diff options
author | Nao Ueda <nao.uedder@gmail.com> | 2020-08-15 21:00:01 +0900 |
---|---|---|
committer | Nao Ueda <nao.uedder@gmail.com> | 2020-08-15 21:00:01 +0900 |
commit | b960d4467987da0c927e79279fab3e91d3c74efe (patch) | |
tree | 9762be59a38bf1bd162b322fda72993a987d50bf /statistic/.ipynb_checkpoints/try_MH-checkpoint.ipynb | |
download | study-b960d4467987da0c927e79279fab3e91d3c74efe.tar.gz study-b960d4467987da0c927e79279fab3e91d3c74efe.tar.bz2 study-b960d4467987da0c927e79279fab3e91d3c74efe.zip |
Initial commit.
Diffstat (limited to 'statistic/.ipynb_checkpoints/try_MH-checkpoint.ipynb')
-rw-r--r-- | statistic/.ipynb_checkpoints/try_MH-checkpoint.ipynb | 215 |
1 files changed, 215 insertions, 0 deletions
diff --git a/statistic/.ipynb_checkpoints/try_MH-checkpoint.ipynb b/statistic/.ipynb_checkpoints/try_MH-checkpoint.ipynb new file mode 100644 index 0000000..dcc5737 --- /dev/null +++ b/statistic/.ipynb_checkpoints/try_MH-checkpoint.ipynb @@ -0,0 +1,215 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "import seaborn as sns\n", + "import matplotlib.pyplot as plt\n", + "from collections import deque\n", + "from scipy import stats\n", + "import sys" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "rand_mu = 1\n", + "rand_sigma = 0.5\n", + "rand_std = np.sqrt(rand_sigma)\n", + "alpha = 11\n", + "_lambda = 13\n", + "B = 1000\n", + "N = 100000 + B" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "def f(x):\n", + " return stats.gamma.pdf(x, alpha, scale=1/_lambda)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "def q(x):\n", + " return stats.norm.pdf(x, loc=rand_mu, scale=rand_std)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "def r(theta, a):\n", + " return (\n", + " (q(theta) * f(a))\n", + " / (q(a) * f(theta))\n", + " )" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "def rand():\n", + " return np.random.normal(rand_mu, rand_std)" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "100000 / 101000 99.01 % \n", + "acceptance ratio: 0.4129108910891089 \n" + ] + } + ], + "source": [ + "data = deque([rand()])\n", + "accept_count = 1\n", + "for i in range(2, N):\n", + " if not i % 1000: # 進捗用\n", + " sys.stdout.write(\"%s / %s %s %% \\r\" % (i, N, np.round(100 * (i / N), decimals=2)))\n", + " a = rand()\n", + " prev = data[len(data) - 1]\n", + " if q(a) * f(prev) > q(prev) * f(a):\n", + " if np.random.rand() < r(prev, a):\n", + " data.append(a)\n", + " accept_count = accept_count + 