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Created May 18, 2018 13:42
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{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "raw",
"metadata": {
"raw_mimetype": "text/html"
},
"source": [
"<script>\n",
" function code_toggle() {\n",
" if (code_shown){\n",
" $('div.input').hide('500');\n",
" $('#toggleButton').val('Show Code')\n",
" } else {\n",
" $('div.input').show('500');\n",
" $('#toggleButton').val('Hide Code')\n",
" }\n",
" code_shown = !code_shown\n",
" }\n",
"\n",
" $( document ).ready(function(){\n",
" code_shown=false;\n",
" $('div.input').hide()\n",
" $('div.prompt').hide();\n",
" $('div.back-to-top').hide();\n",
" $('nav#menubar').hide();\n",
" $('.breadcrumb').hide();\n",
" $('.hidden-print').hide();\n",
" });\n",
"</script>\n",
"\n",
"<form action=\"javascript:code_toggle()\"><input type=\"submit\" id=\"toggleButton\" value=\"Show Code\"></form>"
]
},
{
"cell_type": "raw",
"metadata": {
"raw_mimetype": "-"
},
"source": [
"# from IPython.core.display import HTML\n",
"import urllib\n",
"response = urllib.urlopen(\"\"\"\n",
" https://gist.githubusercontent.com/sgttwld/c060b18a9d6ce7c3a10e3c6dce2c0d3a/raw\n",
"\"\"\")\n",
"css = str(response.read().decode(\"utf-8\"))\n",
"HTML(\"<style type='text/css'>\"+css+\"</style>\")"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"import seaborn as sns\n",
"import _lib.pr_func as pr\n",
"from _lib.utility import *\n",
"from scipy.interpolate import InterpolatedUnivariateSpline\n",
"%config InlineBackend.figure_format = 'retina'\n",
"sns.set_style(\"whitegrid\")\n",
"#import sys\n",
"#sys.path.append('..')"
]
},
{
"cell_type": "markdown",
"metadata": {
"inputHidden": false,
"outputHidden": false
},
"source": [
"# The Legendre Transform and Rate Distortion\n",
"## _When is it true, that dU/dI = 1/beta?_"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Legendre Transform\n",
"The Legendre transform of a function $f:x\\mapsto f(x)$ is given by a function $g:u\\mapsto g(u)$, such that $\\pm\\frac{dg}{du}(u) = x(u)$. $g$ can be obtained by treating $x$ as a function of $u$ and defining\n",
"$$\n",
"u := \\frac{df}{dx}, \\quad g(u) := \\pm (u\\, x(u) - f(x(u)))\n",
"$$\n",
"since then automatically $\\pm \\frac{dg}{du} = x(u) + u \\frac{dx}{du} - \\frac{df}{dx} \\frac{dx}{du} = x(u)$. Similarly, if $g$ is given, then $f$ can be reconstructed by \n",
"$$\n",
"x := \\pm \\frac{dg}{du}, \\quad f(x) := x \\, u(x) \\mp g(u(x)) \\, .\n",
"$$\n",
"\n",
"### Free Energy principle for one-step decision-making with fixed prior\n",
"The Legendre transform can be applied to single-task decision-making with utility function $U$ and fixed prior $p_0$. If we consider the equilibrium Free Energy as a function of $\\tau := \\frac{1}{\\beta}$,\n",
"$$\n",
"F(\\tau) = \\tau \\log Z_\\tau = \\tau \\log \\sum_a p_0(a) e^{U(a)/\\tau} \\, ,\n",
"$$\n",
"then $F$ is the Legendre transform of the expected utility $\\bar U := \\mathbb E[U]$ as a function of $D_{KL}(p(a)\\|p_0(a))$. This can be seen by explicitly differentiating $F$: We have\n",
"\n",
"$$\n",
"\\frac{dF}{d\\tau} = \\log Z_\\tau + \\frac{\\tau}{Z_\\tau} \\sum_a p_0(a) e^{U(a)/\\tau} \\frac{-U(a)}{\\tau^2} = \\log Z_\\tau -\\frac{\\bar U}{\\tau} = \\frac{1}{\\tau} ( F(\\tau) - \\bar U) , \n",
