layer = (1..outputs).map { Neuron.new(prng) }

 @input_layer.product(@hidden_layer).each do |source, dest|
 synapse = Synapse.new(source, dest, prng)
 source.synapses_out << synapse
 dest.synapses_in << synapse
 end
 @hidden_layer.product(@output_layer).each do |source, dest|
 synapse = Synapse.new(source, dest, prng)
 source.synapses_out << synapse
 dest.synapses_in << synapse
 end
 add_synapses(@input_layer, @hidden_layer)
 add_synapses(@hidden_layer, @output_layer)
 end

 def train(inputs, targets)
 def train(inputs:, targets:)
 feed_forward(inputs)

 @output_layer.zip(targets).each do |neuron, target|
 neuron.output_train(0.3, target)
 neuron.output_train(rate: 0.3, target: target)
 end
 @hidden_layer.each { |neuron| neuron.hidden_train(0.3) }
 @hidden_layer.each { |neuron| neuron.hidden_train(rate: 0.3) }
 end

 def feed_forward(inputs)
 @input_layer.zip(inputs).each do |neuron, input|
 neuron.output = input
 end
 @hidden_layer.each { |neuron| neuron.calculate_output }
 @output_layer.each { |neuron| neuron.calculate_output }
 @input_layer.zip(inputs) { |neuron, input| neuron.output = input }
 @hidden_layer.each(&:calculate_output)
 @output_layer.each(&:calculate_output)
 end

 def current_outputs
 @output_layer.map { |neuron| neuron.output }
 @output_layer.map(&:output)
 end

 private

 def add_synapses(source_layer, dest_layer)
 source_layer.product(dest_layer) do |source_neuron, dest_neuron|
 synapse = Synapse.new(source_neuron: source_neuron,
 dest_neuron: dest_neuron)

 source_neuron.synapses_out << synapse
 dest_neuron.synapses_in << synapse
 end
 end
end

require 'benchmark'

(ARGV[0] || 5).to_i.times do
x = Benchmark.measure do |x|
 xor = NeuralNetwork.new(2, 10, 1)
10000.times do
 xor.train([0, 0], [0])
 xor.train([1, 0], [1])
 xor.train([0, 1], [1])
 xor.train([1, 1], [0])
ARGV.fetch(0) { 5 }.to_i.times do
results = Benchmark.measure do
 xor = NeuralNetwork.new(inputs: 2, hidden: 10, outputs: 1)

10_000.times do
 xor.train(inputs: [0, 0], targets: [0])
 xor.train(inputs: [1, 0], targets: [1])
 xor.train(inputs: [0, 1], targets: [1])
 xor.train(inputs: [1, 1], targets: [0])
 end

 xor.feed_forward([0, 0])
 puts xor.current_outputs
 xor.feed_forward([0, 1])
Expand All@@ -135,5 +140,7 @@ def current_outputs
 xor.feed_forward([1, 1])
 puts xor.current_outputs
 end
 puts x

 puts results
end


, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Highlight search terms from Google/DuckDuckGo/Bing referrer\n(function() {\n var ref = document.referrer;\n var terms = [];\n \n if (ref.includes('google.com') || ref.includes('duckduckgo.com') || ref.includes('bing.com')) {\n var url = new URL(ref);\n var q = url.searchParams.get('q') || url.searchParams.get('p');\n if (q) {\n terms = q.split(/\\s+/).filter(function(t) { return t.length > 2; });\n }\n }\n \n if (terms.length === 0) return;\n \n var style = document.createElement('style');\n style.textContent = '.userscript-highlight { background: #fbbf24; color: #1a1a2e; padding: 1px 3px; border-radius: 2px; }';\n document.head.appendChild(style);\n \n function highlight(node) {\n if (node.nodeType === 3) { // text node\n var text = node.textContent;\n var found = false;\n terms.forEach(function(term) {\n var regex = new RegExp('(' + term.replace(/[.*+?^${}()|[\\]\\\\]/g, '\\\\') + ')', 'gi');\n if (regex.test(text)) {\n found = true;\n var frag = document.createDocumentFragment();\n var parts = text.split(regex);\n parts.forEach(function(part, i) {\n if (i % 2 === 0) {\n frag.appendChild(document.createTextNode(part));\n } else {\n var span = document.createElement('span');\n span.className = 'userscript-highlight';\n span.textContent = part;\n frag.appendChild(span);\n }\n });\n node.parentNode.replaceChild(frag, node);\n }\n });\n } else if (node.nodeType === 1 && node.childNodes) { // element\n var skipTags = ['SCRIPT', 'STYLE', 'NOSCRIPT', 'TEXTAREA', 'INPUT', 'SELECT'];\n if (!skipTags.includes(node.tagName)) {\n Array.from(node.childNodes).forEach(highlight);\n }\n }\n }\n \n highlight(document.body);\n \n // Re-highlight on dynamic content\n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1 || node.nodeType === 3) highlight(node);\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Highlight Search Terms");
}
} catch(__e) { console.warn('[Userscript:Highlight Search Terms]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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159 changes: 83 additions & 76 deletions
time/bench_neural_net.rb

