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@xiangze
Last active April 30, 2018 18:52
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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# State space model in Edward variational bayes"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/apple/Library/Python/3.6/lib/python/site-packages/statsmodels/compat/pandas.py:56: FutureWarning: The pandas.core.datetools module is deprecated and will be removed in a future version. Please use the pandas.tseries module instead.\n",
" from pandas.core import datetools\n"
]
}
],
"source": [
"import pandas as pd\n",
"from datetime import datetime\n",
"import matplotlib.pyplot as plt\n",
"%matplotlib inline\n",
"import statsmodels.api as sm\n",
"import numpy as np"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Nile dataset"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"data=pd.read_csv(\"https://raw.githubusercontent.com/statsmodels/statsmodels/master/statsmodels/datasets/nile/nile.csv\",index_col='year',)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x1176326d8>"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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l6Pc5C4LAnHMsJAp368NdLsxE0/j04ycRS2fxtquHcXYujtloOm8BGILASTlX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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x11008aa90>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"data.plot()"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"y=data[\"volume\"]\n",
"T = 1000\n",
"N = len(y)\n",
"T = 1000"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {},
"outputs": [],
"source": [
"def plotseries(y,mu_samples,title,ax=0):\n",
" #axis=0 stan, 1 pymc\n",
" mu_lower, mu_upper = np.percentile(mu_samples, q=[2.5, 97.5], axis=ax)\n",
" pred = mu_samples.mean(axis=ax)\n",
" plt.plot(list(range(len(y)+1)), list(y)+[None], color='black')\n",
" plt.plot(list(range(1, len(y)+1)), pred)\n",
" plt.fill_between(list(range(1, len(y)+1)), mu_lower, mu_upper, alpha=0.3)\n",
" plt.title(title)\n",
" plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Edward"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/usr/local/Cellar/python3/3.6.3/Frameworks/Python.framework/Versions/3.6/lib/python3.6/importlib/_bootstrap.py:219: RuntimeWarning: compiletime version 3.5 of module 'tensorflow.python.framework.fast_tensor_util' does not match runtime version 3.6\n",
" return f(*args, **kwds)\n",
"/Users/apple/Library/Python/3.6/lib/python/site-packages/h5py/__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n",
" from ._conv import register_converters as _register_converters\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"1.3.5\n"
]
}
],
"source": [
"import tensorflow as tf\n",
"import edward as ed\n",
"from edward.models import Normal, InverseGamma, Empirical\n",
"\n",
"print(ed.__version__)"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [],
"source": [
"def genq():\n",
"# return Normal( loc=tf.Variable(tf.random_normal([])), scale=tf.nn.softplus(tf.Variable(tf.random_normal([]))))\n",