1\n", + " else:\n", + " data.append(prev)\n", + " else:\n", + " data.append(a)\n", + " accept_count = accept_count + 1\n", + "print(\"\\nacceptance ratio: %s \" % str(accept_count / N))" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "<matplotlib.axes._subplots.AxesSubplot at 0x7fa011009d90>" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXQAAAD4CAYAAAD8Zh1EAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjMsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+AADFEAAAgAElEQVR4nO3dd3wUZf4H8M83QOidIN3QDgSphiagYKWp550FvDu7Hh6e/eeBvYsdFRv2Xo9DlGYDKQIS6b1GDAQIARISSH9+f+xs2N3M7MzuzpaZfN6vV17ZnZ2deWZn5jvPPG1EKQUiInK+pHgngIiI7MGATkTkEgzoREQuwYBOROQSDOhERC5RPV4rbtasmUpNTY3X6omIHOm33347qJRK0fssbgE9NTUV6enp8Vo9EZEjicjvRp+xyIWIyCUY0ImIXIIBnYjIJRjQiYhcggGdiMglGNCJiFyCAZ2IyCVMA7qItBWR+SKySUQ2iMitOvMME5FcEVmt/T0QneQSkRvNWpuFwwXF8U6G41npWFQK4E6l1EoRqQ/gNxH5Xim1MWC+RUqpMfYnkYjcLCv3OCZ8shIDOzTBZzcOindyHM00h66UylJKrdReHwWwCUDraCeMiKqG4tJyAMDeI4VxTonzhVSGLiKpAPoAWK7z8SARWSMic0Sku8H3bxSRdBFJz87ODjmxRERkzHJAF5F6AP4L4DalVF7AxysBnKyU6gXgZQAz9JahlJqmlEpTSqWlpOiOLUNERGGyFNBFpAY8wfxjpdT0wM+VUnlKqXzt9WwANUSkma0pJSKioKy0chEAbwPYpJR63mCeFtp8EJH+2nJz7EwoEREFZ6WVy2AA/wCwTkRWa9PuAdAOAJRSrwO4BMBNIlIK4DiAsUopFYX0EhGRAdOArpRaDEBM5pkKYKpdiSIiotCxpygRkUswoBMRuQQDOhGRSzCgE1FcsfmEfRjQiSioPUeO4/FZG1FeHt3IK0GbXpAVDOhEFNQtn67Cm4t2YU3mkXgnhUwwoBNRUKVRzpmTfRjQiYhcggGdiMglGNCJiFyCAZ2IyCUY0ImIXIIBnYjIJRjQqcoqLSuPdxKIbMWATlXSom3Z6HTvHKzafTjeSany3NzK/ZFvNuLhbzbEbH0M6FQl/bzF85Dy9AxnB/Si0jKMnbYUa13Qi9ONPf/fWbIL7y7JiNn6GNCJHGxz1lEs23kI981YH++kUAJgQCcicgkGdCIil2BAJyJyCQZ0IiKXYEAnIld69NuNWLL9YLyTEVMM6ETkSm8v3oW/vbU83smIKQZ0IiKXYECPky37juJgflG8k0FELsKAHifnT1mI4c8uiHcyiMhFGNDj6GhhaRzWWYIZq/bEfL1ERpRy82gusVU93gmg2Jo4fR1mrc1Cp+b1cGrrhvFODlEFETeO5hJbzKFXMftzCwEAx0vK4pyS2Niy7yj+OHQs3smoEg4XFGP7gfx4J6NKc2VAT884hH1a4CLnKSwps+02/PwpCzH06fmW5t2fV4hX5m9nEUCYRry4EOc8/3O8k1GluTKgX/L6Upzr8ANrz5HjOO3R75FxsCDeSYmp3OMl6Hr/XLz04/aYr3vCxyvxzLwt2LzvaMzX7Qb789hqy0isxt13dEAvKCrF9JWZup8dLYp9haOdZqzag5yCYnyR/ke8kxJThwqKAQD/W6W/X6OpoNhTDFXukhx6ebnCg1+vx85sFoPE28Wv/hKToG4a0EWkrYjMF5FNIrJBRG7VmUdE5CUR2S4ia0Wkb3SS6+/BmRtwxxdrkJ5xyPZlZx8twh2fr0ZhFSlrJvfZkZ2P95f+jhs//C3eSSEAB45G/w7GSg69FMCdSqlTAAwEMEFEugXMMxJAZ+3vRgCv2ZpKA/vzPOXk3pxVUWkZco+X2LLsp+ZuxvRVezBzzV5blpdoXJIJJSIfpgFdKZWllFqpvT4KYBOA1gGzXQTgA+WxDEAjEWlpe2pNjJ22DL0e/q7i/ZQftqIkyg8CXrn7MFInzsIWh5S7smWYMSdUhhaWlKGotAz5RaUVGRozTtguskdI7dBFJBVAHwCBI960BuBb2JupTcuKIG0hW7Xb/7mKU37Yhub1a+GKAe2its7Zaz2buHBrNrq0qB+19VDsSAI/3bLr/XPRoFZ1NK6bjN9zjuHrCYMN53XKxXv1H85/HmqisFwpKiL1APwXwG1KqbzAj3W+UilbICI3iki6iKRnZ2eHltIwFZeGVwburRhzau5GKYUfN+13bPrJWF5hKX7PiV/b+qLSMhwrjqzRgVIK367di6LSMtzxxZpKn6/afRhXvvMrdh0sQFk5j2GrLAV0EakBTzD/WCk1XWeWTABtfd63AVCp8FkpNU0plaaUSktJSQknvbqiEbSmr/R0j/9sRXxamUS6TV/+lonr3k/Hp79WrVYyVVWww8Xus2PUi4vQ7YF5ES1jyfYc3PzJKjw1Z4vu53d8sQYLt2Zj+LML8OKP2yJaV6ys2n0YL8U5rVZauQiAtwFsUko9bzDbTABXaq1dBgLIVUpFvbjF21XYropQPQdsbFv7xYo/Qu61GO5ts7djVVbu8YppT8zehBUZsWkPS7ER/PiITpnLjuzI+0Z4z9l9eSeOT6PULtuZE/H6YuHiV3/B899vjWsarOTQBwP4B4CzRGS19jdKRMaLyHhtntkAdgLYDuBNAP+KTnL13frZasPPEuVmrbi0HHf/dy0ufX1p3NIwbeHOitcsiiHLbD5WDhcUQwU5M3/YuJ/NhcNkWimqlFoMk0u98kSHCXYlyomCHaC+n3s7ziSy48VlOOWBubh31Cm44YwO8U5OhUe+2Yh3luxCxuTRUV2PUyoTLbMpHtsxeNaWfUc9wzF0bqb7+brMXFz/QXrIy83JL0Lt5Gqokxyd8QZf/nEbRvZoiU7N60Vl+XZxdE/RRJCoJ38kmaojxz0XnbcX77IpNfZ4Z0lipSfRJeKx6R28a9E2/Wd96hafWjiWT3vsB4x5eXEkSTOUV1iC577fisvfiOzuOha7gwHdhNOLJhLhnC4oKkV+mEMxlJSVO34fxJsCsDbzCD5fsdvW5f7j7cif12lnE9GdNpTt6/EefsVR7tNiB9cHdKfGArN0l5ersINkOD77dTfOem5BWN/t/uA8nPpg6K0i8gpL0PneOZj6U+wH6so+WoTUibMwd/2+mK/bLkeOncjtXjh1Cf7z33W2Lt8ol+2fhmI8/M0GFJfGNhhW1aaOrg/okYrVoPuFJWU4XFCMCR+vRF7hiRPRKAcz5cdtOPXBeThyzP4yeb2LycTp64LmgHLyi3DAYs9Fqw7le7btK4MB2Ozm3e5pC3dW9Pz9cFlGTNYdDX997RfDzw4VFFu+YHkPh+PFoVdUPjl7M95dkoFvDIbQMKt7AoD1e3Pxy3bji4feHdwz8/SbQwZatfuwqypgq2xAV0rhw6UZtgXESO4ENmXloev9c3HB1MWYtS4LnyzXvzUuKCrF4Mk/YfnOHHyrnSA5BpWsZifKxqw8bNsffLgCEU/OfOkO82Zjpz32A/o/8aPpfE7wP5c9ok8v4HkvWO/q1EsUFJVi9EuLsGFvLgBgbabn/6PfbjRcx3++Wqs7vVTLKUcyguWx4jJc8ZZx8Y5eU8GFWyt3XCwsKcOX6X9U/B5Zucdx8au/YNJ0kzsXB2X2q2xAX7cnF/d/vQF3fVm5l1oojHLw6/fk4qaPfkOphXK3dXs8J0zm4eNB59uUlYc9R47jaYu5Dy2BupMf/mYjzn1hoenXJ05fh3FvLqt43+W+OdhzJHg6q6Jfth/EmgTpwl5SVo7FFopDjPz2+2Fs2JuHyXM2+00PNnbM52EO82xHGfrnJp3/tu4/ivyiUjw5exP+76u1WKj9NvnaM329519+Uanf3XHltCa+KhvQvWV6h48F75QUboXcLZ+twpz1+5ARxy7a4Qq2xUWl5ZgXg3LljJxjFQOrhbMLDuQVBr2V3m3jY+nyi0pxxVvLcdErS2xbZiSenbcFf7ehwjIerBTBhOq8FxbiuvdWVAxfW2BQ99Tr4e/Q86ETg/tNW7gDqRNn2Z6eaHJ9QFfw5Ja/37gfAPDpr7vx7dr4Dom7O8GDvPciFixHUlhShue/34qigLFyXl2wHbsOFuDa91ZEXGl7JIIewP2f+BF/D3Kb/p12PNjhw6W/27asUOlVdO4IeKCFWYhUSuH1n3dYHr0xHPEutVi+y/yZCYEVqU/M3qw7X3rGoaCjuB42KAaNRX2c6wM6AIx5eTFu0DorTJq+Djd/ssryd+0+EIvLynHGM+bPuIz3CQAEPwDfXrwLL/24De8tyfCb/vTcLRj+7AL8tPkAJny8MsopDC7999CGOYhG7jDaNmUFjpNnje+2bjuQj8lzNpvur3B+nYpDSAW81505jBWEYWWYx4WIYP2eXFzy+tKgla59Hv0+ovRFwtEBPZHKtBSAzfvyThTRRBAbfLfL9wQIZZHRbK6pgIrijKIgzdEyD0d2J+K7DeVxaoZm5++olMLUn7b5ja/jq7CkDEcLS5BfVIrlIY5fEm6lowhQWub5bvrvh3HmM/NDavL39Fz9XGzF8k0TYHlVtnkrxA5zyudilJ3vKbYJ5