View file


Open in desktop

Original file line numberDiff line numberDiff line change
@@ -1,131 +1,136 @@
# from bryanbibat's gist: https://gist.github.com/2348802

class Synapse
 WEIGHT_RANGE = (-1.0..1.0).freeze

 attr_accessor :weight, :prev_weight
attr_accessor :source_neuron, :dest_neuron
attr_reader :source_neuron, :dest_neuron

 def initialize(source_neuron, dest_neuron, prng)
self.source_neuron = source_neuron
self.dest_neuron = dest_neuron
self.prev_weight = self.weight = prng.rand(-1.0..1.0)
 def initialize(source_neuron:, dest_neuron:)
@source_neuron = source_neuron
@dest_neuron = dest_neuron
@prev_weight = @weight = rand(WEIGHT_RANGE)
 end
end

class Neuron

 LEARNING_RATE = 1.0
 MOMENTUM = 0.3
 WEIGHT_RANGE = (-1.0..1.0).freeze

attr_accessor :synapses_in, :synapses_out
attr_accessor :threshold, :prev_threshold, :error
attr_reader :synapses_in, :synapses_out
attr_reader :threshold, :prev_threshold, :error
 attr_accessor :output

 def initialize(prng)
self.prev_threshold = self.threshold = prng.rand(-1.0..1.0)
self.synapses_in = []
self.synapses_out = []
 def initialize
@synapses_in = []
@synapses_out = []
@prev_threshold = @threshold = rand(WEIGHT_RANGE)
 end

 def calculate_output
 # calculate output based on the previous layer
 # use logistic function

 activation = synapses_in.inject(0.0) do |sum, synapse| 
 sum + synapse.weight * synapse.source_neuron.output 
 end
 activation -= threshold
 activation = synapses_in.sum do |synapse|
 synapse.weight * synapse.source_neuron.output
 end - @threshold

 self.output = 1.0 / (1.0 + Math.exp(-activation))
 end

 def derivative
 output * (1 - output)
 @output = 1.fdiv(Math.exp(-activation) + 1)
 end

 def output_train(rate, target)
self.error = (target - output) * derivative
 def output_train(rate:, target:)
@error = (target - @output) * derivative
 update_weights(rate)
 end

 def hidden_train(rate)
self.error = synapses_out.inject(0.0) do |sum, synapse|
sum + synapse.prev_weight * synapse.dest_neuron.error
 def hidden_train(rate:)
@error = synapses_out.sum do |synapse|
 synapse.prev_weight * synapse.dest_neuron.error
 end * derivative
 update_weights(rate)
 end

 private

 def derivative
 @output * (1 - @output)
 end

 def update_weights(rate)
 synapses_in.each do |synapse|
 temp_weight = synapse.weight
 synapse.weight += (rate * LEARNING_RATE * error * synapse.source_neuron.output) +
 (MOMENTUM * ( synapse.weight - synapse.prev_weight))
 synapse.prev_weight = temp_weight
 update_synapses_in(rate)
 update_thresholds(rate)
 end

 def update_synapses_in(rate)
 @synapses_in.each do |synapse|
 prev_weight = synapse.weight
 synapse.weight += LEARNING_RATE * rate * @error * synapse.source_neuron.output
 synapse.weight += MOMENTUM * (synapse.weight - synapse.prev_weight)
 synapse.prev_weight = prev_weight
 end
 temp_threshold = threshold
 self.threshold += (rate * LEARNING_RATE * error * -1) + 
 (MOMENTUM * (threshold - prev_threshold))
 self.prev_threshold = temp_threshold
 end

 def update_thresholds(rate)
 prev_threshold = @threshold
 @threshold += LEARNING_RATE * rate * @error * -1
 @threshold += MOMENTUM * (@threshold - @prev_threshold)
 @prev_threshold = prev_threshold
 end
end

class NeuralNetwork
attr_accessor :prng

def initialize(inputs, hidden, outputs)
self.prng = Random.new
def initialize(inputs:, hidden:, outputs:)
 @input_layer = Array.new(inputs) { Neuron.new }
 @hidden_layer = Array.new(hidden) { Neuron.new }
@output_layer = Array.new(outputs) { Neuron.new }

 @input_layer = (1..inputs).map { Neuron.new(prng) }
 @hidden_layer = (1..hidden).map { Neuron.new(prng) }
 @output_layer = (1..outputs).map { Neuron.new(prng) }

 @input_layer.product(@hidden_layer).each do |source, dest|
 synapse = Synapse.new(source, dest, prng)
 source.synapses_out << synapse
 dest.synapses_in << synapse
 end
 @hidden_layer.product(@output_layer).each do |source, dest|
 synapse = Synapse.new(source, dest, prng)
 source.synapses_out << synapse
 dest.synapses_in << synapse
 end
 add_synapses(@input_layer, @hidden_layer)
 add_synapses(@hidden_layer, @output_layer)
 end

 def train(inputs, targets)
 def train(inputs:, targets:)
 feed_forward(inputs)

 @output_layer.zip(targets).each do |neuron, target|
 neuron.output_train(0.3, target)
 neuron.output_train(rate: 0.3, target: target)
 end
 @hidden_layer.each { |neuron| neuron.hidden_train(0.3) }
 @hidden_layer.each { |neuron| neuron.hidden_train(rate: 0.3) }
 end

 def feed_forward(inputs)
 @input_layer.zip(inputs).each do |neuron, input|
 neuron.output = input
 end
 @hidden_layer.each { |neuron| neuron.calculate_output }
 @output_layer.each { |neuron| neuron.calculate_output }
 @input_layer.zip(inputs) { |neuron, input| neuron.output = input }
 @hidden_layer.each(&:calculate_output)
 @output_layer.each(&:calculate_output)
 end

 def current_outputs
 @output_layer.map { |neuron| neuron.output }
 @output_layer.map(&:output)
 end

 private

 def add_synapses(source_layer, dest_layer)
 source_layer.product(dest_layer) do |source_neuron, dest_neuron|
 synapse = Synapse.new(source_neuron: source_neuron,
 dest_neuron: dest_neuron)

 source_neuron.synapses_out << synapse
 dest_neuron.synapses_in << synapse
 end
 end
end

require 'benchmark'

(ARGV[0] || 5).to_i.times do
x = Benchmark.measure do |x|
 xor = NeuralNetwork.new(2, 10, 1)
10000.times do
 xor.train([0, 0], [0])
 xor.train([1, 0], [1])
 xor.train([0, 1], [1])
 xor.train([1, 1], [0])
ARGV.fetch(0) { 5 }.to_i.times do
results = Benchmark.measure do
 xor = NeuralNetwork.new(inputs: 2, hidden: 10, outputs: 1)