" return Normal( loc=tf.Variable(tf.random_normal([])), scale=tf.nn.softplus(tf.Variable(tf.random_normal([]))))"
]
},
{
"cell_type": "code",
"execution_count": 53,
"metadata": {},
"outputs": [],
"source": [
"def nile_st_vb(y,N,T):\n",
" muZero = Normal(loc=0.0, scale=1.0)\n",
" sigmaW = InverseGamma(concentration=1.0, rate=1.0)\n",
" \n",
" mu = [0]*N\n",
" mu[0] = Normal(loc=muZero, scale=sigmaW)\n",
" for n in range(1, N):\n",
" mu[n] = Normal(loc=mu[n-1], scale=sigmaW)\n",
" \n",
" sigmaV = InverseGamma(concentration=1.0, rate=1.0)\n",
" y_pre = Normal(loc=tf.stack(mu), scale=sigmaV)\n",
" \n",
" qmuZero = genq()\n",
" qsigmaW = genq()\n",
" qmu = [ genq() for n in range(N) ]\n",
" qsigmaV = genq()\n",
" \n",
" latent_vars = {m: qm for m, qm in zip(mu, qmu)}\n",
" latent_vars[muZero] =qmuZero\n",
" latent_vars[sigmaW]= qsigmaW\n",
" latent_vars[sigmaV] = qsigmaV\n",
" \n",
" return ed.KLqp(latent_vars, data={y_pre: y}) ,qmu\n"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/usr/local/lib/python3.6/site-packages/edward/util/random_variables.py:52: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n",
" not np.issubdtype(value.dtype, np.float) and \\\n",
"/usr/local/lib/python3.6/site-packages/edward/util/random_variables.py:53: FutureWarning: Conversion of the second argument of issubdtype from `int` to `np.signedinteger` is deprecated. In future, it will be treated as `np.int64 == np.dtype(int).type`.\n",
" not np.issubdtype(value.dtype, np.int) and \\\n"
]
}
],
"source": [
"inference,qmu=nile_st_vb(y,N,T)"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"1000/1000 [100%] ██████████████████████████████ Elapsed: 41s | Loss: 8264293.000\n"
]
}
],
"source": [
"inference.run(n_iter=T)"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {
"scrolled": false
},
"outputs": [],
"source": [
"qmu_sample=np.array([ _qmu.sample(1000).eval() for _qmu in qmu])"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(100, 1000)"
]
},
"execution_count": 29,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"qmu_sample.shape"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {},
"outputs": [
{
"data": {
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EYO7cuZx88sn88pe/BIhKAAC+9rWvkZ2dHdS5d7YArKiigZ4DGEgXUGtrKx0d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\nRK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x14d428d68>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plotseries(y,qmu_sample,\"Edward VB\"+ed.__version__,1)"
]
},
{
"cell_type": "code",
"execution_count": 59,
"metadata": {},
"outputs": [],
"source": [
"def simpleplot(qmu_sample,ax=1):\n",
" mu_lower, mu_upper = np.percentile(qmu_sample, q=[2.5, 97.5], axis=ax)\n",
" pred = qmu_sample.mean(axis=ax)\n",
" plt.plot(list(range(1, len(y)+1)), pred)\n",
" plt.fill_between(list(range(1, len(y)+1)), mu_lower, mu_upper, alpha=0.3)\n",
" plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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nUyhmDLRjgohxQaeG1whsGMUyaEuBZtdBMW2gkDFYFkhEtoe4TG5ZTuRepECy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\nAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x14d6d0c88>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"mu_lower, mu_upper = np.percentile(qmu_sample, q=[2.5, 97.5], axis=1)\n",
