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Created October 25, 2017 01:38
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{
"cells": [
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"%matplotlib inline\n",
"import numpy as np\n",
"import matplotlib.pylab as plt"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def compute_sum(r, p):\n",
" if p == 1:\n",
" return r\n",
" elif np.isposinf(p):\n",
" return np.sum(1/(np.arange(r) + 1))\n",
" else:\n",
" q = p/(p-1)\n",
" q_over_p = 1/(p-1)\n",
" seq = np.arange(r) + 1\n",
" denom = np.sum(seq ** q) ** (1/p)\n",
" return np.sum([1/np.sum(((seq ** q_over_p)/denom)[m:]) for m in range(r)])"
]
},
{
"cell_type": "code",
"execution_count": 60,
"metadata": {},
"outputs": [],
"source": [
"\n",
"p_values = [1, 2, 3, 4, 5, 10, 100, 1000]\n",
"S_10_values = [compute_sum(10, p) for p in p_values]\n",
"S_20_values = [compute_sum(20, p) for p in p_values]\n",
"S_30_values = [compute_sum(30, p) for p in p_values]"
]
},
{
"cell_type": "code",
"execution_count": 61,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.legend.Legend at 0x7fbb48c4a0f0>"
]
},
"execution_count": 61,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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VWcwKp9PFo+9+H4hT+mboNXD3t5A+0mN1zvAcrGZrvTKr2crVfa/meOXx1ohQ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\nAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fbb489935f8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.semilogx(p_values, S_10_values, '--o')\n",
"plt.semilogx(p_values, S_20_values, '--o')\n",
"plt.semilogx(p_values, S_30_values, '--o')\n",
"plt.xlabel('p')\n",
"plt.ylabel('S_r(p)')\n",
"plt.legend(('S_10', 'S_20', 'S_30'))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python [conda root]",
"language": "python",
"name": "conda-root-py"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.5.4"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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