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theref/fingerprint.ipynb

Created November 4, 2016 10:19
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Fingerprint Class example
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fingerprint.ipynb
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"%matplotlib inline\n",
"from new_fingerprint import *\n",
"import axelrod as axl"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"[0.0, 0.5]"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"list(np.arange(0, 1, 0.5))"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"axelrod.strategies.memoryone.WinStayLoseShift"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"s = axl.WinStayLoseShift\n",
"p = axl.TitForTat\n",
"f = AshlockFingerprint(s, p)\n",
"s"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Begin Spatial Tournament\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": []
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Spatial Tournament Finished\n"
]
}
],
"source": [
"f.fingerprint(turns=5, repetitions=1, granularity=0.25, cores=4)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'axelrod.result_set.ResultSetFromFile'>\n",
"<class 'pandas.core.frame.DataFrame'>\n"
]
},
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>0.0</th>\n",
" <th>0.25</th>\n",
" <th>0.5</th>\n",
" <th>0.75</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0.00</th>\n",
" <td>3.0</td>\n",
" <td>1.8</td>\n",
" <td>1.4</td>\n",
" <td>1.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>0.25</th>\n",
" <td>3.0</td>\n",
" <td>1.4</td>\n",
" <td>1.0</td>\n",
" <td>2.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>0.50</th>\n",
" <td>3.0</td>\n",
" <td>2.0</td>\n",
" <td>0.4</td>\n",
" <td>3.2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>0.75</th>\n",
" <td>3.0</td>\n",
" <td>2.8</td>\n",
" <td>3.2</td>\n",
" <td>1.4</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" 0.00 0.25 0.50 0.75\n",
"0.00 3.0 1.8 1.4 1.0\n",
"0.25 3.0 1.4 1.0 2.0\n",
"0.50 3.0 2.0 0.4 3.2\n",
"0.75 3.0 2.8 3.2 1.4"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"print(type(f.results))\n",
"g = f._generate_data(f.results, f.probe_players.keys())\n",
"print(type(g))\n",
"g"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"{(0, 2): [[('C', 'C'), ('C', 'C'), ('C', 'C'), ('C', 'C'), ('C', 'C')]],\n",
" (0, 3): [[('C', 'C'), ('C', 'D'), ('D', 'C'), ('D', 'D'), ('C', 'D')]],\n",
" (0, 4): [[('C', 'D'), ('D', 'C'), ('D', 'D'), ('C', 'D'), ('D', 'D')]],\n",
" (0, 5): [[('C', 'C'), ('C', 'D'), ('D', 'D'), ('C', 'D'), ('D', 'D')]],\n",
" (0, 6): [[('C', 'C'), ('C', 'C'), ('C', 'C'), ('C', 'C'), ('C', 'C')]],\n",
" (0, 7): [[('C', 'D'), ('D', 'C'), ('D', 'D'), ('C', 'D'), ('D', 'D')]],\n",
" (0, 8): [[('C', 'C'), ('C', 'D'), ('D', 'D'), ('C', 'D'), ('D', 'D')]],\n",
" (0, 9): [[('C', 'D'), ('D', 'D'), ('C', 'C'), ('C', 'C'), ('C', 'C')]],\n",
" (0, 10): [[('C', 'C'), ('C', 'C'), ('C', 'C'), ('C', 'C'), ('C', 'C')]],\n",
" (0, 11): [[('C', 'C'), ('C', 'C'), ('C', 'C'), ('C', 'D'), ('D', 'D')]],\n",
" (0, 12): [[('C', 'D'), ('D', 'D'), ('C', 'D'), ('D', 'D'), ('C', 'D')]],\n",
" (0, 14): [[('C', 'C'), ('C', 'C'), ('C', 'C'), ('C', 'C'), ('C', 'C')]],\n",
" (0, 15): [[('C', 'C'), ('C', 'C'), ('C', 'C'), ('C', 'D'), ('D', 'C')]],\n",
" (1, 13): [[('D', 'C'), ('D', 'C'), ('D', 'D'), ('C', 'D'), ('D', 'C')]],\n",
" (1, 16): [[('D', 'C'), ('D', 'C'), ('D', 'D'), ('C', 'D'), ('D', 'C')]],\n",
" (1, 17): [[('D', 'C'), ('D', 'D'), ('C', 'D'), ('D', 'D'), ('C', 'D')]]}"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"f.results.interactions"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"OrderedDict([((0.0, 0.0), Joss-Ann Tit For Tat),\n",
" ((0.0, 0.25), Joss-Ann Tit For Tat),\n",
" ((0.0, 0.5), Joss-Ann Tit For Tat),\n",
" ((0.0, 0.75), Joss-Ann Tit For Tat),\n",
" ((0.25, 0.0), Joss-Ann Tit For Tat),\n",
" ((0.25, 0.25), Joss-Ann Tit For Tat),\n",
" ((0.25, 0.5), Joss-Ann Tit For Tat),\n",
" ((0.25, 0.75), Joss-Ann Tit For Tat),\n",
" ((0.5, 0.0), Joss-Ann Tit For Tat),\n",
" ((0.5, 0.25), Joss-Ann Tit For Tat),\n",
" ((0.5, 0.5), Joss-Ann Tit For Tat),\n",
" ((0.5, 0.75), Joss-Ann Tit For Tat),\n",
" ((0.75, 0.0), Joss-Ann Tit For Tat),\n",
" ((0.75, 0.25), Joss-Ann Tit For Tat),\n",
" ((0.75, 0.5), Joss-Ann Tit For Tat),\n",
" ((0.75, 0.75), Joss-Ann Tit For Tat)])"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"f.probe_players"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"[(0, 2),\n",
" (0, 3),\n",
" (0, 4),\n",
" (0, 5),\n",
" (0, 6),\n",
" (0, 7),\n",
" (0, 8),\n",
" (0, 9),\n",
" (0, 10),\n",
" (0, 11),\n",
" (0, 12),\n",
" (1, 13),\n",
" (0, 14),\n",
" (0, 15),\n",
" (1, 16),\n",
" (1, 17)]"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"f.edges"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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"text/plain": [
"<matplotlib.figure.Figure at 0x11483ac88>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"f.plot()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
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" <th>0.0</th>\n",
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" <tbody>\n",
" <tr>\n",
" <th>0.00</th>\n",
" <td>3.0</td>\n",
" <td>1.8</td>\n",
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" <td>2.0</td>\n",
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" <tr>\n",
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"</table>\n",
"</div>"
],
"text/plain": [
" 0.00 0.25 0.50 0.75\n",
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"execution_count": 10,
"metadata": {},
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}
],
"source": [
"f.data"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"anaconda-cloud": {},
"kernelspec": {
"display_name": "Python [Root]",
"language": "python",
"name": "Python [Root]"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
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Display the source blob
Display the rendered blob
Raw
fingerprint_example.ipynb
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"%matplotlib inline\n",
"from new_fingerprint import *\n",
"import axelrod as axl"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"[0.0, 0.5]"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"list(np.arange(0, 1, 0.5))"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"axelrod.strategies.memoryone.WinStayLoseShift"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"s = axl.WinStayLoseShift\n",
"p = axl.TitForTat\n",
"f = AshlockFingerprint(s, p)\n",
"s"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Begin Spatial Tournament\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": []
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Spatial Tournament Finished\n"
]
}
],
"source": [
"f.fingerprint(turns=5, repetitions=1, granularity=0.25, cores=4)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'axelrod.result_set.ResultSetFromFile'>\n",
"<class 'pandas.core.frame.DataFrame'>\n"
]
},
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>0.0</th>\n",
" <th>0.25</th>\n",
" <th>0.5</th>\n",
" <th>0.75</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0.00</th>\n",
" <td>3.0</td>\n",
" <td>1.8</td>\n",
" <td>1.4</td>\n",
" <td>1.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>0.25</th>\n",
" <td>3.0</td>\n",
" <td>1.4</td>\n",
" <td>1.0</td>\n",
" <td>2.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>0.50</th>\n",
" <td>3.0</td>\n",
" <td>2.0</td>\n",
" <td>0.4</td>\n",
" <td>3.2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>0.75</th>\n",
" <td>3.0</td>\n",
" <td>2.8</td>\n",
" <td>3.2</td>\n",
" <td>1.4</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" 0.00 0.25 0.50 0.75\n",
"0.00 3.0 1.8 1.4 1.0\n",
"0.25 3.0 1.4 1.0 2.0\n",
"0.50 3.0 2.0 0.4 3.2\n",
"0.75 3.0 2.8 3.2 1.4"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"print(type(f.results))\n",
"g = f._generate_data(f.results, f.probe_players.keys())\n",
"print(type(g))\n",
