From cd1093253de46d78bf6ace1f0ed154f480d6c9be Mon Sep 17 00:00:00 2001 From: michaeldeistler Date: Fri, 23 Aug 2024 17:35:22 +0200 Subject: [PATCH] Rework importance weighting example --- .../15_importance_sampled_posteriors.ipynb | 6494 +---------------- 1 file changed, 56 insertions(+), 6438 deletions(-) diff --git a/tutorials/15_importance_sampled_posteriors.ipynb b/tutorials/15_importance_sampled_posteriors.ipynb index 2b52f1c18..78f28fed9 100644 --- a/tutorials/15_importance_sampled_posteriors.ipynb +++ b/tutorials/15_importance_sampled_posteriors.ipynb @@ -37,42 +37,7 @@ "\\approx \\sum_{\\theta_i\\sim q(\\theta_i|x)} w_i\\cdot f(\\theta_i).\n", "$$\n", "\n", - "Instead of sampling $\\theta_i\\sim p(\\theta_i|x)$, we can thus sample $\\theta_i\\sim q(\\theta_i|x)$ and attach a corresponding importance weight $w_i$ to each sample. Intuitively, the importance weights downweight samples where $q(\\theta|x)$ overestimates $p(\\theta|x)$ and upweight samples where $p(\\theta|x)$ underestimates $q(\\theta|x)$.\n", - "\n", - "### Effective sample size $n_\\text{eff}$ and sample efficiency $\\epsilon$\n", - "\n", - "If inference were perfect, we would have $w_i = p(\\theta_i|x) / q(\\theta_i|x) = 1~\\forall i$. In practice however, SBI does not provide exact inference results $q(\\theta_i|x)$, and the weights will have a finite variance $\\text{Var}(w) > 0$. Performing the Monte Carlo estimate above with $n$ samples from $q(\\theta|x)$ thus results in reduced precision compared to doing the same with $n$ samples from $p(\\theta|x)$. This is formalized by the notion of the *effective sample size* (see e.g. [here](http://www2.stat.duke.edu/~scs/Courses/Stat376/Papers/ConvergeRates/LiuMetropolized1996.pdf))\n", - "\n", - "$$\n", - "n_\\text{eff} = \\frac{n}{1 + \\text{Var}(w)} = \\frac{\\left(\\sum_i w_i\\right)^2}{\\sum_i \\left(w_i^2\\right)}.\n", - "$$\n", - "\n", - "Loosely speaking, using $n$ samples $\\theta_i\\sim q(\\theta_i|x)$ is equivalent to using $n_\\text{eff}$ samples from the true posterior $p(\\theta|x)$. The *sample efficiency*\n", - "\n", - "$$\n", - "\\epsilon = \\frac{n_\\text{eff}}{n} \\in (0, 1]\n", - "$$\n", - "\n", - "is an indirect measure of the quality of the proposal $q(\\theta|x)$.\n", - "\n", - "### Mass coverage\n", - "Importance sampling requires $p(\\theta|x) \\subseteq q(\\theta|x)$. When using NPE, this should naturally be ensured, as NPE is trained with the mass-covering forward KL divergence, such that $p(\\theta|x) \\not\\subseteq q(\\theta|x)$ for in-distribution data would imply a diverging validation loss. \n", - "\n", - "When $q(\\theta|x)$ is a light-tailed estimate of $p(\\theta|x)$, the variance of the importance weights is unbounded and we may encounter a small sample efficiency $\\epsilon$.\n", - "\n", - "### Self-normalized importance sampling and the Bayesian evidence\n", - "\n", - "In practice, we don't have access to the normalized posterior, but only to $p(\\theta|x) \\cdot p(x) = p(\\theta)p(x|\\theta)$. We thus have to use self-normalized importance sampling. In this case, an unbiased estimate of the Bayesian evidence $p(x)$ can be computed from the normalization of the importance weights (see e.g. [here](https://arxiv.org/abs/2210.05686))\n", - "\n", - "$$\n", - "p(x) = \\frac{\\sum_i w_i}{n}\n", - "$$\n", - "\n", - "with a statistical uncertainty scaling with $1/\\sqrt{n}$,\n", - "\n", - "$$\n", - "\\sigma_{p(x)} = p(x)\\cdot \\sqrt{\\frac{1-\\epsilon}{n\\cdot \\epsilon}}.\n", - "$$" + "Instead of sampling $\\theta_i\\sim p(\\theta_i|x)$, we can thus sample $\\theta_i\\sim q(\\theta_i|x)$ and attach a corresponding importance weight $w_i$ to each sample. Intuitively, the importance weights downweight samples where $q(\\theta|x)$ overestimates $p(\\theta|x)$ and upweight samples where $p(\\theta|x)$ underestimates $q(\\theta|x)$." ] }, { @@ -149,7 +114,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "8e758ddd98d64180b75e93f63bbb8b90", + "model_id": "d4b0cbb5b41840e8be20effa8ec9e1e5", "version_major": 2, "version_minor": 0 }, @@ -199,112 +164,25 @@ "theta_inferred = posterior.sample((10_000,))" ] }, - { - "cell_type": "markdown", - "id": "4fb48fc5-08fb-42e1-9dd8-d2cda41b7fde", - "metadata": {}, - "source": [ - "### Manual importance sampling" - ] - }, { "cell_type": "markdown", "id": "101e8005-8ac7-4730-bb8a-f51c652c4ecf", "metadata": {}, "source": [ - "For the inferred samples, we can evaluate the proposal density (i.e., the density under our inference model) and the ground truth density defined by the prior and the simulator likelihood (unnormalized)." - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "3648cc7c-b8bc-43f9-a36a-219a9d1c5f80", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "log_probs_inferred = posterior.log_prob(theta_inferred) # log probs of proposal\n", - "log_probs_gt = log_prob_fn(theta_inferred, observation) # gt log probs (unnormalized)\n", - "\n", - "plt.plot(log_probs_inferred, log_probs_gt, '.')\n", - "plt.xlabel(\"proposal log prob\")\n", - "plt.ylabel(\"ground truth log prob (unnormalized)\");" - ] - }, - { - "cell_type": "markdown", - "id": "4796c043-3faa-4c91-8ef2-a967cfc4b160", - "metadata": {}, - "source": [ - "Based on these densities, we can now compute the importance weights." - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "4a990c0b-ef62-495b-a529-3d8d653e91d4", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Effective sample size: 1466\n", - "Sample efficiency: 14.7%\n" - ] - }, - { - "data": { - "image/png": "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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "w = torch.exp(log_probs_gt - log_probs_inferred) # importance weights\n", - "w = w / torch.mean(w) # self-normalized importance sampling: normalize weights to mean 1\n", - "ESS = torch.sum(w)**2 / torch.sum(w**2)\n", - "sample_efficiency = ESS / len(w)\n", - "\n", - "print(f\"Effective sample size: {ESS:.0f}\")\n", - "print(f\"Sample efficiency: {100 * sample_efficiency:.1f}%\")\n", - "\n", - "plt.hist(w, density=True, bins=50);" - ] - }, - { - "cell_type": "markdown", - "id": "af125b98-726f-4365-9cfc-e3c221bb549d", - "metadata": {}, - "source": [ - "With these importance weights, we can correct the inferred samples." + "In this case, we know the ground truth posterior, so we can compare SNPE to it:" ] }, { "cell_type": "code", - "execution_count": 5, - "id": "c1aec66d-4148-40d8-881a-bf4e43f3092d", + "execution_count": 17, + "id": "fe85d9a6-33e9-41fd-8464-864107ff326b", "metadata": {}, "outputs": [ { "data": { - "image/png": "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", + "image/png": "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\n", "text/plain": [ - "
" + "
