Refactorings

benchmarks/Measures.py

@@ -2,7 +2,6 @@
 
 import numpy as np
 import pandas as pd
-from pyFTS.common import FuzzySet,SortedCollection
 
 
 # Autocorrelation function estimative
@@ -33,6 +32,7 @@ def mape(targets, forecasts):
 
 
 def smape(targets, forecasts, type=2):
+    return mape(targets, forecasts)
     if type == 1:
         return np.mean(np.abs(forecasts - targets) / ((forecasts + targets)/2))
     elif type == 2:
@@ -52,8 +52,10 @@ def UStatistic(targets, forecasts):
     naive = []
     y = []
     for k in np.arange(0,l-1):
-        y.append((forecasts[k ] - targets[k ]) ** 2)
-        naive.append((targets[k + 1] - targets[k]) ** 2)
+        #y.append((forecasts[k ] - targets[k ]) ** 2)
+        y.append(((forecasts[k + 1] - targets[k + 1]) / targets[k]) ** 2)
+        #naive.append((targets[k + 1] - targets[k]) ** 2)
+        naive.append(((targets[k + 1] - targets[k]) / targets[k]) ** 2)
     return np.sqrt(sum(y) / sum(naive))
 
 
@@ -109,39 +111,3 @@ def coverage(targets, forecasts):
         else:
             preds.append(0)
     return np.mean(preds)
-
-
-def pmf_to_cdf(density):
-    ret = []
-    for row in density.index:
-        tmp = []
-        prev = 0
-        for col in density.columns:
-            prev += density[col][row]
-            tmp.append( prev )
-        ret.append(tmp)
-    df = pd.DataFrame(ret, columns=density.columns)
-    return df
-
-
-def heavyside_cdf(bins, targets):
-    ret = []
-    for t in targets:
-        result = [1 if b >= t else 0 for b in bins]
-        ret.append(result)
-    df = pd.DataFrame(ret, columns=bins)
-    return df
-
-
-# Continuous Ranked Probability Score
-def crps(targets, densities):
-    l = len(densities.columns)
-    n = len(densities.index)
-    Ff = pmf_to_cdf(densities)
-    Fa = heavyside_cdf(densities.columns, targets)
-
-    _crps = float(0.0)
-    for k in densities.index:
-        _crps += sum([ (Ff[col][k]-Fa[col][k])**2 for col in densities.columns])
-
-    return _crps / float(l * n)

benchmarks/benchmarks.py

@@ -5,14 +5,13 @@ import numpy as np
 import pandas as pd
 import matplotlib as plt
 import matplotlib.colors as pltcolors
-import matplotlib.cm as cmx
 import matplotlib.pyplot as plt
 from mpl_toolkits.mplot3d import Axes3D
 # from sklearn.cross_validation import KFold
-from pyFTS.benchmarks import Measures, naive, ResidualAnalysis, ProbabilityDistribution
+from pyFTS.benchmarks import Measures, naive, ResidualAnalysis
 from pyFTS.partitioners import Grid
 from pyFTS.common import Membership, FuzzySet, FLR, Transformations, Util
-from pyFTS import fts, chen, yu, ismailefendi, sadaei, hofts, hwang, pwfts, ifts
+from pyFTS import fts, chen, yu, ismailefendi, sadaei, hofts, hwang, pfts, ifts
 
 colors = ['grey', 'rosybrown', 'maroon', 'red','orange', 'yellow', 'olive', 'green',
           'cyan', 'blue', 'darkblue', 'purple', 'darkviolet']
@@ -23,113 +22,21 @@ styles = ['-','--','-.',':','.']
 
 nsty = len(styles)
 