/kH7Fhkg2A181Zk5Rai6/1zcJlBLzHvTlq6IwcjpizCh8v8b7+jcZcVyiIT6aIXrt2HjuG0x2Kb64l0v73w/VYMC7gT234gH89+txU3faSfE/7zK0vQ46HvcNtnq3D5tGUhjdy3Ya9xTj2UWP97zrGgFYOBQu1NXCktNlwwfRfxv1WZhvVe0czkRPtBOla5PqCbsVLpWVhSjl9NyuB+z/G00d4Y5MRyikTpjOUbVH0rrzfvyzOs2EoUL/64rVKFuLcJn1Fl7WYt17cpy/Nfr44iUqGMJaSUp/7JTCJ1vrr98zWm56qZZ+dt0T2+jhWXGRZxzbDY1HXj3ryodgissgE92vUTgTHxNxcNWxvvrvgjpizC+I8S58HH4ZS9z12fFbSzjJ1803e3t724xST7jo9itN+3mvRnCExDtBVE+PCNqfO3647BXlxajqfn6pedl5SZb9/1H6Rj1EuLcO17KyJKXzBVNqBHy8F8Tw+8XQc9OXbvdeMpk7JGX0YXG73z6Zm5W7AzoFUDYE+laqQXvXBq9a1eLLzdzg8XFONgvj1j1ttxnUrPOIQ3ft7hN+1oYQkOHPVvQTL+o5VBO8uEw0pRQ36Rf3FKtB+35z0EDH/bgNUvjtFFzkyRz12UXtJ9D+3i0nIcKrB+DKZnRHYHEUx0xpp0udcW7MC0hTuw6oHzKn0WGFzDbao0J8htrO8i527Yh41ZeVh493DTeWMt2Kq9jzCLdLhT3xYFNw3riP+M6BrR8iJ1ic5498OfXYCD+cWYc+vQsJZZUFSKJBHUTq4WdL5dBwvQpnEd/LDpQFjr8Yq0eV1OfhGKSsvRqlHtkC8Yb/y803wmm4Vzx7lgSzaUUhAR3Kr1OUkEzKEHuH/GetPOBE/N3VypQ5LdN5Tesjorp4O3N+rmfebd+a2wq0QlMC749prt9sC8iB9jFui1BTvMZ4qDg9qYNIfDGGbi3SUZ6P7gPPR4yPy3UkClOwHAU7EfjnBzy6c99gNOn/wTXv95R9g9SCNRXFqOvTotiQKLfYJdt6ycAl+me8YYSpRgDjg8oPv29ntzoT1X9sBWKqZimAM2e9TeiCmLLHXntyroAW/hiA/Mnf2sja9hdeiAWN5cZOUex7YDlYuu9KzcfRipE2eFPBjZI99Ya3Hl+/t4u9+XWmxKqNfqRK8ZYrClGf3uoV7nfYcO8H438Jg6WhhZeXdRaVmlSuZ/f7rKb6TJSP26S78Jqd7FM94cHdC95dQA8PjsTWEtIxZVNXuPHMeaTPPWAmZuCOVJLiFks5ftzMHgyT+F9ST3UIJuVm4hCopKketzsgX2TjQbisFuby32ZASMWi/o/YzeisWzn/8ZGQetj8EdadO2nzab9259N6Cjlx6lFN5c5NluvYu20YX8yLES3PrZKkvzWqU3iFYo+jzyfaWLgpUKylCMN2hmmogcHdAjE5v8nwC4/XPjZ57qMRs7+lBBMf6w2GnHShnm2GnLsOfI8aBtmX09NmuTpdxJ4Ml+34z1uPhV//FOAgP6lB8ie8ju+j25yNGpJPXtj+DbSWm/wUPAg22f96k7RwtLMezZBbYUUVkZwvXa90J/NJuejJxjWLAlvED69Wr/YTOUim9fh2NhDOkbyI6HXieKKhzQI3fBy4tRFpAb0Du3Qx1s/9nvgge1HdkFtudCfFlpYjbTYHxrM1v3+xdr7MwuwN/fWo7jxWWYv/lAxA8mGPPyYox8cVGl6b5FETd+aB4Yb/88slE4Q2VHx5Szn/vZ0nxl5cHXFY2WL4nSt8Fr8768oBcDK+m18oCPWKvyrVy8HSfmbzmAP51UP+i8v2w/iNM7nXi47bo9ueh8kuehsUYHgKVb0jge7Lrlq96uzkFO7MKSE0HBqGy/tFzhjKeDj1vz2KxNOJhfhEnT12LGansGTfOOqheorFyhWpLotgIJN8dq5t7/2fuUoEhZCdbxahkV7dXuPXLirmvElMoX/VAt33Wo0kBoXrPXZWHkqS10P4vm6V5lc+g/bPKUR3pzute8uwIjpwSvULzireX4erV/jzDvCWLncKz+K4juYe5bFBFOLmrrgXzDsv3tB/J1fxe9TQq3JUYo7gvSgumDCEZMDFZZ/bHOw0p8mwV2f2AugNhd05fuzME5z584zn/ZkYN5G6LfSiMRBj4Lpb7D6mln1GP5Xx+vxPwtkTUfDUeVzaHrNXHLs1Dj/tmv/s2wVu0O3gNUIDHP8ZSWlfudPou3HTQc0+a9XzIqXq/IOIT+7ZuEtC6jR4sFszbT2oMgxk5bZj5TCD791d6HJHtZ7djk3Se+7Z4LbCgDjlRgr8ioDuwW/7iu64+AjIfV4/rCqcZj4B8uiG0FP1CFc+jhWhowCt7OEK76kegbwpCcS3weGScC3DdjHbZYaJ/+zLwtOFxQHPW8lO8Y3sEudlab6sWb1SDlfSbr5jBG6oulCZ9Yb9UhYi036/2NrFbmx9