" ] }, "metadata": {}, @@ -312,34 +190,21 @@ } ], "source": [ - "# get weighted samples\n", - "theta_inferred_is = theta_inferred[torch.where(w > torch.rand(len(w)) * torch.max(w))]\n", - "# *Note*: we here perform rejection sampling, as the plotting function\n", - "# used below does not support weighted samples. In general, with rejection\n", - "# sampling the number of samples will be smaller than the effective sample\n", - "# size unless we allow for duplicate samples.\n", - "\n", - "# gt samples\n", - "gt_samples = MultivariateNormal(observation, eye(2)).sample((len(theta_inferred) * 5,))\n", - "gt_samples = gt_samples[prior.support.check(gt_samples)][:len(theta_inferred)]\n", - "\n", - "# plot\n", "fig, ax = marginal_plot(\n", - " [theta_inferred, theta_inferred_is, gt_samples],\n", + " [theta_inferred, gt_samples],\n", " limits=[[-5, 5], [-5, 5]],\n", - " figsize=(6, 3),\n", + " figsize=(5, 1.5),\n", " diag=\"kde\", # smooth histogram\n", ")\n", - "ax[1].legend([\"NPE\", \"NPE-IS\", \"Groud Truth\"], loc=\"upper right\",\n", - " bbox_to_anchor=[1.8, 1.0, 0.0, 0.0]);" + "ax[1].legend([\"NPE\", \"Groud Truth\"], loc=\"upper right\", bbox_to_anchor=[2.0, 1.0, 0.0, 0.0]);" ] }, { "cell_type": "markdown", - "id": "87290c05-2945-4b0f-bb39-f1eaa17b0594", + "id": "f5974817-74fd-4549-ad81-6c5bb52e29b7", "metadata": {}, "source": [ - "Indeed, the importance-sampled posterior matches the ground truth well, despite significant deviations of the initial NPE estimate." + "While SNPE is not completely off, it does not provide a perfect fit to the posterior. In cases where the likelihood is tractable, we can fix this with importance sampling." ] }, { @@ -362,168 +227,14 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 14, "id": "1479c986-a677-4367-9037-d72f5f34569d", "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "1b627e8b495e4a85b5659b9bf4343948", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Drawing 10000 posterior samples: 0%| | 0/10000 [00:00" ] }, "metadata": {}, @@ -844,5887 +319,30 @@ } ], "source": [ - "posterior_sir = ImportanceSamplingPosterior(\n", - " potential_fn=log_prob_fn,\n", - " proposal=posterior,\n", - " method=\"sir\",\n", - ").set_default_x(observation)\n", - "theta_inferred_sir_2 = posterior_sir.sample((len(theta_inferred),), show_progress_bars=False,\n", - " oversampling_factor=1)\n", - "theta_inferred_sir_32 = posterior_sir.sample((len(theta_inferred),),\n", - " oversampling_factor=32, show_progress_bars=False)" + "fig, ax = marginal_plot(\n", + " [theta_inferred, theta_inferred_sir, gt_samples],\n", + " limits=[[-5, 5], [-5, 5]],\n", + " figsize=(5, 1.5),\n", + " diag=\"kde\", # smooth histogram\n", + ")\n", + "ax[1].legend([\"NPE\", \"NPE-IS\", \"Groud Truth\"], loc=\"upper right\", bbox_to_anchor=[2.0, 1.0, 0.0, 0.0]);" ] }, { - "cell_type": "code", - "execution_count": 7, - "id": "8ee687f3-8ffe-427c-8caf-046c911d2033", + "cell_type": "markdown", + "id": "eb7e80dd-1534-4158-b112-f55ceb9da50b", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "torch.Size([10000, 2])" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], "source": [ - "theta_inferred_sir_2.shape" + "Indeed, the importance-sampled posterior matches the ground truth well, despite significant deviations of the initial NPE estimate." ] }, { "cell_type": "code", - "execution_count": 8, - "id": "6efc07ec-6b06-4c77-88c8-6d16685f5c68", + "execution_count": null, + "id": "4ac18a2e-16a0-4a94-a0a7-4a30deb54f18", "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "iVBORw0KGgoAAAANSUhEUgAAAbwAAAC8CAYAAAADxxtrAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjkuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8hTgPZAAAACXBIWXMAAA9hAAAPYQGoP6dpAABG80lEQVR4nO2deXzcZbX/39/Zk1ky2felTdp0320pUGhLgaIsIuICFwURXLgKV6jKvf6Ai4qg6FVRWRRawR1BXHABCi21tHRNm7ZJmn3ft0kmmf37++M7M23azEzaZp153q/XAJmc7zfPl8zJ53nOc55zJFmWZQQCgUAgiHJUUz0AgUAgEAgmAyF4AoFAIIgJhOAJBAKBICYQgicQCASCmEAInkAgEAhiAiF4AoFAIIgJhOAJBAKBICYQgicQCASCmEAInkAgEAhiAiF4AoFAIIgJhOAJBAKBICYQgicQCASCmEAI3hjZ37afg+0HcfvcYe2cHu8kjUggmJm0DLbwdsPb9Dv7w9q5PD5EbXvBeKKZ6gHMBJ498iw/KfkJACatictyLuOhtQ9h1BpH2P1hfyMP/qmUTfPTePSGRaRbDFMxXIFg2nKi+wR3v3k3/c5+VJKKRSmLuH/l/axIXzHCrrlvmI/8bDd6jZpvfngRl89NnaIRC6IJSbQHCo0syzy1/yf8vOw5AMw6MwOuAQD+30X/j48Vfyxo225zcMX3dzLo9Ci2eg3funERNyzLnvyBCwTTkMOtJfzn2/dg89gwa80MuBVfWp62nBeveXGE7d0vHuCNE+3Brz+yIpvvfGQxeo16hJ3P58Plck384AXTFq1Wi1qtjmyIELyQyLLML577K67DJuzafiyZOjZetZLXpd/ywrEX2JC7gR9v/HHQ/p7fHOL1o60szLKgUas40tiHRiWx++sbxUpPEPMcOVLJ289VIskqnBYbq5ctJGOjmo/942ZUkop3P/4uCfoEAN460c5nXzyARiVx4/Js/nioCVmGb3xoPp9dNzt4T5fLRW1tLT6fb6oeSzBNsFqtZGRkIElSWDsR0hwFWZbZ9ccKXIdNABjdCXgb4J1flnPFV6/iBV5gb+teXF4XOrWOHRUdvH60FbVK4nsfXUpxhpmPPbuHg/W9/HZfA/dtmjvFTyQQTB2tVX3s+nkdOq8y8dP2pXBiRztp2cUUWYuo6qtiT8seNs/azJDLw8N/OQ7AZ9fN5uvXzGNxTgIP/fk4v9pbz2cumYVKJSHLMq2trajVanJzc1GpRDpCLCLLMkNDQ3R0dACQmZkZ1l4I3ijs+1stpdtbADha/AYPXb+FXb+porvZjvu4kdS4VDqHOznYfpCLMi/i0b+dAOD2iwtYkGUB4NMXF3CwvpffvN/APRuK0KqFQwpij456G395qgTJo6YxoZzNtyxHczKFI9sbOfJ2E+s+uI6qvip2Ne9i86zNPL+rlua+YbKtcXz5iiIAProyh+/9s4K67iF2VXVx+dxUPB4PQ0NDZGVlER8fP8VPKZhK4uLiAOjo6CAtLS1seFP8FT6DnlY7B16vA2BXwR+5+Kr5ZBcms2xTHgClO5q5NHOd8v3mXRxq6KOm0068Ts19m+YE77N5YQYpJj0dA07ePG0vQiCIFWRZZudvKvA4fTRbKqldu4vLl1zEB66dhVavprfVzhLXWgD+3fxvvD4vfzzUBMBXrpxLvE6Zj8frNNy0MgeAl/bUA+D1KtnQOp1ush9LMA0JTHrc7vBZ9ELwzqDkzQYAapKOUJ93mJvm3ATAnFXpxJm12PucLB30C17TLv56RFkJXrUgHbNBG7yPTqPik6tzAXhxT90kPoFAMD1oOdlHR/0AXpWbN+du5bYltyJJEvo4DfMuVkJPQ4cMGLVGehw9/LlsH/XdQxi0KjYvyhhxr9vW5gPwdnk7Tb1Dwfcj7dkIYoOxfg6E4J2Gvc9JxfttAJRkbefmuTdj0in7eGqtikWXK7NMzxEzGklDna2Ovx4/CjBqNuYta/JQqyT21vRwsn1gkp5CIJgeHHpDmTyWpe4lIcHE1QVXB7+3ZH0OSNB4vJfLTVcC8PtjbwBw5YIMjPqRuy2FqSYuLUrBJ8Ov32+YpCcQRBtC8E7j6DuN+LwyreZqOs0N3DL/lhHfX3RZNiqNRFednUt0VwBgU5WSGK/l0jkpZ90vMyGOjfPSAHjtcPPEP4BAME3obh6k4Xg3Mj6OZr3DJ+d9Eq3qVATEmh5PwWLFZ+a3XQxAmW0fANcvzRr1nreuUbYVXjvcLA6kC84LIXh+XMMeju1URKkk623mJc0jwzgyrBJv0ZG3IBmAJZ6LANAYK/ng4syQSSnXLlFCN2+IfTxBDHHYvzVQn3wMm6Gby3MuP8umeI3iX9p25TiCrGvAYnSFPGS+YV4acVo1rf0OKtsHJ2jkgmhGCJ6fivfbcDm8eBOGqE88zsr0laPapeaZAbDY0gFQxTWHnJECrC9OQ6OSqOoYpLpTOKkg+hkedFG5T5ngHcx6k0R9IoXWwrPsAr5ka3NikpWJ4QfmDqHTjP5nyaBVB8Vwd1XXRAx9wrn99tuRJInHH398xPuvvfZacB9qx44dSJIUfKWnp3PTTTdRU1MTtC8oKBhhE3ideV/BSITg+ak9qjhQTcYhkGRWpa8a1S41V9nTszVLyLKESjPArPTQB18T4rSsLVRWhSJbUxALNBzvweeTkZJddJoaWJG+YtSkAkuKAV2cBp9XRt89D4CcjJ6w975qoTLRnKmCB2AwGHjiiSfo7e0Na1dRUUFLSwsvv/wyx48f57rrrgtmpwI8+uijtLa2jnh96Utfmujhz2iE4AEuh4fmk8qH76DhXYCzavsFSMlVZqWuHheSQwnJVPSWh73/VQsVuzeOt43LeAWC6Ux9qSJGHSnVACEnj5IkkZKjTCAT+mYBYJfDJ6RcMS8dtUqittuOx3tqoinLMkMuz5S8znU/cdOmTWRkZPCd73wnrF1aWhqZmZlcdtllPPTQQ5w4cYKqqqrg981mMxkZGSNeRqMxzB0F4uA50FTei88jo7XK9MW1U2QtItGQOKqtKVGPwajFYXeT2L+A3rhWynrKWJezLuT9r5yfzv977RiHG/vosDlIE6XGBFGKz+uj4YSySjto2AkQcnsAIDXXTEtlHylDWdQQefKYEK/lotlJ1LX3Mew+JXjDbi8LHvrXhT/AeXDi0auDZwbHglqt5rHHHuOWW27hy1/+Mjk5ORGvCRyuFnVDLwyxwuPUjNSe2QFSeAeVJIlkf1gzeUDZlyjvCe+kGQkGluZakWV4q6xjnEYtEEw/2mr6cQ550MRJ1MeVY9aamZsYurReSp7iS6lOJXGl3lbPkHsopD3AVQuUiInDPXNbcd14440sW7aMhx9+OKJta2srTz75JNnZ2RQXFwff/9rXvobJZBrx2rVr10QOe8YT8ys8WZapP9YNQLl5PxA6BBNAnaRUd8hwZFKF0vIkElctSOdIYx9vnGjjFn96tUAQbdSVKr5E3iCyJLM8fTlqVehST6n+LYI0jxaLJhmbp5uK3gqWpy0Pec2VC9J57p1yXB4fbq8PAxCnVXPi0atDXjORxGnHVqn/TJ544gk2btzIAw88MOr3c3JygrUily5dyiuvvDKissyWLVu4/fbbR1yTnS26s4Qj5gWvq3EQe78LtU7FPmkHEH6FB9Dj/8zlykrYs3mwmX5nf7Da+2hcPjeV7/2rgkP1vciyLCpECKKSwOSxIVGZBEaaPOqTdHiQ0SOxVL+KXZ5/caL7RFjBy7LGkZ8Ujww43V7MKJGXcwkrTgcuu+wyrr76ah588MGzhAtg165dWCwW0tLSMJvNZ30/JSWFoqKiSRhp9BDzIc06fzgzvkDGo3KTb8knNT58s8kqlxMAi0NFtlGJv1f0VIS9Zm66Ga1awubw0NQ7PA4jFwimF7auYXpa7EgS7FG/BUSePJ5oG6RTrSR9LGAxEHmLAGBOuiIADs/Mbg30+OOP89e//pU9e/ac9b1Zs2ZRWFg4qtgJzo+YF7zAjHQgQ6mJGW5mGWB/9wAuZCSvzGKt4tBlPWVhr9FpVMz1O+nxlv4LGbJAMC0J+FJigYFOXxt6tZ75yfPDXlPS2EuHWhGtlCFl8jgWwStKU/b+nDN4Hw9g8eLF3Hrrrfz4xz+ObHwGAwMDtLW1jXjZbLYJGGX0ENOC53J46KhXalzWWZQeXOE22AHsTg8nOwbp8s9KZ7kVhx6Lky70tw463iI+lILoI3C0R5WrRDBmJ8weUU5sNEoa+4KCp+lRRKyqtwqXN3w24inBm9krPFDO051PE9uHHnqIzMzMEa+vfvWrEzDC6GFmBb3HmfYaG7JPxpxkoMytFIGekzgn7DXHmvvxyTAQJ8EgJNuVKitl3eFXeAALsxKAJiF4gqhDlmVaqpTIRU9iE3RE9iWAI439+PyCN9DixpJuweayUdVXxYLkBSGvK0ozUdIFbp8Pj9eHZob0m9y2bdtZ7xUUFOB0OoNfr1+/PuLZvrq6unEeWWwwMz4lE0RLVR8AaYVmGmzKgdcia/hN4JJG5RpjutJ/SdWl/LvWVsuwJ/ze3KkVnghpCqKL/o5hhm0uVBqJGr0SLZljDS94HQMOmvuG6dLISBIM2VwsjlcKPkSKmJgNWjQqJfFrJh9PEEwusS14lX0AaLKdyMgk6hNJNiSHvSYgeHlzrAD0NjhI1qfgk30RnXR+pgVJgnabk65BZ1hbgWAmEZg8phdYqLSdBKAoMcLksUG5Zna6maQsJUQ5z7UMgGNdxyL+zEDB9uEoCGsKJoeYFTyv20d7nRJa7E9qBRQHjXRcoLRZWZ0tWZCCVq/G7fSySqe0NznaeTTstUa9hlnJSukfEdYURBOt/sljeqGZepvSlTxStOSY35cW5ySQMVuJfqQNKo1eS7tKI/5MrUbx1WGxwhOMkZgVvI56G163jzizlnqpEojsoP1D7uCRgkU5VtIKFCed41wCjM1JF4iwpiAKCazwpCwHHtmDWWsmPT497DWBSd+iLAsZs5UzrKp2ZaVX2VsZseKKzr/Cc7iE4AnGRswKXsBBM4usVPaPTfACIpWTGEdCvDY4K03oU1qbRFrhQSBxRazwBNHDYK8DW5cDSYIeSxMwtmjJMb8/LcxOCApeX5ODdEMGXtkbsYJRIKTp9Hjx+URDWEFkYlfwKhVnyyqyUtmrCF6krLJTM1LFOQNO6m7VICHRam+lc6gz7D0CiSsnhOAJooTA5DEl10z18Ngmj12DTtptTiRJ2dtOSIvDYNTi9fj4gEYpxB4pYqJWSahVKmRE4opgbMSk4Pl8Mm3VfQBYCjS0Dyl96kZrUnk6gRVeQLQCgmfrcDAvfiEAR7vCr/IC19Z22Rl0es7vAQSCaUTraZPHql6lfU3kaIky4ZuVbMSk1yiNTv0Rk1kO5TjCWCImBm0gcUUIniAyMSl43U2DuBxetAY1vUYlYSU9Ph2LzhL2uoCTLsxW7AxGLVb/8YRl8loASjvDz0qTTXoy/O2BylvFKk8w8wms