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/env
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__pycache__
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@@ -0,0 +1,144 @@
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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {
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"collapsed": false
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},
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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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"Populating the interactive namespace from numpy and matplotlib\n"
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]
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}
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],
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"source": [
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"%pylab inline\n",
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"from network.network import is_valid_network, get_number_of_activated_antenna, pp_network\n",
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"from numpy import mean\n",
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"from random import Random"
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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": 3,
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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"random = Random(666)\n",
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"def get_some_possible_network(n, box_size):\n",
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" number_of_square = box_size * box_size\n",
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" for i in range(n):\n",
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" yield random.randint(0, 2 ** number_of_square - 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": 4,
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": [
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"box_size = 5\n",
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"n = 1000000\n",
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"\n",
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"valid_networks = []\n",
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"for network in get_some_possible_network(n, box_size):\n",
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" if is_valid_network(network, box_size):\n",
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" valid_networks.append(network)"
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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": 5,
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"metadata": {
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"collapsed": false
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},
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"outputs": [
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{
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"data": {
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truncated
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"text/plain": [
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"<matplotlib.figure.Figure at 0x7fe8f4a177b8>"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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}
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],
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"source": [
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"hist(list(map(get_number_of_activated_antenna, valid_networks)), bins=100)\n",
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"None"
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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": 6,
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"metadata": {
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"collapsed": false
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},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"6.2328431372549016"
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]
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},
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"execution_count": 6,
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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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"source": [
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"mean(list(map(get_number_of_activated_antenna, valid_networks)))"
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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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"collapsed": false
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},
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"outputs": [],
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"source": [
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"valid_networks[0]"
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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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"collapsed": true
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},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.4.3+"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 0
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}
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@@ -0,0 +1,189 @@
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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 23,
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"metadata": {
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"collapsed": false
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},
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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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"Populating the interactive namespace from numpy and matplotlib\n"
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]
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}
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],
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"source": [
|
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"%pylab inline\n",
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"from network.network import is_valid_network, get_number_of_activated_antenna, pp_network\n",
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"from numpy import mean\n",
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"\n",
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"def get_all_possible_network(box_size):\n",
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" number_of_square = box_size * box_size\n",
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" return iter(range(2**number_of_square))\n",
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"\n"
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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": 24,
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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"box_size = 5\n",
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"\n",
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"valid_networks = []\n",
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"for network in get_all_possible_network(box_size):\n",
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" if is_valid_network(network, box_size):\n",
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" valid_networks.append(network)"
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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": 18,
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"metadata": {
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||||
"collapsed": false
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||||
},
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||||
"outputs": [
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||||
{
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||||
"data": {
|
||||
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAXEAAAEACAYAAABF+UbAAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nC5aY7wAAAABJRU5ErkJggg==\n",
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x7efec2a57908>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"output_type": "display_data"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"hist(list(map(get_number_of_activated_antenna, valid_networks)), bins=100)\n",
|
||||
"None"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 19,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"2.4126984126984126"
|
||||
]
|
||||
},
|
||||
"execution_count": 19,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"mean(list(map(get_number_of_activated_antenna, valid_networks)))"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 15,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"■-□-■-□-■\n",
|
||||
"□-■-□-■-□\n",
|
||||
"■-□-■-□-■\n",
|
||||
"□-■-□-■-□\n",
|
||||
"■-□-■-□-■\n",
|
||||
"\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"print(pp_network(valid_networks[-1], box_size))"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 20,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"63"
|
||||
]
|
||||
},
|
||||
"execution_count": 20,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"len(valid_networks)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 16,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"18446744073709551616"
|
||||
]
|
||||
},
|
||||
"execution_count": 16,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"2**64"
|
||||
]
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3",
|
||||
"language": "python",
|
||||
"name": "python3"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.4.3+"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 0
|
||||
}
|
||||
@@ -0,0 +1,160 @@
|
||||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 2,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"Populating the interactive namespace from numpy and matplotlib\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"%pylab inline\n",
|
||||
"from network.network import is_valid_network, get_number_of_activated_antenna, pp_network\n",
|
||||
"from numpy import mean\n",
|
||||
"from random import Random"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 2,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"random = Random(666)\n",
|
||||
"def get_some_possible_network(n, box_size):\n",
|
||||
" number_of_square = box_size * box_size\n",
|
||||
" for i in range(n):\n",
|
||||
" yield random.randint(0, 2 ** number_of_square - 1)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 15,
|
||||
"metadata": {
|
||||
"collapsed": true
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"box_size = 8\n",
|
||||
"n = 10**7\n",
|
||||
"\n",
|
||||
"valid_networks = []\n",
|
||||
"for network in get_some_possible_network(n, box_size):\n",
|
||||
" if is_valid_network(network, box_size):\n",
|
||||
" valid_networks.append(network)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 16,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x7f3178a0edd8>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"output_type": "display_data"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"hist(list(map(get_number_of_activated_antenna, valid_networks)), bins=100)\n",
|
||||
"None"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 17,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"18.0"
|
||||
]
|
||||
},
|
||||
"execution_count": 17,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"mean(list(map(get_number_of_activated_antenna, valid_networks)))"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 19,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"□-□-□-□-■-□-□-□\n",
|
||||
"■-□-□-□-□-■-□-■\n",
|
||||
"□-■-□-■-□-□-■-□\n",
|
||||
"□-□-■-□-□-■-□-■\n",
|
||||
"■-□-□-□-■-□-■-□\n",
|
||||
"□-■-□-□-□-□-□-□\n",
|
||||
"□-□-□-□-□-□-■-□\n",
|
||||
"■-□-□-□-□-■-□-■\n",
|
||||
"\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"print(pp_network(valid_networks[0], 8))"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"collapsed": true
|
||||
},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3",
|
||||
"language": "python",
|
||||
"name": "python3"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.4.3+"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 0
|
||||
}
|
||||
@@ -0,0 +1,187 @@
|
||||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 9,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"Populating the interactive namespace from numpy and matplotlib\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"%pylab inline\n",
|
||||
"from network.network import is_valid_network, get_number_of_activated_antenna, pp_network\n",
|
||||
"from numpy import mean\n",
|
||||
"\n",
|
||||
"def get_all_possible_network(box_size):\n",
|
||||
" number_of_square = box_size * box_size\n",
|
||||
" return range(2**number_of_square)\n",
|
||||
"\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 10,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"box_size = 3\n",
|
||||
"\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 11,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAXEAAAEACAYAAABF+UbAAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nC5aY7wAAAABJRU5ErkJggg==\n",
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x7faff062fd68>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"output_type": "display_data"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"hist(list(map(get_number_of_activated_antenna, valid_networks)), bins=100)\n",
|
||||
"None"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 26,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"6.208433278626436"
|
||||
]
|
||||
},
|
||||
"execution_count": 26,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"mean(list(map(get_number_of_activated_antenna, valid_networks)))"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 15,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"■-□-■-□-■\n",
|
||||
"□-■-□-■-□\n",
|
||||
"■-□-■-□-■\n",
|
||||
"□-■-□-■-□\n",
|
||||
"■-□-■-□-■\n",
|
||||
"\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"print(pp_network(valid_networks[-1], box_size))"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 20,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"63"
|
||||
]
|
||||
},
|
||||
"execution_count": 20,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"len(valid_networks)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 4,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"344239"
|
||||
]
|
||||
},
|
||||
"execution_count": 4,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"sum(list(map(get_number_of_activated_antenna, valid_networks)))"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 12,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"random.choice?"
