- Otimizações em pfts.forecastAheadDistribution

- Correção da Issue #1

benchmarks/benchmarks.py

@@ -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',
+                    ax.scatter(x, y+cc, c=alpha, marker='s', linewidths=0, cmap='Oranges', edgecolors=None)
-                               norm=pltcolors.Normalize(vmin=0, vmax=1), vmin=0, vmax=1, 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):

chen.py

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

common/SortedCollection.py

@@ -214,4 +214,11 @@ class SortedCollection(object):
             return self._items[g: l-1]
         else:
             return self._items[g - 1: l - 1]
-        raise ValueError('No item found inside keys: %r,%r' % (ge,le))
+        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,))

pfts.py

@@ -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):
+    def gridCount(self, grid, resolution, index, 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):
         #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,parameters=None):
-    def forecastAheadDistribution(self, data, steps, resolution):
 
         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)
-                qtle = self.forecastInterval(path)
+                    grids[k - self.order] = self.gridCount(grids[k - self.order], resolution, index, np.ravel(qtle))
-
+                else:
-                grids[k - self.order] = self.gridCountIndexed(grids[k - self.order], resolution, index, np.ravel(qtle))
+                    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])])