10_000.times do
 xor.train(inputs: [0, 0], targets: [0])
 xor.train(inputs: [1, 0], targets: [1])
 xor.train(inputs: [0, 1], targets: [1])
 xor.train(inputs: [1, 1], targets: [0])
 end

 xor.feed_forward([0, 0])
 puts xor.current_outputs
 xor.feed_forward([0, 1])
Expand All@@ -135,5 +140,7 @@ def current_outputs
 xor.feed_forward([1, 1])
 puts xor.current_outputs
 end
 puts x

 puts results
end


, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Strip utm_, fbclid, gclid, etc. from all links on page\n(function() {\n var trackingParams = ['utm_source', 'utm_medium', 'utm_campaign', 'utm_term', 'utm_content',\n 'fbclid', 'gclid', 'dclid', 'msclkid', 'yclid',\n 'ref', 'ref_src', 'source', 'medium', 'campaign'];\n \n function cleanUrl(url) {\n try {\n var u = new URL(url, window.location.origin);\n var changed = false;\n trackingParams.forEach(function(p) {\n if (u.searchParams.has(p)) {\n u.searchParams.delete(p);\n changed = true;\n }\n });\n return changed ? u.toString() : url;\n } catch (e) {\n return url;\n }\n }\n \n function cleanLinks() {\n document.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n \n cleanLinks();\n \n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1) {\n if (node.tagName === 'A') cleanLinks();\n node.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Remove Tracking Parameters from Links");
}
} catch(__e) { console.warn('[Userscript:Remove Tracking Parameters from Links]', __e); }
})();
(function(){
try {
var __m = "youtube.com";
var __re = new RegExp('^' + "youtube\\.com" + '

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Refactor the neural net benchmark#3

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159 changes: 83 additions & 76 deletions
time/bench_neural_net.rb

View file


Open in desktop

Original file line numberDiff line numberDiff line change
@@ -1,131 +1,136 @@
# from bryanbibat's gist: https://gist.github.com/2348802

class Synapse
 WEIGHT_RANGE = (-1.0..1.0).freeze

 attr_accessor :weight, :prev_weight
attr_accessor :source_neuron, :dest_neuron
attr_reader :source_neuron, :dest_neuron

 def initialize(source_neuron, dest_neuron, prng)
self.source_neuron = source_neuron
self.dest_neuron = dest_neuron
self.prev_weight = self.weight = prng.rand(-1.0..1.0)
 def initialize(source_neuron:, dest_neuron:)
@source_neuron = source_neuron
@dest_neuron = dest_neuron
@prev_weight = @weight = rand(WEIGHT_RANGE)
 end
end

class Neuron

 LEARNING_RATE = 1.0
 MOMENTUM = 0.3
 WEIGHT_RANGE = (-1.0..1.0).freeze

attr_accessor :synapses_in, :synapses_out
attr_accessor :threshold, :prev_threshold, :error
attr_reader :synapses_in, :synapses_out
attr_reader :threshold, :prev_threshold, :error
 attr_accessor :output

 def initialize(prng)
self.prev_threshold = self.threshold = prng.rand(-1.0..1.0)
self.synapses_in = []
self.synapses_out = []
 def initialize
@synapses_in = []
@synapses_out = []
@prev_threshold = @threshold = rand(WEIGHT_RANGE)
 end

 def calculate_output
 # calculate output based on the previous layer
 # use logistic function

 activation = synapses_in.inject(0.0) do |sum, synapse| 
 sum + synapse.weight * synapse.source_neuron.output 
 end
 activation -= threshold
 activation = synapses_in.sum do |synapse|
 synapse.weight * synapse.source_neuron.output
 end - @threshold

 self.output = 1.0 / (1.0 + Math.exp(-activation))
 end

 def derivative
 output * (1 - output)
 @output = 1.fdiv(Math.exp(-activation) + 1)
 end

 def output_train(rate, target)
self.error = (target - output) * derivative
 def output_train(rate:, target:)
@error = (target - @output) * derivative
 update_weights(rate)
 end

 def hidden_train(rate)
self.error = synapses_out.inject(0.0) do |sum, synapse|
sum + synapse.prev_weight * synapse.dest_neuron.error
 def hidden_train(rate:)
@error = synapses_out.sum do |synapse|
 synapse.prev_weight * synapse.dest_neuron.error
 end * derivative
 update_weights(rate)
 end

 private

 def derivative
 @output * (1 - @output)
 end

 def update_weights(rate)
 synapses_in.each do |synapse|
 temp_weight = synapse.weight
 synapse.weight += (rate * LEARNING_RATE * error * synapse.source_neuron.output) +
 (MOMENTUM * ( synapse.weight - synapse.prev_weight))
 synapse.prev_weight = temp_weight
 update_synapses_in(rate)
 update_thresholds(rate)
 end

 def update_synapses_in(rate)
 @synapses_in.each do |synapse|
 prev_weight = synapse.weight
 synapse.weight += LEARNING_RATE * rate * @error * synapse.source_neuron.output
 synapse.weight += MOMENTUM * (synapse.weight - synapse.prev_weight)
 synapse.prev_weight = prev_weight
 end
 temp_threshold = threshold
 self.threshold += (rate * LEARNING_RATE * error * -1) + 
 (MOMENTUM * (threshold - prev_threshold))
 self.prev_threshold = temp_threshold
 end

 def update_thresholds(rate)
 prev_threshold = @threshold
 @threshold += LEARNING_RATE * rate * @error * -1
 @threshold += MOMENTUM * (@threshold - @prev_threshold)
 @prev_threshold = prev_threshold
 end
end

class NeuralNetwork
attr_accessor :prng

def initialize(inputs, hidden, outputs)
self.prng = Random.new
def initialize(inputs:, hidden:, outputs:)
 @input_layer = Array.new(inputs) { Neuron.new }
 @hidden_layer = Array.new(hidden) { Neuron.new }
@output_layer = Array.new(outputs) { Neuron.new }

