Should be feature complete. Onto debugging and extracting data.
This commit is contained in:
Alex Huddleston
parent
2ba5366c4c
commit
5e2d49fa69
1 changed files with 205 additions and 86 deletions
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@ -2,51 +2,23 @@
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"execution_count": null,
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"metadata": {},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"Using TensorFlow backend.\n"
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]
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}
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],
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"outputs": [],
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"source": [
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"# Setting up our imported libraries.\n",
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"from functools import reduce\n",
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"import numpy as np\n",
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"from keras.models import Sequential\n",
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"from keras.layers import Dense"
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"from keras.layers import Dense\n",
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"from os.path import join"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"execution_count": null,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"240000\n",
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"240\n",
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"[[ 0.28588903 0.05308564 0.99171479 ..., 0.92657084 0.09114427\n",
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" 0.76495161]\n",
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" [ 0.50998915 0.74032164 0.04898317 ..., 0.77742777 0.46720853\n",
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" 0.01731216]\n",
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" [ 0.31522802 0.11448062 0.40291163 ..., 0.87519373 0.31255597\n",
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" 0.7202333 ]\n",
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" ..., \n",
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" [ 0.13906598 0.99536312 0.36709839 ..., 0.68740262 0.9536678\n",
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" 0.53053495]\n",
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" [ 0.13696298 0.91392043 0.5846018 ..., 0.84365665 0.92837426\n",
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" 0.18738981]\n",
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" [ 0.05775272 0.7919279 0.51444914 ..., 0.53078037 0.67684536\n",
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" 0.25327729]]\n"
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]
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}
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],
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"outputs": [],
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"source": [
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"# Generating dummy data.\n",
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"\n",
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@ -58,36 +30,9 @@
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"execution_count": null,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Epoch 1/5\n",
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"1000/1000 [==============================] - 4s 4ms/step - loss: 0.0986 - acc: 0.0250\n",
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"Epoch 2/5\n",
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"1000/1000 [==============================] - 0s 155us/step - loss: 0.0936 - acc: 0.0350\n",
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"Epoch 3/5\n",
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"1000/1000 [==============================] - 0s 157us/step - loss: 0.0912 - acc: 0.0380\n",
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"Epoch 4/5\n",
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"1000/1000 [==============================] - 0s 154us/step - loss: 0.0901 - acc: 0.0370\n",
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"Epoch 5/5\n",
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"1000/1000 [==============================] - 0s 162us/step - loss: 0.0894 - acc: 0.0370\n"
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]
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},
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{
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"data": {
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"text/plain": [
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"<keras.callbacks.History at 0x7fe4fae027f0>"
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]
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},
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"execution_count": 3,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"outputs": [],
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"source": [
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"# Sets up a Sequential model, Sequential is all\n",
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"# that should need to be used for this project,\n",
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@ -106,35 +51,209 @@
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"\n",
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"model.compile(optimizer='sgd',\n",
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" loss='mean_squared_error',\n",
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" metrics=['accuracy'])\n",
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"\n",
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"# Train the network.\n",
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"\n",
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"model.fit(data, output, epochs=5, batch_size=10)\n",
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"\n",
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"# Generating predictions should look like this:\n",
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"\n",
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"# predictions = model.predict(testing_data, batch_size=10)"
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" metrics=['accuracy'])"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"execution_count": null,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"0xf23456789abcdef0\n"
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]
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}
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],
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"outputs": [],
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"source": [
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"with open('data/0.bin', 'rb') as f:\n",
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" data = f.read()\n",
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"\n",
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"print(hex(int.from_bytes(data, byteorder='little', signed=False)))"
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"def format_input(acc, elem):\n",
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" hex_elem = (elem - (elem >> 4 << 4))\n",
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" for x in range(16):\n",
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" if x == hex_elem:\n",
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" acc.append(1)\n",
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" else:\n",
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" acc.append(0)\n",
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" hex_elem = (elem >> 4) % 16\n",
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" for x in range(16):\n",
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" if x == hex_elem:\n",
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" acc.append(1)\n",
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" else:\n",
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" acc.append(0)\n",
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" return acc\n",
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"\n",
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"with open('data/0.bin', 'rb') as f:\n",
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" data = f.read(8)\n",
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" counter = 0\n",
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"\n",
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" while(data):\n",
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" bin_data = reduce(format_input, list(data), [])\n",
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" bin_data.reverse()\n",
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" bin_data = bin_data[16:]\n",
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" print(bin_data)\n",
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"\n",
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" print(counter)\n",
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" \n",
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" for i in range(int(len(bin_data)/240)):\n",
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" for x in range((i*16),(i*16) + 15):\n",
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" temp = []\n",
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" for y in range(16):\n",
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" temp.append(bin_data[(x*16) + y])\n",
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" print(temp)\n",
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" \n",
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" data = f.read(8)\n",
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" counter += 1"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"collapsed": true
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},