|
||||
]
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3",
|
||||
"language": "python",
|
||||
"name": "python3"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.4.3+"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 0
|
||||
}
|
||||
+112
@@ -0,0 +1,112 @@
|
||||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# algorithme\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"1. Posons $X^{(0)}$ un reseau quelconque.\n",
|
||||
"\n",
|
||||
"2. copie $X^{(0)}$ dans $X^{(1)}$\n",
|
||||
"\n",
|
||||
"3. pour change antenne dans $X^{(1)}$\n",
|
||||
" \n",
|
||||
" 1. si `antenne` peut etre allume et $U(0, 1) < 0.5$\n",
|
||||
" 1. On allume `antenne` dans $X^{(1)}$ \n",
|
||||
" 2. sinon\n",
|
||||
" 1. On eteint `antenne` dans $X^{(1)}$ "
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 2,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"Populating the interactive namespace from numpy and matplotlib\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"%pylab inline\n",
|
||||
"from network.network import (is_valid_network,\n",
|
||||
" get_number_of_activated_antenna,\n",
|
||||
" pp_network,\n",
|
||||
" switch_antenna_in_network,\n",
|
||||
" turn_off_antenna,\n",
|
||||
" can_antenna_be_turned_on,\n",
|
||||
" turn_on_antenna)\n",
|
||||
"from numpy import mean\n",
|
||||
"from random import Random\n",
|
||||
"from itertools import cycle"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 3,
|
||||
"metadata": {
|
||||
"collapsed": true
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"random = Random(666)\n",
|
||||
"\n",
|
||||
"def get_all_antennas(box_size):\n",
|
||||
" return range(box_size * box_size)\n",
|
||||
" \n",
|
||||
"def get_new_network_with_gibbs(network, box_size):\n",
|
||||
" for antenna in get_all_antennas(box_size):\n",
|
||||
" if can_antenna_be_turned_on(antenna, network, box_size) and random.choice([True, False]):\n",
|
||||
" network = turn_on_antenna(antenna, network, box_size)\n",
|
||||
" else:\n",
|
||||
" network = turn_off_antenna(antenna, network)\n",
|
||||
" return network"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"collapsed": true
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"networks = [0]\n",
|
||||
"box_size = 5\n",
|
||||
"\n",
|
||||
"for i in range(100):\n",
|
||||
" last_network = networks[-1]\n",
|
||||
" networks.append(get_new_network_with_gibbs(last_network, box_size))\n",
|
||||
"\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3",
|
||||
"language": "python",
|
||||
"name": "python3"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.4.3+"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 0
|
||||
}
|
||||
@@ -0,0 +1,258 @@
|
||||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 2,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"Populating the interactive namespace from numpy and matplotlib\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"%pylab inline\n",
|
||||
"from network.network import (is_valid_network,\n",
|
||||
" get_number_of_activated_antenna,\n",
|
||||
" can_antenna_be_turned_on,\n",
|
||||
" pp_network,\n",
|
||||
" turn_off_antenna,\n",
|
||||
" turn_on_antenna)\n",
|
||||
"from numpy import mean\n",
|
||||
"from random import Random\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 2,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"random = Random(666)\n",
|
||||
"\n",
|
||||
"def create_new_network(network, box_size):\n",
|
||||
" number_of_square = box_size * box_size\n",
|
||||
" for antenna in range(number_of_square):\n",
|
||||
" if can_antenna_be_turned_on(antenna, network, box_size) and random.choice([True, False]):\n",
|
||||
" network = turn_on_antenna(antenna, network)\n",
|
||||
" else:\n",
|
||||
" network = turn_off_antenna(antenna, network)\n",
|
||||
" return network\n",
|
||||
"\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 12,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"box_size = 8\n",
|
||||
"n = 15000\n",
|
||||
"\n",
|
||||
"valid_networks = [0]\n",
|
||||
"\n",
|
||||
"for random_antenna in range(n):\n",
|
||||
" network = valid_networks[-1]\n",
|
||||
" new_network = create_new_network(network, box_size)\n",
|
||||
" valid_networks.append(new_network)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 13,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x7f6ffacf3f60>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"output_type": "display_data"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"hist(list(map(get_number_of_activated_antenna, valid_networks)), bins=100)\n",
|
||||
"None"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 14,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"15.072261849210053"
|
||||
]
|
||||
},
|
||||
"execution_count": 14,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"mean(list(map(get_number_of_activated_antenna, valid_networks)))"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 11,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"□-■-□-□-■\n",
|
||||
"□-□-■-□-□\n",
|
||||
"□-□-□-□-□\n",
|
||||
"□-■-□-■-□\n",
|
||||
"■-□-□-□-■\n",
|
||||
"\n",
|
||||
"□-□-□-□-■\n",
|
||||
"□-□-□-■-□\n",
|
||||
"■-□-■-□-■\n",
|
||||
"□-□-□-□-□\n",
|
||||
"■-□-■-□-□\n",
|
||||
"\n",
|
||||
"□-□-■-□-□\n",
|
||||
"□-■-□-□-□\n",
|
||||
"□-□-■-□-■\n",
|
||||
"□-□-□-■-□\n",
|
||||
"■-□-□-□-□\n",
|
||||
"\n",
|
||||
"□-□-□-□-□\n",
|
||||
"□-□-□-□-□\n",
|
||||
"■-□-■-□-■\n",
|
||||
"□-□-□-■-□\n",
|
||||
