203 lines
298 KiB
Plaintext
203 lines
298 KiB
Plaintext
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{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "6ea68943a7d38c92",
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"metadata": {
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"collapsed": false
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},
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"source": [
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"# Здесь сравниваются все виды разбиения"
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]
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},
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{
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"cell_type": "markdown",
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"id": "87721a41fa7d5a43",
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"metadata": {},
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"source": [
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"Тестовые \"левые\" данные"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"id": "initial_id",
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"metadata": {
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"ExecuteTime": {
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"end_time": "2024-04-18T06:35:02.910179Z",
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"start_time": "2024-04-18T06:34:59.833600Z"
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},
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"collapsed": true
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},
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"outputs": [
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{
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"data": {
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"image/png": "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"text/plain": [
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"<Figure size 1000x500 with 1 Axes>"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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}
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],
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"source": [
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"import matplotlib.pylab as plt\n",
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"from pyFTS.data import Enrollments\n",
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"import warnings\n",
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"warnings.filterwarnings('ignore')\n",
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"\n",
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"fig, ax = plt.subplots(nrows=1, ncols=1, figsize=[10,5])\n",
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"\n",
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"df = Enrollments.get_dataframe()\n",
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"plt.plot(df['Year'],df['Enrollments'])\n",
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"data = df['Enrollments'].values"
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]
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},
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{
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"cell_type": "markdown",
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"id": "5567b1483bbee8bf",
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"metadata": {},
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"source": [
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"Загрузка сигмоиды"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 7,
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"id": "38fe5ded4fc50b3d",
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"metadata": {
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"ExecuteTime": {
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"end_time": "2024-04-18T06:41:50.847475Z",
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"start_time": "2024-04-18T06:41:50.739969Z"
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}
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},
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"outputs": [
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{
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"data": {
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"image/png": "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"text/plain": [
|
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"<Figure size 640x480 with 1 Axes>"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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}
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],
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"source": [
|
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"from service.api.generate import generate_sine_series\n",
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"\n",
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"df = generate_sine_series(length=500, frequency=50, amplitude=100)\n",
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"plt.plot(df)\n",
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"\n",
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"data = df.values.reshape(-1)"
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]
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},
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{
|
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|
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"cell_type": "markdown",
|
||
|
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"id": "12914e2f00fc7fd6",
|
||
|
|
"metadata": {
|
||
|
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"collapsed": false
|
||
|
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},
|
||
|
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"source": [
|
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|
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"Grid"
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|
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]
|
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|
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},
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||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 8,
|
||
|
|
"id": "3d6024de3ba53ea9",
|
||
|
|
"metadata": {
|
||
|
|
"ExecuteTime": {
|
||
|
|
"end_time": "2024-04-18T06:41:53.824984Z",
|
||
|
|
"start_time": "2024-04-18T06:41:53.701349Z"
|
||
|
|
},
|
||
|
|
"collapsed": false
|
||
|
|
},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 1500x500 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"from pyFTS.partitioners import Grid\n",
|
||
|
|
"\n",
|
||
|
|
"fs = Grid.GridPartitioner(data=data,npart=5)\n",
|
||
|
|
"\n",
|
||
|
|
"fig, ax = plt.subplots(nrows=1, ncols=1, figsize=[15,5])\n",
|
||
|
|
"\n",
|
||
|
|
"fs.plot(ax)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"id": "123ce57e233c80b1",
|
||
|
|
"metadata": {},
|
||
|
|
"source": [
|
||
|
|
"CMeans"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 15,
|
||
|
|
"id": "57982a811cfcb14a",
|
||
|
|
"metadata": {
|
||
|
|
"ExecuteTime": {
|
||
|
|
"end_time": "2024-04-18T07:06:09.680074Z",
|
||
|
|
"start_time": "2024-04-18T07:06:09.480746Z"
|
||
|
|
},
|
||
|
|
"collapsed": false
|
||
|
|
},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 1500x500 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"from pyFTS.partitioners import KMeans\n",
|
||
|
|
"\n",
|
||
|
|
"fs = KMeans.KMeansPartitioner(data=data,npart=10)\n",
|
||
|
|
"\n",
|
||
|
|
"fig, ax = plt.subplots(nrows=1, ncols=1, figsize=[15,5])\n",
|
||
|
|
"\n",
|
||
|
|
"fs.plot(ax)"
|
||
|
|
]
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"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": "3.11.9"
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"nbformat": 4,
|
||
|
|
"nbformat_minor": 5
|
||
|
|
}
|