rQgKJBO1pbGY3ZEs2LGgN6gnvpx21QSgUdVMnO4+Omj0+MkRLLO4tEDd1Ld+Zg1tqsivd6d3Z2PIM0UXOudvHeHQU+1i4UP23eH9IQGvH24MwNMV9nlQror8zfrjs98HbLTst35UTcasCsOWGw8ue84yUhPQYv4+CxmA6+5f1lIh0hL5p8c6xRCyguD+hWBvAyc+176RH1Bj6a4CN02qFKBXSjp4wE3m5FIjAY/p/B09BDW2boafB+J9TB/Pf5tAuPRQbdqEVKoikuLcdDcchxJTrf1k6x8pdXl+DZUB6UXoVUqYCeiMy681uVbWNg9F4/nPiA62j5Zs1evwpkOsFKN/uSMmV652e1g8/K3Ucw1eBuu6pzZEBXSuH9BD25Qr1zttIZxEqTr36P/1Dx+snZmyM64AN7AxJw55fR7WiUCM36wmX1LrD9pNlRTgk5MqB/t3F/XCocrPhdL1cbpOziYH4R7v5v5MUyvqy0aAkmlEefuYGVbveUeBI1UxdPjgzoxxOg7W4s5YTw2DAKXdf758Y7Ca5v5RINiZqpiydHBvSqpigOFU9OcLnBg7udZv2eXPSN8UOwyZ0cGdDD6Z0YT/Ecjc7NlidwU8dQvLFwJ3PoVUypDYOx6TEN6CLyjogcEJH1Bp8PE5FcEVmt/T1gfzL9/bg59mMkxFM8HyNHRPZ7ZX50nq5lZSyX9wBMBfBBkHkWKaXG2JIiF4o0J8ncG5G77Muz9tSuUJnm0JVSCwG4497WoXbFaLwYInI2u8rQB4nIGhGZIyLdjWYSkRtFJF1E0rOzozP+tBs5afwKCh1L1Kqi6Ox1OwL6SgAnK6V6AXgZwAyjGZVS05RSaUqptJSUFBtWTeR84T79iZwrWvViEQd0pVSeUipfez0bQA0RaWbyNSIislnEAV1EWoj2CBYR6a8tMyf4t4iIqq5oFbOZtnIRkU8BDAPQTEQyATwIoAYAKKVeB3AJgJtEpBTAcQBjVSzHXyUiIgAWArpSapzJ51PhadZIREQWHI/S+EGO7ClKRORk01dG/pQrPQzoREQuwYBOROQSDOhERC7BgE5E5BKOC+glURp2kojI6RwX0P8XpdphIiKnc1xAL2IOnYhIl+MCOhER6WNAJyJyCccFdI4dTUSkz3EBnYiI9DkuoB85VhzvJBARJSTHBfSC4uiMUkZE5HSOC+gsQyci0ue8gM6ITkSky3kBnXl0IiJdjgvoRESkz3EBPYkZdCIiXY4L6GzlQkSkz3EB/e3Fu+KdBCKihOS4gE5ERPoY0ImIXIIBnYjIJRjQiYhcggGdiMglGNCJiFyCAZ2IyCUY0ImIXIIBnYjIJRjQiYhcggGdiMglGNCJiFzCNKCLyDsickBE1ht8LiLykohsF5G1ItLX/mT6ri+aSycici4rOfT3AIwI8vlIAJ21vxsBvBZ5sowxnhMR6TMN6EqphQAOBZnlIgAfKI9lABqJSEu7EhgoiVl0IiJddpShtwbwh8/7TG1aJSJyo4iki0h6dnZ2WCtjPCci0mdHQNcLsUpvRqXUNKVUmlIqLSUlJbyVMaITEemyI6BnAmjr874NgL02LFcXnylKRKTPjoA+E8CVWmuXgQBylVJZNiyXiIhCUN1sBhH5FMAwAM1EJBPAgwBqAIBS6nUAswGMArAdwDEA10QrsQBQWFIezcUTETmWaUBXSo0z+VwBmGBbioiIKCzsKUpE5BIM6ERELsGATkTkEgzoREQuwYBOROQSDOhERC7BgE5E5BIM6ERELsGATkTkEgzoREQuwYBOROQSDOhERC7BgE5E5BIM6ERELsGATkTkEgzoREQuwYBOROQSDOhERC7BgE5E5BIM6ERELuG4gF6rhuOSTEQUE46LjgKJdxKIiBKS8wI64zkRkS7nBfR4J4CIKEE5L6Azi05EpMt5AT3eCSAiSlCOC+iM6ERE+hwX0BnPiYj0OS+gswydiEiXAwN6vFNARJSYHBfQk6s5LslERDHhuOjYt13jeCeBiCghOS6gJzkuxUREsWEpPIrICBHZIiLbRWSizudXi0i2iKzW/q63P6keDWrViNaiiYgczTSgi0g1AK8AGAmgG4BxItJNZ9bPlVK9tb+3bE5nhdvP/VO0Fk1E5GhWcuj9AWxXSu1UShUD+AzARdFNlrFaNarFa9VERAnNSkBvDeAPn/eZ2rRAfxWRtSLylYi01VuQiNwoIukikp6dnR1GcomIyIiVgK7X8lsFvP8GQKpSqieAHwC8r7cgpdQ0pVSaUiotJSUltJR6E8N26EREuqwE9EwAvjnuNgD2+s6glMpRShVpb98EcJo9yauM8ZyISJ+VgL4CQGcRaS8iyQDGApjpO4OItPR5eyGATfYl0R+7/hOR0zWpmxyV5VY3m0EpVSoiNwOYB6AagHeUUhtE5BEA6UqpmQBuEZELAZQCOATg6qikFsyhE5HzPXJR96gs1zSgA4BSajaA2QHTHvB5PQnAJHuTRkTkTjWrR6e1nuP6XbLEhYicrknd6HSQdFxAJyJyutNObhKV5TouoCcxi05EpMtxAZ09RRNT60a1450EoirPcQGdEtO1Q9rHOwlEVZ4jA/qFvVpFdfn/PLNDVJdPieM/I7pamm9o52ZRTklwk0ZaSyfF1th+uqOcxI0jA7rXdVHIFY7r3w53n191T55wi06UChwNIvbS7zsn5O+IALueHIWf7jwz6HyN69jbEaRZvZohzX/N4NjdAf1j4MmW53336n5hrSOcQNihWd2w1hXMm1emmc7z/rX9K14netGiowN6zzYNbV/mk3/pgWpJ+hWvo3u21J2eqOrVrNzNIKW+cSC5Z1RXfH/HGRXvbzm7c1TSFS6zoBtqkPQSEdMeyDVsfPRhw9rGTdaMAkZy9dDXXzc5vPqmUNodDO/aPOQg969hHfHkX3pgXP/QgvrMfw8JaX4r2lu4SJz5p/DGnYoHRwf0wEzhyU3rRHV9wwx27MMXdsfjF58KABjQXr850sAOTdCqYS20aFDLb/rZXZuHlIZeIVzELkvzP2E+u3EgZgU5KYZ2TkGd5BMXgTssjj1/2sn+jwXs3qoBLujVCk9c3MNyWq38Dh1S6iHNZ10X92mN5y/r5TdPy4a1Ar8WlNXYNaZnS9tyZ60a1Uazevo5/r4nNzb8LFRP/MX4979qkPVcuJk7zwvtGQV3j+gKEUGXk+qH9D29DEqstWlcG4M6NA3ru5/eMNDm1FTmyIBulIMYE+UcdCuDE/qq01NRy6TnV4sGtfDLpLPx7S1D8PRfe1ZMD6XYKLVpHQzsaHww3Tf6FL/3KmBQzIEdmqJ5g1oY1sX/wjS2X1usfuBcnNKyQaVlfn/7GZWmBaoZkHu8aVhHvDyuj+n3AGD5PWdj1i1D8JLF+ScM71TxetKorvhL3zZ+n0//1+mYGEJ588lNPTk0s2A9vGtz/HBH8DsErwfG6D3/5YSXx/XBC5f39ps2rEsK3r26H576aw+kamn64p+DLK3PSLDxQv7cR28EbI9QGwb7ZgJi7aPrBqBtk/AvtL6x5J5R5sfNG/84DQ9cEHz/xpMjA7qvf5914gSXCEd6ufS0NkE/H9ypGb745yBU14pkfrrzTCy4a5jfPGbNKpvVq4nL+rVF2ya1ce3g9hjUsSnqWLg1fvuqNCz4v+FBtzHwBL68X1t0bVE5F/TeNf393p99ykloZFBG3NlCLso3OKWd3Bhjenoqrfue3Eh3/qY+6TypQS10b9UQdS3mvvwuUtrL6kmCi7UA1bJhbYw/s6OlZb04tjdGnNoCgKdIQ++38lXbZD9lTB6NjMmjce2Q9tj15Ch8csMAfDnePyj/fWA7dGper9Kd2rj+7TC8a3PUSa5esYXhdrkY3Mlz0RcIXhzbGzNvHlxpnj46D1s/5xTPXVKqSTFEc4Niu6Gdm2H2LUMtp/P0TpFXNNepWQ0D2pvnmOtbOL70zt3kgKK2RnWScUrLBmjT2NpFxLeOoU3j2qhRLbr9aBwf0O88r4vlogEzz1x64vb96tNTdefp375JReem1o1rVxz83pOwad1kv3K5Zy7x5Lpp9NgAAA43SURBVMbbN6vnt5xFd5+FBy7oBhHBxkdGIGPyaOx6chQ+uzH827LAIqiUejUx97bgOeztj4/Eud1OCjqPN0A01cnx9WrTECf5BKdebU8E8a4t/HP8gWXygQFr5f3n4quAAHjOKcHTBgDbnxhVKcdrRa82+hecQL3bVp4vY/Jo3BWkqEFEcHrHZuiXeqII7pPrB+B+LffeuG6y374+v3uLyssA8O2/h/gFhcZ1QusyflHv1ujZphEu7tMal6cFL7N+7rLeWHn/ueiQUi/ofDcM1W8FVrtGNXRrVfkur5vPnZ/vHemfTDILVsrY++pcmPQsu+dsS/NZ9dX40/HmlWnoYpIJGK4VJSYJ0LZJHXx3+5mYEsaxapXjAzrgCbIAMMinOOKKAe0sfdeoguqhC7tjxb36rSa8OU/dXqsCnNf9RBC6NK0tPrpuAG72uZMwIiIY2KEp/jagHZrXr+l3Ihi5NUjFpZVcb3ULlX3esvjAXF3vto3w9c3BK6rm3zUM/bWg5i3f9l53aiT5r7tJ3WQ0r++fc/XmGr3sbEwTLAe8+dERFTnuGRNO5HBvOatTRfHQhOGdcFFv8ya0X44fhBfH9sbpnZr5Dco00EJZ7KmtG1YEhS/HDzK9QAPAn3t77lY6Nj+RsXjh8t546pKefhdc3/qIW87ujIa1a6BJ3eSKe8A+7Rph62MjcW1AC5uWjfz3UVqqZzmBxYentm6Aebed4XdeWjW0czNcoNM8uX2zurgyoPzfLM+7/uHzDc8F36K2UPLOLRrWwrndTjLM+Pn69Z6zsfrB8wB40h+suCtSjgzo3lusjlpOYmCHptj86AgM1m7h7h7RxVKFXOtGtbFG+6HN+