"□-□-■-□-■\n",
|
||||
"\n",
|
||||
"■-□-□-□-□\n",
|
||||
"□-□-□-■-□\n",
|
||||
"■-□-□-□-□\n",
|
||||
"□-■-□-■-□\n",
|
||||
"■-□-□-□-■\n",
|
||||
"\n",
|
||||
"□-■-□-□-■\n",
|
||||
"□-□-□-□-□\n",
|
||||
"■-□-□-□-□\n",
|
||||
"□-■-□-■-□\n",
|
||||
"■-□-■-□-□\n",
|
||||
"\n",
|
||||
"□-□-□-□-■\n",
|
||||
"□-□-□-□-□\n",
|
||||
"■-□-□-□-□\n",
|
||||
"□-■-□-□-□\n",
|
||||
"□-□-□-□-□\n",
|
||||
"\n",
|
||||
"■-□-□-□-□\n",
|
||||
"□-■-□-□-□\n",
|
||||
"□-□-■-□-■\n",
|
||||
"□-■-□-■-□\n",
|
||||
"□-□-□-□-■\n",
|
||||
"\n",
|
||||
"□-□-□-■-□\n",
|
||||
"□-■-□-□-□\n",
|
||||
"■-□-□-□-■\n",
|
||||
"□-□-□-□-□\n",
|
||||
"□-■-□-□-■\n",
|
||||
"\n",
|
||||
"■-□-□-□-□\n",
|
||||
"□-■-□-■-□\n",
|
||||
"■-□-■-□-■\n",
|
||||
"□-□-□-■-□\n",
|
||||
"□-□-□-□-□\n",
|
||||
"\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"for network in valid_networks[-10:]:\n",
|
||||
" print(pp_network(network, box_size))"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 14,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"ename": "ValueError",
|
||||
"evalue": "Cannot take a larger sample than population when 'replace=False'",
|
||||
"output_type": "error",
|
||||
"traceback": [
|
||||
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
|
||||
"\u001b[1;31mValueError\u001b[0m Traceback (most recent call last)",
|
||||
"\u001b[1;32m<ipython-input-14-e2f587eeb987>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mrandom\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mchoice\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m3\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m4\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m111\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mreplace\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mFalse\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
|
||||
"\u001b[1;32mmtrand.pyx\u001b[0m in \u001b[0;36mmtrand.RandomState.choice (numpy/random/mtrand/mtrand.c:11010)\u001b[1;34m()\u001b[0m\n",
|
||||
"\u001b[1;31mValueError\u001b[0m: Cannot take a larger sample than population when 'replace=False'"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"random.choice([1,2,3,4], 111, replace=False)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"collapsed": true
|
||||
},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3",
|
||||
"language": "python",
|
||||
"name": "python3"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.4.3+"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 0
|
||||
}
|
||||
@@ -0,0 +1,179 @@
|
||||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 1,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"Populating the interactive namespace from numpy and matplotlib\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"%pylab inline\n",
|
||||
"from network.network import (is_valid_network,\n",
|
||||
" create_network,\n",
|
||||
" get_number_of_activated_antenna,\n",
|
||||
" get_activated_antennas,\n",
|
||||
" can_antenna_be_turned_on,\n",
|
||||
" pp_network,\n",
|
||||
" turn_off_antenna,\n",
|
||||
" turn_on_antenna)\n",
|
||||
"\n",
|
||||
"from network.impSamp import AntennaGenerator\n",
|
||||
"from numpy import mean\n",
|
||||
"from random import Random"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 43,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"random = Random(666)\n",
|
||||
"\n",
|
||||
"def get_random_network(box_size, random):\n",
|
||||
" antenna_generator = AntennaGenerator(box_size, random)\n",
|
||||
" antennas = []\n",
|
||||
" antenna, network_prob = antenna_generator.get_random_antenna()\n",
|
||||
"\n",
|
||||
" while antenna != -1:\n",
|
||||
" antennas.append(antenna)\n",
|
||||
" antenna, p = antenna_generator.get_random_antenna()\n",
|
||||
" network_prob *= p\n",
|
||||
" \n",
|
||||
" return create_network(antennas), network_prob\n",
|
||||
" "
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 5,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"box_size = 4\n",
|
||||
"antannas = [0,1,2,3]\n",
|
||||
"\n",
|
||||
"for i in range(1000):\n",
|
||||
" network = get_random_network(box_size)\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 6,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"box_size = 5\n",
|
||||
"for i in range(100000):\n",
|
||||
" network, p = get_random_network(box_size)\n",
|
||||
"# print(\"network\", network)\n",
|
||||
"# print(pp_network(network, box_size))"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"ds = list(reversed(range(10)))"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 47,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"all_networks = set()\n",
|
||||
"\n",
|
||||
"\n",
|
||||
" "
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 45,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"5.582214353409138"
|
||||
]
|
||||
},
|
||||
"execution_count": 45,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"get_estimation(1000000, random)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 37,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"25318"
|
||||
]
|
||||
},
|
||||
"execution_count": 37,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"len(all_networks)"
|
||||
]
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3",
|
||||
"language": "python",
|
||||
"name": "python3"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.4.3+"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 0
|
||||
}
|
||||
Whitespace-only changes.