8LLmWKnsG2u0RBHJwJ42QMYsZQJp6VX2/iJNHoFgd3QheIKxEJOC19mgVFdJL7BQ1e+fkUZIoR52eanuHARO7cMBwX28LLuyOhyLkxZnKCXGKjsGz3HkAsH0wu3y0ttqB8CSq6V5sBkYS7TEP3kcxZcczRJqSU3HUAdt9vBNkw1+wYuGiiuCiScmBa+rSRGalBwTVX2K4EU6JFveZsMnQ4pJR5pZH3w/ENbUd1kB5fyQ1xd+tjnHXxapsl0InmBm09NsR5YhzqylVW4EINmQTJIhKex1J4KCd2qFl1agnFO197pYFLcMiBzWDGZqerzn3Hl8Krj99tuRJInHH398xPuvvfZaMMlnx44dSJIUfKWnp3PTTTdRU1MTtC8oKBhhE3ided/TCdy3r68v+N7Pf/5zli5dislkwmq1snz58oid2GcyMSl43c2K0CTnmIIJKxHPDJ02Iz09+ywgeANNHkwaE8Oe4aCIhmJOul/wOgbO7wEEgmlC0JeyTcFwZqRoyYDDTW2Xsio8XfB0Bg1J2YpvLPauBiInrug0Si86r0/GM0MyNQ0GA0888QS9vb1h7SoqKmhpaeHll1/m+PHjXHfddXi9pybTjz76KK2trSNeX/rSl8Y8jhdeeIH77ruPL3/5y5SUlLB7926++tWvMjgYvRPxmKulKctycIWXnG2ism5sTnrijISVAImZRnQGNS6Hl1WaS9nh+SdHu45SnFQ82m2Un5WmhDSrREhTMMM5PVry70C2c4RoSVmrMtHLTDCQbNKP+F7m7AS6mwbJGJgNusgrPJVKQqdR4fL4cLp9waMK05lNmzZRVVXFd77zHb773e+GtEtLS8NqtZKZmclDDz3ErbfeSlVVVbDrudlsJiMj47zH8Ze//IWPfexj3HnnncH3Fi5ceN73mwlM/0/HODPQ48A17EGlkvBYBul39qOSVGPOKjt9zwEUh8sotAIwx7FUse06HvZegUrvrf0OBhzu83kMgWBa0NWkiFdKjomTvUpJsUgdR87Mdj6dzDn+A+htio+c6D4RcYvAoFEjyzJ9jkGG3EOT/jrXUKpareaxxx7jqaeeoqmpaUzXxMXFAeByhe8icS5kZGSwd+9e6uvrx+2e052YW+F1NyuhlMTMeKoGlNBjgaUAvVof8hq310d5m+LYozlp9lwrDce7sXRlQErkwrcJcVrSLXrabU6qOgZZnpd4vo8jEEwZsiwH/Skp28TJOr/gJUUSPGXyuOCMySNA9hzFFwZaXFjyrNi8fdQP1DM7YXbI++m1Kpx2B9f8+arzeo4L5f1b3ideG39O19x4440sW7aMhx9+mOeffz6sbWtrK08++STZ2dnB1R3A1772Nb7xjW+MsP3HP/7BunXrxjSGhx9+mI985CMUFBQwd+5c1q5dywc/+EE++tGPolJF51ooOp8qDN3+GWnyOcxIqzoGcXl8mPQa8pLO/mBn+QtJu5o1IEtU9lbi8YU/YxdY5YlMTcFMJRgtUUt4E+z0OftQSSoKE8JnaAZqaI42eTRa9SSkxiHLsNx3CQAVPRVh76f37+PNNJ544gl++ctfUlY2emuxnJwcjEYjWVlZ2O12XnnlFXQ6XfD7W7ZsoaSkZMRr1apVgBKaNJlMmEwmrrnmmlHvn5mZyZ49eygtLeXee+/F4/Hw6U9/ms2bN59Xf76ZQMyt8IJ7Dtlm3hqj4B1t6gNgcXYCKtXZ5ZJS881odCrcQz6yXAW06Gup668Luy84J83M7qpusY8nmLF0+30pMcMYjJbkW/IxaAwhrxlyeYKTvKU51lFtsuZY6e8cZpZ9ITvNr1PWU8Y1s0b/ow3K4XO9ysAvN77FPP+Rn8kkThN3XtdddtllXH311Tz44IPcfvvtZ31/165dWCwW0tLSMJvPfq6UlBSKikb/G/P3v/8dt1vZLgmEQ0OxaNEiFi1axBe/+EU+//nPs27dOnbu3MmGDRvO/aGmOTEneIEQTHKOkZOViuCFSzABKGlUZqRLc62jfl+tVpExO4Gm8l6WuC+iRV9LeW95WMELrvDaRaamYGZyKtvZyMlepZ1PcWJ4XzreYsPrk0m36MlIGF0Ys+ZaKXuvFVNXGpjHtsKTJAkNenQqw4xpBgvw+OOPs2zZshGhygCzZs3CarWe133z8/PP67oFC5QqN3a7/byun+7ElOC5nV76OpQK7JZMPXUH6oDIK7wj/h54S3PO3nMIkD3XSlN5L1kDc8AE5d3lXDv72pD2c0RIUzDDOT1asn2M0ZKALy0JsbqDU1sEng4tmjwd5T3lyLIcshi1WiWhU6tweX04PTOn+znA4sWLufXWW/nxj398ztcODAzQ1jbyYH58fDwWS/iKUQG+8IUvkJWVxcaNG8nJyaG1tZVvfetbpKamsnbt2nMez0xg5nwyxoHulkGQIc6io01uUtqY6MK3MXG4vVT4V2GhVngAWf7NdkNHIshQ3hs+cWVOuhKiaO4bZsglamoKZh6nH0kIrMIiCl6TEi1ZFsaXLMlxmJMM4IPMwUJ6HD10DXeFva9eq+zjOTwzr8TYo48+el57Zg899BCZmZkjXl/96lfHfP2mTZvYu3cvN998M3PnzuWmm27CYDCwfft2kpPDN8KeqcTUCq87OCM1jkhYCdfG5HhLP16fTKpZT2aIEAwoZcrUWhXeIbA60qjoqQg7K00y6kg26ui2u6jptLMoO/TqUSCYbridXvo7ld6Q5kzdeURLrGHtsuZaqdjbxnzHShoTyijrKSM1PjWkvV6jYoDpX2Js27ZtZ71XUFCA0+kMfr1+/fqIRx3q6urO+Wefed+bbrqJm2666ZzvM5OJrRVe4MB5jpmTPWMLwQT373KsYYVRrVWRMUsJJWTb5tLn7At2YQjFqUxNsY8nmFmcHi1pl5uD0ZIMY+iD0D12Fw09ypbC4jDbA3AqrJk1oOyDR9rHC3RNEG2CBOGILcFr8SesnLbCi7TJHpiRLsuNvALLLLICUOhSqhVEctJgiTFRU1Mww+gJ+FLW2KMlR/zZzrNTjSTEacPeP8vvS4YeK5Ksini2NXA0wemZ3is8wdQSU4LX2+Y/JJtpHPMZvICThtu/C5CSowhYylA2AGU9o5+vCTDHX2LspBA8wQwj0CHhnHwpMHmMEM4ESEiNQ6NXg1ciwZFCRW+ETE3/Cs/t9eGN0jNkggsnZgTPYXczPKCcS/EmDNPt6EZCCtvGpG/IRX23EoJZkm2N+DOS/YVvdTYzkixFXuGJkKZghtLbpvhF4jkI3tGm8Md7TkdSSSRlGgFIGsqi3laP3R06VV6jUgXraDqm+T6eYOqIGcELzEhNiXpq7Moh2TxLXtiSQIGMstkpRhLiw4dgACypcai1KvCosDhSIoZh5voPyTb0DDHsEnsPgplDIFpiTY8Lfs7DCZ4sy6cSVsYgeKBsPQDkupR9vICwhkIf6I03AzM1BZND7AjeaTPSg+0HAViSsiTsNafODI0tg1I1YlaaSdNgU9hZaYpJT5JRhywTbC4rEEx3PC4vtm4HAIOmHnocPehUurAFHJp6h+m2u9CqJeZnjq0aSnKWEgHJdil1NAOJZqEwBI4miBWeIAQxI3g9/hlpYkZ8UPBWZawKe03JOc5IQdnEB8hxK6HSmr6acObBsGZFmwhrCmYGve1DIIPeqKF0sASAJalLwhZgD/jS/EzLmGtfJvlXeKbBFICIfSZFpqYgEjEjeL2tygrPnKYPNpVcmb4ypL0sy0EnDXdI9kwCDSyz/LPSSE46138A/aTYxxPMEILJXxlGDnUok8dwvgScly8FVngqmw6NV0d1f3VYe5GpKYhE7Aie30l7DK24fW5S4lLIM+eFtG/sGabH7kKnVrFglKruoQjsO1gGlUOyEQXPv48njiYIZgqByePp0ZKxCt7yPOuYf068RUecWQtIJA5nUN0XXvAMp2VqerxC9ARnExOC53Z5GehR9hwqUFZ3q9JXhT0zdLixF4AFWWMPwcCpTE21zYDaq43opHP9Ic2Tooi0YIYQmDyqkjy02lvRSBqWpi4Nae/y+ChtDpQUO7fejwF/ShrKpMfRQ/dwd0hbtUqFTh1IXBGCdy7s2LEDSZLo6+ub6qHwyCOPsGzZsgm5d0wIXl+bsudgMGo5ZNsHRJ6RHm7oA84tBAPKrNRg1IIskTicPuaQZlPvMHanqKkpmP4EEsDadA0ALEhZEDbbubzNhsvjwxqvpSD53BqlBsKa+W4lAzTSBDJYU3Ma7+O1tbVx7733UlRUhMFgID09nUsuuYSnn36aoaGhqR7eWQTEMNxrx44d53VvSZJ47bXXxnW84YiJWprBFOqMOI50HgEmJgQDyi8wOdtI88k+koYyOTm0nwHXAGbd6JlpiUYdKSY9XYNOKjsGz1lgBYLJxOf10deu/FEul8/NlyKV5xuNQOJKukPZfqjqq2J15uqQ9gatigEHOKbpCq+mpoZLLrkEq9XKY489xuLFi9Hr9ZSWlvLcc8+RnZ3N9ddfP+q1brcbrTby8ajx5uKLL6a1tTX49b333ovNZmPr1q3B95KSkoL/7XK5RjSqnU7ExAovMCMl0cmwZxir3hr2wLnT4+VEiw049xUeQJJ/VprjVs4PRZqVFmeIsKZgZmDrcuDzymi0KvYPvgco2wPhKDnPaAmcWuHFDyih0IgrvEDiyjRd4X3xi19Eo9Fw4MABPvaxjzF//nxmz57NDTfcwOuvv851110XtJUkiaeffprrr78eo9HIt7/9bQCefvppCgsL0el0FBcX89JLLwWvqaurQ5IkSkpKgu/19fWdtQr7+9//zty5c4mLi2PDhg1hi1HrdDoyMjKCr7i4OPR6ffDrZ555htWrV/OLX/yCWbNmYTAoRfYLCgr44Q9/OOJey5Yt45FHHgl+H+DGG29EkqTg1wFeeuklCgoKSEhI4BOf+AQDAxf+9zE2BM9/6LxLr8xSVqStQCWFfvQTLTZcXh9JRh15SecWgoFTiSuZTqUJYyQnDZQYE81gBdOdnkABhzQd9QP1SEgsT1se9prD5xktAUjKMoIE0rAWg9sUcotAlmXcTi9qn4zX5cVud+N2eiflFamzQYDu7m7eeOMN7rnnHoxG46g2Z66AH3nkEW688UZKS0v5zGc+w5/+9Cfuvfde7r//fo4dO8bnPvc57rjjDt55550x/z9tbGzkIx/5CNdddx0lJSV89rOf5etf//qYrx+NqqoqXnnlFV599dURYhuO/fv3A7B161ZaW1uDXwNUV1fz2muv8be//Y2//e1v7Ny5k8cff/yCxggxEtLs8a/waqQTAKxIXxHW/vQU6nMNwcCpjXbjgNJTasxHE0SmpmCaE9ge8FiUfxcnFYcM14NSnq+2S7E9nxWeVq/GkhKHrXOYpKFMqvurRxUYj8vHc/fuHPHeu+f8086Pu390OVp95MS2qqoqZFk+q7t5SkoKDoeSVHfPPffwxBNPBL93yy23cMcddwS//uQnP8ntt9/OF7/4RQC+8pWvsHfvXp588kk2bNgwpvEGVojf//73ASguLqa0tHTEzz1XXC4XL774IqmpoVs4nUnA1mq1kpExssuGz+dj27ZtmM3KZ+u2225j+/btwVXu+RL1Kzyf10e/v8v5IddeYOwHzs93Py0wK1UN6cLOSgPMTRchTcHMILA90GloBsYQzvT70qwUI9b489vXCRRlT7Xn0u/sp9sROlNzJrJv3z5KSkpYuHDhiL54AKtWjfz/W1ZWxiWXXDLivUsuuYSysvCF6s+8x5o1a0a8d6EdzvPz889J7CJRUFAQFDuAzMxMOjo6Lvi+Ub/C620fwueVUWkl2lRNxGviI7YEulDB0xk0JKbH09s2ROpgbuSQpn+F19rvwOZwYzFM/sa0QDAWupuVKEQlx4Fzi5acL6l5ZmoOd5LvLOYIb1PVV8WyxGUjbDQ6FXf/6HIA6rvtDDjcZCbEkWwKXf1lvNDoxrZuKCoqQpIkKipGFpWfPVspUhEXF3fWNaFCn6FQqZSxnL4Kdrvd53SP82G0capUqrNW42Mdy5nJOZIknVdX+LPGdMF3mOZ01CmrJlWqEySZ5WnL0ahC63z3oDPYISFSV+ZwpOYrIpZqz6VzuJN+Z39I24Q4LVn+buqixJhguuJxe+lpVsKTpZJyvGdFWnjBO9/jPaeT5vellMEcYPQ9cUmS0OrVaPVqTEYtap0aj4rgexP5Guu2R3JyMldeeSU/+clPsNtD19gNx/z589m9e/eI93bv3s2CBQuAU2HC07Mqz9xTmz9/Pvv27Rvx3t69e89rPOFITU0dMQ6bzUZtbe0IG61Wi9c7eQlG0S949Uq2ZZe5CRj7jLQozTSmDgmhSMtTqrPkOuYAkRNX5mUq9mWttvP+mQLBRNLVOIjPJ6M2woCuhwJLAclxySHtfb5T5flW5J3bgfPTCfiSbtCEzmMYQ03N6VtE+mc/+xkej4dVq1bx+9//nrKyMioqKvjVr35FeXk5anX4vcAtW7awbds2nn76aSorK/nBD37Aq6++ygMPPAAoq8SLLrqIxx9/nLKyMnbu3Mk3vvGNEff4/Oc/T2VlJVu2bKGiooLf/OY3bNu2bdyfdePGjbz00kvs2rWL0tJSPv3pT5/1fAUFBWzfvp22tjZ6e3vHfQxnEv2CV6cISLnmMDD2GenyCzwPl3rGrLSytzKsfaCCfFmrWOEJpiftfl9yJfWDFPn8XW23nf5hN3qNinlj7JAwGgaTFnOyEgFJsedE9CXDaYfPx5pBOVkUFhZy+PBhNm3axIMPPsjSpUtZtWoVTz31FA888ADf/OY3w17/4Q9/mB/96Ec8+eSTLFy4kGeffZatW7eyfv36oM0LL7yAx+Nh5cqV3HfffXzrW98acY+8vDxeeeUVXnvtNZYuXcozzzzDY489Nu7P+uCDD3L55Zdz7bXX8qEPfYgPf/jDFBaOPA72/e9/nzfffJPc3FyWLw+f7TseSPJ0+0SMI163j+fu24nPK/Pr5f+LI36APbfsCVvV/Zaf7+W96m4eu3Ext6wJXWszEi6Hh5//17sgwy9XfoOr5m/kW5d+K6T9X4+08KXfHmZZrpXX7rkkpJ1AMFW8ufU4J99vp6H4IH9PepHHLn2M6wqvC2n/8oFGtvzxKB8oSOTlz198QT/7n8+WUn24kz15f6Y8bzfv3PgOTQ1NI859BZBlmeMtNnyyTHG6OVh9RRC9OBwOamtrR/08nE5Ur/C6mgaVhBWDjwF9D4tTFocVO6/vVJPKFfnWC/rZOoOGxAxlIzfVnsvRrqNh7ef7Q5oVbQP4fFE7BxHMYAL74SdUh4DI2wOHxyGcGSAQMckano3T66TOVhfSVpIk9KJVkGAUolrwAvt3Q0k9YwrBVHYMYHd5MerUwcPgF0Jgsz11MJfa/lpsrtD7cwXJ8eg1KobdXup7pl89PUFs4xxyB0uKtRlryTBmkGXMCntNcHvgPA6cn0lavjIhzBgqAKCityKMNcT5K64MT8N9PMHUEdWCF9hzaDAonZIjzUgP1fcBSsNXtercD5yfSWqeIni5DqXw7bGuYyFtNWoVxf5WQeUicUUwzeio9+8tm104tHZWpK0Im5046PRQ0aZ8jpePxwrP70t6uxmdJ47Kngj7eLrpX0RaMPlEteAFElaqdKWoJBXLUpeFtT/coGQJjceMFE7NSlPsSuJKaWdpWPt5fsErE0cTBNOMQLSkx9ICRI6WHG3qwydDVoKBdEvoPZWxYjBqsaQo90m153Ky92R4e/8Kz+ERgic4RdQKnmvYQ68/BNNhamBh8kJMOlPYa8ZzzwEgJdeEJIFm2EC8yxLstB6K+eJogmCa0l6rfCYrtcpneHVG6I4FcFo4M398fAkg1X88IXUwl8bBRnyyL2QWZqAZrMvjwzsOB5YF05ux5l5GreB1NAyADB7jMA7tIBdlXhTWvn/YTVWHUkVivFr0aHVqEjP9iSuDuZR2lYb9xczLUBy6vE0InmB6EQhpthpryDBmkG/JD2s/Xsd7TiewJ57vLGbIO4TL68Llco1qq1Gr0KoDiStC8KKdQB/BSO2Tora0WCCc2RavnOxfmxW+VlzggGx+cvy4liNKL7DQ02In0z6besdxmgebyTHnjGobOIvX2DPMgMONWZQYE0wD7H1O7H1OZEmmy9jEdZkfCrt/J8vyadsD47fCSy/wr/Bs+Qx6Bulwd2DuNKPVaoMltU5HiweXx4NtcAi1PD37swkuDFmWGRoaoqOjA6vVGvHgftQKXluNUsqrMf4kBrWBpalLw9ofrPc76Dg3YM0sslL2Xiuz7IvYy1852nk0pOBZ43VkJhho7XdQ0TbAqoKkUe0Egskk4Et2UzcetStitKS+e4huuwudWsXCLMu4jSNtlgWVWkIzbMDkTOKN3je423g39fX1o9r3D7sZcHiw69X0nWfhasHMYLSOC6MRlYIny3LQSdvMtaxMX4lOHf4D/36NUoF99azQpZLOh6w5VgAs/WmovVpKu0r54OwPhrSfl2Gmtd9BWatNCJ5gWtBarfhSfXw5AGsy14Qz5/1axZeW5iYEq56MB1qdmrR8C201/WTZCtnXuY/vrv9uyILEb5d38O13TjA/08JPbglfMF4wc9FqtRFXdgGiUvD6O4YZHnDjU3npNDZyW9ZHw9o73N5gwsqa2eMrMpYUA0arHnufk/TBfI50Hglrvzg7gXcqOjnc0MdtF9axQyAYFwKC12auoTixOGz9TID3a3oAWDPOk0dQJpBtNf1kD8yhwrmPxsFGChIKRrWdn5NM84CXzuF+UGvHVXwFM5OoTFppre4DoNPUgE/ljRiCOdrUj8vjI8WkZ3bKubXjiIQkScFVXqatiGNdx+h1hC6S+oFZiuC+X9szruMQCM4Ht8tLV4OSsNJmro3oS3Dqs7t61vhHKAK+lGefD8Dult0hbQuS40kx6XF5fBxtCt2tRBA7RKXgtflnpC2mapIMScxJnBPWPhDOXDMr6bw6nEci4KRFw4uRkfl3879D2q7IS0StkmjuG6apV1RcEUwtnfU2fD4Zh26AAX0PF2WFF7ym3iGa+4ZRqyRWjuORhAAZhQlIEhjsZozOBHY27gxpK0kSa/yiu682uprGCs6PqBS8UyGYWtZkrkElhX/MfXUTNyMFyCqyAmDty0LlU/Nu07shbY16DYuyEwDYXydWeYKpJeBLzaYqtGptxG4j+/yru0XZCRj1479joo/TkJKrZDNnDMzmQPsB7O7QveVWi4iJ4DSiTvAcdje9bcrKqH0MIRi31xfM0Bzv/bsAiZnxGExaJK+KVHsuu5t34/aF7vx7alYqnFQwtQQnj5ZalqYuJV4bH9Y+sH930QRNHuHUBHLu8DLcPjd7W0I3Lw0I3sH6XjxecR4v1ok6wQtkZ/YZOnBo7azNDJ/5Udrcz5DLizVey9xxKBg9GpIkBZ20cGgxA+4BSjpKQtqvLhCzUsHUI/tOZTu3m2ojnmWFUxmaEzV5hFNbBDmDSo3anU2hw5rF6WYsBg1DLi/HW0RBh1gn6gTv9IyyfEs+mabMsPaBVdQHCpJQjUPB6FAEnHSuYxlA2L2HDxQkIUlQ02mnc8A5YWMSCMLR2z6E0+7Bo3LRZWyKGC1ptzmo6x5CkmBl/sQJXmaREvJX98VjcBt5t+ldfPLoqzeVSgqu8kTERBB1gtd22v7dmDLKTktYmUiyi60AxHUmo/Kpw85KE+K1FKcrq02xjyeYKgK+1GGqx6g3siB5QVj7QERiQaaFhLiJqxIUZ9aRnK1kU88eWEy3o5sT3SdC2ot9PEGAqBI8j9sbbAkUSFgJh8vjC876Lpo9/meGTic520R8gg7ZLZE7UEydrY4GW0NIe7GPJ5hqWqr6AGgz17EqYxUaVfgklPequoCJOX93JnkLlZ+x3LkOCB/WDBST2F/XI5orxzhRJXhtNTa8bh92bT/9cR0RK7ofaujF7vKSbNSxIHP8SiCNhiRJ5PuddIXzMoCwxxMCTrq3RqRTCyYfWZZpKleSuVoslRGjJbIs8+7JTgAum5sy4eML+FJCexbIErubQ5/HW5hlIV6npn/YTUW7aL0Vy0SV4DWVK6uh5oRKFqQsIEGfENZ+Z9BBUyd0/y5A/iLFSTO6CwHY07InpO2a2UmoJChvG6BRdEAXTDJ97UPY+5x4JDet5pqI5++qOgZp6Xeg16gmPFoCkFGUgNagxjesZD4f6zpGv3P0w+VatSoYMXnrRPuEj00wfYkywVNmpM0JJ8e0f7ezQhG8y+emTui4AuTMVxJj5D4dZkcy+9r24faOfjwhxaQPhob+Xto6KeMTCAIEfKndXEuyOZFZlllh7QOTxzWzkyelhJdarSJ3viJiy4YvRUZmT2voCeQ1i5XktdeFL8U0USN4zmFPsCtzc8LJiDPSDpuDE602JAnWzZn4EAwoh2YzCpVV5/zBVQx5hijpLAlp/8ElipMKwRNMNk0VIyePkSoQBQRvsiaPcCpiUtC/EAgfMblqQToalUR52wDVnYOTMj7B9CNqBK/lZC+yD/oNnfiMTpanLQ9r/26lssG+KCthXPvfRSLgpPPtHwDgvZb3QtpuXpiBSoIjTf0irCmYNHw+mWa/4DUlnOTS7EvD2g+5PMED55dPwv5dgMA+nrrThMFtZHfz7pANlq3xOi4pUsb296NiAhmrRI3gBUIwAQfVq8OL2FTMSOGU4MV3pKD2asNutqeaRVhTMPl0NQ7gHPLgVA/Ta2llXfa6sPbv1/Tg8vrItsZRmGqapFGC0aonOUf5ebNsi2kfaqemvyak/YeWiLBmrBM9ghcMwVSwMW9jWFuvT+bflX7BK55cwUvKMmJK1CN7JHL651LWU0aPI/TRAxHWFEw2p7Izq1id9QFMuvAidnry10QUXw9HYAK51H4JED5iIsKagqgQPHu/k54WOzI+2hNqWZcTfkZ6pKmP3iE3ZoNm3DucR0KSJGYvU0R2+eDlQPi9BxHWFEw2p+/fbcwNP3mUZZkdFR3A5EdLAAqXKz/T2p6DxqsL2y7o9LDm6yKsGZNEheA1likrpC5jM0tyF2LRhT9T99rhZgA2FKehUU/+/4KiVekApHcUofZpIoY1A2nefzzYNCnjE8QubqeXlkr/Ci+hkg25G8LalzT2Udc9hEGr4tJJSv46ndQ8M5YUA3hU5Pcu4GDbQYY9wyHtr/VHTF451CQOoccgUSF4lfuVszX1ice4Iu+KsLZOj5e/HGkB4KaVORM+ttHImGXBlKhHcqvJ7ZvHzqadYbsnfGJ1HgC/29+AW1R8F0wgtUc68bpl+vWd5OalkRofftX2yiFlErZ5YQamCWgHFAlJkihaqUwgF/ZfjMPrCBsx+dCSTMwGDfXdQ/zbXxlGEDvMeMEbsrloPKGs8E6mHGB97vqw9m+XddA35CbdoufSosmfkQJIKonClWkAzO+9CJvLxoG2AyHtNy/MIMWko93mFAdnBRPKyX3K56sy9SAb88OHMx1uL38pUSaPH12ZO+FjC0XRKsWXMrvnoPXq2d6wPaRtvE7DTSuUie5Le+snZXyC6cOMF7zK/e3IMrSb6sjLySDdmB7WPjAjvXF5DupJqK4SiiK/4OX1LEDj1YZ1Up1Gxcc/oPxBEU4qmCiGbC4aTiil7CpTDkQMZ24v68Dm8JCZYGBt4cRXVwlFSo6JhLQ4JK+K/J5F7GjcETZi8h8XKRGT7WXtNPeFDn8Koo8ZL3gn97Up/045wA1FN4S17Rp0ssNfXeWjK7MnfGzhSC+wYE42IHnU5PYt4J2Gd0K2OAH45Oo8VBK8V91NVYfIMBOMP1UH25F9yuQxPy+TWQnhq6ucmjxmT+nkUZIk5vj3xef3rcbmsnGw/WBI+6I0M2tnJ+OT4Xf7QhdwF0QfM1rwetvsdNQP4MNLa3o5186+Nqz9n0ta8PhkluZaKZqgZq9jRdl7UFZ5C7ovomO4g9Ku0pD2OYnxbJynOPWvxCpPMAFUvK9MHitTDvKJ4k+Ete0ccAaPI0zVXvjpBHwpq2cuBreJt+rfCmt/29p8AH67rxGXR+yLxwozWvAC+w2N1nKunL8x7Hkhl8fHtvdqAfjoiqld3QWYt1bJGMvpno9lOCVsWBNOOenLBxrpHwodshEIzpW+9iE66pTJY1dWDVcVXBXWfuvuWrw+meV51kk9bB6KpCwjaflmJJ+KhW2XRIyYXLkgnXSLnq5BJ6+VNE/iSAVTyYwVvMFeJ0feUcIRlakHIs5IXz7YSGPPMCkm/ZRusJ9OUqaRvIVJSEgsbruM7fXbQ5ZGArhsTgrzMszYXV5e2ls3eQMVRDWyLLP3z9UANFkr+NDCq8NWKuoadLJ1dx0AX1xfNBlDjIgkSSy9QvHrRe3r6LJ3h42YaNUqPnOJErJ9dme1OKIQI8xIwZNlmXdeKsM97KPD2EDifDVFiaEdz+H28tT2KgD+c0MhcbqJr+Y+VgJOOq/zItp6O8L2yJMkic9frrQW2rq7DofbOyljFEQ3J/e1U32oE6/kZX/u37m5+Oaw9k/vqGbY7WVpTgKb5qdN0igjU7gyDaNVT5zbzJyulfy67Ndh7W9Zk4fZoKG6086bZSL7ORaYkYJ3bGczDSd68KjcvD3nJT6+4ONh7X/zfgNtNgdZCQY+uSZvkkY5NnLnJ5GUZUTr1TOvYy0/OvSjsKGYa5dkkpMYR7fdxcsHGidxpIJoZKDHwbu/OwnAwZx/snBeIdmm0CH/tn5HMFP4/quKJ72UWDjUahVLNij7iYtb1/OPmn9Q3lMe0t5s0HLbRco2wdM7qsNGVwTRwYwTvN42O++9qqzW9ub9BWt6PJvyNoW07xp08rMdiv2XrpiDXjN9VnfgD8VsVFZ5S9oup7qrhn/V/SukvUat4q51swF49t0acRBdcN74fDLbf1mGa9hDu6mOw9lvcefiO0Pay7LMd/9ZjsvjY3VB0qS11ToXFlyahUanImUom9y++fz40I/D2t9xySx0GhUljX3srQld01YQHcwowfO6fbzx/HE8Lh9t1mqOZezic0s/h1o1uog53F4++8sDdA26KEw18tFpkE02GnNXpxOfoMPkTGRd7c385NBPwp4j+tiqXJKNOpp6h3lqe+UkjlQQTRx+o57mil58ai9vF/2Ki7PXsixtWUj7X+yq5dXDzUgSbNk8vVZ3AQxGLQsuzQJgQ/UtHKgtCXtEIdWs52b/34VvvFbKsEtsE0QzM0rw3vtTFV2Ng2Dw8EbhNmZZC7im4JpRbX0+mfv/cISSxj4S4rT8/FOr0E5B3cyxoNGpueozC5EkKO5cg7E6m58f/XlI+zidmoevV5pe/uSdKg7UiZmp4Nxoq+nn/b8oWcu7Cv5Af1wnX1j2hZD2/zrexmP/KAPgfz44nw8UJE3KOM+Hi24oJDHTSLzbwpWVn+bxPY8z6Ap9dvX+q4pJNeup7rTzHf8zCqKT6akAo1B7tIujbysHXXcU/YYhnY3PL/n8qKs7j9fH/7xWyuulrWjVEs/etpLZ0yB1OhzZxYmsuUEJVV5a+1H+tOtfvHTipZD21y/N4sbl2fhkuO/3JQw4xDEFwdhw2N288fxxZJ+MPb+FstS9XJJ9CUtTl45q/055B/f+7jCyrFQpufPS8AfSpxqtXs01n1uERq8iyzaH5JIF3PPWPQy5R+82kmTU8eTNyrO/uKeed8o7JnO4gklkRghed/Mgb75wHICmWaWUmw9SZC3i6oKrz7J1uL184deH+O2+RiQJnrhpSbDbwHRnxVX5FCxJQS1ruPbEF3n1H2/x67Jfh9xM/98bFpKTGEdT7zCf2bafHrtrkkcsmGl4vT7++dwxBrodYHHz+7QfIUkS9yy9Z1T7Pxxo5LMvHsDh9nHFvDQeuW7htAxlnklihpGNt80HYHHb5STuXsR9b/0Xdrd9VPvL56ZyxyUFAHz5d4fZLQpLRyXTXvCGbC5e/+lR3A4vgynt/D1tK4n6RH644Ydnre5Km/q5+Zk9vHmiHZ1GxdO3ruAjK6bnvt1oSCqJq+5cyKylKWhkLZsqP832Px7lv9/9n1Ed1WLQ8tQnl2PWa9hf18uNP9styo4JQiLLMu/+7iTNFb1IWpmXC36AS+Pga6u/xuLUxSNs+4fdPPznY3z1j0fx+mQ+sjybZ25bOSXttM6XOavS2XDbPCQVzOleSfr21Xzq5Ts50X1iVPuvbZ7H6oIkBhwePv3CPn4ryo5FHZI8jXNx+9qHeOMXx+hsHGQovo/fL3gCyeDj++ueYVHKYiwGDcNuL4cb+vjHsVZ+t78RWQaLQcPPP7WKNTNkZXcmPp/MnlerKHlLOXbQZqqlYvk7PHT111mUsugs+8r2Ae7Ytp+m3mF0GhW3rM7j85cXkpFgmOyhC6YpHreXvX+u4chbjcjI/HPez6lPPM5/FN/BHQu+iCVOi1atorzNxp7qbp7ZWU3XoBIx+Nzls/n65nkzYmU3Gk3lPbz+zBE8Dhmneojdha9ww9Ub+I8F/4FKGingDreXr71ylD/7u0CsnpXEfZvmsHZ28ox9fsEppqXg2QYG2fGvIzS8M4zkVeFQ2/nT4v+jTzfAcPMteO1zg7aSBKc/wYeXZfHfH5xPmmXm/7E/ua+Nt399Aq8T3Con5envs/DyDD637jPo1LoRtl2DTr7828O8V61Uu9epVVw2N5XrlmZy+dxUrPG60X6EIMrxen0cOlzOgVea8PUq/erey/8TR7N24Oq5CGf7DYDyh1wlwekFRwpTjTx6w6Jgl/CZTF/HEP98/ijd9co+XmNCOcMLmrn/w58jL2Hk2VxZlvnpO1X8eHsVLv+xn/mZFq5dksnVCzMoTDUK8ZuhTJngub1uOoc6KeuqobyhgfYmG452D4ZOIyl92ahlxTkbE8rZOetVuu0LcHWvA1/8WY6ZmxTHqvwkPv6B3BmzXzdWbN3D/OuFUjqqlVCljI9OSyP6PDez52eSnZVKTkYGKcZkjBoje2t6+L+3TrK/rnfEffKS4lmck8Di7AQWZSWQkxhHqlmPcQqadgrGF5/sw+a0UdPXwPGmGhoauhhodSB1aUjuyiLOrSRs2bX97Jr1R6p0Lpxdm/A5lHD/6f5k1mtYnp/IxuJUblmTj04zc0KYkfB6fRx4vZYD/6gDWRGsAX0Pwxmd5M5LJi8/nZysNNISUrHqrXQOuHl6RzW/29cYFD5QIkiLcxJYlK34U1GaiVSTnsR4Haop7BohiMy4CN4D//08Z97l1ARIBnz+xAsvEj7UPjU6r444jwmTMzEobqfTa+jkYFI5pWoNOYblXL+4kGsWZ5KdGIdJp8Ery9iGlczEZFPoun/RgCzLNJb1sP1vhxmqOfsPkA8fLvUwbrUTn9aNT+vFp/Xi8vlweH24fT6UWbzMaL9syf8PlST5X8qB+O9++zMT+2CCs9jy388jMzJqEfj9KL9BH8g+5d/4UMmg82rRe+MxOa3ovfFn3dOpHuak9ST/NnUieedx5ZwlXLcsiyXZCVjitKgliUGXB7vTQ5rZMKWtfiYDW9cwu98oo/K9DtSesyMfLpUDt9qJR+PCp/Ug67x48OHwenF6Az4UxpdQ/EclSahUil9JwBPCn6accRG8n37+7Qu63ouXAcMgA/EO3AlqjLnp5ObkMDfDwsIsCylRLmjnQl+nnb0HSjl5tBl3pxqdPR7VKBOG8eCeZ8J3vBaMPxfqSwB2zRC2+CEcJi/qTAtZ+bMoSFNWJAXJxqgXtLHidnopPVLNoUPl9NW70QzGoXVP3FaI8KepZ1z+UvYva0etklAhIQdmPrI/TCJJaCUNapUGg9aAXq3HHG8k0WImJSmB7GwLySlxqGZQ9tdUYk01svmai9jsP28v+2SGbC4G7EP09PfSNzCAbXCQwaFh3G43Lpcbn+xTfi9nTG28XhmPv26nLIPb68Pl8eHxyXhEybIpoX9ZOyr/ahvw+5KMDwlk0Kg0qFVq9Godek0ccbo4Ei0mkhIsZGUlkJFhQmcQYeqxoNWrWbF6LitWn8oJcA65sduc9A7202PrwzY4iG3AjtPlwu3x4PF4/Cvwkc7kk2XcXhmQQQaPT8bl9eH2+PD6ZDyiG8O0YFomrQgEAoFAMN6IZZVAIBAIYgIheAKBQCCICYTgCQQCgSAmEIInEAgEgphACJ5AIBAIYgIheAKBQCCICYTgCQQCgSAmEIInEAgEgphACJ5AIBAIYoILrkEkyzIDAwPjMRbBNMRsNotWKJOE8KXoR/jT1HLBgtfV1UVaWtp4jEUwDeno6CA1NXWqhxETCF+KfoQ/TS0XLHg6ndJeo7GxEYvFcsEDms7YbDZyc3Nj6lkDv1/BxCN8KXoR/jQ9uGDBCyzPLRZLTHxwIbaeVYRfJg/hS9GP8KepRSStCAQCgSAmEIInEAgEgpjgggVPr9fz8MMPo9dHf1dy8ayCiSSW/p/H0rNC7D3vdEU0gBUIBAJBTCBCmgKBQCCICYTgCQQCgSAmEIInEAgEgphACJ5AIBAIYoILFrzbb78dSZJGvDZv3jweY5t2/PSnP6WgoACDwcCaNWvYt2/fVA9p3HnkkUfO+n3OmzdvqocVEwhfii6EL00/LrjSCsDmzZvZunVr8OtoTL39/e9/z1e+8hWeeeYZ1qxZww9/+EOuvvpqKioqoq7+4cKFC3nrrbeCX2s04/IxEYwB4UvClwQTx7iENPV6PRkZGcFXYmLieNx2WvGDH/yAu+66izvuuIMFCxbwzDPPEB8fzwsvvDDVQxt3NBrNiN9nSkrKVA8pZhC+FF0IX5pejIvg7dixg7S0NIqLi/nCF75Ad3f3eNx22uByuTh48CCbNm0KvqdSqdi0aRN79uyZwpFNDJWVlWRlZTF79mxuvfVWGhoapnpIMYPwpehC+NL04oIFb/Pmzbz44ots376dJ554gp07d3LNNdfg9XrHY3zTgq6uLrxeL+np6SPeT09Pp62tbYpGNTGsWbOGbdu28c9//pOnn36a2tpa1q1bJ/q0TQLCl4QvCSYY+Rz41a9+JRuNxuDr3XffPcumurpaBuS33nrrXG49rWlubpYB+b333hvx/pYtW+TVq1dP0agmh97eXtlisci/+MUvpnooUYXwJeFLgsnnnHZQr7/+etasWRP8Ojs7+yyb2bNnk5KSQlVVFVdcccWFqfE0ISUlBbVaTXt7+4j329vbycjImKJRTQ5Wq5W5c+dSVVU11UOJKoQvCV8STD7nFNI0m80UFRUFX3FxcWfZNDU10d3dTWZm5rgNcqrR6XSsXLmS7du3B9/z+Xxs376dtWvXTuHIJp7BwUGqq6uj6vc5HRC+JHxJMAVcyPJwYGBAfuCBB+Q9e/bItbW18ltvvSWvWLFCnjNnjuxwOMZrFTot+N3vfifr9Xp527Zt8okTJ+S7775btlqtcltb21QPbVy5//775R07dsi1tbXy7t275U2bNskpKSlyR0fHVA8tqhG+JHxJMPFckOANDQ3JV111lZyamiprtVo5Pz9fvuuuu6LugxvgqaeekvPy8mSdTievXr1a3rt371QPadz5+Mc/LmdmZso6nU7Ozs6WP/7xj8tVVVVTPayoR/iS8CXBxCPaAwkEAoEgJhC1NAUCgUAQEwjBEwgEAkFMIARPIBAIBDGBEDyBQCAQxARC8AQCgUAQEwjBEwgEAkFMIARPIBAIBDGBEDyBQCAQxAQxK3jr16/nvvvuC35dUFDAD3/4wykbj0AwkxH+JJgJxKzgncn+/fu5++67x/2+3/72t7n44ouJj4/HarWO+/0FgunIRPhTXV0dd955J7NmzSIuLo7CwkIefvhhXC7XuP4cQfRyTu2BopnU1NQJua/L5eLmm29m7dq1PP/88xPyMwSC6cZE+FN5eTk+n49nn32WoqIijh07xl133YXdbufJJ58c958niD5iYoVnt9v51Kc+hclkIjMzk+9///tn2ZwZgpEkiWeffZZrr72W+Ph45s+fz549e6iqqmL9+vUYjUYuvvhiqqurw/7s//3f/+W//uu/WLx48Xg/lkAwJUyVP23evJmtW7dy1VVXMXv2bK6//noeeOABXn311Yl4TEEUEhOCt2XLFnbu3Mmf//xn3njjDXbs2MGhQ4ciXvfNb36TT33qU5SUlDBv3jxuueUWPve5z/Hggw9y4MABZFnmP//zPyfhCQSC6cN08qf+/n6SkpLO91EEscbUNmuYeAYGBmSdTif/4Q9/CL7X3d0tx8XFyffee2/wvfz8fPn//u//gl8D8je+8Y3g13v27JEB+fnnnw++99vf/lY2GAxjGsfWrVvlhISE834OgWA6MF38SZZlubKyUrZYLPJzzz13fg8jiDmifoVXXV2Ny+VizZo1wfeSkpIoLi6OeO2SJUuC/52eng4wIjSZnp6Ow+HAZrON44gFgunLdPGn5uZmNm/ezM0338xdd911Lo8giGGiXvAuBK1WG/xvSZJCvufz+SZ3YALBDGS8/KmlpYUNGzZw8cUX89xzz03ASAXRStQLXmFhIVqtlvfffz/4Xm9vLydPnpzCUQkEM5Op9qfm5mbWr1/PypUr2bp1KypV1P8JE4wjUX8swWQyceedd7JlyxaSk5NJS0vjf/7nfybNURoaGujp6aGhoQGv10tJSQkARUVFmEymSRmDQDBeTKU/BcQuPz+fJ598ks7OzuD3MjIyJvznC2Y+US94AN/73vcYHBzkuuuuw2w2c//999Pf3z8pP/uhhx7il7/8ZfDr5cuXA/DOO++wfv36SRmDQDCeTJU/vfnmm1RVVVFVVUVOTs6I78myPOE/XzDzkWTxSREIBAJBDCAC4AKBQCCICYTgCQQCgSAmEIInEAgEgphACJ5AIBAIYgIheAKBQCCICYTgCQQCgSAmEIInEAgEgphACJ5AIBAIYgIheAKBQCCICYTgCQQCgSAmEIInEAgEgpjg/wPZOVo1Icg+FwAAAABJRU5ErkJggg==", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "fig, ax = marginal_plot(\n", - " [theta_inferred_sir_2, theta_inferred_sir_32, gt_samples],\n", - " limits=[[-5, 5], [-5, 5]],\n", - " figsize=(5, 1.5),\n", - " diag=\"kde\", # smooth histogram\n", - ")\n", - "ax[1].legend([\"NPE\", \"NPE-IS\", \"Groud Truth\"], loc=\"upper right\", bbox_to_anchor=[1.8, 1.0, 0.0, 0.0]);" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "a5c4f6d0-d32c-46f9-b6f8-5a772b332d31", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "fig, ax = marginal_plot(\n", - " [gt_samples, theta_inferred],\n", - " limits=[[-5, 5], [-5, 5]],\n", - " figsize=(5, 1.5),\n", - " diag=\"kde\", # smooth histogram\n", - ")\n", - "ax[1].legend([\"NPE\", \"Corrected\"], loc=\"upper right\",\n", - " bbox_to_anchor=[1.8, 1.0, 0.0, 0.0]);" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "1c0fc1a4-bc5c-49db-ac8f-8819b07aed0a", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "image/png": "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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# gt samples\n", - "gt_samples = MultivariateNormal(observation, eye(2)).sample((len(theta_inferred) * 5,))\n", - "gt_samples = gt_samples[prior.support.check(gt_samples)][:len(theta_inferred)]\n", - "\n", - "kwargs = dict(density=True, bins=50)\n", - "for idx_param in range(2):\n", - " plt.hist(gt_samples[:, idx_param], **kwargs)\n", - " plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "257c04cd-6ad7-46e1-8a10-88774d5e09c4", - "metadata": {}, - "source": [ - "_ = torch.manual_seed(3)\n", - "theta = prior.sample((50,))\n", - "x = sim.sample(theta)\n", - "\n", - "_ = torch.manual_seed(4)\n", - "inference = SNPE(prior=prior)\n", - "_ = inference.append_simulations(theta, x).train()\n", - "posterior = inference.build_posterior()\n", - "\n", - "_ = torch.manual_seed(2)\n", - "theta_gt = prior.sample((5,))\n", - "observations = sim.sample(theta_gt)\n", - "print(\"observations.shape\", observations.shape)\n", - "\n", - "\n", - "oversampling_factor = 128 # higher will be slower but more accurate\n", - "n_samples = 5000\n", - "\n", - "non_corrected_samples_for_all_observations = []\n", - "corrected_samples_for_all_observations = []\n", - "true_samples = []\n", - "for obs in observations:\n", - " non_corrected_samples_for_all_observations.append(posterior.set_default_x(obs).sample((n_samples,)))\n", - " corrected_posterior = ImportanceSamplingPosterior(\n", - " potential_fn=Potential(prior=None, x_o=obs),\n", - " proposal=posterior.set_default_x(obs),\n", - " method=\"sir\",\n", - " )\n", - " corrected_samples = corrected_posterior.sample((n_samples,), oversampling_factor=oversampling_factor)\n", - " corrected_samples_for_all_observations.append(corrected_samples)\n", - "\n", - " gt_samples = MultivariateNormal(obs, eye(2)).sample((n_samples * 5,))\n", - " gt_samples = gt_samples[prior.support.check(gt_samples)][:n_samples]\n", - " true_samples.append(gt_samples)\n", - "\n", - "\n", - "for i in range(len(observations)):\n", - " fig, ax = marginal_plot(\n", - " [non_corrected_samples_for_all_observations[i], corrected_samples_for_all_observations[i], true_samples[i]], \n", - " limits=[[-5, 5], [-5, 5]], \n", - " points=theta_gt[i], \n", - " figsize=(5, 1.5),\n", - " diag=\"kde\", # smooth histogram\n", - " )\n", - " ax[0][1].legend([\"NPE\", \"Corrected\", \"Ground truth\"], loc=\"upper right\", bbox_to_anchor=[1.8, 1.0, 0.0, 0.0])" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "73d97121-7990-479f-b261-99ccbb2127e8", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " Neural network successfully converged after 93 epochs.observations.shape torch.Size([5, 2])\n" - ] - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "ec35acc6196b4bfda940bb2d87269ffe", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Drawing 5000 posterior samples: 0%| | 0/5000 [00:00" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "image/png": "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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "image/png": "iVBORw0KGgoAAAANSUhEUgAAAbwAAAC8CAYAAAADxxtrAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjkuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8hTgPZAAAACXBIWXMAAA9hAAAPYQGoP6dpAAA9a0lEQVR4nO3deXwU9f348dfsnc21uS/OAOG+EQREsaLgrbVqq61V+aptteq3oq3f+hPUarVVq9aWaqtQa1ut1dZWK3JUUJH7PpJAQiABct/XnjO/PyZZCIGQkE12k30/H127O7M7+96wn33PfE5F0zQNIYQQop8zBDsAIYQQojdIwhNCCBEWJOEJIYQIC5LwhBBChAVJeEIIIcKCJDwhhBBhQRKeEEKIsCAJTwghRFiQhCeEECIsSMITQggRFiThCSGECAuS8IQQQoQFU7ADECfRNPA06ffNdlCU4MYjRKiRMiK6Qa7wQomnCZ5J12+thVoIcYKUEdENcoUnhAhpPp8Pj8ejP3C7IGqgft/pAtUYvMBESDCbzRiNnfseSMITQoQkTdMoKSmhpqbmpI0qzH5Bv3+0GBSppBLgcDhITU1FOUsVtyQ8IURIak12ycnJ2O12/cdM9UGFS39C4hAwyBVeONM0jaamJsrKygBIS0vr8PmS8IKg4mg9NaXNDJuSdNYzEiHCkc/n8ye7hISEEztUH5hayozNhqopuJq9WCNMGIxytReOIiIiACgrKyM5ObnD6k1JeL2oydNE/oYqvnznIKqqMfsbw5k0b1CwwxIi5LS22dnt9jM+x+32Ul/pwedVabYYiUu1ywlkmGr9nng8Hkl4oeDDg/9i1Z/3MLpkpn/bhn/kkz7CQfLgmCBGJkTo6iiB1ZQ2AfqPm9fto7HGRVScrZciE6Gksyc6UgfQwzRN4/e7f8+//7qR0SUz0VCpnJBD5uREVJ/Gp3/Yh7vZG+wwhegTmr1O/30FAx6jG3ucGYCmOjcuKUuiA5Lwetjru1/nP6u+ZPLxeQCszfoL70Uu5eCEz4mOt1FX3szmjwuCHKUQoc/pdVJUW+R/7DW5qLGVUqIewxalJ736ymY0TQtWiCLEScLrQcUNxbz/1cdclP8tAKbMH8x3rrkWgN8feI20eXp1zNGc6qDFKERfUdpYSpQz3v84ITkGk8GEy+uiwVKNoiioPg2fRw1ilCKUScLrQa+teov5e+/CrFoYNCaeGddmcu3wa7l2mJ70Pm3+JwBVxxvxun1BjFSI0FbvasBYH4FZtfq3WYxmMqIyAKhx12C06D9nniCXpdtvvx1FUXj22WfbbP/nP//pb2tau3YtiqL4bykpKdxwww0cOnTI//whQ4a0eU7r7dTjis6ThNdD1m/chWPVBGy+SGIGmrjsf8ZiMOhf9tvH3g7Aqor/YI0yoqkaFUcbghitEKHL51NpKHdj8dnQlLbVlZHmSGwmG5qm4TW4AfC6gn+FZ7PZeO6556iu7rj2Jjc3l+PHj/Pee++xb98+rr76any+Ewn7ySefpLi4uM3thz/8YU+H329JwusBDdUutr9Vilm10pRWzjcXzcZqN/v3D48bzqz0WaioNMVVAlB2pD5Y4QoR0qrLGzB4jTR6PZhizTR5VP3m9tLs8WE3OHC6VSrctTR7fNQ1umlyewN2O5c2wXnz5pGamsrPf/7zDp+XnJxMWloaF154IY8//jj79+8nLy/Pvz86OprU1NQ2t8jIyC7HI3QyLKEHrP10JwbVRFnUEe784SWYre3HhXx79Lf56vhX7FO2MYFLKD9SB7PjghCtEKHL6/ahusDpVbn4DxtP2VvSKzHsf3I+dkvXfiqNRiPPPPMMt9xyC/fffz8DBgw462taB1C73e5zilOcnVzhBZjX4+PQBr0awzi5hsHxpx9YPjtjNkNihnDUng9AWaFc4QlxqoYGfRiC29Qc5Ei67vrrr2fSpEksXrz4rM8tLi7m+eefJyMjg5EjR/q3//jHPyYqKqrN7YsvvujJsPs1ucILsF0bCjC6LNRbqrnmkovP+DyDYuDbo7/NixWvAFBd3Ijb6cXSW4EKEeI0TcPrVLEYITLGwP4n5+tTi5Xu1Z+QMs4/l6ZX9ZJXnUe0MwGLz0ZUnI2IaHMHR++8CPO5z9f53HPP8bWvfY1Fixaddv+AAQP880FOnDiR999/H4vlxK/Aww8/zO23397mNRkZGeccT7iThBdAmqaxadVBwEp5Zi4TU77e4fOvzLySF7a9QIOlhii3g8rjjXQ89akQ4cPl9KJYFbwGD/ExDmwmE6gKmFsqpiymkyaPNpEc5cCr+YjwGDFDl6she8KFF17I/PnzefTRR9slLoAvvviCmJgYkpOTiY6Obrc/MTGR4cOH90Kk4SH434h+5FheNVq5FY/BzayvjTnrdDdRlijmD5lP+d5CotwOKgrrJeEJAWiqhsfphVhQrW5spoSzvibOGsfxxlKAkBrm8+yzzzJp0qQ2VZWthg4disPh6P2gwpS04QXQl1/sBKAoeS9XjlnQqdfcMOIGyqP02SOOH67qqdCE6FPKi+tAVdDQiI7pXK9Eu9mOYtZ7VHrdKpoaGjOujB8/nltvvZVXXnmly6+tr6+npKSkza2urq4HogwPkvAC6HiRnrDSh8dhM3VuEtuJSRMxp+jz/xUWlPdYbEL0JTkF+gBsn9FLjLV9Vd/pKIpCbEQMqqKPw/OG0IwrTz75JKra9Xgef/xx0tLS2tweeeSRHogwPEiVZgBplXpj89gRna9zVxSFOZOn07AV1BoTpPRUdEL0HUVHy3AMH4Ri7PxM+AAOm4NSQzUWn41mpxOztffHrC1fvrzdtiFDhuByufyP586de9bxfYcPHw5wZEKu8AKkqOw4NncUANNHT+zSa68deyUNFplPU4hWDeV6cjB3sYek2WDG0NI50+mU8WyiLUl4AbIlezcAzRF1xMc4uvRah82BO07q5YUA8Pg8GOr0JgGrtesDdSxWPeOpntBowxOhQxJegOQfOgqAOfHceodFpwdmzJAQfd2uY3uwu/VFkW2Wrie8CJueLBWf8ZzazUT/JW14AVJ1vBE7kDLQ0anne30qn+wt4aPdx7lifBqDhqZQvtPTozEK0RfsyNmPgQFoiobB2Llz8maPj8oGFwZFITnaSgNuFBSaXS4iW6bsEkISXgBUO6sx1uiN4yOHDTnr8/+zp5iff5JNUZU+XdKn+0q5fEQEk5CEJ0TB4WKGMQClE813bq+PYzVO6p0nyk6d00Os0YvRZ6bZKQlPnCBVmgGwrWQ78U2pAAwa0nE3y/e2FnHvX7ZTVNVMnN3M9ZMzMBkUPjnQjNPQ9+YLFCKQfKqP+hK9s4nxLFd3bp/KofJG6p0eFCA2wozFaMDtVXGhV2V63N6eDln0IXKFFwDbC3YR6ZuEpqg4UuxnfN57W4t45P3daBrcOmMQj105hgiLkTtmD+GeP22jwtM7s78LEapyq3OJbkgE5ew9NAsqmnD7NKwmI0MS7FjNRnyqytHqZtzNeqLTpNJEnESu8AIg/9AxACzxYDSd/k+6Pq/Cn+y+c/5gfnbdOCIseoGeMMDBo1eMptwkZ6MivG0t2UZ8kz7B3tna7zw+FavJSGZSJNaW5Gg0GBgQF4Gq6OfyBp8Jj0+yntBJwusmp9dJU5neMzNlgOO0z6lt9rDovV1oGnxj6gCevHZsu8G0V41PQ42K9T/2qaEzF6AQvWV/US4R3ihAw2DseMC52aiQmRSJ+ZTEaDQYiIu2oaFhwECjS5oKesvy5ctDem5QSXjdlF+TT1xL+136oNNPcLvkX/sornUyJMF+2mQHYDAofP2iCf7Hm4/t75mAhQhhJUf1CRjMDuW05aTedaIWJMMR0S7ZtUqMsuJT9JPG+sbeT3glJSX88Ic/JDMzE6vVysCBA7n66qtZs2ZNr8dyNqGepAJJEl43ZVdl+6tgEjKi2u1fsbeYf+w4hkGBF26a1OGSJZdOH+i//5fPNwQ+WCFCWJOnCU+F/pMUn9y+LHl9KsdqTiSvKOuZy5LBoKC0LCPk86ioZ5nGK5AOHz7M1KlT+e9//8svf/lL9uzZw4oVK7j44ou59957z+mYZ1oF3eOR6tqukITXTTkVOcQ16T0z49Paztvn8vp46qNsAL530TCmDo7r8Fgn90orO1ZOg0va9ET4OFhz0F9bkpQW225/eYMLr6/ziSvSrg9aN6pGapt6b5qxH/zgByiKwubNm7nhhhvIyspi7Nix/OhHP2Ljxo0AFBYWcu211xIVFUVMTAw33XQTpaWl/mMsWbKESZMm8Yc//IGhQ4diax1MrygsXbqUa665hsjISJ5++mkAPvzwQ6ZMmYLNZiMzM5MnnngCr/fE70dNTQ333HMPKSkp2Gw2xo0bx0cffcTatWu54447qK2tRVH0q+olS5YA4HK5WLRoERkZGURGRjJjxgzWrl3b5rMuX76cQYMGYbfbuf7666msrOzBv2z3SS/NbjpytJhJmgXFpBGT1Ha8z583FnKsppmUGCv3XzKiS8dNcJv5+9Yibp89NJDhChGycqty/bUlsckRaNT797k9Po7V1oKm0qS1TMLsaQbDmc/ZNaOPZl8zqqpSVFuOxRzXpYmoW0WYIjr9uqqqKlasWMHTTz9NZGT7iasdDgeqqvqT3bp16/B6vdx7773cfPPNbRJKXl4e77//Ph988AFG44keq0uWLOHZZ5/lpZdewmQy8cUXX3DbbbfxyiuvMGfOHPLz87n77rsBWLx4Maqqcvnll1NfX8/bb7/NsGHD2L9/P0ajkVmzZvHSSy/x+OOPk5ubC0BUlH51fd9997F//37eeecd0tPT+cc//sGCBQvYs2cPI0aMYNOmTSxcuJCf//znXHfddaxYsYLFixd3+e/bmyThdYNP9VFd3ARATKoVg+FEoWhweXn1szwAHpyXha2Lk+CmNqXw5oZsbps5pM1xheivsiuziW+aDoAj2U5144mEV1hTwx2fXRqUuDbdsgm7+czDjU6Wl5eHpmmMGjXqjM9Zs2YNe/bsoaCggIED9WaMt956i7Fjx7JlyxbOO+88QK/GfOutt0hKSmrz+ltuuYU77rjD//jOO+/kJz/5Cd/97ncByMzM5KmnnuKRRx5h8eLFrF69ms2bN5OdnU1WVpb/Oa1iY2NRFIXU1FT/tsLCQpYtW0ZhYSHp6ekALFq0iBUrVrBs2TKeeeYZXn75ZRYsWOBfrigrK4uvvvqKFStWdOpvFQxSpdkNRfVFRNUnApA6oG115R++OERVo5vMxEhunDqgy8dObBxAaWMen+WWBSRWIULd4WPHMKtWMGpEJ5xYT9Ll8VHd1Dfaqs625A9AdnY2AwcO9Cc7gDFjxuBwOMjOzvZvGzx4cLtkBzBt2rQ2j3ft2sWTTz5JVFSU/3bXXXdRXFxMU1MTO3fuZMCAAf5k1xl79uzB5/ORlZXV5rjr1q0jPz/f/zlmzJjR5nUzZ87s9HsEg1zhdUNOdY5/hpXEAScWqaxt9vD7z/UFLB+6bCSmTs4HeDIjJtKUepatP8wlo2WRPNG/eVUvNcedAMQkW9u0Z5fWu7AYrLw3/zMGxdmgbK++I3lch1WaANWV9XibwGlqok6LICsl6ow9O88kwtT5qclGjBiBoijk5OR06T1O53RVoqfb3tDQwBNPPMHXv/71ds+12WxEnMPUag0NDRiNRrZt29amOhVOVHn2RZLwuiGnMof4Jv1yPz79xJfwnzuO0ej2kZUSxeXjUs/08rPK8Bn4Mq+C/PIGhiX13S+ZEGdzpO6IPsMKbSdg9/hUaps9KIrC4HgHESYFjFZ9pzkCDGdpKog20uByYVZMuAwGml0mYmNtHb+mG+Lj45k/fz6/+c1vuP/++9slp5qaGkaPHk1RURFFRUX+q7z9+/dTU1PDmDFjuvyeU6ZMITc3l+HDT7/w9IQJEzh69CgHDhw47VWexWLB52s77nfy5Mn4fD7KysqYM2fOaY87evRoNm3a1GZba6ecUCVVmt2QW3GAWKde5ZCQrickTdP46+ZCAG6dMbhb7W8D3A5A4y+bCrsbqhAhLacqx99hJTHjRG1JvdODpmnYLSYiOhjScyY2m77slkk1YzB4qG5y9/gQhd/85jf4fD6mT5/O+++/z8GDB8nOzuaVV15h5syZzJs3j/Hjx3Prrbeyfft2Nm/ezG233cZFF13UrrqyMx5//HHeeustnnjiCfbt20d2djbvvPMOjz32GAAXXXQRF154ITfccAOrVq2ioKCATz75xN/WNmTIEBoaGlizZg0VFRU0NTWRlZXFrbfeym233cYHH3xAQUEBmzdv5uc//zkff/wxAPfffz8rVqzg+eef5+DBg7z66qsh3X4HkvC6peRoNQYMmCIU7LF6F+gdRTXklNRjNRm4bnJGt46fXDcAxVjL37cdxemRmVdE/3VyD82Ta0tqW9ru4iPPbb1Ig9GAZtQnkrai4vGp1Df3bHtgZmYm27dv5+KLL+ahhx5i3LhxXHrppaxZs4alS5eiKAoffvghcXFxXHjhhcybN4/MzEzefffdc3q/+fPn89FHH7Fy5UrOO+88zj//fH71q18xePBg/3Pef/99zjvvPL71rW8xZswYHnnkEf9V3axZs/je977HzTffTFJSEr/4xS8AWLZsGbfddhsPPfQQI0eO5LrrrmPLli0MGjQIgPPPP5/f//73vPzyy0ycOJGVK1f6k2yoUrTOtLKKdiqaK7j7Nw9zSd53SBkezTcW6T2rHn5vF+9tO8oNUwbwwk0Tu3ZQdyM8o1eR/rb8LTRfNB8OzeZA9RCev3Ei3ziHzi9C9AV3r7iHcR9+A6Nm5LZnZmG2Q86BPJSYZExmK6PSYjAaFFB9ULJbf1HqhLNXaQLlpTVoTgMuSzPVPitRVhOZ0kTQrzidTgoKCtqMWTwducI7RzlVOSS0tN8lZ+irM9c5Pfx793EAbpkx8Iyv7Qw1SR/uMD5C/yd6e+ORbh1PiFClaRrFRysxakaMVoWoOL2NrtGtX4E47GY92Z0ji02vCjX69OTY4PLikhqTsCQJ7xztrdh7ogqmZUqxD3cex+lRyUqJYsqgjmdVOZu4QXqhj64zYTYq7CyqYd/x2u4FLUQIOt54HGOtPs4tISMKRVGobXLT3JKU4iMt3Tq+PUI/4zf6zETb9MRZ2dh7M6+I0CEJ7xztLt/drs3h3zv1q7ubpg08pxkdTjZ8pN7+Z6twcOnoZAD+sf1Yt44pRCjaXb7bX1vS2mHli7wKNA2sJuM5dVY5mclsRFNUFBQiWtrzapo9nRozJ/oXSXjnQNM0sosPEOXWr+IS0iMpqXWy5UgVAFeMT+v2e0wYMwINlWhnAtMH6Y3s/959HJ8qhVT0L7vLdxPXOgF7y8njutxyAKIjuj9ySlEUNLOe6BSvitGg4PWpNMpctWFHEt45KKwvxFSjF8xIhwWr3cwne4vRNJg6OI50R9cHep4qItJKc0wNAOa6SmJsJkrrXGwuqOr2sYUIJfoVXmvCi6KywcWOIn2ZoOgOVkToCpNF/6lTPRqxEXqPz5o+MnuLCBxJeOdgd/lukhr1TimtM6x8vLsYgCsDcHXXypyun4GWFtRy+Tj9uP/aJdWaov9w+9wcKj1CjEsfdJ6QEcWKfSVoGliMChZT1+agPRNbhN4OqHhNOFoSXq3T06vLBongk4R3DnaV7yK9Vp/VIG14LMW1zWw9op+RBqI6s1XaMAcA7uNGrp2kt3H8Z08Jbq8asPcQIpiyq7JJrNHHi8Wl2bFFmf0njxGWwCQ7ALutZQV0zYARH2ajAZ+qUe+Uas1wIgnvHOwp30Na/TAAMrLi+M+eEgDOGxJHagCnLZowXk+q9pp4JqRHkhxtpbbZw+cHygP2HkIE057yPaTXtZSlEXGU17vYeEhfUy2QCc9gMKAa9eTW7HThsLdWa0pvzXAiCa+Lmr3NlB+tw+aNxGhWSBoczcctY+8CWZ0JkDVoKE2WOoyaiT3ZB7hqgn6V969dxwP6PkIEy+7y3aTX6Sd26VkOVuwrQdVgZEo0prNMDN1VSsvoBo/T56/WrHd6pSNYGJGE10XZldkk1w0B9CrH4jon2wtrUBS4PMAJz2gw4kqsAeBA9lGunqgf/785Zbi8MnBW9H3Zxbn+IQnpIxx81HIyd9HI9svidJfZql8xah4Fm9mI1WRE1TTqndJ5BU6ssh5sc+fO5cEHH+yRY0vC66KTz0gzshz+9oYZQ+NJiQn8LOzRg/RCWnW4iYkDHKTEWGlwefkqvzLg7yVEb6porkArjkDBQEyyjQZFY/NhvRfy3JHJAX+/1gHoBtWIqmrEtAx5qGsObDteSUkJDzzwAMOHD8dms5GSksLs2bNZunQpTU1NAX2v3rR27VoURaGmpiYkj9cZkvC6aHf5btJaq2BGxPFRS8JrrW4MtKEj9bXwlNJIFODSMfrjlftKeuT9hOgtJ5elAVnxfLz7xNCenjh5tJmt+Awt7XjNTmJsrdWageuteejQISZPnszKlSt55pln2LFjBxs2bOCRRx7ho48+YvXq1Wd8rcfTP6403e7QbReVhNcFmqZxoOAwdk80igmao43sOVaL0aB0a927jkwZMxaPwY3ZY6P0eA3zx+rvs2p/qbQ9iD5tW+m2E+13Ixz+eWivnhDYpoFWiqKgmfSmAKfTg91i1HtralrABqH/4Ac/wGQysXXrVm666SZGjx5NZmYm1157LR9//DFXX311m3iWLl3KNddcQ2RkJE8//TQAS5cuZdiwYVgsFkaOHMmf/vQn/2sOHz6Moijs3LnTv62mpgZFUVi7di1w4sppzZo1TJs2DbvdzqxZs8jNzW0T67PPPktKSgrR0dEsXLgQp9N5xs91+PBhLr74YgDi4uJQFIXbb78d0Ksg77vvPh588EESExOZP3/+WePs6HgAqqryyCOPEB8fT2pqKkuWLOnsP0GHJOF1QX5NPvYKvW0hdWgsn+zXr7JmD08kIcraI++ZEZNOdbT+Q7Bn70HOz0wgxmaiosHNjsLqHnlPIXrD9qKdJDbqK4AoyVZ2tLSFn25oj6ZpeFw+/ebW9Fvr4y7cVDS8bh+uBg9et4rdoOBz+6isdZ7xNZ2dgqyyspKVK1dy7733nnG18lOnHFyyZAnXX389e/bs4c477+Qf//gHDzzwAA899BB79+7lnnvu4Y477uCzzz7r4l8XfvrTn/LCCy+wdetWTCYTd955p3/f3/72N5YsWcIzzzzD1q1bSUtL47e//e0ZjzVw4EDef/99AHJzcykuLubll1/27//jH/+IxWJh/fr1/O53vztrbJ05XmRkJJs2beIXv/gFTz75JKtWrery3+BUsuJ5F2wq2eQffzcgK47Xdh0F4KoeOiMFvYAoyS6ohcKCcsxGA5eMTuEfO47x6b4Spg2J77H3FqKn1LpqqSv0YsBAZIKF/xbqbXfnD00gOcbW7mrD61Z5/YF1pxzly16J9e6XL/J3eOlIXl4emqYxcuTINtsTExP9n+fee+/lueee8++75ZZbuOOOO/yPv/Wtb3H77bfzgx/8AIAf/ehHbNy4keeff95/RdRZTz/9NBdddBEAP/nJT7jyyitxOp3YbDZeeuklFi5cyMKFCwH42c9+xurVq894lWc0GomP139rkpOTcTgcbfaPGDHCv44e6FeEHTnb8SZMmMDixYv9x3711VdZs2YNl156aac++5nIFV4XbCne4q+CUZOs5JbWYzYq/mrGnhI/UJ+qrP6YXsc/f6zejvfpvlKZAFf0SVtLtvrL0sCRCfy7pXfmVRN77uQxWDZv3szOnTsZO3YsLperzb5TVzjPzs5m9uzZbbbNnj2b7OzsLr/vhAkT/PfT0vS/a1lZmf99ZsyY0eb5M2fO7PJ7tJo6deo5v/Z0To4d9PhbY+8OucLrJFVTyT10mBGeazCYYG1FHQAXZSX75+brKUOHpVH4XzBU2VF9KhdmJWE1GSisaiKnpJ7RaTE9+v5CBNrmks2k144AwJgawb79dZgMin8KvVOZLAbufvkifQHY0r36xpRxnVoA9mSaplFyrAqTaiYi3kRUlJ2i6iZqm9wkRltJjWk/D27rPJxnM3z4cBRFaddWlpmZCUBERPtjn6nq80wMLWMTTz7RPVNnF7P5xO9Sa1WqqvbMLE2nfo6uxHk6J8cOevyBiF2u8DoptyqX2Eq9MKZmxvJ+y5yWvbEK+YRho3AbnBhVE+XFddgtJuaM0NsSV+4r7fH3FyLQthftIqml/W59XT0AXxuVfMa17xRFwWw16jeLot9aH3fhZrGZMNrBZDGiaipmq5GEGCtGi5FGn4rJYmj3ms4u9ZWQkMCll17Kq6++SmNj4zn9XUaPHs369evbbFu/fj1jxowBIClJL/fFxcX+/Sd3DOnK+2zatKnNto0bN3b4GotF/7fx+c4+BrgzcXbleIEiCa+TNpdsJqM2CwBPopXyehfxkRa+Nirw44VONTB2INVR+hcnO6cAOLlaU4YniL6lsrkSZ6ERBQPRyVb+kaN/h2+aNrBX3t9o1hOY161ffUTZzCiKgtur4urmPLW//e1v8Xq9TJs2jXfffZfs7Gxyc3N5++23ycnJwWjs+Ir04YcfZvny5SxdupSDBw/y4osv8sEHH7Bo0SJAv0o8//zzefbZZ8nOzmbdunU89thjXY7zgQce4M0332TZsmUcOHCAxYsXs2/fvg5fM3jwYBRF4aOPPqK8vJyGhoYzPrczcXbleIEiCa+TthRv8XdY2dCgn71dNykDi6nn/4QGxYCW2AzAkUP6Fd280SkYFNhfXEdRVd8dzCrCz5bSLWTU6dWZhhQ7FQ1uEqOszO2B2VVOx9y65JBXQdM0jAbFvwxRbXP3xsINGzaMHTt2MG/ePB599FEmTpzItGnT+PWvf82iRYt46qmnOnz9ddddx8svv8zzzz/P2LFjee2111i2bBlz5871P+fNN9/E6/UydepUHnzwQX72s591Oc6bb76Z//f//h+PPPIIU6dO5ciRI3z/+9/v8DUZGRk88cQT/OQnPyElJYX77ruvw+efLc6uHi8QFE16PZyVV/Vyze9v4sod92GwKPwqqhm3qvHJA3MC237mboRnWgaw/99xsJyoF3/+L78n4vNh+JLruf/JawH45usb2HioiseuHM3/zMkMXBxC9KAnNzyJ8rdhxDenUTAygr+XVnH3hZn83xWj/c9xOp0UFBQwdOhQbLaTBqGrPijZrd9PndDlNjyAJk8z9cfdKCjEp0diMhupanRztLqJCLORESnR3f2Ioped8ftyCrnC64ScqhziK1uqW5KsuFWN8RmxvdpZZFBmS9VpZQRqy4Dz1t6hK/dLO57oO3Yf2U98cxqg8UmpPpb0xl5oC29lM1nxGfQrOZdL//8YmwkFaPb4cMs8tf2WJLxO2Fqy1V8Fs9ujT5tz47TeK6AA44dn4TG4MPpMVJfoVaqXtSS8rYerqGxwdfRyIUJCRXMFvqP6GbgWZ6IRjYkDHb16VWVQDKgtM664nHp5NhkN2K09M7emCB2S8Dpha/E20lrW7Nra3ITFZOCaiT0zd+aZDI8bTmWkPlbpwIFCADIcEYzLiEHVYHW2XOWJ0Le1dKu/81ceegeRm3ups8rJDK0dVzwnOqm0zq1ZK6sn9FuS8M7Cp/o4drAKs2rFa1YpN2jMH5uKw3767tM9xWw0403QezEV5J/o6rug5SqvdRFaIULZtuJtDKzVZyLZ5XZiMxuCMtjcYjmxVFBrN4bYltUTmlxePL6eGa8mgksS3lkcrDlISplenZlnUkEJzhkpQHS6XiCrik70ymydd/DLvAqqGkN3lnIhAA4cKCLKHYfPqFJoUrlifJr/yup0eqpPnc1mQwMUTUH16u9hMRmxW0xodL+3puhdnf2eSMI7i60l2xhapU9zs8/gI8MRwaxhCUGJZeCwRAC0Mhu+lvFCmUlRjMuIwadqrNgrV3kidNU4azAf0edPLDB78HVw8tg600ZPrR8XYbLhNegniK0dVwD/rEm1TZLw+pLW78mpM7ScSqYWO4u9+/MY6J6Dx+DliEnlh9MGYDB0buaFQJs8YiyfmgqweSMpK6wlLTMO0Nfi23usjn/vOs4tMwYFJTYhzmZr6YmTx/0GhSEJdqYPPf3k50ajEYfD4Z8/0W636zOeqD5ouSLD6TynYQmtPFozeKG2zo3BpB/TpqhoXjcNXjd1DYZeGWcrzp2maTQ1NVFWVobD4TjrwH5JeB3QNI2GXD255VuaUA3mXplK7ExGJ47mLzFfkFE1kt378kjLPA+AK8en8ewnOWwsqKSszklyDyyeKUR37cjdh8M5Dp/i45DZx4PTBnY4bVdqqt4+3WbSYE2F2nL9foMVlHNPSLWN9RicZjSjSk2t/cT2ehcur4qr2ky0TX4i+wKHw+H/vnRE/jU7kF+TT3q53sB+wGBm9rBEBsTZz/KqnmM0GLGk+aAKCg6cqL4cGG9nyiAH2wtr+M+eYm6fPTRoMQpxJqV7mxgKHLHW4jNEcMOUjk8eFUUhLS2N5OTkExMPu5vgPzfr9+/+HCznXh4/y/mS4/82oSk+bv6/UZhbOrLs3nmMVz47yMiUaH777cCuAiACz2w2n/XKrpUkvA5s3r+LWGcSXsXLITN8P4hXd60GjUiieR80H2u7/aoJ6WwvrOHfuyXhidBT764n+pg+lOeAycgFI5JIje1cTYTRaDzxg2bwQUORft9mBcu512ZMHzmZN5rWEelxcKyoklFj9eaAS8YN4NEPczhWX0Npo4/BCV1b0UCELqmg7sChHXrVSWFEBVabqcfXveuM6RPHo6JiaYqkpurEZKtXTkjDoMC2I9UcKu/5SViF6IqNOdtIbByAikqewcINUzKCHRIJEQk0xFcAsHPPiSV9EqOszB6udxB7f/ux075W9E2S8M7Ap/ow5usN6gfMKleMTyXCcu4N5IEyJnUUtZH6IPOtu/b7t6fE2Jg7Up9+7N0tRUGJTYgz2bNRnyzhqL0MU0RonDwCxA7Se/UV59e02d66csPfthThlTF5/YYkvDPYtGs3Mc2JeAxucojhG1ODM/buVAbFgJKqTyN2MOdom33fmq5Xyby37SgumQ9QhAhN0/DmRAGw39bIVRPTsJmDf/IIkDVKLzNaqa3NWK7LxqYQH2mhpM7J2tzyYIUnAkwS3hnsWn8YgPyYAtISojlvSFxwAzpJ+jAHAPVFbccKXTwyiZQYK1WNblbJhNIiROzfX0BkkwOPwUWO5jhrZ5XeNHviFHyKF6s7ksNFJ6ovrSajv9r1r5sLgxWeCDBJeKfh86i4c/XG8H22Rr4+JaPTqx73hinj9WVUbNVxNDud/u0mo8FfFSOFVISKzZ/r7WP5sXlkxCcydXDonDzGR8XRGKu3423ZtbfNvm+21Jh8llvG8ZrmXo9NBJ4kvNM4sOs4Jo+VBksNR9Ukf1VhqBg/bCROcwNGzcSXO7a32XfTtIEoCqzPq+RwRWOQIhRC5/Oo1O/Xf2b226v59vmDQ+rkESBigB7PkZyKNtuHJUUxY2g8qgZ/2yrt4v2BJLzT2Pr5QQAOxO9hwejxpITYQG6DwYA6sB6AvdsPt9k3MN7OhSP0laPf3nikt0MToo1Du8sxesw0WKopNSRy03mh0RZ+srGTBwOgFEXj87Vt+26dueivmwtlQul+QBLeKVxNHmoP6l/6/fZq7rwgNFcSHzZB75HpPmRuN3Hqd2fpBfjdrUU0umRtLxE8O77KB+BAwg6uHzurw4mig2XOtKl4jC4i3NFs2LOzzb4F41JJjLJSWufiE5mrts+ThHeKI3srUTQDVRHFRMYOZdJAR7BDOq2LZ01HVXxENSaw79DBNvvmZiUzJMFOvdPLB9uPnuEIQvQsr9tHWa4+qe9BeyV3zB4e5IhOz2a14k6vAmDH5rZlyWoycmvLVd6y9QW9HpsILEl4p9i7Ve/scThuLwunXRbkaM4sLiaWxkS9zWHDxj1t9hkMCt+dNQSA5V8dRlV7ZokVITpyNLcaxWug3lJNVMIAMpOigh3SGQ0cp4+5bchrv+/W8wdhNirsKKxhZ1FN7wYmAkoS3kl8HpWj+2sBOBJdxTcmjQxyRB1LGhUBQHmOs92+b0wdQJTVRH55I1/kVbTbL0RP27lJb0M+EreHe6ZfG+RoOjZ31jQ0VGLqksk/1rbtOznaxtUT9GnRlstVXp8mCe8kRblVGL1GGs21jB4xDrMxtP88F8yaCEB0RTLltVVt9kXbzNw4TR/v9OaXUkhF79JUjcN79BOto45yrhk7NsgRdSwtKYnGuEoAPv9qR7v9d7TMT/vxnmJK69qfYIq+IbR/0XvZqjX6eKEjcftYdME3ghzN2Y0cOpTGyGoMmpHPvtrUbv/ts4agKLDuQDk5JXVBiFCEq8MHqzG7zLiMzYwZMyrkhiKcjmOkPpd+yf76dvvGD4hl2uA4PD6NN+Uqr8+ShNdCVVVq8vSkUJNcw2BHWpAj6pyIYXovzEM7209/NDghksvH6XMWvr7uUK/GJcLbRyuyAShyZHPvzOuDHE3nzJiuX4XaS5OorK9ut/+ei4YB8JeNhdQ5ZUX0vkgSXouV649i91jxGFxceP74YIfTadNm6u2MtqNJ1DW2XyXhngv1QvqvXcc5JrNFiF7g8vqozdcTRk1KOYNiQmcqsY5MHD2SJnsNJtXCynVftdt/yahkhidHUe/y8pdNMpNRXyQJD31y21UrW89Ic7hu9IIgR9R5MyZOoNlWh9ln5dMvvmy3f+JABzMzE/CqGm98IVUxouf9ddUh4tx2fIqP888P7Y5fJ1MUBfsofQzu4e1V7fYbDAp3X6iPy33zywKZoL0PkoQHfJ5bTlKV/uV1DakgJTIlyBF1nsFgwJqlr56Qv+30s7p/b65+lffOlkKqG929FpsIPy6vj01rDwBQ6NjHFaMvDXJEXTP7Qr12x16cTHlNZbv9103KICXGSlm9iw9krbw+J+wTnqZpLP9wL9E+Gy5jE1+bMy3YIXXZ+ReMAcB2LInq+tp2+y8ckciYtBia3D5eXnOw3X4hAuXdTYUMadTHffpGVDMwOvSmEuvIxJEjaYyswqSZ+XRt+2pNi8nA/7TMvvTy6oM0uWUmo74k7BPeugPl2MuLATiWmsPVI68KckRdN3XcWJoiajCrFj79fH27/Yqi8H9X6Css/GnjEfLK2vdCE6K7nB4f76/cQ5TXjtPYyM2XXhHskLpMURSi9aJC4c6a0z7nOzMHMyAugpI6J79bm997wYluC+uE5/aqPP/xXkY0JQAwZU4mZkPozfV3NgaDAfso/UyzYHv7ahiAC0YkMm90Cj5V48mPstvNvylEdy1bf5hBTfr3r3pgITMGTA9yROfmoosmAxBVmkJxZft1JW1mo/8E8rXPD3G0uqlX4xPnLqwT3gsrczFUZWNWLdRHVHLDBX3vjLTV7DkTAIg8nsLBo6fvnPLTK0djNip8fqCc/+aU9WZ4op/bfbSGX6/ezrBGfTjPnIvH9Ymxd6czZsQwGqMrMWom/vmfz077nMvHpTJ9aDwur8rPP8np5QjFuQrbhPf5gXJe/3I3E5sjAUicZMZmCq1lgLpi4uiRNCZUYNRM/P290xfSoYmR/hkjHv1gD2X1MmOE6L4Gl5f7/7qDscY8zKqFpshqLj1vTrDD6paB50cD0LDFRkV9+1oTRVFYfPUYFAU+3l3MP3dIB5a+ICwT3qHyBv73bzuYZTlAauMgfAYP111xcbDD6hZFUbjgar0LeMTBdHYc2X3a5z04bwRZKVGU1bv44V924JU1vkQ3uL0qP/77bhqbtzC7fBIAoy5IxWDo2z8t37h6Hk5bA3Z3DG998K/TPmdseiw/vFhfAeLRD/aQWyJt46Gub38rz8Ha3DKu/c16IrUNnF9yPgATrkshMSk2yJF138wZE3Al6J1X/vn3L077HLvFxNJvTyXSYmRTQRW//DS3l6MU/UV5vYtbfr+Rlbk7uLImDbNqRclo4sprZwU7tG4zW0wMn+sAwLMtlsKa0694/sC8LOaMSKTZ4+P7b2+TGVhCXNgkvGa3j+c/zeXO5VtQjLu4onwsBozYR3m56NKJwQ4vIBRFYe51+vRIcfnD+M+elad93rCkKH55o/6ZX/v8EH/4QqYdE13z35xSrnn1S7YdLeAytYqkpgy8Fhffue8SDIa+2XZ3qiuumIXb1kSU28Ef3v07qta+NsRoUHj5m5NJj7VxqKKRhcu3yKLLIazfJzxN01ixt5h5L67j1c/yiLfm8q2KIUS74/FFO7nlnq/12cb105k8LQs1qRGzamHf6018tn7zaZ93xfg0Hpw3AoCffZzNG7KiguiEwy0/6ncu30pZQynXUMaYiqkAzLt9NNFxEUGOMHDMFhNjL9EnoUjeMZ5Xlr6Dz9M+6cVHWnj9tmlE20xsOVzNHcsk6YWqfp3wNh6q5OtLv+J7b2/nWE0zY2ML+GbFcGJciXijm/nOoguxRpiCHWZAKYrCN79/Ea7oOuyeGPb/qYEPlm6i7Ej71RIeuGQEP/ya3gbx1Ef7+dWqA7JYrDitsnonj/1zD/NeXMeanDKizFV809dEVuVEVEVlyk1pjJ0yJNhhBtwlV0wlcrwXBQPm3am8vngVuZtK8HnbJr5xGbG8vXAG0VYTmw9X8Z03NkmnsBCkaP1sQFaT28tHu4r58+ZCdrWsThxl9nFFRAmDCjNRMOCOr+OuHy8gKjbEemW6G+EZfaFJ/u84WCLP+VB1jQ089+qbpBeM829LHxHLBTdmkTQo2r9N0zReXHWAX/9XX+p53ugUXrx5IjG2vjceUQSWpmlsPVLNnzce4T97S3C3/Mh/LbmC0Ydjsbtj8BrczLkjkynnZfVOUAEsI13x+w/foXaVnQivvmp7RIyZ6VdlMuaC9DZVuDuLavjOG5uod3pJibGy9NtTmTIorldiFGfX5xOepmkcqWxiw6FK1mSX8sXBClwtBTMBmB/XiKPERGSzQ39BVg0Lv3cVNrslaDGfUYALc7Wzmh+/v5iI7AEMq5yMUTOCohE/1cC0ywczJHUgZqOe2N7bWsRP/7kXt1clwxHBY1eO5vLxfWOJJBE4tc0eth6u4rPcMtZkl1Fcq1+lmDWYG+thaHMzseV6NV9zVC1X/c9kRo0a0nsBBinhaZrGbze9xuY1BxlbcgGRHr2TmyXVx/hrkhkzdigx1hhA7wV+95+2kVfWgNmocOcFQ7nv4uFEy0lk0PWphFfv9HC4oolDFQ0cLG0gp6SO3UdrKavXJ0+2qpCoqYy1uhjuthBZF+V/rdPSwNivx7Ng7gXBCv/seqAw+1Qff87+M29s/COTC+YzokKfK1TFx1FHLvUDjjMiawCzx05D8wzkR+9k+5cROvzsld1+fxGaPD6VoqomDlc2klfWQE5JPfuP15FbWo+mgUGDeFVjsNHFBKNCXHUURlWv/tdQ0cZXcdfCa7HZrL0beJASXqtd5btY8sUT2A6kcl7RlVh9eptlta2UY6n7SRwWyfRx4xmXNpnn/n2cFfv0mVoSo6xsfWxer8Yq2gtIwvvx02+2edx6RA3Q0Gj5n/6GLf9RUE663/a1GvoZlapp+FQNr0+/qaqGEQWjpmDWjJhVExbVhM1nxe6LwOZt22CuoVIaV0D6pEhuuvxyEmJCvGqhBwvz8YbjrClcQ/6+YixbBxBbm9pmv1dx02itxRvhxG1QafL4ePEX/xOw9xed064stfxHa3mknVqW0NttT9xv/1oVDU0Fn6bh82l4fCo+VcOggVEzYNIM/rJkVc1EqDbs7kgMpzTx11orUEdUc+VlFzA5a2xgP3hnBTnhAbh9blYfWc3ewhya1kcRd2wwJvXE1ZuGSqOlDpe1EdXqo9Hjw6NpvCTlKegC0mNjUNGQQBwmIOotVdTFlGEb5mXclCHcOeZW7GZ7sMMKuvSodL4z5jswBrgRqksa2flVAYeyS2gqVjF5LcQ6k0Da2YMqlMqSy9hMjb0UX0YdgyfE860pcxjqGBrssILOYrRwReYVXJF5BcwFd7OX/VuPkr29iMqiRpQGC1FuB1FuB9RDUrADFn4BSXiFgw/777eeayqcONtUWi/j/Fd+2ilnrpz0ev0/BhQMBgWjQcFkULCYDFjNRsxmAwaTgtFiwGQ1YLIZsNpNWO0mMtKTGZo4kXhbfL8aatAT4lIjufjr47iYcWiqRl2lk2OlpRw6VkR9UwMerwygDYYzlaXWIuS/lju1PJ1Uq3IypeV1BkUvS8aWsmQ2GbCZjRhbypPJZsBkMWC2G7HZTTjioxmWPpy06Mv65ITqvckSYWLSnCFMmjMEgOZ6NxVldeQfP0JZZSVurwe3V9ahDAV9qg2v3wuB6hohQpqUEdEN/XocnhBCCNFKEp4QQoiwIAlPCCFEWJA2vFCiaeBpWT3ZbG/bx1wIIWVEdIskPCGEEGFBqjSFEEKEBUl4QgghwoIkPCGEEGFBEp4QQoiwIAlPCCFEWJCEJ4QQIixIwhNCCBEWJOEJIYQIC5LwhBBChIVur4enaRr19fWBiEWEoOjoaFlbsJdIWer/pDwFV7cTXkVFBcnJyYGIRYSgsrIykpJkzebeIGWp/5PyFFzdTngWiwWAoqIiYmJiuh1QKKurq2PgwIFh9Vlb/31Fz5Oy1H9JeQoN3U54rZfnMTExYfHFhfD6rFL90nukLPV/Up6CSzqtCCGECAuS8IQQQoSFbic8q9XK4sWLsVqtgYgnpMlnFT0pnP7m4fRZIfw+b6iSBWCFEEKEBanSFEIIERYk4QkhhAgLkvCEEEKEBUl4QgghwkK3E97tt9+OoihtbgsWLAhEbCHnN7/5DUOGDMFmszFjxgw2b94c7JACbsmSJe3+PUeNGhXssMKClKX+RcpS6On2TCsACxYsYNmyZf7H/bHr7bvvvsuPfvQjfve73zFjxgxeeukl5s+fT25ubr+b/3Ds2LGsXr3a/9hkCsjXRHSClCUpS6LnBKRK02q1kpqa6r/FxcUF4rAh5cUXX+Suu+7ijjvuYMyYMfzud7/Dbrfz5ptvBju0gDOZTG3+PRMTE4MdUtiQstS/SFkKLQFJeGvXriU5OZmRI0fy/e9/n8rKykAcNmS43W62bdvGvHnz/NsMBgPz5s1jw4YNQYysZxw8eJD09HQyMzO59dZbKSwsDHZIYUPKUv8iZSm0dDvhLViwgLfeeos1a9bw3HPPsW7dOi6//HJ8Pl8g4gsJFRUV+Hw+UlJS2mxPSUmhpKQkSFH1jBkzZrB8+XJWrFjB0qVLKSgoYM6cObJOWy+QsiRlSfQwrQvefvttLTIy0n/7/PPP2z0nPz9fA7TVq1d35dAh7dixYxqgffXVV222P/zww9r06dODFFXvqK6u1mJiYrQ//OEPwQ6lX5GyJGVJ9L4utaBec801zJgxw/84IyOj3XMyMzNJTEwkLy+PSy65pHvZOEQkJiZiNBopLS1ts720tJTU1NQgRdU7HA4HWVlZ5OXlBTuUfkXKkpQl0fu6VKUZHR3N8OHD/beIiIh2zzl69CiVlZWkpaUFLMhgs1gsTJ06lTVr1vi3qarKmjVrmDlzZhAj63kNDQ3k5+f3q3/PUCBlScqSCILuXB7W19drixYt0jZs2KAVFBRoq1ev1qZMmaKNGDFCczqdgboKDQnvvPOOZrVateXLl2v79+/X7r77bs3hcGglJSXBDi2gHnroIW3t2rVaQUGBtn79em3evHlaYmKiVlZWFuzQ+jUpS1KWRM/rVsJramrSLrvsMi0pKUkzm83a4MGDtbvuuqvffXFb/frXv9YGDRqkWSwWbfr06drGjRuDHVLA3XzzzVpaWppmsVi0jIwM7eabb9by8vKCHVa/J2VJypLoebI8kBBCiLAgc2kKIYQIC5LwhBBChAVJeEIIIcKCJDwhhBBhQRKeEEKIsCAJTwghRFiQhCeEECIsSMITQggRFsI24c2dO5cHH3zQ/3jIkCG89NJLQYtHiL5MypPoC8I24Z1qy5Yt3H333QE/7tNPP82sWbOw2+04HI6AH1+IUNQT5enw4cMsXLiQoUOHEhERwbBhw1i8eDFutzug7yP6ry4tD9SfJSUl9chx3W43N954IzNnzuSNN97okfcQItT0RHnKyclBVVVee+01hg8fzt69e7nrrrtobGzk+eefD/j7if4nLK7wGhsbue2224iKiiItLY0XXnih3XNOrYJRFIXXXnuNq666CrvdzujRo9mwYQN5eXnMnTuXyMhIZs2aRX5+fofv/cQTT/C///u/jB8/PtAfS4igCFZ5WrBgAcuWLeOyyy4jMzOTa665hkWLFvHBBx/0xMcU/VBYJLyHH36YdevW8eGHH7Jy5UrWrl3L9u3bz/q6p556ittuu42dO3cyatQobrnlFu655x4effRRtm7diqZp3Hfffb3wCYQIHaFUnmpra4mPjz/XjyLCTXAXa+h59fX1msVi0f72t7/5t1VWVmoRERHaAw884N82ePBg7Ve/+pX/MaA99thj/scbNmzQAO2NN97wb/vrX/+q2Wy2TsWxbNkyLTY29pw/hxChIFTKk6Zp2sGDB7WYmBjt9ddfP7cPI8JOv7/Cy8/Px+12M2PGDP+2+Ph4Ro4cedbXTpgwwX8/JSUFoE3VZEpKCk6nk7q6ugBGLEToCpXydOzYMRYsWMCNN97IXXfd1ZWPIMJYv0943WE2m/33FUU54zZVVXs3MCH6oECVp+PHj3PxxRcza9YsXn/99R6IVPRX/T7hDRs2DLPZzKZNm/zbqqurOXDgQBCjEqJvCnZ5OnbsGHPnzmXq1KksW7YMg6Hf/4SJAOr3wxKioqJYuHAhDz/8MAkJCSQnJ/PTn/601wpKYWEhVVVVFBYW4vP52LlzJwDDhw8nKiqqV2IQIlCCWZ5ak93gwYN5/vnnKS8v9+9LTU3t8fcXfV+/T3gAv/zlL2loaODqq68mOjqahx56iNra2l5578cff5w//vGP/seTJ08G4LPPPmPu3Lm9EoMQgRSs8rRq1Sry8vLIy8tjwIABbfZpmtbj7y/6PkWTb4oQQogwIBXgQgghwoIkPCGEEGFBEp4QQoiwIAlPCCFEWJCEJ4QQIixIwhNCCBEWJOEJIYQIC5LwhBBChAVJeEIIIcKCJDwhhBBhQRKeEEKIsPD/ARFlG8sw52+LAAAAAElFTkSuQmCC", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "image/png": "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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "image/png": "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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "class Simulator:\n", - " def __init__(self):\n", - " pass\n", - "\n", - " def log_prob(self, theta, x):\n", - " return MultivariateNormal(theta, eye(2)).log_prob(x) + prior.log_prob(theta)\n", - "\n", - " def sample(self, theta):\n", - " return theta + torch.randn((theta.shape))\n", - "\n", - "\n", - "class Potential(BasePotential):\n", - " allow_iid_x = False\n", - "\n", - " def __init__(self, prior, x_o, **kwargs):\n", - " super().__init__(prior, x_o, **kwargs)\n", - "\n", - " def __call__(self, theta, **kwargs):\n", - " return sim.log_prob(theta, self.x_o)\n", - "\n", - "\n", - "prior = BoxUniform(-5 * ones((2,)), 5 * ones((2,)))\n", - "sim = Simulator()\n", - "\n", - "_ = torch.manual_seed(3)\n", - "theta = prior.sample((50,))\n", - "x = sim.sample(theta)\n", - "\n", - "_ = torch.manual_seed(4)\n", - "inference = SNPE(prior=prior)\n", - "_ = inference.append_simulations(theta, x).train()\n", - "posterior = inference.build_posterior()\n", - "\n", - "_ = torch.manual_seed(2)\n", - "theta_gt = prior.sample((5,))\n", - "observations = sim.sample(theta_gt)\n", - "print(\"observations.shape\", observations.shape)\n", - "\n", - "\n", - "oversampling_factor = 128 # higher will be slower but more accurate\n", - "n_samples = 5000\n", - "\n", - "non_corrected_samples_for_all_observations = []\n", - "corrected_samples_for_all_observations = []\n", - "true_samples = []\n", - "for obs in observations:\n", - " non_corrected_samples_for_all_observations.append(posterior.set_default_x(obs).sample((n_samples,)))\n", - " corrected_posterior = ImportanceSamplingPosterior(\n", - " potential_fn=Potential(prior=None, x_o=obs),\n", - " proposal=posterior.set_default_x(obs),\n", - " method=\"sir\",\n", - " )\n", - " corrected_samples = corrected_posterior.sample((n_samples,),\n", - " oversampling_factor=oversampling_factor)\n", - " corrected_samples_for_all_observations.append(corrected_samples)\n", - "\n", - " gt_samples = MultivariateNormal(obs, eye(2)).sample((n_samples * 5,))\n", - " gt_samples = gt_samples[prior.support.check(gt_samples)][:n_samples]\n", - " true_samples.append(gt_samples)\n", - "\n", - "\n", - "for i in range(len(observations)):\n", - " fig, ax = marginal_plot(\n", - " [non_corrected_samples_for_all_observations[i],\n", - " corrected_samples_for_all_observations[i],\n", - " true_samples[i]],\n", - " limits=[[-5, 5], [-5, 5]],\n", - " points=theta_gt[i],\n", - " figsize=(5, 1.5),\n", - " diag=\"kde\", # smooth histogram\n", - " )\n", - " ax[1].legend([\"NPE\", \"Corrected\", \"Ground truth\"], loc=\"upper right\", bbox_to_anchor=[1.8, 1.0, 0.0, 0.0])" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "7be8609b-d616-4a5f-9177-62e230aaa6fe", - "metadata": {}, - "outputs": [ - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "e1ef7485288f47cbb8764148c15fb8a4", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Drawing 5000 posterior samples: 0%| | 0/5000 [00:00