-def get_point_methods():
-    return [naive.Naive, chen.ConventionalFTS, yu.WeightedFTS, ismailefendi.ImprovedWeightedFTS,
-                  sadaei.ExponentialyWeightedFTS, hofts.HighOrderFTS, pwfts.ProbabilisticWeightedFTS]
-
-
-def sliding_window(data, windowsize, train=0.8,models=None,partitioners=[Grid.GridPartitioner],
-                   partitions=[10], max_order=3,transformation=None,indexer=None):
-    if models is None:
-        models = get_point_methods()
-
-    objs = {}
-    lcolors = {}
-    rmse = {}
-    smape = {}
-    u = {}
-
-    for train,test in Util.sliding_window(data, windowsize, train):
-        for partition in partitions:
-            for partitioner in partitioners:
-                pttr = str(partitioner.__module__).split('.')[-1]
-                if transformation is not None:
-                    data_train_fs = partitioner(transformation.apply(train), partition)
-                else:
-                    data_train_fs = partitioner(train, partition)
-
-                for count, model in enumerate(models, start=0):
-
-                    mfts = model("")
-                    _key = mfts.shortname + " " + pttr+ " q = " +str(partition)
-
-                    mfts.partitioner = data_train_fs
-                    if not mfts.isHighOrder:
-
-                        if _key not in objs:
-                            objs[_key] = mfts
-                            lcolors[_key] = colors[count % ncol]
-                            rmse[_key] = []
-                            smape[_key] = []
-                            u[_key] = []
-
-                        if transformation is not None:
-                            mfts.appendTransformation(transformation)
-
-                        mfts.train(train, data_train_fs.sets)
-
-                        _rmse, _smape, _u = get_point_statistics(test, mfts, indexer)
-                        rmse[_key].append(_rmse)
-                        smape[_key].append(_smape)
-                        u[_key].append(_u)
-
-                    else:
-                        for order in np.arange(1, max_order + 1):
-                            if order >= mfts.minOrder:
-                                mfts = model("")
-                                _key = mfts.shortname + " n = " + str(order) + " " + pttr + " q = " + str(partition)
-                                mfts.partitioner = data_train_fs
-
-                                if _key not in objs:
-                                    objs[_key] = mfts
-                                    lcolors[_key] = colors[count % ncol]
-                                    rmse[_key] = []
-                                    smape[_key] = []
-                                    u[_key] = []
-
-                                if transformation is not None:
-                                    mfts.appendTransformation(transformation)
-
-                                mfts.train(train, data_train_fs.sets, order=order)
-
-                                _rmse, _smape, _u = get_point_statistics(test, mfts, indexer)
-                                rmse[_key].append(_rmse)
-                                smape[_key].append(_smape)
-                                u[_key].append(_u)
-
-    ret =  "Model\t&Order\t&Scheme\t&Partitions\t&RMSE AVG\t&RMSE STD\t& SMAPE AVG\t& SMAPE STD\t& U AVG\t& U	STD	\\\\ \n"
-    for k in sorted(objs.keys()):
-        mfts = objs[k]
-        ret +=   mfts.shortname + "\t &"
-        ret += str( mfts.order ) + "\t &"
-        ret += mfts.partitioner.name + "\t &"
-        ret += str(mfts.partitioner.partitions) + "\t &"
-        ret += str(round(np.mean(rmse[k]),2)) + "\t &"
-        ret += str(round(np.std(rmse[k]), 2)) + "\t &"
-        ret += str(round(np.mean(smape[k]), 2)) + "\t &"
-        ret += str(round(np.std(smape[k]), 2)) + "\t &"
-        ret += str(round(np.mean(u[k]), 2)) + "\t &"
-        ret += str(round(np.std(u[k]), 2)) + "\t &"
-        ret += str(len(mfts)) + "\\\\ \n"
-
-    print(ret)
-
-
-
-
 def allPointForecasters(data_train, data_test, partitions, max_order=3, statistics=True, residuals=True,
-                        series=True, save=False, file=None, tam=[20, 5], models=None, transformation=None,
-                        distributions=False):
+                        series=True, save=False, file=None, tam=[20, 5], models=None, transformation=None):
 
     if models is None:
-        models = get_point_methods()
+        models = [naive.Naive, chen.ConventionalFTS, yu.WeightedFTS, ismailefendi.ImprovedWeightedFTS,
+                  sadaei.ExponentialyWeightedFTS, hofts.HighOrderFTS,  pfts.ProbabilisticFTS]
 
     objs = []
 
-    if transformation is not None:
-        data_train_fs = Grid.GridPartitionerTrimf(transformation.apply(data_train),partitions)
-    else:
-        data_train_fs = Grid.GridPartitionerTrimf(data_train, partitions)
+    data_train_fs = Grid.GridPartitioner(data_train, partitions, transformation=transformation).sets
+
+#    if transformation is not None:
+#        data_train_fs = Grid.GridPartitionerTrimf(transformation.apply(data_train),partitions)
+#    else:
+#        data_train_fs = Grid.GridPartitionerTrimf(data_train, partitions)
 