ObS3rqqH7799xC/lg++zfbaN/W/ZR3SuZlhyxk9j1/cA7/eew6eufREWbs3FxwY4M7yqUxsoF2c/nlmB2RMHl1xC/nd7WeYthAJ5qLerZExeTTO7+6fW7ZyorZvVhf1a3lOJu8vkCSeCtevdYoCAl0e0LzN9ze3OmLdkoln4b1rQmteZ1R0dsd5XSr6QYgIXrjMPLfVL7UJLupt/SQ2agLaL7VJxXHQIaUu1j104tht0aBWxQXo0rS2yJg8Gi0bVi4W+ODa/hVFML5FSHrt7OvVrI7k6kkVGSavQR2a4t2r+2HjI+cD8BQjZkwejQEBF6i0k5ugS4v6+OcZJ3L0Rhfk+0afYqk1yfy7huGRi041nS9wO/TUTa7mt58vTWtbqTnlJWmeYthRPSrfQQGeY+CGocHrwZo3qIUGtaIzGFeg+Fcbh2Fc/7YY3jXF74D17piMyaMrpnnLqG/4IF13OTVreILD21elmd5mfnDdiXLnejWr49TW/q1NKso9Ibg0rS0mTl9neXuMdG/VEDueGIWt+49WVFimpTZBxuTRuODlxVi3J9cvl3nOKc3x3KW9MKaXf+Ww2a1toCcu7qFbRnjJaW3Qp11j5B4vxtrMXPw9hPbKwIkLzoThnQzbVfuWkX89YXCl5oRDOjXDzcM7YVDHpmgYpPjh+9vPwLkvLATguXDrVXoa1UfMudV6OXBSCBfqUBndQWx4+HwkV0/yu7i1amStdU/D2jXQM6Co6f1r+/sVDQ3s0BQX92mN288xLlIabqFV0iStkrF5A/O0XT+0A64f2gG3f74a/1u1x3R+M6lN6yAj55jftPO7n4R5G/YbfqdWjWqY/Nee+GyF5xHKGx4+vyKuvPo344ewNdaKIsef2REzV+/B3tzCSJMfNkcGdBHRzX0ECqyNfvfqfrjmvRUAgLvO+xMu7OXJNZ1tUE7rvbL/a1hHy1dYEaBakmDNA+fZ8jSOakmi2/rEN/CJeHo8igj+alKx6/XDHWdiY1ae7mdGxVUigk7NPRe+wNHivDlHvQuB9xa9ad1kvwtuMO2a1PErHvBKShLcdX4X0+93Pqk+xp/ZEcFKlJJsuj+dMWFw2G2+x5/ZMeQiCd/ig3H92+LTX//AcxbuFKxKrp6kWyfRMaUuRvdoaflpO753UFOv6IObP1mFm4YFr7C+qHcrSwF96aSzkF9YCkD/wrfg/4YjdeIsv2m3n/unoAE9kNWK+nH92mHpjhxcN6Q9Zq6O/GIUCUcG9HAN79ocyyadjTKlLLUprp1cDVseG1GppltXwK1ksNyjnQSCXU9aC5K+OjWvVxGc7TCmZ0vUq1UdZ3aufNt83+huGNihaaVbcj2tGtVG/9QmuCPEts16zJovtmlsT78FvUpTq/TS6A0k1SxccR6+8FRcP7SDpQ4ykeqYUg93nGd+Mf3230OQX1TqN21Mz1YVrZ+s8L0z69qiPp69NLC/QW0gxH6FgZX0I3vY08y5cd1kfHjdAADAdUM74NFvN9qy3HBUiYBeLUlwzyhPG+0WIXY8CfXJIrEa3Nd7kUmU0YRFBMO76N+G16pRzfLJXKNaEr4YH1n7a6d77rJe+GLFH5Y6kSVXT6qoSwqX2SE0rEsKRvdoWVGEYiawODKYvw1oh8zDx3U/8557qU3rBl2mt+jsnlFd8cTszZbWm37fOUF77IbruiHtcd2Q9pXuDmKlSgT0HU+Mivo6AjvxRNvLV/TFx8t+R3edZmJu85e+rTF9pT23sj3bNMTazFxblhUtzevXws1nRX/YBe9QCWb9IGrVqIZX/tY3Kml4PEjjhb7tGuHhC7tXtNox8h/tLufKQalo36xe0CKhV67oi9nrssIeJsKqObcONbxQRVOVCOixFKscc+tGtXG3xZECne7ZS3qFNIyAkW2Pj0SSCDreM1v389vO6YzxH61EuybRHUIiUTz651MxsEOTSkM3JAoRwVUWmgU2qZuMp7T+HmZ9Kkb3bFlpTKYPru1vuaOQVae0bKBb9xVtDOg2GdmjJWas2ptwA1q5QVKSoFZS5A82MRtga8SpLS1X2rpBvZrVcXk/a/01YmVA+6YY0L5JpWEsoukMneaSoXbgShQM6DZpUKsGPo2glycReRoifB7hGDaR+vC6/rY2GIglR3YsIorE8C7OGQ6VYm9o5xRLzaITEXPoVOW88Y80HC8pi3cyKMoeuqAb+hkMZ+1WDOhU5SRXTwrrgRHkLFfH8ClPiYJHNRGRSzCgExG5BAM6EZFLMKATEbkEAzoRkUswoBMRuQQDOhGRSzCgExG5hBg9vzDqKxbJBvB7mF9vBuCgjclxgqq2zdxe96tq22zX9p6slNIdvyJuAT0SIpKulEqLdzpiqaptM7fX/araNsdie1nkQkTkEgzoREQu4dSAPi3eCYiDqrbN3F73q2rbHPXtdWQZOhERVebUHDoREQVgQCcicgnHBXQRGSEiW0