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< > BotCompany Repo | #329 // Simple perceptron [not really a perceptron]

Lua code - Snippet producer

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get("#265") -- json.encode
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images = {"#1000061", "A", "#1000056", "H", "#1000064", "P", "#1000063", "X", "#1000062", "B"}
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features = {"#326", "#327", "#328"}
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-- make a letter lookup map
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image2letter = {}
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for i=1, #images, 2 do image2letter[images[i]] = images[i+1] end
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-- find out which results to load
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toget = {}
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for i=1, #images, 2 do table.insert(toget, images[i]) end
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for i=1, #features do table.insert(toget, features[i]) end
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results = getirresults(unpack(toget))
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--map: feature -> letter -> value range
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map = {}
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for image, subresults in pairs(results) do
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  letter = image2letter[image]
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  for feature, result in pairs(subresults) do
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    _, _, value = string.find(result, ": ([0-9.]+)")
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    if value ~= nil then
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      value = tonumber(value)
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      if map[feature] == nil then map[feature] = {} end
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      submap = map[feature]
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      range = submap[letter]
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      if range == nil then
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        range = {value, value}
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      else
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        range = {math.min(range[1], value), math.max(range[2], value)}
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      end
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      submap[letter] = range
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    end
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  end
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end
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-- get serpent to serialize map into Lua code
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serpent = go("#158")
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--if all features are within the known value ranges for the letter, just take the intersection of all the matching letters within each feature.
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-- serpent options:
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-- block(map, {comment=false}) is that indented stuff
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-- line(map, {comment=false}) is all in one line
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-- dump(map) is...?
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-- line(map, {comment=false, compact=true, sparse=true}) is in one line, very compact
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-- line(map, {comment=false, compact=true, sparse=false}) is the same
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recognizer = "  map = "..serpent.line(map, {comment=false, compact=true, sparse=false}).."\n"..[[
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  allletters = {}
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  outmap = {}
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  for feature, submap in pairs(map) do
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    f = otherresults[feature]
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    if f == nil then error("Need recalc, waiting for "..feature) end
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    _, _, value = string.find(f, ": ([0-9.]+)")
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    if value ~= nil then
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      value = tonumber(value)
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      for letter, range in pairs(submap) do
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        allletters[letter] = true
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        if value < range[1] or value > range[2] then
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          outmap[letter] = true -- can't be this letter
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        end
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      end
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    end
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  end
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  candidates = {}
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  for letter, _ in pairs(allletters) do
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    if not outmap[letter] then
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      table.insert(candidates, letter)
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    end
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  end
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  if #candidates ~= 0 then
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    return "Candidates: "..table.concat(candidates, ", ")
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  end
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]]
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print(recognizer)
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snippet = {
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  type = 26, -- Lua code - Image recognition
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  text = recognizer,
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  title = "A machine-made letter recognizer, v3"
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}
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return json.encode(snippet)
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--todo: otherwise, expand all value ranges uniformly and try again.
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--more todo: repeat & increase ranges exponentially, finally at least one letter will match.

Author comment

I'm redefining the term PERCEPTRON... ^^

It means a production facility for a general "decider" based on a list of examples and feature extractors.

test run  test run with input  produce a snippet  download  show line numbers   

Travelled to 12 computer(s): aoiabmzegqzx, bhatertpkbcr, cbybwowwnfue, gwrvuhgaqvyk, ishqpsrjomds, lpdgvwnxivlt, mqqgnosmbjvj, pyentgdyhuwx, pzhvpgtvlbxg, tslmcundralx, tvejysmllsmz, vouqrxazstgt

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Image recognition results

Recognizer Recognition Result Visualize Recalc
#308 javax.imageio.IIOException: Can't get input stream from URL! [visualize]

Snippet ID: #329
Snippet name: Simple perceptron [not really a perceptron]
Eternal ID of this version: #329/2
Text MD5: bd993491c90d73e238bdb59189c62608
Author: stefan
Category: meta letter recognizers
Type: Lua code - Snippet producer
Public (visible to everyone): Yes
Archived (hidden from active list): No
Created/modified: 2017-09-05 17:12:14
Source code size: 2846 bytes / 93 lines
Pitched / IR pitched: No / No
Views / Downloads: 1016 / 165
Version history: 1 change(s)
Referenced in: [show references]