{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## Unfolding" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "histogram s ideálním rozlišením má hodnoty binů $m_i$ náhodně rozdělené kolem středních hodnot $\\mu_i$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\\nu_i= \\mu_{tot}\\ P(\\mathrm{ udl. v\\ binu}\\ i) = \\\n", "\\mu_{tot} \\int dy\\ P(\\mathrm{bin\\ i\\ |\\ detek. event\\ s\\ hod.}\\ y)\\ \\varepsilon(y)\\ f_{true}(y)$$\n", "\n", "$\\varepsilon(y)$ je účinnost detekce, $f_{true}$ hledané rozdělení" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Převod mezi měřenou hodnotou $x$ a skutečnou veličinou $y$ charakterizuje podmíněná pravděpodobnost $s(x|y)$ = prst naměření $x$, pokud skutečně nastane $y$. Platí\n", "$P(\\mathrm{bin\\ i\\ |\\ det. event\\ s\\ hod.}\\ y) = \\int_{bin\\ j} dy\\ s(x|y)$ zároveň s normalizací rozlišení (*resolution function*, pro 2D *point spread function*/PSF/ )\n", "$ \\int s(x|y) dx = 1 $\n", "\n", "Úlohu lze převést na maticový problém \n", "\n", "$$R_{ij}=\\frac{\\int_{bin\\ i} dx\\ \\int_{bin\\ j} dy\\ s(x|y)\\ \\varepsilon(y)\\ f_{t}(y)}{\\int_{bin\\ j} dy\\ f_{t}(y)} \\approx \\frac{\\int_{bin\\ i} dx \\ \\int_{bin\\ j} dy\\ s(x|y)\\ \\varepsilon(y)}{\\int_{bin\\ j} dy }$$\n", "poslední přibliž. rovnost platí, pokud $f_{t}(y)$ je zhruba konstatní v rámci každého binu. Hodnoty prvků matice získáme výpočtem z modelu.\n", "\n", "Pak pro $\\nu_i=E (n_i)$, kde $n_i$ je obsah i-teho binu měřeného signálu, platí\n", "\n", "$\\nu_i=\\sum R_{ij} \\mu_j + \\beta_i$, tedy $\\mathbf{\\mu}=R^{-1} (\\mathbf{\\nu} - \\mathbf{\\beta})$ , resp. pro odhad $\\mathbf{\\hat{\\mu}} = R^{-1} ({\\mathbf{n} -\\mathbf{\\beta}})$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Matice ovšem většinou není čtvercová, hledání hodnot $\\mu_i$ je záležitostí lineární regrese (obvykle obyčejné LS). Obecné řešení dá (vzhledem k počtu hledaných proměnných) relativně nestabilní výsledek.\n", "K omezení oscilací je obvykle k minimalizované hodnotě ($-\\log L(\\mu)$) přidán ještě člen $S(\\mu)$\n", "sledující \"hladkost\" rozdělení : **regularizace**\n", "\n", "A) z prvních, druhých, .. diferencí $\\sum{(\\mu_{i}-\\mu_{i-1})^2}$, $\\sum{(2\\mu_{i}-\\mu_{i-1}-\\mu_{i+1})^2}$ \n", "B) z maximalizace **entropie**" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "případná podmínka celkového počtu událostí: minimalizujeme $$\\phi(\\mu,\\lambda)= \\alpha \\log L(\\mu) + S(\\mu) + \\lambda \\left[ n_{tot} - \\sum \\nu_i \\right]$$ " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Určení nejistot podle \n", "$$\\frac{\\partial \\mu}{\\partial n}=C = -A^{-1} B$$\n", "\n", "$A_{ij}=\\frac{\\partial^2 \\phi}{\\partial \\mu_i \\partial \\mu_j}$, $B_{ij}=\\frac{\\partial^2 \\phi}{\\partial \\mu_i \\partial n_j}$, $\\lambda$ je zahrnuto mezi $\\mu$\n", "\n", "Pokud z naměřených dat určíme kovarianční matici $V=cov(n_i,n_j)$, dostaneme\n", "$cov(\\mu_i,\\mu_j) = C^{T} V C$. Kromě nejistot hodnot $\\mu$ nás ale zajímá také bias $\\hat \\mu - E(\\mu)$\n", " " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**ad A**: Tichonovova funkce (vyšší hodnota=hladší průběh)\n", "\n", "$$S(f)=-\\int \\left( \\frac{d^k f(y)}{dy^k} \\right)^2\\ d y$$\n", "\n", "možno kombinovat $k$ " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**ad B**: $$H = - \\sum p_i \\log p_i = - \\sum \\frac{\\mu_i}{\\mu_{tot}}\\ \\log\\frac{\\mu_i}{\\mu_{tot}}= S(\\mu)\\ \\mu_{tot}$$\n", "odpovida $\\log \\Omega$ počtu kombinací " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "- zobecnění na více dimenzí - u entropie přímočaré, u Tichonova konstruujeme n-dim diference\n", "- pro astron. snímky (izolované body) max.ent. vychází lépe, nevyhlazuje tolik píky" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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YTpTdxkxbmf+O0xE1uIJsdiRjGovja3ltEOHeXLKGdGnFPuvqwV0vhGIWfRIP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\nYmLjOeszP6TrSx+wNe0KltY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JOB94GMAY02OMaba2qrBwALEi4gDigGMW1xNyxph3gcZT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\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "%matplotlib inline\n", "from scipy import stats\n", "tau=2.\n", "import numpy as np\n", "from matplotlib import pyplot as pl\n", "xf=np.r_[:10:0.05]\n", "x=xf\n", "lor=lambda a,m,s:a/(1+(x-m)**2/s**2)\n", "yf=lor(2,5,0.3)+lor(1,3,.4)+lor(1.6,3.8,.14)+lor(1,6,3.4)\n", "yf+=lor(.5,6.2,.1)+lor(.5,6.6,.14)+lor(.6,1.2,.14)\n", "x=np.r_[:10:0.2]\n", "y=lor(2,5,0.3)+lor(1,3,.4)+lor(1.6,3.8,.14)+lor(1,6,3.4)\n", "y+=lor(.5,6.2,.1)+lor(.5,6.6,.14)+lor(.6,1.2,.14)\n", "pl.plot(x,y)\n", "pl.plot(xf,yf)\n", "pl.title('original')" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#puvodni spektrum 50 bin\n", "#merime v 25 binech (!?)\n", "n1=len(y)\n", "psf=np.exp(-np.r_[-1.5:1.5:0.2]**2/2./0.2**2)\n", "m,mc=len(psf),np.argmax(psf)\n", "psf/=psf.sum()\n", "z=np.convolve(psf,y,\"same\").reshape(n1//2,2).sum(1)/2.\n", "xm=(x[::2]+x[1::2])/2.\n", "pl.plot(xm,z)\n", "rmatf=np.zeros((n1,n1))\n", "psfs=psf#np.exp(-(np.r_[-1.5:1.5:0.2]-0.2)**2/2./0.2**2)\n", "psfs/=psfs.sum()\n", "for i in range(n1-1):\n", " jl,jh=i-mc,i+m-mc\n", " kl,kh=0,m\n", " if jl<0:\n", " kl=-jl\n", " jl=0\n", " if jh>n1:\n", " kh=n1-jh\n", " jh=n1\n", " rmatf[i][jl:jh]+=psfs[kl:kh]\n", "rmat=rmatf.reshape(n1//2,2,n1).sum(1)/2.\n", "pl.plot(xm,rmat.dot(y))\n", "pl.title('vysledek konvoluce (bez sumu)')" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "pl.imshow(rmat,interpolation=\"nearest\",origin=\"lower\")" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[]" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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SrY2j1hwbpZTaBKQB92uttznovMKJdFUl3YvXsjnoNKP+ejVvDzeemtaPsd3D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\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "rmatq=rmat.reshape(n1//2,n1//2,2).sum(2)\n", "irmatq=np.linalg.inv(rmatq)\n", "pl.plot(xm,irmatq.dot(z))\n", "pl.plot(xf,yf,':')" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[]" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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Gre/gr0mHnDu4Hqo67Uz1rAIgvfTs1kuSdP7J4C6dN6nlqWSYMllhquKXLfex5OLE9vci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\ndi8LXOIBTwAAAABJRU5ErkJggg==\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#fyzikalne probiha konvoluce s jemnejsim krokem \n", "ncum=30\n", "psff=np.exp(-np.r_[-1.5:1.5:0.05]**2/2./0.2**2)\n", "#m,mc=len(psf),np.argmax(psf)\n", "psff/=psff.sum()\n", "zf=np.convolve(psff,yf,\"same\")\n", "pl.plot(xf,zf)\n", "pl.plot(x,zf.reshape(n1,4).mean(1))\n", "zpoi=np.array([stats.poisson(mval).rvs(1) for mval in