"pred = qmu_sample.mean(axis=1)\n",
"plt.plot(list(range(1, len(y)+1)), pred)\n",
"plt.fill_between(list(range(1, len(y)+1)), mu_lower, mu_upper, alpha=0.3)\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Articifial data"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {},
"outputs": [],
"source": [
"import math"
]
},
{
"cell_type": "code",
"execution_count": 54,
"metadata": {},
"outputs": [],
"source": [
"sy=[100*(math.sin(i*10/N)+1) for i in range(N)]"
]
},
{
"cell_type": "code",
"execution_count": 51,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x15f911b38>"
]
},
"execution_count": 51,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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IsDg3hQ8ONfrFEoO2SPSbSmsJElioE0S8Zklu3xKD+2pbrQ5FAW/tb6C712i3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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x15f9334a8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"sy.plot()"
]
},
{
"cell_type": "code",
"execution_count": 55,
"metadata": {},
"outputs": [],
"source": [
"inference_sin,qmu_sin=nile_st_vb(sy,N,T)"
]
},
{
"cell_type": "code",
"execution_count": 56,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"1000/1000 [100%] ██████████████████████████████ Elapsed: 51s | Loss: 20429.459\n"
]
}
],
"source": [
"inference_sin.run(n_iter=T)"
]
},
{
"cell_type": "code",
"execution_count": 57,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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6CbRRSvUCXgRmiUhMdc9VSv1bKdVHKdUnKSmpNjGqVVpayvLly0lLS8NkCvlj\nzvWiV69epKSkhNxQs8Zg4cKF9OjRg44dOxodJSjZbDaGDx9OZmYmbrf78k8IcFddAUXkIWAYcL9S\nSgEopRxKqbP+77cABwBDrjnw6aefUlpaqndda0FESEtLY9WqVRQVFRkdR6uh06dPs379er3u19Ko\nUaPIzc1l7dq1Rkeptasq9CIyGHgJGKGUKq0yPUlEzP7v2wOdgB/qIuiVWrhwIQkJCfTv39+IxYeM\n9PR0HA4Hy5cvNzqKVkMVQ4p1t03t3HvvvYSHh4dE12VNhlfOBjYBnUXkmIg8CkwBooGV5w2j7A98\nKyLbgfnAE0qpBr9li8vlYvHixQwfPhyr1drQiw8pt912G02aNNHDLINIRkYGHTp0oHv37kZHCWoR\nEREMGjSIjIwM/J0WQctyuRmUUtXdSfjdi8y7ADD86MXatWvJz8/XLZo6YDabGTFiBAsWLMDpdGKz\n2YyOpF1CYWEhn332Gc8884weUlwH0tPTyczMZOvWrfTu3dvoOFctJI9SZmRkVG6NtdpLS0ujoKCA\nNWvWGB1Fu4xPPvkEp9OpT5KqI8OGDcNkMgX9Hm3IFXqlFBkZGZX9a1rt3XPPPURGRurRN0EgIyOD\npKQkbrnlFqOjhITExET69+8f9Ot+yBX6LVu2cPz4cd2iqUNhYWEMGTKEzMxMvF6v0XG0i3A4HCxd\nupQRI0ZgNpuNjhMy0tPT2blzZ1DfdS3kCn1GRgZms5mhQ4caHSWkpKWlcfLkSb7++mujo2gXsWbN\nGoqKivSxqTpWcUP1YB59E5KFvl+/fiQmJhodJaQMHToUi8US9LuwoSwjI4PIyEgGDBhgdJSQ0rZt\nW3r16hXU/fQhVej37dvHzp07dbdNPYiLi+Ouu+4K6lZNKPN6vWRmZjJkyBDCwsKMjhNy0tPT2bhx\nI6dPnzY6ylUJqUJfUYQqdrW0upWamsr333/Pnj17jI6inefrr7/m5MmTupFTT9LS0lBKBe0tBkOq\n0GdkZNCzZ09SUlKMjhKSRowYAQR3X2WoysjIwGKx6GNT9aRbt260a9cuaNf9kCn0p0+fZuPGjbpF\nU49at25N7969g7qvMlRlZmZy5513EhcXZ3SUkFT1uk/BeIvBkCn0S5YsQSmlu23qWVpaGl999RUn\nT540OormV9Gdptf9+pWamorD4WDFihVGR7liIVPoMzIyaNu2Lddff73RUUJaamoqSikWL15sdBTN\nr6I7oaJrTasft912GwkJCUG5RxsShb6kpIRVq1aRmpqqr+9Rz7p160b79u2Dtq8yFGVmZtKrVy/a\ntGljdJSQZrFYGD58eOWd64Ik23VrAAAgAElEQVRJSBT6Tz/9lPLycr3r2gBEhNTUVH2N+gBx+vRp\nNm3apI9NNZDU1FTy8vJYv3690VGuSEgU+szMTOLi4ujX76J3LtTqUFpaGk6nMyj7KkPN4sWL9bGp\nBjRo0CDCwsKCrvsm6Au92+1m8eLFDBs2TF97voHceuutJCYmBt3KHooyMzNp27YtPXr0MDpKoxAZ\nGck999xTeXOXYBH0hX7Dhg3k5ubqFk0DslgsDBs2LCj7KkNJcXExK1euJC0tTR+bakCpqakcPnyY\nb775xugoNVajQi8i74lItojsqDItQURWisg+/9d4/3QRkTdFZL+IfCsiN9RXePC1aGw2G/fee299\nLkY7T2pqKvn5+XzxxRdGR2m0Pv30UxwOh27kNLDhw4cjIkE1IKGmLfrpwODzpv0S+Ewp1Qn4zP8z\nwBB894rtBEwGptY+ZvWUUmRmZjJgwACio6PrazFaNSr6KoNpZQ81mZmZxMfHc/vttxsdpVFJTk7m\nlltuCap1v0aFXim1Djj/3q+pwH/83/8HSKsyfYby+RKIE5HmdRH2fDt37uSHH37QIw4MEBkZycCB\nA4OurzJUuN1ulixZwtChQ/WxKQOkpqaybds2jhw5YnSUGqlNH31TpVTF6ZGngKb+71sCR6vMd8w/\n7RwiMllEskQkKycn56oCxMXF8bvf/U6fKGKQir7Kb7/91ugojc769ev1sSkDVfzdg+UiZ3VyMFb5\nmnRX1KxTSv1bKdVHKdUnKSnpqpbbqlUrfv/739OsWbOrer5WO8HYVxkqMjMzsdvt+tiUQTp37kyX\nLl2CZt2vTaE/XdEl4/+a7Z9+HGhdZb5W/mlaiGnatCl9+/YNmpU9VOhjU4EhNTWVNWvWkJ+fb3SU\ny6pNof8YmOT/fhKQWWX6g/7RN32BgipdPFqISUtLY+vWrUHTVxkKduzYwcGDB3W3jcFSU1Nxu90s\nW7bM6CiXVdPhlbOBTUBnETkmIo8CfwbuEZF9wED/zwDLgB+A/cA7wH/VeWotYARbX2UoqNiDGj58\nuMFJGrebb76Zpk2bBsUerQTCiIk+ffqorKwso2NoV6lLly60bt2alStXGh2lUbjxxhsxm818+eWX\nRkdp9B5//HHmzp1LTk4Odru9wZcvIluUUn0uN1/QnxmrGS+Y+iqD3fHjx8nKytLdNgEiNTWVoqIi\n1qxZY3SUS9KFXqu1tLS0oOmrDHYVXWS60AeGAQMGEBkZGfDdN7rQa7UWTH2VwS4jI4OOHTvStWtX\no6NoQHh4OPfeey+ZmZl4vV6j41yULvRarZlMJkaMGMGyZctwOBxGxwlZBQUFrF69mvT0dH0RswCS\nlpbGiRMnCOTjjLrQa3UiLS2N4uJiPv/8c6OjhKxPPvkEl8ulL/kRYIYOHYrZbA7oy3brQq/Vibvv\nvjso+iqDWUZGBsnJydx8881GR9GqSEhIoH///gG97utCr9WJsLAwhgwZEvB9lcHK4XCwbNkyRowY\ngdlsNjqOdp60tDR27drF3r17jY5SLV3otTqTmprKqVOn2Lx5s9FRQs7q1aspKirS3TYBqmIUVKC2\n6nWh1+pMMPRVBquMjAwiIyMZMGCA0VG0arRt25ZevXoF7LqvC71WZ+Lj47nzzjsDdmUPVl6vl8zM\nTAYPHkxYWJjRcbSLSE1NZdOmTZw+fdroKBfQhV6rU2lpaezZs4c9e/YYHSVkbN68mVOnTulumwCX\nlpaGUiogr/ukC71WpyqK0aJFiwxOEjoWLVqE2Wxm6NChRkfRLqFHjx6kpKQE5B6tLvRanWrVqhU3\n3nijLvR1RCnFokWLuPPOO4mPjzc6jnYJIkJ6ejqrVq2isLDQ6Djn0IVeq3Pp6els3ryZY8eOGR0l\n6O3evZu9e/cycuRIo6NoNZCeno7T6eSTTz4xOso5dKHX6lx6ejpAQO7CBpuKPSN9EbPgcOutt5Kc\nnBxwe7RXXehFpLOIbK/yKBSR50Xk9yJyvMr0n9RlYC3wdenShS5dugTcyh6MFi5cSN++fWnZsqXR\nUbQaMJvNpKamsnTpUsrLy42OU+mqC71S6nulVE+lVE+gN1AKVHyy/17xO6WUvnZtI5Sens7atWs5\ne/as0VGC1uHDh9m6dWvlHpIWHNLT0ykuLuazzz4zOkqluuq6GQAcUEodrqPX04Jceno6Ho+HJUuW\nGB0laFV0felCH1zuvvtuoqOjA2qPtq4K/XhgdpWfnxaRb0XkPRGpdqiAiEwWkSwRycrJyamjGFqg\n6N27Ny1btgyolT3YLFq0iG7dutGpUyejo2hXwG63M3ToUD7++GM8Ho/RcYA6KPQiYgNGAPP8k6YC\nHYCewEngb9U9Tyn1b6VUH6VUn6SkpNrG0AKMyWQiPT2dFStWUFxcbHScoJOTk8MXX3yhW/NBauTI\nkeTk5LBhwwajowB106IfAmxVSp0GUEqdVkp5lFJe4B3gpjpYhhaERo0aRXl5ecANNQsGH3/8MV6v\nVxf6IDVkyBDsdjsLFy40OgpQN4X+Pqp024hI8yq/Swd21MEytCDUr18/kpKSWLBggdFRgs7ChQtJ\nSUmhZ8+eRkfRrkJUVBT33nsvCxcuRClldJzaFXoRiQTuAaputv4iIt+JyLfAXcALtVmGFrzMZjNp\naWkBN9Qs0OXn57Ny5UpGjx6tbxkYxEaNGsXRo0cD4rLdtSr0SqkSpVSiUqqgyrQHlFLdlVI9lFIj\nlFInax9TC1ajRo2iuLiYTz/91OgoQWPJkiW4XC5GjRpldBStFoYPH47VamX+/PlGR9Fnxmr16667\n7iIuLk5331yB+fPn06pVK266SR/eCmbx8fEMGDCABQsWGN59owu9Vq9sNhsjRozg448/xul0Gh0n\n4BUVFbF8+XJGjRqFyaQ/nsFu9OjR/PDDD2zfvt3QHHpN0urdqFGjyM/PZ/Xq1UZHCXhLly7F4XAw\nevRoo6NodSA1NRWz2Wz4Hq0u9Fq9GzRoEFFRUYav7MFgwYIFNGvWjFtvvdXoKFodaNKkCXfeeSfz\n5883tPtGF3qt3oWFhTFs2DAWLVqE2+02Ok7AKikpYdmyZYwcOVJ324SQUaNG8f3337Nr1y7DMui1\nSWsQY8eO5cyZM6xZs8boKAFr+fLllJaW6m6bEJOeno6IMG/evMvPXE90odcaxODBg4mKiuKjjz4y\nOkrAmjdvHk2aNKFfv35GR9HqULNmzejfvz8fffSRYd03utBrDSI8PJwRI0awcOFCXC6X0XECTklJ\nCYsXL2bUqFFYLBaj42h1bNy4cezevZudO3casnxd6LUGM27cOM6ePcvnn39udJSAs3TpUkpLSxk3\nbpzRUbR6UHHcZe7cuYYsXxd6rcEMGjSImJgY3X1Tjblz51bu4muhp2nTptx1113MnTvXkO4bXei1\nBhMWFkZqaiqLFi3SJ09VUVRUxLJlyxgzZgxms9noOFo9GTduHPv27TPk5Cld6LUGNXbsWPLy8gLq\nNmtG+/jjjykvL9fdNiFu5MiRWCwWQ7pvdKHXGtQ999xDbGysYX2VgWju3Lm0atWKW265xegoWj1K\nTExk4MCBhoy+0YVea1B2u5309HQWLVqkL10M5OXlsXz5csaOHatPkmoExo0bx8GDB8nKymrQ5eo1\nS2twEyZMoLCwkKVLlxodxXAZGRm4XC7dbdNIpKamYrVaG3yPVhd6rcHdddddNG3alFmzZhkdxXBz\n5syhXbt23HjjjUZH0RpAfHw8v/rVr+jbt2+DLrfWZ2aIyCGgCPAAbqVUHxFJAOYCKcAhYKxSKq+2\ny9JCg8ViYfz48UydOpX8/Hzi4uKMjmSIU6dOsWrVKn71q1/pO0k1Ir///e8bfJl11aK/SynVUynV\nx//zL4HPlFKdgM/8P2tapQkTJuB0OgPm5slGmDNnDl6vl/vvv9/oKFqIq6+um1TgP/7v/wOk1dNy\ntCB144030rFjRz788EOjoxhm5syZ9O7dmy5duhgdRQtxdVHoFfCpiGwRkcn+aU2r3Cv2FND0/CeJ\nyGQRyRKRrJycnDqIoQUTEWHChAmsXr2aEydOGB2nwe3evZstW7YwceJEo6NojUBdFPrblVI3AEOA\np0TknHO4lW/A6AWDRpVS/1ZK9VFK9UlKSqqDGFqwmTBhAkop5syZY3SUBvfhhx9iMpkYP3680VG0\nRqDWhV4pddz/NRtYBNwEnBaR5gD+r9m1XY4Wejp37kzv3r0bXfeN1+vlww8/5J577qFZs2ZGx9Ea\ngVoVehGJFJHoiu+BQcAO4GNgkn+2SUBmbZajha4HHniArVu3smPHDqOjNJiNGzdy6NAh3W2jNZja\ntuibAutF5Bvga2CpUmo58GfgHhHZBwz0/6xpF5gwYQIWi4Xp06cbHaXBzJw5k4iICNLS9BgFrWGI\nkTesrdCnTx/V0KcEa4Fj5MiRbNy4kaNHj2K1Wo2OU6/Kyspo3rw5w4cP54MPPjA6jhbkRGRLlWHt\nF6XPjNUM99BDD3H69GmWL19udJR6t2jRIgoKCnjkkUeMjqI1IrrQa4YbMmQIycnJjaL75t1336Vd\nu3bccccdRkfRGhFd6DXDWa1WJk6cyOLFizlz5ozRcerNwYMH+fzzz3n44Yf1lSq1BqXXNi0gPPTQ\nQ7hcrpC+0Nn06dMRER566CGjo2iNjC70WkDo3r07vXv35v333zc6Sr3weDy8//77DBo0iNatWxsd\nR2tkdKHXAsYjjzzC9u3bG/ymDA3h888/5+jRo/ogrGYIXei1gDFx4kQiIyN5++23jY5S5959910S\nEhJITU01OorWCOlCrwWMmJgYJkyYwKxZs8jPzzc6Tp05ffo0Cxcu5IEHHsButxsdR2uEdKHXAsoT\nTzxBWVlZSJ1MNG3aNFwuF08++aTRUbRGSp8ZqwWcm2++meLiYnbs2BH0d15yu920a9eOLl26sHLl\nSqPjaCFGnxmrBa0nnniCXbt2sX79eqOj1NqSJUs4duwYTz31lNFRtEZMF3ot4IwbN47Y2NiQOCj7\nz3/+k9atWzNs2DCjo2iNmC70WsCJiIhg0qRJzJs3j1OnThkd56rt3buXlStX8tOf/hSLxWJ0HK0R\n04VeC0jPPPMMbrebf/zjH0ZHuWpTp07FarXy2GOPGR1Fa+R0odcCUseOHUlNTWXq1KmUlpYaHeeK\nFRQU8N577zF69GiaNr3glsma1qCuutCLSGsRWS0iu0Rkp4g855/+exE5LiLb/Y+f1F1crTF58cUX\nOXv2LDNmzDA6yhX797//TWFhIT//+c+NjqJpVz+80n8v2OZKqa3+2wluAdKAsUCxUuqvNX0tPbxS\nq45SiptvvpmCggJ2794dNFd8dDgctG/fnq5du7Jq1Sqj42ghrN6HVyqlTiqltvq/LwJ2Ay2v9vU0\n7XwiwosvvsjevXtZsmSJ0XFqbNasWZw4cYKXXnrJ6CiaBtTRCVMikgKsA7oBLwIPAYVAFvAzpVTe\npZ6vW/Taxbjdbjp06EBKSgpr1641Os5leb1eunfvjtVqZdu2bUF/wpcW2BrshCkRiQIWAM8rpQqB\nqUAHoCdwEvjbRZ43WUSyRCQrJyentjG0EGWxWHjuuedYt24dGzZsMDrOZS1btoxdu3bxi1/8Qhd5\nLWDUqkUvIlZgCbBCKfV6Nb9PAZYopbpd6nV0i167lJKSEtq3b0+PHj0C+jICSin69+/PkSNH2L9/\nf8jf6FwzXr236MXXXHkX2F21yPsP0lZIB3Zc7TI0DSAyMpKXXnqJVatWBfRlEVauXMn69ev5xS9+\noYu8FlBqM+rmduAL4DvA65/8K+A+fN02CjgE/FQpdfJSr6Vb9NrllJaW0q5dO7p37x6QI1mUUtx0\n001kZ2ezd+9efTlirUHUtEV/1edlK6XWA9V1Qi672tfUtIuJiIjg5Zdf5mc/+xlffPEF/fr1MzrS\nOTIzM8nKyuLdd9/VRV4LOPoyxVrQKC0tpX379lx77bV8/vnnRsep5PF46NmzJ06nk507d+rr2mgN\npt5b9NrVUUqRU+wgu9BBmctDqdODUorESDsJUTbiwq1