"$$\n",
"and therefore,\n",
"$$\n",
"I := - \\frac{dF}{d\\tau} = \\frac{1}{\\tau} (\\bar U - F(\\tau)) = \\frac{\\bar U}{\\tau} - \\log Z_\\tau = D_{KL}(p(a)\\|p_0(a)) \\, .\n",
"$$\n",
"and\n",
"$$\n",
"f(I) = \\tau I + F(\\tau) = \\bar U\n",
"$$\n",
"Hence $F:\\tau \\mapsto F(\\tau)$ is the Legendre transform of $\\bar U: I\\mapsto \\bar U(I)$ and it follows that $\\frac{d\\bar U}{dI} = \\tau = \\frac{1}{\\beta}$.\n",
"\n",
"__Note:___ The same calculation works for multi-task decision-making, i.e. when $U$ is a function of $w$ and $a$. Here, the equilibrium Free Energy is given by \n",
"$$\n",
"\\frac{1}{\\beta} \\sum_w \\rho(w) \\log Z(w) . \n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Rate distortion\n",
"In the limit case of a perfectly optimal prior, $p_0$ is replaced by $p(a) = \\sum_w p_\\beta(a|w) \\rho(w)$, in particular, there might be a different prior for each $\\beta$. The above derivation cannot be followed since there is no closed from expression for the equilibrium Free Energy in terms of $\\beta$ anymore. In particular, by taking the derivative of $F$ with respect to $\\tau$ as in the derivation above, the $\\tau$-dependency of the prior would be neglected."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Simulations"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"def RD(U,pw,beta):\n",
" pa = pr.func(vars=['a'], val='unif').normalize()\n",
" test = 0\n",
" for i in range(0,1000):\n",
" pagw = (pa*pr.exp(beta*U)).normalize(['a'])\n",
" pa = pr.sum(['w'],pagw*pw)\n",
" if np.linalg.norm(test-pagw.val)<1e-10:\n",
" break\n",
" test= pagw.val\n",
" EU = pr.sum(U*pagw*pw)\n",
" IWA = pr.sum(pw*pagw*pr.log(pagw/pa))\n",
" return EU.val, IWA.val\n",
"\n",
"def nonRD(U,pw,beta): \n",
" pa = pr.func(vars=['a'], val='unif').normalize()\n",
" pagw = (pa*pr.exp(beta*U)).normalize(['a'])\n",
" EU = pr.sum(U*pagw*pw)\n",
" DKL = pr.sum(pw*pagw*pr.log(pagw/pa))\n",
" return EU.val, DKL.val"
]
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {},
"outputs": [
{
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Zr3uNGIah+chy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"text/plain": [
"<matplotlib.figure.Figure at 0x10acb7c50>"
]
},
"metadata": {
"image/png": {
"height": 480,
"width": 933
}
},
"output_type": "display_data"
}
],
"source": [
"# setup\n",
"N,K = 20,20\n",
"pr.set_dims([('w',N),('a',K)])\n",
"U = pr.func(val=utility(N,K,6.0),vars=['w','a'])\n",
"# U = pr.func(val=np.eye(N),vars=['w','a'])\n",
"pw = pr.func(vars=['w'],val='unif').normalize()\n",
"\n",
"# RD curve\n",
"EUs = []\n",
"Is = []\n",
"betas = np.linspace(0.3,5,1000)\n",
"\n",
"for beta in betas:\n",
" EU,I = RD(U,pw,beta) \n",
" EUs.append(EU)\n",
" Is.append(I)\n",
" \n",
"# nonRD curve\n",
"EUs_non = []\n",
"Is_non = []\n",
"\n",
"for beta in betas:\n",
" EU,I = nonRD(U,pw,beta) \n",
" EUs_non.append(EU)\n",
" Is_non.append(I)\n",
"\n",
"with plt.rc_context({\"figure.figsize\": (16,8)}):\n",
" plt.title(\"Rate distortion curves\")\n",
" plt.plot(Is,EUs,label=\"optimal prior\")\n",
" plt.plot(Is_non,EUs_non, label=\"non-optimal prior\")\n",
" plt.legend()\n",
" plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 60,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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vLczJjSWq74AAAAAACMQQpQAH6mQqGQ1pbmfPC9r8nw8EgeWbU59/7bmly4eOJR523a2ZWe/qFs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\nUrP0aODFalrf4bh+845VO3y2/8gMduyZC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"text/plain": [
"<matplotlib.figure.Figure at 0x10acf5510>"
]
},
"metadata": {
"image/png": {
"height": 482,
"width": 928
}
},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"||dU/dI - 1/beta|| = 2.44547712642e-07\n"
]
}
],
"source": [
"# calc derivative of EU wrt I\n",
"f_non = InterpolatedUnivariateSpline(Is_non, EUs_non, k=5)\n",
"dfdx_non = f_non.derivative()\n",
"dUdx_non = dfdx_non(Is_non)\n",
"# compare against 1/beta\n",
"with plt.rc_context({\"figure.figsize\": (16,8)}):\n",
" plt.title(\"Comparison of ${dU}/{dI}$ and ${1}/{\\\\beta}$ -- non-optimal prior\")\n",
" plt.plot(Is_non,1.0/betas,label=\"1/beta\",linestyle=\"-\",color=\"xkcd:silver\")\n",
" plt.plot(Is_non,dUdx_non,label=\"dU/dI\",linestyle=\":\",color=\"xkcd:denim blue\")\n",
" plt.legend()\n",
" plt.show()\n",
" print \"||dU/dI - 1/beta|| = {}\".format(np.linalg.norm(dUdx_non-1.0/betas)) "
]
},
{
"cell_type": "code",
"execution_count": 61,
"metadata": {},
"outputs": [
{
"data": {
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"text/plain": [
"<matplotlib.figure.Figure at 0x10525cf50>"
]
},
"metadata": {
"image/png": {
"height": 482,
"width": 919
}
},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"||dU/dI - 1/beta|| = 13.0328221758\n"
]
}
],
"source": [
"# calc derivative of EU wrt I\n",
"f = InterpolatedUnivariateSpline(Is, EUs, k=5)\n",
"dfdx = f.derivative()\n",
"dUdx = dfdx(Is)\n",
"# compare against 1/beta\n",
"with plt.rc_context({\"figure.figsize\": (16,8)}):\n",
" plt.title(\"Comparison of ${dU}/{dI}$ and ${1}/{\\\\beta}$ -- optimal prior\")\n",
" plt.plot(Is,1.0/betas,label=\"1/beta\",linestyle=\"-\",color=\"xkcd:silver\")\n",
" plt.plot(Is,dUdx,label=\"dU/dI\",linestyle=\":\",color=\"xkcd:denim blue\")\n",
" plt.legend()\n",
" plt.show()\n",
" print \"||dU/dI - 1/beta|| = {}\".format(np.linalg.norm(dUdx-1.0/betas)) "
]
},
{
"cell_type": "code",
"execution_count": 65,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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"text/plain": [
"<matplotlib.figure.Figure at 0x109e27a90>"
]
},
"metadata": {
"image/png": {
"height": 482,
"width": 932
}
},
"output_type": "display_data"
}
],
"source": [
"# zommed in\n",
"with plt.rc_context({\"figure.figsize\": (16,8)}):\n",
" plt.title(\"Comparison of ${dU}/{dI}$ and ${1}/{\\\\beta}$ -- optimal prior, zoomed in\")\n",
" plt.scatter(Is,1.0/betas,label=\"1/beta\",marker='.',color=\"xkcd:silver\")\n",
" plt.scatter(Is,dUdx,label=\"dU/dI\",marker='.',color=\"xkcd:denim blue\")\n",
" plt.xlim([0,0.01])\n",
" plt.legend()\n",
" plt.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernel_info": {
"name": "python2"
},
"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.10"
},
"nteract": {
"version": "0.4.3"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
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