     count = 1
 
@@ -152,10 +59,10 @@ def allPointForecasters(data_train, data_test, partitions, max_order=3, statisti
                         mfts.appendTransformation(transformation)
                     mfts.train(data_train, data_train_fs, order=order)
                     objs.append(mfts)
-                    lcolors.append(colors[(count + order) % ncol])
+                    lcolors.append(colors[count % ncol])
 
     if statistics:
-        print(print_point_statistics(data_test, objs))
+        print(getPointStatistics(data_test, objs))
 
     if residuals:
         print(ResidualAnalysis.compareResiduals(data_test, objs))
@@ -165,53 +72,16 @@ def allPointForecasters(data_train, data_test, partitions, max_order=3, statisti
         plotComparedSeries(data_test, objs, lcolors, typeonlegend=False, save=save, file=file, tam=tam,
                            intervals=False)
 
-    if distributions:
-        lcolors.insert(0,'black')
-        pmfs = []
-        pmfs.append(
-            ProbabilityDistribution.ProbabilityDistribution("Original", 100, [min(data_test), max(data_test)], data=data_test) )
 
-        for m in objs:
-            forecasts = m.forecast(data_test)
-            pmfs.append(
-                ProbabilityDistribution.ProbabilityDistribution(m.shortname, 100, [min(data_test), max(data_test)],
-                                                                data=forecasts))
-        print(getProbabilityDistributionStatistics(pmfs,data_test))
-
-        plotProbabilityDistributions(pmfs, lcolors,tam=tam)
-
-def get_point_statistics(data, model, indexer=None):
-    if indexer is not None:
-        ndata = np.array(indexer.get_data(data[model.order:]))
-    else:
-        ndata = np.array(data[model.order:])
-
-    if model.isMultivariate or indexer is None:
-        forecasts = model.forecast(data)
-    elif not model.isMultivariate and indexer is not None:
-        forecasts = model.forecast(indexer.get_data(data))
-
-    if model.hasSeasonality:
-        nforecasts = np.array(forecasts)
-    else:
-        nforecasts = np.array(forecasts[:-1])
-    ret = list()
-    ret.append(np.round(Measures.rmse(ndata, nforecasts), 2))
-    ret.append(np.round(Measures.smape(ndata, nforecasts), 2))
-    ret.append(np.round(Measures.UStatistic(ndata, nforecasts), 2))
-
-    return ret
-
-
-def print_point_statistics(data, models, externalmodels = None, externalforecasts = None, indexers=None):
-    ret = "Model		& Order     & RMSE		& SMAPE      & Theil's U		\\\\ \n"
-    for count,model in enumerate(models,start=0):
-        _rmse, _smape, _u = get_point_statistics(data, model, indexers[count])
-        ret += model.shortname + "		& "
-        ret += str(model.order) + "		& "
-        ret += str(_rmse) + "		& "
-        ret += str(_smape)+ "		& "
-        ret += str(_u)
+def getPointStatistics(data, models, externalmodels = None, externalforecasts = None):
+    ret = "Model		& Order     & RMSE		& MAPE      & Theil's U		\\\\ \n"
+    for fts in models:
+        forecasts = fts.forecast(data)
+        ret += fts.shortname + "		& "
+        ret += str(fts.order) + "		& "
+        ret += str(round(Measures.rmse(np.array(data[fts.order:]), np.array(forecasts[:-1])), 2)) + "		& "
+        ret += str(round(Measures.smape(np.array(data[fts.order:]), np.array(forecasts[:-1])), 2))+ "		& "
+        ret += str(round(Measures.UStatistic(np.array(data[fts.order:]), np.array(forecasts[:-1])), 2))
         #ret += str(round(Measures.TheilsInequality(np.array(data[fts.order:]), np.array(forecasts[:-1])), 4))
         ret += "	\\\\ \n"
     if externalmodels is not None:
@@ -219,20 +89,9 @@ def print_point_statistics(data, models, externalmodels = None, externalforecast
         for k in np.arange(0,l):
             ret += externalmodels[k] + "		& "
             ret += " 1		& "
-            ret += str(round(Measures.rmse(data, externalforecasts[k][:-1]), 2)) + "		& "
-            ret += str(round(Measures.smape(data, externalforecasts[k][:-1]), 2))+ "		& "
-            ret += str(round(Measures.UStatistic(data, externalforecasts[k][:-1]), 2))
-            ret += "	\\\\ \n"
-    return ret
-
-
-def getProbabilityDistributionStatistics(pmfs, data):
-    ret = "Model		& Entropy     & Empirical Likelihood		&  Pseudo Likelihood		\\\\ \n"
-    for k in pmfs:
-        ret += k.name + "		& "
-        ret += str(k.entropy()) + "		& "
-        ret += str(k.empiricalloglikelihood())+ "		& "
-        ret += str(k.pseudologlikelihood(data))
+            ret += str(round(Measures.rmse(data[fts.order:], externalforecasts[k][:-1]), 2)) + "		& "
+            ret += str(round(Measures.smape(data[fts.order:], externalforecasts[k][:-1]), 2))+ "		& "
+            ret += str(round(Measures.UStatistic(np.array(data[fts.order:]), np.array(forecasts[:-1])), 2))
             ret += "	\\\\ \n"
     return ret
 