 @input_layer = (1..inputs).map { Neuron.new(prng) }
 @hidden_layer = (1..hidden).map { Neuron.new(prng) }
 @output_layer = (1..outputs).map { Neuron.new(prng) }

 @input_layer.product(@hidden_layer).each do |source, dest|
 synapse = Synapse.new(source, dest, prng)
 source.synapses_out << synapse
 dest.synapses_in << synapse
 end
 @hidden_layer.product(@output_layer).each do |source, dest|
 synapse = Synapse.new(source, dest, prng)
 source.synapses_out << synapse
 dest.synapses_in << synapse
 end
 add_synapses(@input_layer, @hidden_layer)
 add_synapses(@hidden_layer, @output_layer)
 end

 def train(inputs, targets)
 def train(inputs:, targets:)
 feed_forward(inputs)

 @output_layer.zip(targets).each do |neuron, target|
 neuron.output_train(0.3, target)
 neuron.output_train(rate: 0.3, target: target)
 end
 @hidden_layer.each { |neuron| neuron.hidden_train(0.3) }
 @hidden_layer.each { |neuron| neuron.hidden_train(rate: 0.3) }
 end

 def feed_forward(inputs)
 @input_layer.zip(inputs).each do |neuron, input|
 neuron.output = input
 end
 @hidden_layer.each { |neuron| neuron.calculate_output }
 @output_layer.each { |neuron| neuron.calculate_output }
 @input_layer.zip(inputs) { |neuron, input| neuron.output = input }
 @hidden_layer.each(&:calculate_output)
 @output_layer.each(&:calculate_output)
 end

 def current_outputs
 @output_layer.map { |neuron| neuron.output }
 @output_layer.map(&:output)
 end

 private

 def add_synapses(source_layer, dest_layer)
 source_layer.product(dest_layer) do |source_neuron, dest_neuron|
 synapse = Synapse.new(source_neuron: source_neuron,
 dest_neuron: dest_neuron)

 source_neuron.synapses_out << synapse
 dest_neuron.synapses_in << synapse
 end
 end
end

require 'benchmark'

(ARGV[0] || 5).to_i.times do
x = Benchmark.measure do |x|
 xor = NeuralNetwork.new(2, 10, 1)
10000.times do
 xor.train([0, 0], [0])
 xor.train([1, 0], [1])
 xor.train([0, 1], [1])
 xor.train([1, 1], [0])
ARGV.fetch(0) { 5 }.to_i.times do
results = Benchmark.measure do
 xor = NeuralNetwork.new(inputs: 2, hidden: 10, outputs: 1)

10_000.times do
 xor.train(inputs: [0, 0], targets: [0])
 xor.train(inputs: [1, 0], targets: [1])
 xor.train(inputs: [0, 1], targets: [1])
 xor.train(inputs: [1, 1], targets: [0])
 end

 xor.feed_forward([0, 0])
 puts xor.current_outputs
 xor.feed_forward([0, 1])
Expand All@@ -135,5 +140,7 @@ def current_outputs
 xor.feed_forward([1, 1])
 puts xor.current_outputs
 end
 puts x

 puts results
end


, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Auto-enable theater mode on YouTube\n(function() {\n function tryTheater() {\n var btn = document.querySelector('button[aria-label=\"Theater mode\"], ytd-player #player button[title=\"Theater mode\"]');\n if (btn && !btn.classList.contains('activated')) {\n btn.click();\n }\n }\n \n // Try immediately\n tryTheater();\n \n // Try after navigation (SPA)\n var lastUrl = location.href;\n setInterval(function() {\n if (location.href !== lastUrl) {\n lastUrl = location.href;\n setTimeout(tryTheater, 500);\n }\n }, 1000);\n \n // Also try on player load\n var observer = new MutationObserver(tryTheater);\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "YouTube Theater Mode Default");
}
} catch(__e) { console.warn('[Userscript:YouTube Theater Mode Default]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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Refactor the neural net benchmark#3

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Refactor the neural net benchmark
#3
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159 changes: 83 additions & 76 deletions
time/bench_neural_net.rb

View file


Open in desktop

Original file line numberDiff line numberDiff line change
@@ -1,131 +1,136 @@
# from bryanbibat's gist: https://gist.github.com/2348802

class Synapse
 WEIGHT_RANGE = (-1.0..1.0).freeze

 attr_accessor :weight, :prev_weight
attr_accessor :source_neuron, :dest_neuron
attr_reader :source_neuron, :dest_neuron

 def initialize(source_neuron, dest_neuron, prng)
self.source_neuron = source_neuron
self.dest_neuron = dest_neuron
self.prev_weight = self.weight = prng.rand(-1.0..1.0)
 def initialize(source_neuron:, dest_neuron:)
@source_neuron = source_neuron
@dest_neuron = dest_neuron
@prev_weight = @weight = rand(WEIGHT_RANGE)
 end
end

class Neuron

 LEARNING_RATE = 1.0
 MOMENTUM = 0.3
 WEIGHT_RANGE = (-1.0..1.0).freeze

attr_accessor :synapses_in, :synapses_out
attr_accessor :threshold, :prev_threshold, :error
attr_reader :synapses_in, :synapses_out
attr_reader :threshold, :prev_threshold, :error
 attr_accessor :output

 def initialize(prng)
self.prev_threshold = self.threshold = prng.rand(-1.0..1.0)
self.synapses_in = []
self.synapses_out = []
 def initialize
@synapses_in = []
@synapses_out = []
@prev_threshold = @threshold = rand(WEIGHT_RANGE)
 end

 def calculate_output
 # calculate output based on the previous layer
 # use logistic function

 activation = synapses_in.inject(0.0) do |sum, synapse| 
 sum + synapse.weight * synapse.source_neuron.output 
 end
 activation -= threshold
 activation = synapses_in.sum do |synapse|
 synapse.weight * synapse.source_neuron.output
 end - @threshold

 self.output = 1.0 / (1.0 + Math.exp(-activation))
 end

 def derivative
 output * (1 - output)
 @output = 1.fdiv(Math.exp(-activation) + 1)
 end