"g"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"{(0, 2): [[('C', 'C'), ('C', 'C'), ('C', 'C'), ('C', 'C'), ('C', 'C')]],\n",
" (0, 3): [[('C', 'C'), ('C', 'D'), ('D', 'C'), ('D', 'D'), ('C', 'D')]],\n",
" (0, 4): [[('C', 'D'), ('D', 'C'), ('D', 'D'), ('C', 'D'), ('D', 'D')]],\n",
" (0, 5): [[('C', 'C'), ('C', 'D'), ('D', 'D'), ('C', 'D'), ('D', 'D')]],\n",
" (0, 6): [[('C', 'C'), ('C', 'C'), ('C', 'C'), ('C', 'C'), ('C', 'C')]],\n",
" (0, 7): [[('C', 'D'), ('D', 'C'), ('D', 'D'), ('C', 'D'), ('D', 'D')]],\n",
" (0, 8): [[('C', 'C'), ('C', 'D'), ('D', 'D'), ('C', 'D'), ('D', 'D')]],\n",
" (0, 9): [[('C', 'D'), ('D', 'D'), ('C', 'C'), ('C', 'C'), ('C', 'C')]],\n",
" (0, 10): [[('C', 'C'), ('C', 'C'), ('C', 'C'), ('C', 'C'), ('C', 'C')]],\n",
" (0, 11): [[('C', 'C'), ('C', 'C'), ('C', 'C'), ('C', 'D'), ('D', 'D')]],\n",
" (0, 12): [[('C', 'D'), ('D', 'D'), ('C', 'D'), ('D', 'D'), ('C', 'D')]],\n",
" (0, 14): [[('C', 'C'), ('C', 'C'), ('C', 'C'), ('C', 'C'), ('C', 'C')]],\n",
" (0, 15): [[('C', 'C'), ('C', 'C'), ('C', 'C'), ('C', 'D'), ('D', 'C')]],\n",
" (1, 13): [[('D', 'C'), ('D', 'C'), ('D', 'D'), ('C', 'D'), ('D', 'C')]],\n",
" (1, 16): [[('D', 'C'), ('D', 'C'), ('D', 'D'), ('C', 'D'), ('D', 'C')]],\n",
" (1, 17): [[('D', 'C'), ('D', 'D'), ('C', 'D'), ('D', 'D'), ('C', 'D')]]}"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"f.results.interactions"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"OrderedDict([((0.0, 0.0), Joss-Ann Tit For Tat),\n",
" ((0.0, 0.25), Joss-Ann Tit For Tat),\n",
" ((0.0, 0.5), Joss-Ann Tit For Tat),\n",
" ((0.0, 0.75), Joss-Ann Tit For Tat),\n",
" ((0.25, 0.0), Joss-Ann Tit For Tat),\n",
" ((0.25, 0.25), Joss-Ann Tit For Tat),\n",
" ((0.25, 0.5), Joss-Ann Tit For Tat),\n",
" ((0.25, 0.75), Joss-Ann Tit For Tat),\n",
" ((0.5, 0.0), Joss-Ann Tit For Tat),\n",
" ((0.5, 0.25), Joss-Ann Tit For Tat),\n",
" ((0.5, 0.5), Joss-Ann Tit For Tat),\n",
" ((0.5, 0.75), Joss-Ann Tit For Tat),\n",
" ((0.75, 0.0), Joss-Ann Tit For Tat),\n",
" ((0.75, 0.25), Joss-Ann Tit For Tat),\n",
" ((0.75, 0.5), Joss-Ann Tit For Tat),\n",
" ((0.75, 0.75), Joss-Ann Tit For Tat)])"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"f.probe_players"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"[(0, 2),\n",
" (0, 3),\n",
" (0, 4),\n",
" (0, 5),\n",
" (0, 6),\n",
" (0, 7),\n",
" (0, 8),\n",
" (0, 9),\n",
" (0, 10),\n",
" (0, 11),\n",
" (0, 12),\n",
" (1, 13),\n",
" (0, 14),\n",
" (0, 15),\n",
" (1, 16),\n",
" (1, 17)]"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"f.edges"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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"text/plain": [
"<matplotlib.figure.Figure at 0x11483ac88>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"f.plot()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>0.0</th>\n",
" <th>0.25</th>\n",
" <th>0.5</th>\n",
" <th>0.75</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0.00</th>\n",
" <td>3.0</td>\n",
" <td>1.8</td>\n",
" <td>1.4</td>\n",
" <td>1.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>0.25</th>\n",
" <td>3.0</td>\n",
" <td>1.4</td>\n",
" <td>1.0</td>\n",
" <td>2.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>0.50</th>\n",
" <td>3.0</td>\n",
" <td>2.0</td>\n",
" <td>0.4</td>\n",
" <td>3.2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>0.75</th>\n",
" <td>3.0</td>\n",
" <td>2.8</td>\n",
" <td>3.2</td>\n",
" <td>1.4</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" 0.00 0.25 0.50 0.75\n",
"0.00 3.0 1.8 1.4 1.0\n",
"0.25 3.0 1.4 1.0 2.0\n",
"0.50 3.0 2.0 0.4 3.2\n",
"0.75 3.0 2.8 3.2 1.4"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"f.data"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"anaconda-cloud": {},
"kernelspec": {
"display_name": "Python [Root]",
"language": "python",
"name": "Python [Root]"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.5.1"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
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