272 lines
211 KiB
Plaintext
272 lines
211 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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"metadata": {},
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"source": [
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"# High Order Interval Fuzzy Time Series\n",
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"\n",
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"SILVA, Petrônio CL; SADAEI, Hossein Javedani; GUIMARÃES, Frederico Gadelha. Interval Forecasting with Fuzzy Time Series.\n",
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"In: Computational Intelligence (SSCI), 2016 IEEE Symposium Series on. IEEE, 2016. p. 1-8."
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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": 1,
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"metadata": {},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"/usr/local/lib/python3.6/dist-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",
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" from pandas.core import datetools\n"
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]
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},
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Populating the interactive namespace from numpy and matplotlib\n"
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]
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},
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"/usr/lib/python3/dist-packages/IPython/core/magics/pylab.py:161: UserWarning: pylab import has clobbered these variables: ['plt']\n",
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"`%matplotlib` prevents importing * from pylab and numpy\n",
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" \"\\n`%matplotlib` prevents importing * from pylab and numpy\"\n"
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]
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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.benchmarks import benchmarks as bchmk\n",
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"from pyFTS.models import ifts\n",
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"\n",
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"from pyFTS.common import Transformations\n",
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"tdiff = Transformations.Differential(1)\n",
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"\n",
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"%pylab inline"
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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": 2,
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"metadata": {},
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"outputs": [],
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"source": [
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"from pyFTS.data import Enrollments\n",
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"\n",
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"enrollments = Enrollments.get_data()"
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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": 3,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"image/png": "iVBORw0KGgoAAAANSUhEUgAAA1gAAALICAYAAABijlFfAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nBcsaWkgCc2M5Ho49/OS
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"text/plain": [
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"<matplotlib.figure.Figure at 0x7f64a8854550>"
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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 pyFTS.partitioners import Grid, Util as pUtil\n",
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"\n",
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"fuzzy_sets = Grid.GridPartitioner(data=enrollments, npart=12)\n",
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"fuzzy_sets2 = Grid.GridPartitioner(data=enrollments, npart=5, transformation=tdiff)\n",
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"\n",
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"pUtil.plot_partitioners(enrollments, [fuzzy_sets,fuzzy_sets2])"
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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": 4,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"[ 14:39:06] Start training\n",
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"[ 14:39:06] Starting batch 1\n",
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"[ 14:39:06] Finish batch 1\n",
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"[ 14:39:06] Starting batch 2\n",
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"[ 14:39:06] Finish batch 2\n",
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"[ 14:39:06] Starting batch 3\n",
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"[ 14:39:06] Finish batch 3\n",
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"[ 14:39:06] Starting batch 4\n",
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"[ 14:39:06] Finish batch 4\n",
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"[ 14:39:06] Starting batch 5\n",
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"[ 14:39:06] Finish batch 5\n",
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"[ 14:39:06] Starting batch 6\n",
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"[ 14:39:06] Finish batch 6\n",
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"[ 14:39:06] Starting batch 7\n",
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"[ 14:39:06] Finish batch 7\n",
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"[ 14:39:06] Starting batch 8\n",
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"[ 14:39:06] Finish batch 8\n",
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"[ 14:39:06] Starting batch 9\n",
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"[ 14:39:06] Finish batch 9\n",
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"[ 14:39:06] Starting batch 10\n",
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"[ 14:39:06] Finish batch 10\n",
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"[ 14:39:06] Starting batch 11\n",
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"[ 14:39:06] Finish batch 11\n",
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"[ 14:39:06] Finish training\n",
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"Interval FTS:\n",
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"A1 -> A2,A3\n",
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"A10 -> A10,A8,A9\n",
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"A2 -> A2,A3,A4\n",
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"A3 -> A2,A3,A4,A5\n",
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"A4 -> A4,A5,A6\n",
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"A5 -> A4,A5,A6,A7\n",
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"A6 -> A4,A5,A6,A7,A8,A9\n",
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"A7 -> A5,A6,A7,A8,A9\n",
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"A8 -> A10,A9\n",
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"A9 -> A10,A8,A9\n",
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"\n"
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]
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}
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],
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"source": [
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"model1 = ifts.IntervalFTS(\"FTS\", partitioner=fuzzy_sets)\n",
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"model1.fit(enrollments, order=1)\n",
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"\n",
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"print(model1)"
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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": 5,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"[ 14:39:07] Start training\n",
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"[ 14:39:07] Starting batch 1\n",
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"[ 14:39:07] Finish batch 1\n",
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"[ 14:39:07] Starting batch 2\n",
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"[ 14:39:07] Finish batch 2\n",
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"[ 14:39:07] Starting batch 3\n",
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"[ 14:39:07] Finish batch 3\n",
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"[ 14:39:07] Starting batch 4\n",
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"[ 14:39:07] Finish batch 4\n",
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"[ 14:39:07] Starting batch 5\n",
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"[ 14:39:07] Finish batch 5\n",
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"[ 14:39:07] Starting batch 6\n",
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"[ 14:39:07] Finish batch 6\n",
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"[ 14:39:07] Starting batch 7\n",
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"[ 14:39:07] Finish batch 7\n",
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"[ 14:39:07] Starting batch 8\n",
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"[ 14:39:07] Finish batch 8\n",
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"[ 14:39:07] Starting batch 9\n",
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"[ 14:39:07] Finish batch 9\n",
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"[ 14:39:07] Starting batch 10\n",
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"[ 14:39:07] Finish batch 10\n",
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"[ 14:39:07] Starting batch 11\n",
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"[ 14:39:07] Finish batch 11\n",
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"[ 14:39:07] Finish training\n",
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"Interval FTS:\n",
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"A1 -> A2,A3\n",
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"A10 -> A10,A8,A9\n",
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"A2 -> A2,A3,A4\n",
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"A3 -> A2,A3,A4,A5\n",
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"A4 -> A4,A5,A6\n",
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"A5 -> A4,A5,A6,A7\n",
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"A6 -> A4,A5,A6,A7,A8,A9\n",
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"A7 -> A5,A6,A7,A8,A9\n",
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"A8 -> A10,A9\n",
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"A9 -> A10,A8,A9\n",
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"\n"
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]
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}
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],
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"source": [
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"model2 = ifts.IntervalFTS(\"FTS\", partitioner=fuzzy_sets2)\n",
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"model2.append_transformation(tdiff)\n",
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"model2.fit(enrollments, order=1)\n",
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"\n",
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"print(model1)"
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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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"metadata": {},
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"outputs": [
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{
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"data": {
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|
||
|
|
"text/plain": [
|
||
|
|
"<matplotlib.figure.Figure at 0x7f64a6475be0>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"bchmk.plot_compared_series(enrollments, [model1, model2], bchmk.colors, \n",
|
||
|
|
" points=False, intervals=True)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 7,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"Model\t& Order & Sharpness\t\t& Resolution\t\t& Coverage & .05 & .25 & .75 & .95\t\\\\ \n",
|
||
|
|
"IFTS FTS\t\t& 1\t\t& 3797.83\t\t& 631.23\t\t& 1.0 &88.61 &443.05 &514.01 &102.8\\\\ \n",
|
||
|
|
"IFTS FTS\t\t& 1\t\t& 2436.2\t\t& 491.02\t\t& 0.81 &96.02 &480.08 &214.85 &105.31\\\\ \n",
|
||
|
|
"\n"
|
||
|
|
]
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"bchmk.print_interval_statistics(enrollments, [model1, model2])"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"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
|
||
|
|
}
|