 def output_train(rate, target)
self.error = (target - output) * derivative
 def output_train(rate:, target:)
@error = (target - @output) * derivative
 update_weights(rate)
 end

 def hidden_train(rate)
self.error = synapses_out.inject(0.0) do |sum, synapse|
sum + synapse.prev_weight * synapse.dest_neuron.error
 def hidden_train(rate:)
@error = synapses_out.sum do |synapse|
 synapse.prev_weight * synapse.dest_neuron.error
 end * derivative
 update_weights(rate)
 end

 private

 def derivative
 @output * (1 - @output)
 end

 def update_weights(rate)
 synapses_in.each do |synapse|
 temp_weight = synapse.weight
 synapse.weight += (rate * LEARNING_RATE * error * synapse.source_neuron.output) +
 (MOMENTUM * ( synapse.weight - synapse.prev_weight))
 synapse.prev_weight = temp_weight
 update_synapses_in(rate)
 update_thresholds(rate)
 end

 def update_synapses_in(rate)
 @synapses_in.each do |synapse|
 prev_weight = synapse.weight
 synapse.weight += LEARNING_RATE * rate * @error * synapse.source_neuron.output
 synapse.weight += MOMENTUM * (synapse.weight - synapse.prev_weight)
 synapse.prev_weight = prev_weight
 end
 temp_threshold = threshold
 self.threshold += (rate * LEARNING_RATE * error * -1) + 
 (MOMENTUM * (threshold - prev_threshold))
 self.prev_threshold = temp_threshold
 end

 def update_thresholds(rate)
 prev_threshold = @threshold
 @threshold += LEARNING_RATE * rate * @error * -1
 @threshold += MOMENTUM * (@threshold - @prev_threshold)
 @prev_threshold = prev_threshold
 end
end

class NeuralNetwork
attr_accessor :prng

def initialize(inputs, hidden, outputs)
self.prng = Random.new
def initialize(inputs:, hidden:, outputs:)
 @input_layer = Array.new(inputs) { Neuron.new }
 @hidden_layer = Array.new(hidden) { Neuron.new }
@output_layer = Array.new(outputs) { Neuron.new }

 @input_layer = (1..inputs).map { Neuron.new(prng) }
 @hidden_layer = (1..hidden).map { Neuron.new(prng) }
 @output_layer = (1..outputs).map { Neuron.new(prng) }

 @input_layer.product(@hidden_layer).each do |source, dest|
 synapse = Synapse.new(source, dest, prng)
 source.synapses_out << synapse
 dest.synapses_in << synapse
 end
 @hidden_layer.product(@output_layer).each do |source, dest|
 synapse = Synapse.new(source, dest, prng)
 source.synapses_out << synapse
 dest.synapses_in << synapse
 end
 add_synapses(@input_layer, @hidden_layer)
 add_synapses(@hidden_layer, @output_layer)
 end

 def train(inputs, targets)
 def train(inputs:, targets:)
 feed_forward(inputs)

 @output_layer.zip(targets).each do |neuron, target|
 neuron.output_train(0.3, target)
 neuron.output_train(rate: 0.3, target: target)
 end
 @hidden_layer.each { |neuron| neuron.hidden_train(0.3) }
 @hidden_layer.each { |neuron| neuron.hidden_train(rate: 0.3) }
 end

 def feed_forward(inputs)
 @input_layer.zip(inputs).each do |neuron, input|
 neuron.output = input
 end
 @hidden_layer.each { |neuron| neuron.calculate_output }
 @output_layer.each { |neuron| neuron.calculate_output }
 @input_layer.zip(inputs) { |neuron, input| neuron.output = input }
 @hidden_layer.each(&:calculate_output)
 @output_layer.each(&:calculate_output)
 end

 def current_outputs
 @output_layer.map { |neuron| neuron.output }
 @output_layer.map(&:output)
 end

 private

 def add_synapses(source_layer, dest_layer)
 source_layer.product(dest_layer) do |source_neuron, dest_neuron|
 synapse = Synapse.new(source_neuron: source_neuron,
 dest_neuron: dest_neuron)

 source_neuron.synapses_out << synapse
 dest_neuron.synapses_in << synapse
 end
 end
end

require 'benchmark'

(ARGV[0] || 5).to_i.times do
x = Benchmark.measure do |x|
 xor = NeuralNetwork.new(2, 10, 1)
10000.times do
 xor.train([0, 0], [0])
 xor.train([1, 0], [1])
 xor.train([0, 1], [1])
 xor.train([1, 1], [0])
ARGV.fetch(0) { 5 }.to_i.times do
results = Benchmark.measure do
 xor = NeuralNetwork.new(inputs: 2, hidden: 10, outputs: 1)

10_000.times do
 xor.train(inputs: [0, 0], targets: [0])
 xor.train(inputs: [1, 0], targets: [1])
 xor.train(inputs: [0, 1], targets: [1])
 xor.train(inputs: [1, 1], targets: [0])
 end

 xor.feed_forward([0, 0])
 puts xor.current_outputs
 xor.feed_forward([0, 1])
Expand All@@ -135,5 +140,7 @@ def current_outputs
 xor.feed_forward([1, 1])
 puts xor.current_outputs
 end
 puts x

 puts results
end


, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Remove or un-stick sticky/fixed headers that block content\n(function() {\n function unstick() {\n document.querySelectorAll('header, nav, [role=\"banner\"], .header, .navbar, .sticky, .fixed-top, [style*=\"position: fixed\"], [style*=\"position:sticky\"]').forEach(function(el) {\n if (el.style.position === 'fixed' || el.style.position === 'sticky' || \n getComputedStyle(el).position === 'fixed' || getComputedStyle(el).position === 'sticky') {\n el.style.position = 'static';\n el.style.top = 'auto';\n el.style.zIndex = 'auto';\n }\n });\n }\n \n unstick();\n \n var observer = new MutationObserver(unstick);\n observer.observe(document.body, { childList: true, subtree: true, attributes: true, attributeFilter: ['style', 'class'] });\n})();", "Kill Sticky Headers");
}
} catch(__e) { console.warn('[Userscript:Kill Sticky Headers]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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Refactor the neural net benchmark#3