@@ -0,0 +1,22 @@
|
||||
from network.network import (get_number_of_activated_antenna,
|
||||
is_valid_network)
|
||||
|
||||
|
||||
def _get_random_networks(box_size, random):
|
||||
'''yields random networks, broken or not'''
|
||||
number_of_square = box_size * box_size
|
||||
while True:
|
||||
yield random.randint(0, 2 ** number_of_square - 1)
|
||||
|
||||
|
||||
def get_random_networks(box_size, random):
|
||||
for network in _get_random_networks(box_size, random):
|
||||
if is_valid_network(network, box_size):
|
||||
yield network
|
||||
|
||||
|
||||
def estimate_number_of_activated_antenna(n, box_size, random):
|
||||
estimate = 0
|
||||
for _, network in zip(range(n), get_random_networks(box_size, random)):
|
||||
estimate += get_number_of_activated_antenna(network)
|
||||
return estimate / n
|
||||
@@ -0,0 +1,27 @@
|
||||
from network.network import is_valid_network, get_number_of_activated_antenna
|
||||
|
||||
def _get_all_possible_network(box_size):
|
||||
'''returns all networks, broken or not'''
|
||||
return range( 2**(box_size*box_size) )
|
||||
|
||||
|
||||
def get_all_possible_network(box_size):
|
||||
if box_size < 1:
|
||||
return
|
||||
for network in _get_all_possible_network(box_size):
|
||||
if is_valid_network(network, box_size):
|
||||
yield network
|
||||
|
||||
|
||||
def _get_random_networks(n, box_size, random):
|
||||
all_networks = list(get_all_possible_network(box_size))
|
||||
n = min(n, len(all_networks))
|
||||
return random.sample(all_networks, n), n
|
||||
|
||||
|
||||
def estimate_number_of_activated_antenna(n, box_size, random):
|
||||
networks, n = _get_random_networks(n, box_size, random)
|
||||
estimate = 0
|
||||
for network in networks:
|
||||
estimate += get_number_of_activated_antenna(network)
|
||||
return estimate / n
|
||||
@@ -0,0 +1,36 @@
|
||||
from network.network import (get_number_of_activated_antenna,
|
||||
can_antenna_be_turned_on,
|
||||
turn_off_antenna,
|
||||
turn_on_antenna)
|
||||
|
||||
def _flip_coin(random):
|
||||
return random.random() < 0.5
|
||||
|
||||
def create_new_network(network, box_size, random):
|
||||
number_of_square = box_size * box_size
|
||||
for antenna in range(number_of_square):
|
||||
if can_antenna_be_turned_on(antenna, network, box_size) and _flip_coin(random):
|
||||
network = turn_on_antenna(antenna, network)
|
||||
else:
|
||||
network = turn_off_antenna(antenna, network)
|
||||
return network
|
||||
|
||||
|
||||
def get_pseudo_random_networks(box_size, random):
|
||||
network = 0
|
||||
while True:
|
||||
network = create_new_network(network, box_size, random)
|
||||
yield network
|
||||
|
||||
def get_random_networks(box_size, random):
|
||||
time_between_sample = 20
|
||||
for i, network in enumerate(get_pseudo_random_networks(box_size, random), 1):
|
||||
if i % time_between_sample == 0:
|
||||
yield network
|
||||
|
||||
|
||||
def estimate_number_of_activated_antenna(n, box_size, random):
|
||||
estimate = 0
|
||||
for _, network in zip(range(n), get_random_networks(box_size, random)):
|
||||
estimate += get_number_of_activated_antenna(network)
|
||||
return estimate / n
|
||||
@@ -0,0 +1,92 @@
|
||||
from random import Random
|
||||
from network.network import create_network, get_number_of_activated_antenna
|
||||
|
||||
|
||||
class AntennaGenerator(object):
|
||||
|
||||
def __init__(self, box_size, random):
|
||||
self.box_size = box_size
|
||||
self._antennas = list(range(box_size * box_size))
|
||||
self.random = random
|
||||
|
||||
def get_random_antenna(self):
|
||||
idx, prob = self.get_random_index_and_its_probability()
|
||||
if idx == -1:
|
||||
self._antennas = []
|
||||
return -1, prob
|
||||
choosen_antenna = self._antennas[idx]
|
||||
self._remove_antenna(idx)
|
||||
return choosen_antenna, prob
|
||||
|
||||
|
||||
class SuccesiveAntennaGenerator(AntennaGenerator):
|
||||
def _remove_antenna(self, idx):
|
||||
ant = self._antennas[idx]
|
||||
if ant == -1:
|
||||
self._antennas = []
|
||||
return ant
|
||||
if (ant + 1) % self.box_size:
|
||||
self._antennas = self._antennas[idx + 2:]
|
||||
else:
|
||||
self._antennas = self._antennas[idx + 1:]
|
||||
|
||||
if ant + self.box_size < self.box_size * self.box_size:
|
||||
self._antennas.remove(ant + self.box_size)
|
||||
|
||||
def get_random_index_and_its_probability(self):
|
||||
idx = self.random.randint(-1, len(self._antennas)-1)
|
||||
prob = (len(self._antennas) + 1) ** -1
|
||||
return idx, prob
|
||||
|
||||
|
||||
class BadAntennaGenerator(SuccesiveAntennaGenerator):
|
||||
'''this antenna generator does not return 'real' probabilities
|
||||
and thus cannot be used in importance_sampling'''
|
||||
def _remove_antenna(self, idx):
|
||||
ant = self._antennas[idx]
|
||||
to_remove = {ant, ant + self.box_size, ant - self.box_size}