Rku4hMjHd6QiEibUVkvohsEpENInKrNr2JiHwvItu0/4216SIiL2nbulZE+vos6ypt/m0icpXP9NNEZJ32nZdERGK/pf5EpJqIrBKRb7X37UVkuZb2z0UkWZteU3u/Xfs81WcZk7TpW0TkfJ/pCXU8iEgjEflKRDZr+3lQFdi/t2vH83oR+VREarlpH4vIOyJyQETW+0yL+j41WkdQSinH/AGoBmAHgA4AkgGsAdAt3ukKIf0tAfTVXtcHsBVANwBPA5ioTZ8I4Cnt9SgAcwAIgIEAlmvTmwDYqf1vrL1urH32K4BB2nfmABiZANt9B4BPAHyrvf8CwFjt9esAbtJe/wvA69rrsQA+11530/Z1TQDttWOgWiIeDwDeB3C99joZQCM3718ArQHsAlDbZ99e7aZ9DOAMAH0BrPeZFvV9arSOoGmN58EQxg87CMA8n/eTAEyKd7oi2J6vAZwLYAuAltq0lgC2aK/fADDOZ/4t2ufjALzhM/0NbVpLAJt9pvvNF6dtbAPgRwBnAfhWO2gPAqgeuE8BzAMwSHtdXZtPAvezd75EOx4ANNCCmwRMd/P+bQ3gDy1QVdf28flu28cAUuEf0KO+T43WEezPaUUu3oPHK1Ob5jjarWYfAMsBnKSUygIA7X9zbTaj7Q02PVNnejxNAXA3gHLtfVMAR5RSpdp73zRWbJf2ea42f6i/Q7x0AJAN4F2tiOktEakLF+9fpdQeAM8C2A0gC5599hvcu4+9YrFPjdZhyGkBXa+80HHtLkWkHoD/ArhNKZUXbFadaSqM6XEhImMAHFBK/eY7WWdWZfKZI7YXnhxnXwCvKaX6ACiA51bZiNO3F1q57kXwFJO0AlAXwEidWd2yj83EdfucFtAzAbT1ed8GwN44pSUsIlIDnmD+sVJqujZ5v4i01D5vCeCANt1oe4NNb6MzPV4GA7hQRDIAfAZPscsUAI1EpLo2j28aK7ZL+7whgEMI/XeIl0wAmUqp5dr7r+AJ8G7dvwBwDoBdSqlspVQJgOkATod797FXLPap0ToMOS2grwDQWatBT4anUmVmnNNkmVZ7/TaATUqp530+mgnAW+t9FTxl697pV2o15wMB5Gq3XvMAnCcijbUc0nnwlDNmATgqIgO1dV3ps6yYU0pNUkq1UUqlwrOvflJK/Q3AfACXaLMFbq/3d7hEm19p08dqLSTaA+gMT0VSQh0PSql9AP4QkS7apLMBbIRL969mN4CBIlJHS5N3m125j33EYp8arcNYPCtUwqycGAVP65AdAO6Nd3pCTPsQeG6n1gJYrf2NgqcM8UcA27T/TbT5BcAr2rauA5Dms6xrAWzX/q7xmZ4GYL32nakIqKCL47YPw4lWLh3gOVm3A/gSQE1tei3t/Xbt8w4+379X26Yt8GnZkWjHA4DeANK1fTwDnhYNrt6/AB4GsFlL14fwtFRxzT4G8Ck89QMl8OSor4vFPjVaR7A/dv0nInIJpxW5EBGRAQZ0IiKXYEAnInIJBnQiIpdgQCcicgkGdCIil2BAJyJyif8HBlhanW094aMAAAAASUVORK5CYII=\n", + "text/plain": [ + "<Figure size 432x288 with 1 Axes>" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "pd.Series(data).plot()" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "<matplotlib.axes._subplots.AxesSubplot at 0x7fa0116ae550>" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "<Figure size 432x288 with 1 Axes>" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fig = plt.figure()\n", + "ax = fig.add_subplot(1,1,1)\n", + "sns.distplot(list(data)[B:],bins=100, label='MH', ax=ax)\n", + "arr = np.arange(0, 2.5, 0.01)\n", + "sns.lineplot(\n", + " data=pd.DataFrame(\n", + " data=f(arr),\n", + " index=arr,\n", + " columns=['theoretical']\n", + " ),\n", + " ax=ax,\n", + " palette=['orange']\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "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": "3.7.6" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} |