77 lines
2.3 KiB
Python
77 lines
2.3 KiB
Python
#!/usr/bin/python
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# -*- coding: utf8 -*-
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import os
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import numpy as np
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import matplotlib.pylab as plt
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#from mpl_toolkits.mplot3d import Axes3D
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import pandas as pd
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from pyFTS.common import Transformations
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tdiff = Transformations.Differential(1)
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from pyFTS.data import TAIEX
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dataset = TAIEX.get_data()
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from pyFTS.benchmarks import benchmarks as bchmk
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from pyFTS.models import pwfts
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'''
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bchmk.sliding_window_benchmarks(dataset, 1000, train=0.8, inc=0.2, methods=[pwfts.ProbabilisticWeightedFTS],
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benchmark_models=False, orders=[1], partitions=[10], #np.arange(10,100,2),
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progress=False, type='distribution',
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distributed=False, nodes=['192.168.0.106', '192.168.0.105', '192.168.0.110'],
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save=True, file="pwfts_taiex_interval.csv")
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'''
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train_split = 2000
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test_length = 200
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from pyFTS.partitioners import Grid, Util as pUtil
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#partitioner = Grid.GridPartitioner(data=dataset[:train_split], npart=30)
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partitioner = Grid.GridPartitioner(data=dataset[:train_split], npart=10, transformation=tdiff)
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from pyFTS.common import fts,tree
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from pyFTS.models import hofts, pwfts
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pfts1_taiex = pwfts.ProbabilisticWeightedFTS("1", partitioner=partitioner)
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pfts1_taiex.append_transformation(tdiff)
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pfts1_taiex.fit(dataset[:train_split], save_model=True, file_path='pwfts')
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pfts1_taiex.shortname = "1st Order"
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print(pfts1_taiex)
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tmp = pfts1_taiex.predict(dataset[train_split:train_split+20], type='point',
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method='heuristic', steps_ahead=10)
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print(tmp)
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'''
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tmp = pfts1_taiex.predict(dataset[train_split:train_split+200], type='diPedro Pazzini
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stribution', steps_ahead=20)
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f, ax = plt.subplots(3, 4, figsize=[20,15])
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tmp[0].plot(ax[0][0], title='t=1')
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tmp[2].plot(ax[0][1], title='t=20')
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tmp[4].plot(ax[0][2], title='t=40')
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tmp[6].plot(ax[0][3], title='t=60')
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tmp[8].plot(ax[1][0], title='t=80')
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tmp[10].plot(ax[1][1], title='t=100')
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tmp[12].plot(ax[1][2], title='t=120')
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tmp[14].plot(ax[1][3], title='t=140')
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tmp[16].plot(ax[2][0], title='t=160')
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tmp[18].plot(ax[2][1], title='t=180')
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tmp[20].plot(ax[2][2], title='t=200')
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f, ax = plt.subplots(1, 1, figsize=[20,15])
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bchmk.plot_distribution(ax, 'blue', tmp, f, 0, reference_data=dataset[train_split:train_split+200])
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''' |