zf.reshape(n1,4).mean(1)*ncum])\n", "pl.plot(x,zpoi/ncum)" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[]" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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XMTGoElpwbXIPSwKLD1NCKvkwvwqtNcatKyHcR5K7MN2RskOU2MK4eLyx7k0pRUyoPzGh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\nFGyAaT91+6U96Mdb36TtK1M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4TT63w91AltlBuNkzwHKt9UhgPKfB51dKDQR+AaRrrcdg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\nSUVORK5CYII=\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# pouziti nezrebinovane matice 50x50\n", "pl.plot(x,rmatf.dot(y))\n", "rmatc=rmatf[:-1,:-1]\n", "irmatc=np.linalg.inv(rmatc)\n", "z0=np.convolve(psf,y,\"same\")\n", "pl.plot((x[:-1]+x[1:])/2,irmatc.dot(z0[:-1]))" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[]" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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HhU7LnBKtnpPay2NSrLSYhh/38OW9Q5qcgGotZa5tvJMiEPPwBee0eu6AnAP4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\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "ypoi=irmatc.dot((zpoi/ncum)[:-1])\n", "pl.plot((x[:-1]+x[1:])/2,ypoi)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "vetsinou nelze invertovat matici primo \n", "hledame reseni soustavy rovnic o `n1` neznamych" ] }, { "cell_type": "code", "execution_count": 36, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Warning: Maximum number of function evaluations has been exceeded.\n" ] }, { "data": { "text/plain": [ "[]" ] }, "execution_count": 36, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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syoHtOM3sNrvE05/vw4SYQJwzIdLJVTpfZlIoDndYsP9QGyqaOvDLN3ZgyfPf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\nKSU25FVjSmwQEsM4DUhEdKQ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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from scipy import optimize as op\n", "meas=(zpoi/ncum)[:-1].ravel()\n", "fun=lambda ym:meas-rmatc.dot(ym)\n", "tkho2=lambda ym:(ym[2:]+ym[:-2]-2*ym[1:-1])/2. #2nd difference\n", "#yfit=op.leastsq(fun,meas)[0]\n", "lamb=1.3\n", "yfit=op.fmin(lambda ym:lamb*(fun(ym)**2).sum()+(tkho2(ym)**2).sum(),meas)\n", "pl.plot((x[:-1]+x[1:])/2,yfit)" ] }, { "cell_type": "code", "execution_count": 40, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[]" ] }, "execution_count": 40, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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QPUCr2UqzpQCACJ+YDp0r2NmXcp2Gp6WW/cXSgV0IcXbac1WgAt4CMjVNe+4kh30B3Hj0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\n8KuuwpTe/rROe3aWlmLZsAHzuvVY1q/HfvgwANrwcEwdO2Bq1QpDs+b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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "yfit1p3=yfit\n", "lamb=0.7\n", "#yfit0p7=op.fmin(lambda ym:lamb*(fun(ym)**2).sum()+(tkho2(ym)**2).sum(),meas)\n", "pl.plot((x[:-1]+x[1:])/2,yfit0p7)\n", "pl.plot((x[:-1]+x[1:])/2,yfit1p3)\n", "lamb=2.7\n", "#yfit2p7=op.fmin(lambda ym:lamb*(fun(ym)**2).sum()+(tkho2(ym)**2).sum(),meas)\n", "pl.plot((x[:-1]+x[1:])/2,yfit2p7)\n", "pl.plot(x2,rmatf.T.dot(y))" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Warning: Maximum number of function evaluations has been exceeded.