|
||||
if (ant + 1) % self.box_size:
|
||||
to_remove.add(ant + 1)
|
||||
|
||||
if ant % self.box_size:
|
||||
to_remove.add(ant - 1)
|
||||
self._antennas = [a for a in self._antennas if a not in to_remove]
|
||||
|
||||
|
||||
class BetterSuccesiveAntennaGenerator(SuccesiveAntennaGenerator):
|
||||
|
||||
def _get_random_index(self, max_number_inclusive):
|
||||
if max_number_inclusive < 1:
|
||||
return self.random.randint(-1, max_number_inclusive)
|
||||
mean_ = max_number_inclusive/2
|
||||
x = int(self.random.expovariate(mean_**-1))
|
||||
if x > max_number_inclusive:
|
||||
return -1
|
||||
return x
|
||||
|
||||
def get_random_index_and_its_probability(self):
|
||||
idx = self._get_random_index(len(self._antennas)-1)
|
||||
prob = (len(self._antennas) + 1) ** -1
|
||||
return idx, prob
|
||||
|
||||
|
||||
def get_random_network(box_size, random, Generator):
|
||||
antenna_generator = Generator(box_size, random)
|
||||
antennas = []
|
||||
antenna, network_prob = antenna_generator.get_random_antenna()
|
||||
|
||||
while antenna != -1:
|
||||
antennas.append(antenna)
|
||||
antenna, p = antenna_generator.get_random_antenna()
|
||||
network_prob *= p
|
||||
|
||||
return create_network(antennas), network_prob
|
||||
|
||||
|
||||
def estimate_number_of_activated_antenna(n, box_size, random, Generator):
|
||||
ret, sum_of_probs = 0, 0
|
||||
for i in range(n):
|
||||
network, p = get_random_network(box_size, random, Generator)
|
||||
ret += get_number_of_activated_antenna(network) / p
|
||||
sum_of_probs += p**-1
|
||||
return ret / sum_of_probs
|
||||
@@ -0,0 +1,97 @@
|
||||
from collections import deque
|
||||
|
||||
|
||||
def _get_print_symbol(network, position):
|
||||
black_square = '\u25A0'
|
||||
white_square = '\u25A1'
|
||||
activated_antennas = set(get_activated_antennas(network))
|
||||
if position in activated_antennas:
|
||||
return black_square
|
||||
return white_square
|
||||
|
||||
|
||||
def pp_network(network, box_size):
|
||||
ret = ""
|
||||
for line in range(box_size):
|
||||
for column in range(box_size):
|
||||
symbol = _get_print_symbol(network, line * box_size + column)
|
||||
ret += symbol + '-'
|
||||
ret = ret[:-1] + '\n'
|
||||
return ret
|
||||
|
||||
|
||||
def get_activated_antennas(network):
|
||||
i = 0
|
||||
while network > 0:
|
||||
if network % 2:
|
||||
yield i
|
||||
network = network >> 1
|
||||
i += 1
|
||||
|
||||
|
||||
def create_network(activated_antennas):
|
||||
return sum(2**activated_antenna for activated_antenna in activated_antennas)
|
||||
|
||||
|
||||
def _are_two_consicutive_horizontaly_antennas_activated(network, box_size):
|
||||
predecessor = -2
|
||||
for antenna in get_activated_antennas(network):
|
||||
if antenna == predecessor + 1:
|
||||
return True
|
||||
if (antenna + 1) % box_size:
|
||||
predecessor = antenna
|
||||
return False
|
||||
|
||||
|
||||
def _are_two_consicutive_verticaly_antennas_activated(network, box_size):
|
||||
invalid_entennas = set()
|
||||
for antenna in get_activated_antennas(network):
|
||||
if antenna in invalid_entennas:
|
||||
return True
|
||||
invalid_entennas.add(antenna + box_size)
|
||||
|
||||
return False
|
||||
|
||||
|
||||
def is_valid_network(network, box_size):
|
||||
if _are_two_consicutive_horizontaly_antennas_activated(network, box_size):
|
||||
return False
|
||||
|
||||
if _are_two_consicutive_verticaly_antennas_activated(network, box_size):
|
||||
return False
|
||||
return True
|
||||
|
||||
|
||||
def has_antenna(antenna, network):
|
||||
return (network >> antenna) % 2
|
||||
|
||||
|
||||
def turn_on_antenna(antenna, network): # error with borders
|
||||
if has_antenna(antenna, network):
|
||||
return network
|
||||
return network + 2**antenna
|
||||
|
||||
|
||||
def turn_off_antenna(antenna, network):
|
||||
if has_antenna(antenna, network):
|
||||
return network - 2**antenna
|
||||
return network
|
||||
|
||||
|
||||
def can_antenna_be_turned_on(antenna, network, box_size):
|
||||
for ant in get_activated_antennas(network):
|
||||
if ant == antenna + 1 and ant % box_size:
|
||||
return False
|
||||
if ant == antenna - 1 and antenna % box_size:
|
||||
return False
|
||||
if ant == antenna + box_size:
|
||||
return False
|
||||
if ant == antenna - box_size:
|
||||
return False
|
||||
if ant > antenna + box_size:
|
||||
return True
|
||||
return True
|
||||
|
||||
|
||||
def get_number_of_activated_antenna(network):
|
||||
return len(list(get_activated_antennas(network)))
|
||||
@@ -0,0 +1,220 @@
|
||||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 1,
|
||||
"metadata": {
|
||||
"collapsed": true
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from network.gibbs import *\n",
|
||||
"from network.network import pp_network, can_antenna_be_turned_on\n",