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159 changes: 83 additions & 76 deletions
time/bench_neural_net.rb

View file


Open in desktop

Original file line numberDiff line numberDiff line change
@@ -1,131 +1,136 @@
# from bryanbibat's gist: https://gist.github.com/2348802

class Synapse
 WEIGHT_RANGE = (-1.0..1.0).freeze

 attr_accessor :weight, :prev_weight
attr_accessor :source_neuron, :dest_neuron
attr_reader :source_neuron, :dest_neuron

 def initialize(source_neuron, dest_neuron, prng)
self.source_neuron = source_neuron
self.dest_neuron = dest_neuron
self.prev_weight = self.weight = prng.rand(-1.0..1.0)
 def initialize(source_neuron:, dest_neuron:)
@source_neuron = source_neuron
@dest_neuron = dest_neuron
@prev_weight = @weight = rand(WEIGHT_RANGE)
 end
end

class Neuron

 LEARNING_RATE = 1.0
 MOMENTUM = 0.3
 WEIGHT_RANGE = (-1.0..1.0).freeze

attr_accessor :synapses_in, :synapses_out
attr_accessor :threshold, :prev_threshold, :error
attr_reader :synapses_in, :synapses_out
attr_reader :threshold, :prev_threshold, :error
 attr_accessor :output

 def initialize(prng)
self.prev_threshold = self.threshold = prng.rand(-1.0..1.0)
self.synapses_in = []
self.synapses_out = []
 def initialize
@synapses_in = []
@synapses_out = []
@prev_threshold = @threshold = rand(WEIGHT_RANGE)
 end

 def calculate_output
 # calculate output based on the previous layer
 # use logistic function

 activation = synapses_in.inject(0.0) do |sum, synapse| 
 sum + synapse.weight * synapse.source_neuron.output 
 end
 activation -= threshold
 activation = synapses_in.sum do |synapse|
 synapse.weight * synapse.source_neuron.output
 end - @threshold

 self.output = 1.0 / (1.0 + Math.exp(-activation))
 end

 def derivative
 output * (1 - output)
 @output = 1.fdiv(Math.exp(-activation) + 1)
 end

 def output_train(rate, target)
self.error = (target - output) * derivative
 def output_train(rate:, target:)
@error = (target - @output) * derivative
 update_weights(rate)
 end

 def hidden_train(rate)
self.error = synapses_out.inject(0.0) do |sum, synapse|
sum + synapse.prev_weight * synapse.dest_neuron.error
 def hidden_train(rate:)
@error = synapses_out.sum do |synapse|
 synapse.prev_weight * synapse.dest_neuron.error
 end * derivative
 update_weights(rate)
 end

 private

 def derivative
 @output * (1 - @output)
 end

 def update_weights(rate)
 synapses_in.each do |synapse|
 temp_weight = synapse.weight
 synapse.weight += (rate * LEARNING_RATE * error * synapse.source_neuron.output) +
 (MOMENTUM * ( synapse.weight - synapse.prev_weight))
 synapse.prev_weight = temp_weight
 update_synapses_in(rate)
 update_thresholds(rate)
 end

 def update_synapses_in(rate)
 @synapses_in.each do |synapse|
 prev_weight = synapse.weight
 synapse.weight += LEARNING_RATE * rate * @error * synapse.source_neuron.output
 synapse.weight += MOMENTUM * (synapse.weight - synapse.prev_weight)
 synapse.prev_weight = prev_weight
 end
 temp_threshold = threshold
 self.threshold += (rate * LEARNING_RATE * error * -1) + 
 (MOMENTUM * (threshold - prev_threshold))
 self.prev_threshold = temp_threshold
 end

 def update_thresholds(rate)
 prev_threshold = @threshold
 @threshold += LEARNING_RATE * rate * @error * -1
 @threshold += MOMENTUM * (@threshold - @prev_threshold)
 @prev_threshold = prev_threshold
 end
end

class NeuralNetwork
attr_accessor :prng

def initialize(inputs, hidden, outputs)
self.prng = Random.new
def initialize(inputs:, hidden:, outputs:)
 @input_layer = Array.new(inputs) { Neuron.new }
 @hidden_layer = Array.new(hidden) { Neuron.new }
@output_layer = Array.new(outputs) { Neuron.new }

 @input_layer = (1..inputs).map { Neuron.new(prng) }
 @hidden_layer = (1..hidden).map { Neuron.new(prng) }
 @output_layer = (1..outputs).map { Neuron.new(prng) }

 @input_layer.product(@hidden_layer).each do |source, dest|
 synapse = Synapse.new(source, dest, prng)
 source.synapses_out << synapse
 dest.synapses_in << synapse
 end
 @hidden_layer.product(@output_layer).each do |source, dest|
 synapse = Synapse.new(source, dest, prng)
 source.synapses_out << synapse
 dest.synapses_in << synapse
 end
 add_synapses(@input_layer, @hidden_layer)
 add_synapses(@hidden_layer, @output_layer)
 end

 def train(inputs, targets)
 def train(inputs:, targets:)
 feed_forward(inputs)

 @output_layer.zip(targets).each do |neuron, target|
 neuron.output_train(0.3, target)
 neuron.output_train(rate: 0.3, target: target)
 end
 @hidden_layer.each { |neuron| neuron.hidden_train(0.3) }
 @hidden_layer.each { |neuron| neuron.hidden_train(rate: 0.3) }
 end

 def feed_forward(inputs)
 @input_layer.zip(inputs).each do |neuron, input|
 neuron.output = input
 end
 @hidden_layer.each { |neuron| neuron.calculate_output }
 @output_layer.each { |neuron| neuron.calculate_output }
 @input_layer.zip(inputs) { |neuron, input| neuron.output = input }
 @hidden_layer.each(&:calculate_output)
 @output_layer.each(&:calculate_output)
 end

 def current_outputs
 @output_layer.map { |neuron| neuron.output }
 @output_layer.map(&:output)
 end

 private

 def add_synapses(source_layer, dest_layer)
 source_layer.product(dest_layer) do |source_neuron, dest_neuron|
 synapse = Synapse.new(source_neuron: source_neuron,
 dest_neuron: dest_neuron)

 source_neuron.synapses_out << synapse
 dest_neuron.synapses_in << synapse
 end
 end
end

require 'benchmark'

(ARGV[0] || 5).to_i.times do
x = Benchmark.measure do |x|
 xor = NeuralNetwork.new(2, 10, 1)
10000.times do
 xor.train([0, 0], [0])
 xor.train([1, 0], [1])
 xor.train([0, 1], [1])
 xor.train([1, 1], [0])
ARGV.fetch(0) { 5 }.to_i.times do
results = Benchmark.measure do
 xor = NeuralNetwork.new(inputs: 2, hidden: 10, outputs: 1)

10_000.times do
 xor.train(inputs: [0, 0], targets: [0])
 xor.train(inputs: [1, 0], targets: [1])
 xor.train(inputs: [0, 1], targets: [1])
 xor.train(inputs: [1, 1], targets: [0])
 end

 xor.feed_forward([0, 0])
 puts xor.current_outputs
 xor.feed_forward([0, 1])
Expand All@@ -135,5 +140,7 @@ def current_outputs
 xor.feed_forward([1, 1])
 puts xor.current_outputs
 end
 puts x

 puts results
end


, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Universal Dark Mode - works on any site\n(function() {\n var enabled = true;\n \n function applyDarkMode() {\n if (!enabled) return;\n \n // Create style element if it doesn't exist\n var style = document.getElementById('universal-dark-mode-style');\n if (!style) {\n style = document.createElement('style');\n style.id = 'universal-dark-mode-style';\n document.head.appendChild(style);\n }\n \n // Dark mode CSS - inverts colors but preserves images/video\n style.textContent = '\n /* Invert everything except media */\n html {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #1a1a2e !important;\n }\n \n /* Restore images, videos, iframes, canvas */\n img, video, iframe, canvas, svg, picture, [style*=\"background-image\"] {\n filter: invert(1) hue-rotate(180deg) !important;\n }\n \n /* Preserve specific elements that should not be inverted */\n .no-dark-mode, .no-dark-mode *,\n [data-theme=\"light\"], [data-theme=\"light\"],\n .ace_editor, .ace_editor *,\n .CodeMirror, .CodeMirror *,\n .monaco-editor, .monaco-editor *,\n .markdown-body pre, .markdown-body pre *,\n .highlight, .highlight *,\n pre code, pre code * {\n filter: none !important;\n }\n \n /* Fix common UI elements */\n .modal, .popup, .dropdown-menu, .tooltip, .popover {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #2d2d44 !important;\n border-color: #444 !important;\n }\n \n /* Scrollbars */\n ::-webkit-scrollbar { background: #1a1a2e !important; }\n ::-webkit-scrollbar-thumb { background: #444 !important; }\n ::-webkit-scrollbar-thumb:hover { background: #555 !important; }\n \n /* Selection */\n ::selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ::-moz-selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ';\n }\n \n function removeDarkMode() {\n var style = document.getElementById('universal-dark-mode-style');\n if (style) style.remove();\n }\n \n // Toggle with Alt+Shift+D\n document.addEventListener('keydown', function(e) {\n if (e.altKey && e.shiftKey && e.key === 'D') {\n e.preventDefault();\n enabled = !enabled;\n if (enabled) {\n applyDarkMode();\n console.log('[Universal Dark Mode] Enabled');\n } else {\n removeDarkMode();\n console.log('[Universal Dark Mode] Disabled');\n }\n }\n });\n \n // Apply on load\n applyDarkMode();\n \n // Re-apply on dynamic content\n var observer = new MutationObserver(function(mutations) {\n if (enabled && !document.getElementById('universal-dark-mode-style')) {\n applyDarkMode();\n }\n });\n observer.observe(document.head, { childList: true });\n \n console.log('[Universal Dark Mode] Loaded - Press Alt+Shift+D to toggle');\n})();", "Universal Dark Mode");
}
} catch(__e) { console.warn('[Userscript:Universal Dark Mode]', __e); }
})();
})();

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Refactor the neural net benchmark#3

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#3
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159 changes: 83 additions & 76 deletions
time/bench_neural_net.rb

View file


Open in desktop

Original file line numberDiff line numberDiff line change
@@ -1,131 +1,136 @@
# from bryanbibat's gist: https://gist.github.com/2348802

class Synapse
 WEIGHT_RANGE = (-1.0..1.0).freeze

 attr_accessor :weight, :prev_weight
attr_accessor :source_neuron, :dest_neuron
attr_reader :source_neuron, :dest_neuron

 def initialize(source_neuron, dest_neuron, prng)
self.source_neuron = source_neuron
self.dest_neuron = dest_neuron
self.prev_weight = self.weight = prng.rand(-1.0..1.0)
 def initialize(source_neuron:, dest_neuron:)
@source_neuron = source_neuron
@dest_neuron = dest_neuron
@prev_weight = @weight = rand(WEIGHT_RANGE)
 end
end

class Neuron

 LEARNING_RATE = 1.0
 MOMENTUM = 0.3
 WEIGHT_RANGE = (-1.0..1.0).freeze

attr_accessor :synapses_in, :synapses_out
attr_accessor :threshold, :prev_threshold, :error
attr_reader :synapses_in, :synapses_out
attr_reader :threshold, :prev_threshold, :error
 attr_accessor :output