YzSZsluA4OaghRURE8Morr/D888/zySef\nMGTIEKMjATB37lx27NjB7Nmzg7bIK6X0KKEQplv0teTxKlweL26vwqsUyguKH/+mSoFHKbxeRbHD\nzYn8cspdnsu+rghYzCbMIphNgs0iRNmtxIRbiLRbKvvMrGYTYVZzta/hdHtxerw4XB4cbq//4cHt\n8WdV0Cw2jORoe+WHvNTp5tCZUhxuD1azCbNJiA230iTKHhAbH6fTSbdu3TCZTHz33XeGH/R0OBxc\nd911REZGsm3btqA5e7dCUbmLo7llZBeV0zYxkrYJEZhM9Vvw62qjUlDmoqDURWyElZgwyzmvqZSi\n3OWlzOXB7fESabcQYTNfsFyvV+HxfxYCYf2+Uo26Re/1KvZmF1Hm9FS2jiNsZqLsviLpcHspKndR\n4nBjEiHSbiHcZsbr9a0cDreHcJuZhAgbFvOP//wyp4fCchd5pU7yS12UOt14vZcIch6318t3xwr4\nYt8ZjuSWEm4zE2EzE2m3EGmzEGk3E241VxbYMKuZpjF2mseGExNmIa/EVe3rNom2k5IYQVyEjWKH\nm+N5ZZwsKMPtufxGPKfIQYTNTOuECPJLXWQXlVNQ6sLh9hJm9W1ErGYTIhATbqVZTBhNY8LO+VB4\nvAqTUKMPr8vjpdTpAQWRdvM5f9+asNlsvP766wwfPpx//OMfPP/881f0/Lr2P//zPxw4cIAVK1YY\nWuTdHi+5pU5cHoW7ouHhVbi9viJmNQs2i2+9cri9lPv3JgtKf1ynDmQXc7KgjE7J0YRZf3wvIoJJ\nwCSCVHwFShwe32ehzIXb40X864DN7Pu8RdgseJWi1Omh3OV7lLk8ON1eTCKE+z+T8ZE2kqPtWGuw\nLrg9XvLLXBw+W3LO58FsFiKsZl/Dy6vweL0XfDYrPlNe5Wucefx/mwoWsxBlt1TmdnsVArRPiiQ6\nrPYNCpfHS6nDQ4nTTanTTanTg9uruKFNfK1f+3JCrkXv8Sq+PZbP2WJnrV/LZILYcN8/uKjcXVk4\ni8pdbD6UR6nTTYu4cFrEhWMW4VRhOScLysgtcfpaG2UunG4vZpNgNZs4WVBOQZmL2HAr17WIweH2\nUuJwU+Lw/dNLnG7KXdVvOcKtZpKi7TSJspEQaSPMasZmNhFuM9OjZSyJUXbCrObKvQWXx8vBMyXs\nzy7m4JkS8stcFJW7KHa48SrfUXSzSejSLJrbOzbhuhaxHMktZcXOU2w5knfOB6BZbBg9WsbSo1Us\nHZOjsFlMJETaMYtQ5HBR5vRgNgmJkXYSo2yIQF6Jb4PocHswiWAxmfAohct97vuLsJmJi7DRNMZO\nQqStRhsLpRQ/+clP2LRpE/v27SMpKekq/ru1d+jQIbp27cqwYcOYN2/eVb2G2+Ol2OEmr9RFbomT\nEoe7cm+r6p6hVOzDCdjMJmLDrcT6u/myi8o5U+zA6/X9bUqcHoodbsIsJiJsFqxmuaC1W+bycKbY\nWbmOHM8vo3V8ONe1iOXa5jFEhV1ZG9DjVZXFPMxqrrb1nFviZPfJQvacKsJmMdGtRQxdm8cQZjVj\nMkFSVBjhNt867HD7CrGIb131eJW/dX75eqWUwuH2crbEydliByVODzFhFuLCbUTazZS7vJQ63Tjc\nXmwWX2Mm0mYmNtxa7fonAm0TI2jXJIoSp5sT+WWcLnRgNQsxYVaiwyy4PF5KHB5/8fb6/3++LApA\n+d7D+WwWE/2vufr1t6Yt+pAq9C6Pl+1H89l7qojZm49QWOau/ENH2S3ERViJC7fhVRUrpZcou4Wk\naDtJ0XZMAqVO3z+r6j+lYmUD2HO6iG+PFVT7T6sQZjURG2YlJtyK3WrC4/G1DqLCLNzWoQndW8Zi\nvsjusVcpPF7fo9Tp4VSBb+NxqrCcM8VOcood5JU4cbq9VE3QKTmKHq1iyS3xfXiP5pVVZmwaY6dJ\npJ3ocAtRdgtmEbyAw+Vh29F8isrdhFvNlLk8hFvN9L+mCS1iw32tPpeH/aeL+f50EW6vItxqpmvz\naLq1jKVZTFhlCy/SZiE+0ord4utGKnd5OFvsxOXxEmE3E2G1YDJBuctbuTHy7cn82Kq3Wkw0jw2j\nTULERbujKuzevZsePXrwyCOP8K9//euS89aX1NRUPvvsM/bs2UOrVq1q9ByH28PR3DLOFDsor2Hh\nqlDRzSAClip7D4VlLlZ/n83WI/mcKXbgOG9jajYJdosJm9mEySQUlbtwVVludJiFlnHhHMkt9e1t\n4dsAx4RbKwtkfISV2Agr5S5vZZdJYbnvUVTuvmCZNrOJuAgrZpPg9HcbFjvcgO+z6PZ6KXf5GkEd\nk6Lo0jyars1iSIq2+wq9v8ETafft8bo8Xk7kl3Miv4zsIgf5Zb69at+yfRsGp9uLVyku8dG8pIQI\nG9c0i6JDkyhEwOF/ve4tY2kVH4HZLHj8fzen24vFLJiqbBhcHi+nCsuxmnzvvWIdVv69g1L/BrjE\n4cZs8u09JETaGNyt2VV3ZTXKQn80t5RFW4/z9roDCNAhKcr3jxBfKzyv1LeC+nbhfFvyonI3ZTXo\nM68QHWbhlvaJ3NahCYlRNk4WlHM8vwylFM1iwmgWG3bJ3bwo/4cqwmbmRH45OcXllbuYEXYzMf7n\nivhaAIVl7mr79CtWntwSJ5sP5fLVwVxOFpQTZjWRkhhJSmIkHZOj6HCZ3U63x8t3xwvYciSP1vER\n9O+URLjtwiJb7vKw62QhO44X8N3xAvJKq+9GirL/uLteU/ERVnq2jqN323g6JUdjtQgt4yJoFhPm\ne68oLGYTUfZzW5kvvPACb7zxBuvXr+fWW2+t8fLqwpIlSxg+fDivvfbaZS9eVu7yUOJwk13k4GRB\nWeX/26sUOUUOjueX+R55ZZz1t+pLHL7iWdGyr2ghAphFaBEXRtvESJRSfHUwF7dX0bVZNC3iwkmM\nshEdZsXh754pdXpwery43L4uneiwHxs9KU0iSIryHaPxehWHzpaw51QReaVOCsvdFJa5yC/17Z25\n/RU0OsxCTJhvjyI6zEJ0mK/rMcxqJsxqoszlIa/URV6JE4Wv6NssJprG2OnaPIaWceF4vYr9OcV8\nd7yA3SeLOJpbSk0rkd1iqswfHWbBbjFht5gru6ZMgn+v00aTKDuRdgtFZS7yy3zdteFWM+E2M3aL\nGafH1/AoKHOxP9vXoCkqd1+wzPZNIunbPpGzxQ52n/LltZpNvj3sKBt5JS7f/1admxPA6fFyqTI7\npFszpk7sXcN3f65GWejf/Gwf/7tqL81iw3jmrk4kRV84nvn8A0FK+Q6S5hQ5EBEibL5+cou56jxU\nfuAibBbCbGbaJUYCcPBsyTndERE2M4lRdiLtZqLtVixmweXxHRS1W8yVXUEVyl0eisrdxIZbL3ow\nqNzlOzbgcHkrWy5OjxeXfyV1uLwopSgsdxMdZjmnlREbYaVVfDhWs6lyZTOJr5UnCDnFDk7kl+F0\n+/pYm8aE0To+4pxiX1Tu61Y4U+wrQkqpym6oihZUscNNXomTsyVOBEiMspEYacdqlsrRRV6lCLOY\nK1s6JQ43JU43h8+WsvNEIU6Pl9hwK3d2TuLOa5Iu2ECFWc00ifa9blyElbKSYq6//nrMZjPbt28n\nKirqUqtHnSksLOT6668nPDyc7du3Y7PZLpinxOHm4JkScoodla1Aj1exP7uYb4/lsz+nmGN5Zee0\nhJOi7CRH+wpTpN1XiEwCCJgQzGbBLL6/55GzpRzO9R00v7VDEwZ2TaZ5bDjgayRYzSbc1fRTV2Uy\ngdnkO+BvMvn2EixVllFWZc9W+TfedqvpnL2J85lNUtn1VJ2EKBut4sNxexSHz5ZS4m/lF5e7+f50\nEYVlrsoNhoLKPm0RaOnvJo27SBdLXVBKkVfqwiRgt5hxebx8efAs6/ad4VRBORaT0CEpik5No3C6\nvWQXOThb7CA+wkarhHBaxUXgUYr8Ul/3reA7NmI1CxE2i/84odn3mfE3Mvtfk8Tgbs2uKm+jK/Rb\nDucyauomerSK5fHb25MYZSMm3Lf7ZLf4ipzb62vROP0tm6qFsoIIlX2MUXYLEXZL5YiZEoebuAgr\nbRMjKw8cuTxeDp/1feBaxoUTF3Hhh76+lTrdnC327cp6/bv2ZpPUOI/XqzhT4iAmzHrZLhOn21u5\n21zq9OCq0lL0jV7wfcgr9qS83ot/6KtyuDx8d6KA9fvOsONEIVazcEObeDokRdEmIYLW8eHYq2QT\n8XX9HNyRxf1pQ3j88ccbpAtHKcXEiROZO3cua9eu5bbbbjvndwVlLo7llXG6sLxyndt9soivD+by\nzbF8Sp0eLCahXZNIWvvfV8v4cFrGnvv+aprFoxQWk4mEKBttEyKItPtauBWF0OVvEHi8yteHj8Jq\nNmG3mC57ILyiL7+wzE1+mZPCMre/IWMjPsLXBVrscFPq8G0AosOsRNrMKAXlbg8lDt+GomKPJC7C\nSoTt3L2ys8UOckucOD2+Boxvft964/Koc/ZmTSaIDrNit5gqDzor8B/4NWMxmSoPNHuUIsruK6zh\nVjOKH/vIrWapHPDg8i+3zOUhp8iXpbr1VSnFqcJyEiJtld2T1bGY5YKDvJei++ivwpyvj5AQYaNt\nk0g6JUfVeJiY13+gp6IvU48nrjsVw9xKnW5cHlXZQv1xBl+/fU5xOfmlLpSCE/llfLYnm61H8ip3\no00CbRIi6NQ0mmuSo+iUHF15wPC9v/83s6dNYfHixQwbNqxe38/06dN5+OGH+b//9//ym9/8BoAz\nxQ5OFZRztsSJy+0rrLtPFrL9aD5bj+RT7PAVyOtbxdGzdRzXtYg5Z4NacZzDZJLKll+EzTfaye3x\n4vIPh63YgCtF5TEmi9nXwkyIbPgGRkOoOAir/IW7vj+bTreX3BInXqUqRxlVHGOo6Je3W0z+/42i\n3O1rKEaHWUiIshETZhpy4u4AAAkXSURBVMXl8ZJX4iS31Inbo7CYBYtJMJt8LXuLyde9lVvioMzp\n5fZOTa46b6Ms9KcLyxGB5OiwOkilNTSXx8vpwnKO5pZVdhH5htKVcvBMCfuyi/ghp6Syr7hFXBhd\nm8VwR8d4/vBoKvm5OaxYu4mendtd8bDNmvj++++54YYbuOmmm/j005Vkl/iG+ZU6fK3OAznFfL4n\nm+1H8yuHp/ZoGcdN7RK4rkVM5V6g1WIiPsJKQqRvBNX5rVyt8fB6Va3OW2iUhV4LHWeLHRzNK+Ns\nseOc3eCKYaP7sovZe7qI708VoRR0TzKz6i8/pUViLP/7QQYpzRJJiLSdM/qhNoqKiujXrx/Hjh1j\nyepNOO1xlaM8thzO49Ndpzl4poRwq5kbU+K5oU08nZtFVxb3uAgrzf39y5F2Xdi1uqELvRYSyl0e\njuWVkV1YXu1InvxSJ8t2nGLd3hy8Xi+F25eT4jnGH//2D8z+yxGE28w0iw2jRWx4tSOKLpuhvJx7\nhwxhwxdf8MepH9D7trsB2He6iLlZRzl0tpSmMXYGdGnKrR0Sz9mwRIVZ6JAUVe3AAE2rLV3otZDj\n9p+UUuz0DfsrLHNR4j87ObfEybLvTrJ2bzZet5vE0kNMHjuM9hVDbP3iInznN0SH+Q7UVTf01Ov1\n9b2WOT0UlJTz8MT7WLfqE17+8xTuHjaKXScKWfN9DtuP5RMfYSWtV0tuaZ+ISYQm0XYSImyE2UyE\nW80N0q+sNV660GuNgtPt5VheKcfyfENEzxY7+NusZZy2NkfMFmLCLPRoFUe3FjF0aR5zwVh8i1mI\nj7ARG26l3O0bYVLscOH1gsvp5G+/fYHPlixg0q/+Rmy3u9hw4Axnip1E2S0M6JrMoGub+vYYYsJp\nmxihu2W0BqULvdaoeL2K4/llHMgpxuX28u4//s7iNV/S/KahWFp1p9ztRfCdyt6lWQxdm0fTMSnq\nokMaTx47yn//v/9HtjeSFn1HUGKKAKBLs2j6d0qiV5s4rGYT8ZFWujSL0QVeM4ThhV5EBgNvAGZg\nmlLqzxebVxd6ra443V72ZxdzIr+Mz5cu4q+/eZ4mzVow5sU/o5I7svukb+SORynMIiRE2irHhYPv\nLMZTp7M5VuhGbOEIimuaxtC7bTy92sRVzme1mOiYHEXLuHAj367WyBla6EXEDOwF7gGOAZuB+5RS\nu6qbXxd6ra4Vlbs4VVDO6i828rsXJnPq2BG69+7LQ8++TKceN3IgxzdyJ6fIwdkS//WDHGWUFORR\nXpRPWPlZxgwdwG3dO53TWo+wm2mTEEHz2PCLXq9I0/5/e/caIlUdxnH8+8O7RbtqoemuabQVS3Sx\nKLsQpUHbBSsoMLq9sHyTZBFE0Zt6FUXYBUIItSzCJJOSkKIs6JWmXTAvlXbxhreytPSFSU8vzn9h\nsqbWnLPH+e/vA8PM/z+HnefhmX2Y858z5/SWqhv9xcBjEXF1Gj8CEBFP/NP2bvRWpp/27WfOnLnM\neuoJdu3cwfEntNDReTannt7J/t/2sWPbFjZ/t4E9u3cyqm0sN91+D9fdcjuDBg/5yxp+69ABlfzy\n2ayeqhv9zUBXRNydxncAF0XEjJptpgPTAcaOHXv+pk2bGh6HWa0DBw6wcOFCli9fzoqVq1i/bi0t\nLcMY1dbO6LZ2ruyawiWTu5D60Tp0AKNaBjOih6dONqvCMd/oa/kTvZnZketpoy/rkjjbgPaacVua\nMzOzXlZWo18JdEgaL2kgMBVYUtJrmZnZvyjl4N+IOCRpBvAexeGV8yJibRmvZWZm/660X3lExFJg\naVl/38zMeqa6y9abmVmvcKM3M8ucG72ZWebc6M3MMudGb2aWuWPiNMWSdgNHcw6EE4EfGxROM+hr\n+YJz7iuc85E5JSJO+q+NjolGf7QkrerJz4Bz0dfyBefcVzjncnjpxswsc270ZmaZy6XRv1h1AL2s\nr+ULzrmvcM4lyGKN3szM6svlE72ZmdXhRm9mlrmmbvSSuiR9LWmjpIerjqcMktolfSRpnaS1kmam\n+eGS3pe0Id0PqzrWRpLUT9Lnkt5J4/GSVqRaL0zXOciKpFZJiyR9JWm9pItzrrOkB9J7eo2kBZIG\n51hnSfMk7ZK0pmbuH+uqwvMp/9WSJjQihqZt9JL6AS8A1wCdwK2SOquNqhSHgAcjohOYCNyb8nwY\nWBYRHcCyNM7JTGB9zfhJ4JmIOA34GZhWSVTleg54NyLOBM6hyD/LOksaA9wHXBARZ1Fct2Iqedb5\nZaDrsLl6db0G6Ei36cDsRgTQtI0euBDYGBHfRcRB4HXghopjariI2B4Rn6XHv1L884+hyHV+2mw+\ncGM1ETaepDbgOmBOGguYBCxKm2SVL4CkFuByYC5ARByMiF/IuM4U18MYIqk/MBTYToZ1joiPgT2H\nTder6w3AK1FYDrRKOvloY2jmRj8G2FIz3prmsiVpHHAesAIYGRHb01M7gJEVhVWGZ4GHgD/SeATw\nS0QcSuMcaz0e2A28lJas5kg6jkzrHBHbgKeBzRQNfi/wKfnXuVu9upbS15q50fcpko4H3gTuj4h9\ntc9FcYxsFsfJSroe2BURn1YdSy/rD0wAZkfEecB+DlumyazOwyg+vY4HRgPH8ffljT6hN+razI1+\nG9BeM25Lc9mRNICiyb8WEYvT9M7uXbp0v6uq+BrsUmCKpB8oluMmUaxdt6ZdfMiz1luBrRGxIo0X\nUTT+XOt8FfB9ROyOiN+BxRS1z73O3erVtZS+1syNfiXQkb6lH0jxRc6SimNquLQ+PRdYHxGzap5a\nAtyVHt8FvN3bsZUhIh6JiLaIGEdR0w8j4jbgI+DmtFk2+XaLiB3AFklnpKnJwDoyrTPFks1ESUPT\ne7w736zrXKNeXZcAd6ajbyYCe2uWeP6/iGjaG3At8A3wLfBo1fGUlONlFLt1q4Ev0u1ainXrZcAG\n4ANgeNWxlpD7FcA76fGpwCfARuANYFDV8ZWQ77nAqlTrt4BhOdcZeBz4ClgDvAoMyrHOwAKK7yF+\np9hzm1avroAojib8FviS4qiko47Bp0AwM8tcMy/dmJlZD7jRm5llzo3ezCxzbvRmZplzozczy5wb\nvZlZ5tzozcwy9yd9eJyjN9Z5XgAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x1684b2668>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"qmu_sin_sample=np.array([ _qmu.sample(1000).eval() for _qmu in qmu_sin])\n",
"plotseries(sy,qmu_sin_sample,\"Edward VB\"+ed.__version__,1)"
]
},
{
"cell_type": "code",
"execution_count": 60,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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VwIpKEq4ue3NaxWqwNWhRSmN2tCOemfNvr07DH47h/jys/SQmnUbVwPOd7fHe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\nzlXMH8BimbaJg4cwAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x168500f60>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"simpleplot(qmu_sin_sample,1)"
]
},
{
"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": "3.6.3"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
@xiangze
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xiangze commented Apr 30, 2018

add artificial data result (sin curve)

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