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"source": [
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"I'm going to need to calculate Manhattan Distances for each of the states at some point.\n",
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"\n",
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"This website might be helpful for that formula:\n",
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"https://heuristicswiki.wikispaces.com/Manhattan+Distance"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"def man_dist(x, y):\n",
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" for a, b in zip(x, y):\n",
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" a_one, a_two = x\n",
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" b_one, b_two = y\n",
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" \n",
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" return (abs(a_one - b_one) + abs(a_two - b_two))\n",
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" \n",
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"def man_dist_state(x, y):\n",
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" return sum(man_dist(a, b) for a, b in zip(x, y))\n",
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"\n",
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"def format_pos(acc, elem):\n",
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" hex_elem = (elem[1] - (elem[1] >> 4 << 4))\n",
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" if hex_elem == 0:\n",
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" acc.append((hex_elem, (3,3)))\n",
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" else:\n",
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" acc.append((hex_elem, ((15 - ((elem[0]) * 2)) % 4,int((15 - ((elem[0]) * 2)) / 4))))\n",
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" hex_elem = (elem[1] >> 4) % 16\n",
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" if hex_elem == 0:\n",
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" acc.append((hex_elem, (3,3)))\n",
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" else:\n",
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" acc.append((hex_elem, ((15 - ((elem[0]) * 2 + 1)) % 4,int((15 - ((elem[0]) * 2 + 1)) / 4))))\n",
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" \n",
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" return acc\n",
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"\n",
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"def generate_pos(acc, elem):\n",
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" if(elem[0] == 0):\n",
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" acc.append((3,3))\n",
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" else:\n",
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" acc.append((((elem[0] - 1) % 4), (int((elem[0] - 1)/4))))\n",
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" \n",
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" return acc\n",
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"\n",
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"def format_man_dist(elem):\n",
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" acc = []\n",
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" for x in range(28, -1, -1):\n",
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" if x == elem:\n",
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" acc.append(1)\n",
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" else:\n",
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" acc.append(0)\n",
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" return acc\n",
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"\n",
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"with open('data/0.bin', 'rb') as f:\n",
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" data = f.read(8)\n",
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" counter = 0\n",
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" \n",
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" while(data):\n",
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" pos_data = reduce(format_pos, enumerate(list(data)), [])\n",
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" pos_data.reverse()\n",
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" pos_data = pos_data[1:]\n",
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" \n",
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" state_pos = []\n",
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" \n",
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" for p in pos_data:\n",
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" state_pos.append(p[1])\n",
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" \n",
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" target_pos = reduce(generate_pos, pos_data, [])\n",
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" \n",
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" print(format_man_dist(man_dist_state(state_pos, target_pos)))\n",
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" \n",
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" data = f.read(8)\n",
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" counter += 1"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"for i in range(29):\n",
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" filename = join('data', str(i) + '.bin')\n",
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" \n",
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" print(i)\n",
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" \n",
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" with open(filename, 'rb') as f:\n",
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" data = f.read(8)\n",
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" counter = 0\n",
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"\n",
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" while(data and counter < 1000):\n",
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" bin_data = reduce(format_input, list(data), [])\n",
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" bin_data.reverse()\n",
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" bin_data = bin_data[16:]\n",
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"\n",
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" pos_data = reduce(format_pos, enumerate(list(data)), [])\n",
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" pos_data.reverse()\n",
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"\n",
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" state_pos = []\n",
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"\n",
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" for p in pos_data:\n",
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" state_pos.append(p[1])\n",
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"\n",
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" target_pos = reduce(generate_pos, pos_data, [])\n",
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"\n",
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" #for i in range(int(len(bin_data)/256)):\n",
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" # for x in range((i*16) + 1,(i*16) + 16):\n",
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" # temp = []\n",
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" # for y in range(16):\n",
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" # temp.append(bin_data[(x*16) + y])\n",
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" # print(temp)\n",
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"\n",
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" target = []\n",
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" target.append(format_man_dist(man_dist_state(state_pos, target_pos)))\n",
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"\n",
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" temp = []\n",
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" temp.append(bin_data)\n",
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"\n",
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" # Train the network.\n",
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"\n",
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" #model.fit(data, output, epochs=5, batch_size=10)\n",
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" model.train_on_batch(np.array(temp), np.array(target))\n",
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"\n",
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" data = f.read(8)\n",
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" counter += 1"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"with open('data/2.bin', 'rb') as f:\n",
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" data = f.read(8)\n",
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" counter = 0\n",
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" \n",
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" while(data):\n",
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" bin_data = reduce(format_input, list(data), [])\n",
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" bin_data.reverse()\n",
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" bin_data = bin_data[16:]\n",
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" \n",
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" temp = []\n",
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" temp.append(bin_data)\n",
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"\n",
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" # Generating predictions:\n",
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"\n",
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" predictions = model.predict(np.array(temp), batch_size=1)\n",
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" print(predictions)"
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]
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},
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{
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