 def initialize(prng)
self.prev_threshold = self.threshold = prng.rand(-1.0..1.0)
self.synapses_in = []
self.synapses_out = []
 def initialize
@synapses_in = []
@synapses_out = []
@prev_threshold = @threshold = rand(WEIGHT_RANGE)
 end

 def calculate_output
 # calculate output based on the previous layer
 # use logistic function

 activation = synapses_in.inject(0.0) do |sum, synapse| 
 sum + synapse.weight * synapse.source_neuron.output 
 end
 activation -= threshold
 activation = synapses_in.sum do |synapse|
 synapse.weight * synapse.source_neuron.output
 end - @threshold

 self.output = 1.0 / (1.0 + Math.exp(-activation))
 end

 def derivative
 output * (1 - output)
 @output = 1.fdiv(Math.exp(-activation) + 1)
 end

 def output_train(rate, target)
self.error = (target - output) * derivative
 def output_train(rate:, target:)
@error = (target - @output) * derivative
 update_weights(rate)
 end

 def hidden_train(rate)
self.error = synapses_out.inject(0.0) do |sum, synapse|
sum + synapse.prev_weight * synapse.dest_neuron.error
 def hidden_train(rate:)
@error = synapses_out.sum do |synapse|
 synapse.prev_weight * synapse.dest_neuron.error
 end * derivative
 update_weights(rate)
 end

 private

 def derivative
 @output * (1 - @output)
 end

 def update_weights(rate)
 synapses_in.each do |synapse|
 temp_weight = synapse.weight
 synapse.weight += (rate * LEARNING_RATE * error * synapse.source_neuron.output) +
 (MOMENTUM * ( synapse.weight - synapse.prev_weight))
 synapse.prev_weight = temp_weight
 update_synapses_in(rate)
 update_thresholds(rate)
 end

 def update_synapses_in(rate)
 @synapses_in.each do |synapse|
 prev_weight = synapse.weight
 synapse.weight += LEARNING_RATE * rate * @error * synapse.source_neuron.output
 synapse.weight += MOMENTUM * (synapse.weight - synapse.prev_weight)
 synapse.prev_weight = prev_weight
 end
 temp_threshold = threshold
 self.threshold += (rate * LEARNING_RATE * error * -1) + 
 (MOMENTUM * (threshold - prev_threshold))
 self.prev_threshold = temp_threshold
 end

 def update_thresholds(rate)
 prev_threshold = @threshold
 @threshold += LEARNING_RATE * rate * @error * -1
 @threshold += MOMENTUM * (@threshold - @prev_threshold)
 @prev_threshold = prev_threshold
 end
end

class NeuralNetwork
attr_accessor :prng

def initialize(inputs, hidden, outputs)
self.prng = Random.new
def initialize(inputs:, hidden:, outputs:)
 @input_layer = Array.new(inputs) { Neuron.new }
 @hidden_layer = Array.new(hidden) { Neuron.new }
@output_layer = Array.new(outputs) { Neuron.new }

 @input_layer = (1..inputs).map { Neuron.new(prng) }
 @hidden_layer = (1..hidden).map { Neuron.new(prng) }
 @output_layer = (1..outputs).map { Neuron.new(prng) }

 @input_layer.product(@hidden_layer).each do |source, dest|
 synapse = Synapse.new(source, dest, prng)
 source.synapses_out << synapse
 dest.synapses_in << synapse
 end
 @hidden_layer.product(@output_layer).each do |source, dest|
 synapse = Synapse.new(source, dest, prng)
 source.synapses_out << synapse
 dest.synapses_in << synapse
 end
 add_synapses(@input_layer, @hidden_layer)
 add_synapses(@hidden_layer, @output_layer)
 end

 def train(inputs, targets)
 def train(inputs:, targets:)
 feed_forward(inputs)

 @output_layer.zip(targets).each do |neuron, target|
 neuron.output_train(0.3, target)
 neuron.output_train(rate: 0.3, target: target)
 end
 @hidden_layer.each { |neuron| neuron.hidden_train(0.3) }
 @hidden_layer.each { |neuron| neuron.hidden_train(rate: 0.3) }
 end

 def feed_forward(inputs)
 @input_layer.zip(inputs).each do |neuron, input|
 neuron.output = input
 end
 @hidden_layer.each { |neuron| neuron.calculate_output }
 @output_layer.each { |neuron| neuron.calculate_output }
 @input_layer.zip(inputs) { |neuron, input| neuron.output = input }
 @hidden_layer.each(&:calculate_output)
 @output_layer.each(&:calculate_output)
 end

 def current_outputs
 @output_layer.map { |neuron| neuron.output }
 @output_layer.map(&:output)
 end

 private

 def add_synapses(source_layer, dest_layer)
 source_layer.product(dest_layer) do |source_neuron, dest_neuron|
 synapse = Synapse.new(source_neuron: source_neuron,
 dest_neuron: dest_neuron)

 source_neuron.synapses_out << synapse
 dest_neuron.synapses_in << synapse
 end
 end
end

require 'benchmark'

(ARGV[0] || 5).to_i.times do
x = Benchmark.measure do |x|
 xor = NeuralNetwork.new(2, 10, 1)
10000.times do
 xor.train([0, 0], [0])
 xor.train([1, 0], [1])
 xor.train([0, 1], [1])
 xor.train([1, 1], [0])
ARGV.fetch(0) { 5 }.to_i.times do
results = Benchmark.measure do
 xor = NeuralNetwork.new(inputs: 2, hidden: 10, outputs: 1)

10_000.times do
 xor.train(inputs: [0, 0], targets: [0])
 xor.train(inputs: [1, 0], targets: [1])
 xor.train(inputs: [0, 1], targets: [1])
 xor.train(inputs: [1, 1], targets: [0])
 end

 xor.feed_forward([0, 0])
 puts xor.current_outputs
 xor.feed_forward([0, 1])
Expand All@@ -135,5 +140,7 @@ def current_outputs
 xor.feed_forward([1, 1])
 puts xor.current_outputs
 end
 puts x

 puts results
end