\n" ] }, { "data": { "text/plain": [ "array([ 0.61313419, 0.62907243, 0.63680082, 0.65075251, 0.66315928,\n", " 0.6830814 , 0.70881221, 0.74068714, 0.77161702, 0.79671915,\n", " 0.81558309, 0.83081872, 0.84915971, 0.86879462, 0.89450897,\n", " 0.92272785, 0.95764345, 0.9843261 , 1.00691082, 1.03087307,\n", " 1.05509518, 1.07106123, 1.07702227, 1.08164475, 1.07471819])" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "yclean=irmatc.dot(z0[:-1])\n", "tkho1=lambda yq:yq[1:]-yq[:-1]\n", "tksum=lambda yq:(tkho1(yq)**2).sum()\n", "op.fmin(tksum,z)" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[]" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "pl.plot(z)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### new generation" ] }, { "cell_type": "code", "execution_count": 43, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 43, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "mu=np.zeros_like(x)\n", "for i in range(50):\n", " mu[np.random.randint(len(mu))]+=np.random.randint(1,10)\n", "pl.bar(x,mu,0.2)" ] }, { "cell_type": "code", "execution_count": 44, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "259" ] }, "execution_count": 44, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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uqb9KEvHYqcdKRRRYEc3Mpht/0aNryFgBjdlXRioFcs4bN7vWWQoExB4SmipU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\nvajUOC7OqVOS5pzjIz85g26/A2+5bUiV7ykCabz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XMj8vnYsna+VnkWC6+/J8Xv7ePK6Z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\nrkJggg==\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#konvoluce s merici funkci\n", "mu2=np.zeros((len(x),4))\n", "mu2[:,2]=mu\n", "psfm=np.exp(-np.r_[-1.5:1.5:0.05]**2/2./0.2**2)\n", "psfm/=psfm.sum()\n", "meas=np.convolve(psfm,mu2.ravel(),\"full\")\n", "pl.plot(meas)\n", "len(meas)" ] }, { "cell_type": "code", "execution_count": 45, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "(50, 229)" ] }, "execution_count": 45, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "m2=len(psfm)\n", "mc=m2//4\n", "mstep=4\n", "n1,n2=len(mu),len(meas)-2*mc\n", "rmatf=np.zeros((n1,n2))\n", "for i in range(len(x)-1):\n", " jl,jh=mstep*i-mc,mstep*i+m2-mc\n", " kl,kh=0,m2\n", " if jl<0:\n", " kl=-jl\n", " jl=0\n", " if jh>n2:\n", " kh=n2-jh\n", " jh=n2\n", " rmatf[i][jl:jh]+=psfm[kl:kh]\n", "import matplotlib\n", "matplotlib.rcParams['figure.figsize'] = [10, 5]\n", "pl.imshow(rmatf,origin=\"lower\")\n", "rmatf.shape" ] }, { "cell_type": "code", "execution_count": 46, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[]" ] }, "execution_count": 46, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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iTl0XMMawqcWKiwsUrAjZyIbBijH2VcbYImPszBpfv4UxtsQYO5H/56/EXyap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\nTQAAAABJRU5ErkJggg==\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "x2=np.r_[0:13:0.05]\n", "x2-=x2[(rmatf.shape[1]-4*rmatf.shape[0]+1)//2]\n", "x2=x2[:rmatf.shape[1]]\n", "#len(x), len(psfm),rmatf.shape\n", "#ym=meas[2*mc:n2-1][::4]\n", "pl.plot(x2,rmatf.T.dot(mu))\n", "#pl.plot(x2,meas[17:-13])" ] }, { "cell_type": "code", "execution_count": 70, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Warning: Maximum number of function evaluations has been exceeded.