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- Otimizações em pfts.forecastAheadDistribution

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- Correção da Issue #1
This commit is contained in:
Petrônio Cândido de Lima e Silva 2017-01-16 18:32:15 -02:00
parent 29719053d4
commit 85a47e225a
4 changed files with 28 additions and 48 deletions
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13
benchmarks/benchmarks.py
View File

@ -108,27 +108,18 @@ def plotComparedIntervalsAhead(original, models, colors, distributions, time_fro
count = 0
for fts in models:
if fts.hasDistributionForecasting and distributions[count]:
density = fts.forecastAheadDistribution(original[time_from - fts.order:time_from], time_to, resolution)
density = fts.forecastAheadDistribution(original[time_from - fts.order:time_from], time_to, resolution, parameters=None)
y = density.columns
t = len(y)
# interpol between time_from and time_from+1
#if interpol:
# diffs = [density[q][0] / 50 for q in density]
# for p in np.arange(0, 50):
# xx = [(time_from - 1) + 0.02 * p for q in np.arange(0, t)]
# alpha2 = np.array([diffs[q] * p for q in np.arange(0, t)]) * 100
# ax.scatter(xx, y, c=alpha2, marker='s', linewidths=0, cmap='Oranges',
# norm=pltcolors.Normalize(vmin=0, vmax=1), vmin=0, vmax=1, edgecolors=None)
for k in density.index:
alpha = np.array([density[q][k] for q in density]) * 100
x = [time_from + k for x in np.arange(0, t)]
for cc in np.arange(0,resolution,5):
ax.scatter(x, y+cc, c=alpha, marker='s', linewidths=0, cmap='Oranges',
norm=pltcolors.Normalize(vmin=0, vmax=1), vmin=0, vmax=1, edgecolors=None)
ax.scatter(x, y+cc, c=alpha, marker='s', linewidths=0, cmap='Oranges', edgecolors=None)
if interpol and k < max(density.index):
diffs = [(density[q][k + 1] - density[q][k])/50 for q in density]
for p in np.arange(0,50):

2
chen.py
View File

@ -1,6 +1,6 @@
import numpy as np
from pyFTS.common import FuzzySet, FLR
import fts
from pyFTS import fts
class ConventionalFLRG:

7
common/SortedCollection.py
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@ -215,3 +215,10 @@ class SortedCollection(object):
else:
return self._items[g - 1: l - 1]
raise ValueError('No item found inside keys: %r,%r' % (ge,le))
def around(self, k):
g = bisect_right(self._keys, k)
l = bisect_left(self._keys, k)
if g != len(self) and l != len(self):
return self._items[g : l]
raise ValueError('No item found around key : %r' % (k,))

50
pfts.py
View File

@ -140,9 +140,9 @@ class ProbabilisticFTS(ifts.IntervalFTS):
idx = np.ravel(tmp) # flatten the array
if idx.size == 0: # the element is out of the bounds of the Universe of Discourse
if math.ceil(instance) <= self.sets[0].lower:
if instance <= self.sets[0].lower:
idx = [0]
elif math.ceil(instance) >= self.sets[-1].upper:
elif instance >= self.sets[-1].upper:
idx = [len(self.sets) - 1]
else:
raise Exception(instance)
@ -348,19 +348,19 @@ class ProbabilisticFTS(ifts.IntervalFTS):
return grid
def gridCount(self, grid, resolution, interval):
for sbin in sorted(grid):
if sbin >= interval[0] and (sbin + resolution) <= interval[1]:
grid[sbin] = grid[sbin] + 1
return grid
def gridCountIndexed(self, grid, resolution, index, interval):
def gridCount(self, grid, resolution, index, interval):
#print(interval)
for k in index.inside(interval[0],interval[1]):
#print(k)
grid[k] += 1
return grid
def gridCountPoint(self, grid, resolution, index, point):
k = index.find_ge(point)
# print(k)
grid[k] += 1
return grid
def buildTreeWithoutOrder(self, node, lags, level):
if level not in lags:
@ -372,8 +372,7 @@ class ProbabilisticFTS(ifts.IntervalFTS):
for child in node.getChildren():
self.buildTreeWithoutOrder(child, lags, level + 1)
def forecastAheadDistribution(self, data, steps, resolution):
def forecastAheadDistribution(self, data, steps, resolution,parameters=None):
ret = []
@ -383,8 +382,6 @@ class ProbabilisticFTS(ifts.IntervalFTS):
index = SortedCollection.SortedCollection(iterable=grid.keys())
#print (index)
grids = []
for k in np.arange(0, steps):
grids.append(self.getGridClean(resolution))
@ -393,24 +390,13 @@ class ProbabilisticFTS(ifts.IntervalFTS):
lags = {}
#print(k)
cc = 0
for i in intervals[k - self.order : k]:
#print(i)
nq = 3 * k
if nq == 0: nq = 1
if nq > 50: nq = 50
st = 50 / nq
#print(st)
quantiles = []
for qt in np.arange(0, 50, st):
for qt in np.arange(0, 50, 2):
quantiles.append(i[0] + qt * ((i[1] - i[0]) / 100))
quantiles.append(i[1] - qt * ((i[1] - i[0]) / 100))
quantiles.append(i[0] + ((i[1] - i[0]) / 2))
@ -419,31 +405,27 @@ class ProbabilisticFTS(ifts.IntervalFTS):
quantiles.sort()
#print(quantiles)
lags[cc] = quantiles
cc += 1
# Build the tree with all possible paths
root = tree.FLRGTreeNode(None)
self.buildTreeWithoutOrder(root, lags, 0)
#print(root)
# Trace the possible paths
for p in root.paths():
path = list(reversed(list(filter(None.__ne__, p))))
#print(path)
if parameters is None:
qtle = self.forecastInterval(path)
grids[k - self.order] = self.gridCountIndexed(grids[k - self.order], resolution, index, np.ravel(qtle))
grids[k - self.order] = self.gridCount(grids[k - self.order], resolution, index, np.ravel(qtle))
else:
qtle = self.forecast(path)
grids[k - self.order] = self.gridCountPoint(grids[k - self.order], resolution, index, np.ravel(qtle))
for k in np.arange(0, steps):
tmp = np.array([grids[k][q] for q in sorted(grids[k])])