|
||||
"from collections import defaultdict\n",
|
||||
"from random import Random\n",
|
||||
"from numpy import matrix"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 2,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"mc = defaultdict(dict)\n",
|
||||
"random = Random(66)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 3,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"prec_network = 0\n",
|
||||
"\n",
|
||||
"for i, network in zip(range(10**5), get_random_networks(2, random)):\n",
|
||||
" if network not in mc[prec_network]:\n",
|
||||
" mc[prec_network][network] = 0\n",
|
||||
" mc[prec_network][network] += 1\n",
|
||||
" prec_network = network"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 6,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"defaultdict(dict,\n",
|
||||
" {0: {0: 964, 1: 3671, 2: 1844, 4: 1759, 6: 1835, 8: 938, 9: 3387},\n",
|
||||
" 1: {0: 891, 1: 3646, 2: 1832, 4: 1839, 6: 1729, 8: 938, 9: 3663},\n",
|
||||
" 2: {0: 1810, 2: 3548, 4: 3517, 6: 3611, 8: 1811},\n",
|
||||
" 4: {0: 1779, 2: 3519, 4: 3490, 6: 3486, 8: 1796},\n",
|
||||
" 6: {0: 1885, 2: 3554, 4: 3465, 6: 3475, 8: 1757},\n",
|
||||
" 8: {0: 3633, 1: 3553, 8: 3654, 9: 3572},\n",
|
||||
" 9: {0: 3436, 1: 3668, 8: 3518, 9: 3527}})"
|
||||
]
|
||||
},
|
||||
"execution_count": 6,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"mc"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 4,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"def get_prob(from_, to):\n",
|
||||
" return mc[from_][to]/ sum(mc[from_].values())\n",
|
||||
"\n",
|
||||
"def get_matrix(mc):\n",
|
||||
" n = len(mc)\n",
|
||||
" mat = [[] for i in range(n)]\n",
|
||||
" for i, from_ in enumerate(mc):\n",
|
||||
" for j in enumerate(mc):\n",
|
||||
" pass\n",
|
||||
" "
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"for key in mc:\n",
|
||||
" print(key)\n",
|
||||
" print( pp_network(key, 2))"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 5,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"□-□\n",
|
||||
"■-□\n",
|
||||
"\n",
|
||||
"1 True\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"ant = 1\n",
|
||||
"print(get_prob(0, 2))\n",
|
||||
"print(ant, can_antenna_be_turned_on(ant, 4, 2))"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"0.5**4"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"m = matrix([[1, 2], [3, 4]])\n",
|
||||
"v = matrix([[1, 3]])"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 8,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from numpy import std"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 10,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"42.73458667121028"
|
||||
]
|
||||
},
|
||||
"execution_count": 10,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"std([1432, 1376, 1461, 1436, 1436, 1363, 1496])"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"collapsed": true
|
||||
},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3",
|
||||
"language": "python",
|
||||
"name": "python3"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.4.3+"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 0
|
||||
}
|
||||
@@ -0,0 +1,35 @@
|
||||
backports-abc==0.4
|
||||
cycler==0.9.0
|
||||
decorator==4.0.6
|
||||
ipykernel==4.2.1
|
||||
ipython==4.0.1
|
||||
ipython-genutils==0.1.0
|
||||
Jinja2==2.8
|
||||
jsonschema==2.5.1
|
||||
jupyter-client==4.1.1
|
||||
jupyter-core==4.0.6
|
||||
MarkupSafe==0.23
|
||||
matplotlib==1.5.0
|
||||
mistune==0.7.1
|
||||
nbconvert==4.1.0
|
||||
nbformat==4.0.1
|
||||
nose==1.3.7
|
||||
notebook==4.0.6
|
||||
numpy==1.10.2
|
||||
path.py==8.1.2
|
||||
pexpect==4.0.1
|
||||
pickleshare==0.5
|
||||
ptyprocess==0.5
|
||||
py==1.4.31
|
||||
Pygments==2.0.2
|
||||
pyparsing==2.0.6
|
||||
pytest==2.8.5
|
||||
python-dateutil==2.4.2
|
||||
pytz==2015.7
|
||||
pyzmq==15.1.0
|
||||
simplegeneric==0.8.1
|
||||
six==1.10.0
|
||||
terminado==0.5
|
||||
tornado==4.3
|
||||
traitlets==4.0.0
|
||||
wheel==0.24.0
|
||||
@@ -0,0 +1,26 @@
|
||||
from unittest import TestCase
|
||||
from network.brute_force import (get_all_possible_network,
|
||||
estimate_number_of_activated_antenna)
|
||||
from random import Random
|
||||
|
||||
|
||||
class TestBruteForce(TestCase):
|
||||
|
||||
def test_get_all_possible_network(self):
|
||||
self.assertEqual(list(get_all_possible_network(0)), [])
|
||||
|
||||
self.assertEqual(list(get_all_possible_network(1)), [0, 1])
|
||||
|
||||
expt = [0, 1, 2, 4, 6, 8, 9]
|
||||
self.assertEqual(list(get_all_possible_network(2)), expt)
|
||||
|
||||
def test_estimate_number_of_activated_antenna(self):
|
||||
random = Random(666)
|
||||
est = estimate_number_of_activated_antenna(7, 2, random)
|
||||
self.assertEqual(est, (0 + 1 + 1 + 1 + 1 + 2 + 2) / 7)
|
||||
|
||||