\n" ] }, { "data": { "text/plain": [ "" ] }, "execution_count": 70, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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O8oaqOrzYVGvvySRv7+4XJ3l5krfYJ0vlrUmOLToEP/UPST7T3b+R5CWxbxauqi5K8ldJ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\nAAOUKQCAAcoUAMCA/wPJ131oe/L83AAAAABJRU5ErkJggg==\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "tkho2=lambda ym:(ym[2:]+ym[:-2]-2*ym[1:-1])/2. #2nd difference\n", "#yfit=op.leastsq(fun,meas)[0]\n", "yini=meas[2*mc:n2-1][::4]\n", "yini*=sum(mu)/sum(yini)\n", "lamb=1.3\n", "\n", "fun=lambda ym:meas[mc:n2+mc]-rmatf.T.dot(ym)\n", "yfit=op.fmin(lambda ym:lamb*(fun(ym)**2).sum()+(tkho2(ym)**2).sum(),yini)\n", "pl.bar(x,yfit,0.2,alpha=0.3)\n", "pl.bar(x,mu,0.2,alpha=0.3)\n", "pl.legend([\"reconstructed\",\"true\"])\n" ] }, { "cell_type": "code", "execution_count": 71, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Warning: Maximum number of function evaluations has been exceeded.\n" ] }, { "data": { "text/plain": [ "" ] }, "execution_count": 71, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "lamb=2.3\n", "yfit2=op.fmin(lambda ym:lamb*(fun(ym)**2).sum()+(tkho2(ym)**2).sum(),yini)\n", "pl.bar(x,yfit2,0.2,alpha=0.3)\n", "pl.bar(x,mu,0.2,alpha=0.3)\n", "pl.title(\"less regularization\")" ] }, { "cell_type": "code", "execution_count": 63, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "(136.61112296730684, 234.0)" ] }, "execution_count": 63, "metadata": {}, "output_type": "execute_result" } ], "source": [ "sum(yfit4),sum(mu)" ] }, { "cell_type": "code", "execution_count": 72, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Warning: Maximum number of function evaluations has been exceeded.\n" ] }, { "data": { "text/plain": [ "" ] }, "execution_count": 72, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "lamb=2.3\n", "yfit3=op.fmin(lambda ym:lamb*(fun(ym)**2).sum()+(tkho2(ym)**2).sum(),mu)\n", "pl.bar(x,yfit3,0.2,alpha=0.3)\n", "pl.title(\"true values at input\")\n", "pl.bar(x,mu,0.2,alpha=0.3)\n", "pl.legend([\"reconstructed\",\"true\"])" ] }, { "cell_type": "code", "execution_count": 78, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Warning: Maximum number of function evaluations has been exceeded.\n" ] }, { "data": { "text/plain": [ "" ] }, "execution_count": 78, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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Lgf3q7o/GYdqWi4gh1ALVlZl5favrEQCHAe+OiGXUTpP/ZUT8a2tLGvCWA8sz\nc/NI7nXUQpZa60jgkcxclZkvAtcDh7a4JtU8FRF7A1TfV/bVjgdKqLoTmBAR4yNiJ2qTCW9ocU0D\nWkQEtTki92fmRa2uRzWZ+XeZOTozx1H7f3JLZvrXdwtl5u+AxyNiYrXpCOC+FpakmseAGRGxa/Xz\n7Ai8gKBd3ACcWt0+Ffh+X+14cF/tqJUyc0NEfAz4EbUrNC7LzN+0uKyB7jDgFODXEbGo2vb3mXlT\nC2uS2tVc4Mrqj8KHgQ+2uJ4BLzPviIjrgLupXc18D66u3uci4ipgJjAiIpYDnwYuAK6JiA9RC78n\n9Fk9rqguSZLUewPl9J8kSVJTGaokSZIKMFRJkiQVYKiSJEkqwFAlSZJUgKFKkiSpAEOVJElSAYYq\nSZKkAv4/PKvazBJkukMAAAAASUVORK5CYII=\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "lamb=2.3\n", "def entr(ym):\n", " rep=np.zeros_like(ym)\n", " rep[ym>0]=ym[ym>0]*np.log(abs(ym[ym>0]))\n", " return rep\n", "\n", "yfit4=op.fmin(lambda ym:lamb*(fun(ym)**2).sum()+(entr(ym)).sum(),yini)\n", "pl.bar(x,yfit4/sum(yfit4)*sum(mu),0.2,alpha=0.3)\n", "pl.bar(x,mu,0.2,alpha=0.3)\n", "pl.title(\"entropy regularization\")\n", "pl.legend([\"reconstructed\",\"true\"])\n" ] }, { "cell_type": "code", "execution_count": 82, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 82, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "yini=meas[2*mc:n2-1][::4]\n", "yini*=sum(mu)/sum(yini)\n", "pl.bar(x,yini,0.2,fc='g',alpha=0.3)\n", "pl.bar(x,mu,0.2,alpha=0.3)\n", "pl.legend([\"initial values\",\"true\"])" ] }, { "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": "3.6.6" } }, "nbformat": 4, "nbformat_minor": 2 }