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@catethos
Created January 12, 2022 09:37
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{
"cells": [
{
"cell_type": "markdown",
"id": "5bb64dca-3a9f-4665-bdb9-d38351b8a21d",
"metadata": {},
"source": [
"# Continuous Distribution Fiting using Julia wrapped in Python"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "c4de4246-2a78-48fe-bc94-f70d55e8bfb8",
"metadata": {},
"outputs": [],
"source": [
"# this is main library needed\n",
"from juliacall import Main as jl\n",
"\n",
"# scipy is just needed to generate data\n",
"# and is not needed\n",
"from scipy import stats\n",
"\n",
"# matplotlib is not needed\n",
"# just for plot\n",
"import matplotlib.pyplot as plt"
]
},
{
"cell_type": "markdown",
"id": "f94aa21b-afc3-4298-889a-950abf3868b2",
"metadata": {},
"source": [
"# Wrapper Class"
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "56a9ed1b-cef4-4bda-87e9-9890df040563",
"metadata": {},
"outputs": [],
"source": [
"class Beta:\n",
" \n",
" def __init__(self, data, weights=50):\n",
" jl.seval(\"using OnlineStats\")\n",
" self.o = jl.FitBeta(weight=jl.ExponentialWeight(weights))\n",
" jl.fit_b(self.o, data)\n",
" \n",
" def fit(self, data):\n",
" jl.fit_b(self.o, data)\n",
" \n",
" @property\n",
" def parameters(self):\n",
" return jl.value(self.o)"
]
},
{
"cell_type": "markdown",
"id": "384a88a0-b942-427f-8540-124ed30c4261",
"metadata": {},
"source": [
"# Simulation"
]
},
{
"cell_type": "markdown",
"id": "3a9dadee-1572-471f-b9c8-234d987fba7c",
"metadata": {},
"source": [
"## generate the base population"
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "ea4235c3-4d9f-46b9-89b1-856f86feda18",
"metadata": {},
"outputs": [],
"source": [
"base_population = stats.beta.rvs(2,1, size=10000)\n",
"beta = Beta(base_population)"
]
},
{
"cell_type": "markdown",
"id": "ac939c94-c1ad-41e3-b73d-a736c3833b97",
"metadata": {},
"source": [
"## simulate streaming data"
]
},
{
"cell_type": "code",
"execution_count": 13,
"id": "c886a658-34d4-4057-bbb0-22f932513c15",
"metadata": {},
"outputs": [],
"source": [
"# simulating the data coming from a new population\n",
"# one by one\n",
"new_population = stats.beta.rvs(1,3, size=100)\n",
"updated_parameter = []\n",
"for x in new_population:\n",
" beta.fit(x)\n",
" updated_parameter.append(beta.parameters)"
]
},
{
"cell_type": "markdown",
"id": "edc36dd7-28b7-4e2b-b40b-3380b1d31673",
"metadata": {},
"source": [
"## Ploting how the parameters change as new data comes in"
]
},
{
"cell_type": "code",
"execution_count": 14,
"id": "11c2ab3a-eb9f-4a9c-9713-f06c5f1482c5",
"metadata": {},
"outputs": [],
"source": [
"alpha = [x[0] for x in updated_parameter]\n",
"beta = [x[1] for x in updated_parameter]"
]
},
{
"cell_type": "code",
"execution_count": 15,
"id": "a1971572-ce79-40d2-aa63-e77f4a738b1b",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[<matplotlib.lines.Line2D at 0x7f413f3c8310>]"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"# the beta parameter going fromo 1 to 3\n",
"plt.plot(beta)"
]
},
{
"cell_type": "code",
"execution_count": 16,
"id": "f21c7483-0b25-4b65-a91a-a7c4518ff327",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[<matplotlib.lines.Line2D at 0x7f413d2cd3d0>]"
]
},
"execution_count": 16,
"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": [
"# the beta parameter going fromo 2 to 1\n",
"plt.plot(alpha)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "e7e7cadb-087c-4f75-872d-54b48d5a3318",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Poetry",
"language": "python",
"name": "poetry-kernel"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.9.9"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
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