est = estimate_number_of_activated_antenna(100, 2, random)
|
||||
self.assertEqual(est, (0 + 1 + 1 + 1 + 1 + 2 + 2) / 7)
|
||||
|
||||
est = estimate_number_of_activated_antenna(3, 2, random)
|
||||
self.assertEqual(est, 1)
|
||||
@@ -0,0 +1,35 @@
|
||||
from unittest import TestCase
|
||||
from random import Random
|
||||
from math import sqrt
|
||||
from collections import defaultdict
|
||||
from network.gibbs import get_random_networks, estimate_number_of_activated_antenna
|
||||
|
||||
|
||||
def standard_deviation(lst):
|
||||
sum_x = 0
|
||||
sum_x_2 = 0
|
||||
for elm in lst:
|
||||
sum_x += elm
|
||||
sum_x_2 += elm * elm
|
||||
|
||||
n = len(lst)
|
||||
return sqrt(sum_x_2 / n - (sum_x / n)**2)
|
||||
|
||||
|
||||
class TestGibbs(TestCase):
|
||||
|
||||
def test_get_random_networks(self):
|
||||
counter = defaultdict(int)
|
||||
random, n = Random(555), 1000
|
||||
for _, network in zip(range(n), get_random_networks(2, random)):
|
||||
counter[network] += 1
|
||||
|
||||
self.assertSetEqual(set(counter.keys()), {0, 1, 2, 4, 6, 8, 9})
|
||||
self.assertLess(standard_deviation(counter.values()) / n, 0.1)
|
||||
|
||||
def test_estimate_number_of_activated_antenna(self):
|
||||
random = Random(666)
|
||||
|
||||
est = estimate_number_of_activated_antenna(100, 2, random)
|
||||
real = (0 + 1 + 1 + 1 + 1 + 2 + 2) / 7
|
||||
assert abs(est - real) < 0.1
|
||||
@@ -0,0 +1,121 @@
|
||||
from unittest import TestCase
|
||||
from random import Random
|
||||
from network.importance_sampling import (AntennaGenerator,
|
||||
SuccesiveAntennaGenerator,
|
||||
BadAntennaGenerator,
|
||||
BetterSuccesiveAntennaGenerator,
|
||||
estimate_number_of_activated_antenna)
|
||||
|
||||
|
||||
class TestSuccesiveAntennaGenerator(TestCase):
|
||||
def test_get_random_antenna(self):
|
||||
self.antenna_genetator = SuccesiveAntennaGenerator(5, Random(678))
|
||||
|
||||
ant, _ = self.antenna_genetator.get_random_antenna()
|
||||
self.assertEqual(ant, 2)
|
||||
expt = [4,5,6,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24]
|
||||
self.assertListEqual(self.antenna_genetator._antennas, expt)
|
||||
|
||||
ant, _ = self.antenna_genetator.get_random_antenna()
|
||||
self.assertEqual(ant, 14)
|
||||
expt = [15,16,17,18,20,21,22,23,24]
|
||||
self.assertListEqual(self.antenna_genetator._antennas, expt)
|
||||
|
||||
ant, _ = self.antenna_genetator.get_random_antenna()
|
||||
self.assertEqual(ant, -1)
|
||||
expt = []
|
||||
self.assertListEqual(self.antenna_genetator._antennas, expt)
|
||||
|
||||
def test_get_random_antenna_bottom_line(self):
|
||||
self.antenna_genetator = SuccesiveAntennaGenerator(5, Random(607))
|
||||
ant, _ = self.antenna_genetator.get_random_antenna()
|
||||
self.assertEqual(ant, 21)
|
||||
expt = [23, 24, ]
|
||||
self.assertListEqual(self.antenna_genetator._antennas, expt)
|
||||
|
||||
def test_get_random_antenna_right_line(self):
|
||||
self.antenna_genetator = SuccesiveAntennaGenerator(5, Random(637))
|
||||
ant, _ = self.antenna_genetator.get_random_antenna()
|
||||
self.assertEqual(ant, 14)
|
||||
expt = [15, 16, 17, 18, 20, 21, 22, 23, 24]
|
||||
self.assertListEqual(self.antenna_genetator._antennas, expt)
|
||||
|
||||
def test_get_random_antenna_last_box(self):
|
||||
self.antenna_genetator = SuccesiveAntennaGenerator(5, Random(631))
|
||||
ant, _ = self.antenna_genetator.get_random_antenna()
|
||||
self.assertEqual(24, ant)
|
||||
expt = []
|
||||
self.assertListEqual(self.antenna_genetator._antennas, expt)
|
||||
|
||||
def test_probability(self):
|
||||
self.antenna_genetator = SuccesiveAntennaGenerator(5, Random(678))
|
||||
ant, prob = self.antenna_genetator.get_random_antenna()
|
||||
self.assertAlmostEqual(prob, 26**-1)
|
||||
self.assertEqual(ant, 2)
|
||||
|
||||
_, prob = self.antenna_genetator.get_random_antenna()
|
||||
self.assertAlmostEqual(prob, 21**-1)
|
||||
|
||||
class TestImportanceSampling(TestCase):
|
||||
|
||||
def test_SuccesiveAntennaGenerator(self):
|
||||
random = Random(666)
|
||||
|
||||
est = estimate_number_of_activated_antenna(150, 2, random, SuccesiveAntennaGenerator)
|
||||
real = (0 + 1 + 1 + 1 + 1 + 2 + 2) / 7
|
||||
print(real)
|
||||
print(est)
|
||||
assert abs(est - real) > 0.1
|
||||
|
||||
def test_BadAntennaGenerator(self):
|
||||
random = Random(666)
|
||||
|
||||
est = estimate_number_of_activated_antenna(2500, 2, random, BadAntennaGenerator)
|
||||
real = (0 + 1 + 1 + 1 + 1 + 2 + 2) / 7
|
||||
assert abs(est - real) > 0.1
|
||||
|
||||
def test_BetterSuccesiveAntennaGenerator(self):
|
||||
random = Random(666)
|
||||
|
||||
est = estimate_number_of_activated_antenna(150, 2, random, BetterSuccesiveAntennaGenerator)
|
||||
real = (0 + 1 + 1 + 1 + 1 + 2 + 2) / 7
|
||||
print(real)
|
||||
print(est)
|
||||
assert abs(est - real) > 0.1
|
||||
|
||||
|
||||
class TestBadAntennaGenerator(TestCase):
|
||||
def test_remove_antenna_easy(self):
|
||||
gen = BadAntennaGenerator(3, Random(666))
|
||||
gen._remove_antenna(2)
|
||||
expt = [0,3,4,6,7,8]
|
||||
self.assertListEqual(gen._antennas, expt)
|
||||
|
||||
gen = BadAntennaGenerator(3, Random(666))
|
||||
gen._remove_antenna(3)
|
||||
expt = [1, 2, 5, 7, 8]
|
||||
self.assertListEqual(gen._antennas, expt)
|
||||
|
||||
gen = BadAntennaGenerator(3, Random(666))
|
||||
gen._remove_antenna(4)
|
||||
expt = [0,2,6,8]
|
||||
self.assertListEqual(gen._antennas, expt)
|
||||
|
||||
gen = BadAntennaGenerator(3, Random(666))
|
||||
gen._remove_antenna(7)
|
||||
expt = [0,1,2,3,5]
|
||||
self.assertListEqual(gen._antennas, expt)
|
||||
|
||||
def test_remove_antenna(self):
|
||||
gen = BadAntennaGenerator(3, Random(666))
|
||||
gen._remove_antenna(gen._antennas.index(2))
|
||||
expt = [0,3,4,6,7,8]
|
||||
self.assertListEqual(gen._antennas, expt)
|
||||
|
||||
gen._remove_antenna(gen._antennas.index(0))
|
||||
expt = [4,6,7,8]
|
||||
self.assertListEqual(gen._antennas, expt)
|
||||
|
||||
gen._remove_antenna(gen._antennas.index(7))
|
||||
expt = []
|
||||
self.assertListEqual(gen._antennas, expt)
|
||||
@@ -0,0 +1,89 @@
|
||||
from unittest import TestCase
|
||||
from network.network import (get_activated_antennas,
|
||||
create_network,
|
||||
pp_network,
|
||||
_are_two_consicutive_horizontaly_antennas_activated,
|
||||
_are_two_consicutive_verticaly_antennas_activated,
|
||||
turn_on_antenna,
|
||||
turn_off_antenna,
|
||||
can_antenna_be_turned_on,
|
||||
has_antenna)
|
||||
|
||||
|
||||
class TestFunctions(TestCase):
|
||||
def test_create_network(self):
|
||||
assert create_network([]) == 0
|
||||
assert create_network([0,2,3,7,8,9]) == 909
|
||||
|
||||
def test_pp_network(self):
|
||||
a = 111
|
||||
expt = """\
|
||||
■-■-■-■
|
||||
□-■-■-□
|
||||
□-□-□-□
|
||||
□-□-□-□
|
||||
"""
|
||||
self.assertEqual( pp_network(a, 4), expt)
|
||||
|
||||
|
||||
def test_get_activated_antennas(self):
|
||||
self.assertListEqual(list(get_activated_antennas(0)), [])
|
||||
self.assertListEqual(list(get_activated_antennas(1)), [0])
|
||||
self.assertListEqual(list(get_activated_antennas(2)), [1])
|
||||
self.assertListEqual(list(get_activated_antennas(2)), [1])
|
||||
|
||||
# 909 is written as 1110001101 in binary
|
||||
expt = [0,2,3,7,8,9]
|
||||
self.assertListEqual(list(get_activated_antennas(909)), expt)
|
||||
|
||||
def test_are_two_consicutive_horizontaly_antennas_activated(self):
|
||||
assert _are_two_consicutive_horizontaly_antennas_activated(160, 4) == False
|
||||
assert _are_two_consicutive_horizontaly_antennas_activated(24, 4) == False
|
||||
assert _are_two_consicutive_horizontaly_antennas_activated(96, 4) == True
|
||||
|
||||
def test_are_two_consicutive_verticaly_antennas_activated(self):
|
||||
assert _are_two_consicutive_verticaly_antennas_activated(24, 4) == False
|
||||
assert _are_two_consicutive_verticaly_antennas_activated(16384, 4) == False
|
||||
assert _are_two_consicutive_verticaly_antennas_activated(17408, 4) == True
|
||||
assert _are_two_consicutive_verticaly_antennas_activated(136, 4) == True
|
||||
assert _are_two_consicutive_verticaly_antennas_activated(1104, 4) == True
|
||||
|
||||
def test_turn_on_antenna(self):
|
||||
self.assertEqual(turn_on_antenna(5, 111), 111)
|
||||
self.assertEqual(turn_on_antenna(4, 111), 111+2**4)
|
||||
|
||||
def test_has_antenna(self):
|
||||
network = create_network([0,2,3,7,8,9])
|
||||
assert has_antenna(0, network)
|
||||
assert has_antenna(3, network)
|
||||
assert has_antenna(9, network)
|
||||
|
||||
assert not has_antenna(4, network)
|
||||
assert not has_antenna(10, network)
|
||||
|
||||
def test_turn_off_antenna(self):
|
||||
network = create_network([0,2,3,7,8,9])
|
||||
assert turn_off_antenna(2, network) == 905
|
||||
assert turn_off_antenna(7, network) == 781
|
||||
assert turn_off_antenna(10, network) == 909
|
||||
assert turn_off_antenna(4, network) == 909
|
||||
|
||||
|
||||
def test_can_antenna_be_turned_on(self):
|
||||
# □-□-□-□-■
|
||||
# □-□-□-□-□
|
||||
# □-□-■-□-□
|
||||
# □-□-□-□-■
|
||||
# □-□-□-□-□
|
||||
|
||||
network = create_network([4, 12, 20])
|
||||
assert can_antenna_be_turned_on(0, network, 5)
|
||||
assert can_antenna_be_turned_on(1, network, 5)
|
||||
assert can_antenna_be_turned_on(5, network, 5)
|
||||
assert can_antenna_be_turned_on(19, network, 5)
|
||||
|
||||
assert not can_antenna_be_turned_on(3, network, 5)
|
||||
assert not can_antenna_be_turned_on(7, network, 5)
|
||||
assert not can_antenna_be_turned_on(11, network, 5)
|
||||
assert not can_antenna_be_turned_on(13, network, 5)
|
||||
assert not can_antenna_be_turned_on(17, network, 5)
|
||||
Reference in new issue
Block a user