@@ -240,7 +99,7 @@ def getProbabilityDistributionStatistics(pmfs, data):
 def allIntervalForecasters(data_train, data_test, partitions, max_order=3,save=False, file=None, tam=[20, 5],
                            models=None, transformation=None):
     if models is None:
-        models = [ifts.IntervalFTS, pwfts.ProbabilisticWeightedFTS]
+        models = [ifts.IntervalFTS, pfts.ProbabilisticFTS]
 
     objs = []
 
@@ -296,7 +155,7 @@ def plotDistribution(dist):
 
 
 def plotComparedSeries(original, models, colors, typeonlegend=False, save=False, file=None, tam=[20, 5],
-                       points=True, intervals=True, linewidth=1.5):
+                       intervals=True):
     fig = plt.figure(figsize=tam)
     ax = fig.add_subplot(111)
 
@@ -305,10 +164,10 @@ def plotComparedSeries(original, models, colors, typeonlegend=False, save=False,
 
     legends = []
 
-    ax.plot(original, color='black', label="Original", linewidth=linewidth*1.5)
+    ax.plot(original, color='black', label="Original", linewidth=1.5)
 
     for count, fts in enumerate(models, start=0):
-        if fts.hasPointForecasting and points:
+        if fts.hasPointForecasting and not intervals:
             forecasted = fts.forecast(original)
             mi.append(min(forecasted) * 0.95)
             ma.append(max(forecasted) * 1.05)
@@ -316,7 +175,7 @@ def plotComparedSeries(original, models, colors, typeonlegend=False, save=False,
                 forecasted.insert(0, None)
             lbl = fts.shortname
             if typeonlegend: lbl += " (Point)"
-            ax.plot(forecasted, color=colors[count], label=lbl, ls="-",linewidth=linewidth)
+            ax.plot(forecasted, color=colors[count], label=lbl, ls="-")
 
         if fts.hasIntervalForecasting and intervals:
             forecasted = fts.forecastInterval(original)
@@ -329,8 +188,8 @@ def plotComparedSeries(original, models, colors, typeonlegend=False, save=False,
                 upper.insert(0, None)
             lbl = fts.shortname
             if typeonlegend: lbl += " (Interval)"
-            ax.plot(lower, color=colors[count], label=lbl, ls="--",linewidth=linewidth)
-            ax.plot(upper, color=colors[count], ls="--",linewidth=linewidth)
+            ax.plot(lower, color=colors[count], label=lbl, ls="-")
+            ax.plot(upper, color=colors[count], ls="-")
 
         handles0, labels0 = ax.get_legend_handles_labels()
         lgd = ax.legend(handles0, labels0, loc=2, bbox_to_anchor=(1, 1))
@@ -345,82 +204,11 @@ def plotComparedSeries(original, models, colors, typeonlegend=False, save=False,
     Util.showAndSaveImage(fig, file, save, lgd=legends)
 
 
-def plotProbabilityDistributions(pmfs,lcolors,tam=[15, 7]):
-    fig = plt.figure(figsize=tam)
-    ax = fig.add_subplot(111)
-
-    for k,m in enumerate(pmfs,start=0):
-        m.plot(ax, color=lcolors[k])
-
-    handles0, labels0 = ax.get_legend_handles_labels()
-    ax.legend(handles0, labels0)
-
-
-def allAheadForecasters(data_train, data_test, partitions, start, steps, resolution = None, max_order=3,save=False, file=None, tam=[20, 5],
-                           models=None, transformation=None, option=2):
-    if models is None:
-        models = [pwfts.ProbabilisticWeightedFTS]
-
-    if resolution is None: resolution = (max(data_train) - min(data_train)) / 100
-
-    objs = []
-
-    if transformation is not None:
-        data_train_fs = Grid.GridPartitionerTrimf(transformation.apply(data_train),partitions)
-    else:
-        data_train_fs = Grid.GridPartitionerTrimf(data_train, partitions)
-
-    lcolors = []
-
-    for count, model in Util.enumerate2(models, start=0, step=2):
-        mfts = model("")
-        if not mfts.isHighOrder:
-            if transformation is not None:
-                mfts.appendTransformation(transformation)
-            mfts.train(data_train, data_train_fs)
-            objs.append(mfts)
-            lcolors.append( colors[count % ncol] )
-        else:
-            for order in np.arange(1,max_order+1):
-                if order >= mfts.minOrder:
-                    mfts = model(" n = " + str(order))
-                    if transformation is not None:
-                        mfts.appendTransformation(transformation)
-                    mfts.train(data_train, data_train_fs, order=order)
-                    objs.append(mfts)
-                    lcolors.append(colors[count % ncol])
-
-    distributions = [False for k in objs]
-
-    distributions[0] = True
-
-    print(getDistributionStatistics(data_test[start:], objs, steps, resolution))
-
-    #plotComparedIntervalsAhead(data_test, objs, lcolors, distributions=, save=save, file=file, tam=tam,  intervals=True)
-
-
-def getDistributionStatistics(original, models, steps, resolution):
-    ret = "Model	& Order     &  Interval & Distribution	\\\\ \n"
-    for fts in models:
-        densities1 = fts.forecastAheadDistribution(original,steps,resolution, parameters=3)
-        densities2 = fts.forecastAheadDistribution(original, steps, resolution, parameters=2)
-        ret += fts.shortname + "		& "
-        ret += str(fts.order) + "		& "
-        ret += str(round(Measures.crps(original, densities1), 3)) + "		& "
-        ret += str(round(Measures.crps(original, densities2), 3)) + "	\\\\ \n"
-    return ret
-
-
 def plotComparedIntervalsAhead(original, models, colors, distributions, time_from, time_to,
-                               interpol=False, save=False, file=None, tam=[20, 5], resolution=None,
-                               cmap='Blues',option=2):
+                               interpol=False, save=False, file=None, tam=[20, 5], resolution=None):
     fig = plt.figure(figsize=tam)
     ax = fig.add_subplot(111)
 
-    cm = plt.get_cmap(cmap)
-    cNorm = pltcolors.Normalize(vmin=0, vmax=1)
-    scalarMap = cmx.ScalarMappable(norm=cNorm, cmap=cm)
-
     if resolution is None: resolution = (max(original) - min(original)) / 100
 
     mi = []
@@ -429,44 +217,26 @@ def plotComparedIntervalsAhead(original, models, colors, distributions, time_fro
     for count, fts in enumerate(models, start=0):
         if fts.hasDistributionForecasting and distributions[count]:
             density = fts.forecastAheadDistribution(original[time_from - fts.order:time_from],
-                                                    time_to, resolution, parameters=option)
+                                                    time_to, resolution, parameters=True)
 
-            Y = []
-            X = []
-            C = []
-            S = []
             y = density.columns
             t = len(y)
 
-            ss = time_to ** 2
-
             for k in density.index:
-                #alpha = [scalarMap.to_rgba(density[col][k]) for col in density.columns]
-                col = [density[col][k]*5 for col in density.columns]
+                alpha = np.array([density[q][k] for q in density]) * 100
 
                 x = [time_from + k for x in np.arange(0, t)]
 
-                s = [ss for x in np.arange(0, t)]
-
-                ic = resolution/10
-
-                for cc in np.arange(0, resolution, ic):
-                    Y.append(y + cc)
-                    X.append(x)
-                    C.append(col)
-                    S.append(s)
-
-            Y = np.hstack(Y)
-            X = np.hstack(X)
-            C = np.hstack(C)
-            S = np.hstack(S)
-
-            s = ax.scatter(X, Y, c=C, marker='s',s=S, linewidths=0, edgecolors=None, cmap=cmap)
-            s.set_clim([0, 1])
-            cb = fig.colorbar(s)
-
-            cb.set_label('Density')
-
+                for cc in np.arange(0, resolution, 5):
+                    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):
+                        xx = [time_from + k + 0.02 * p for q in np.arange(0, t)]
+                        alpha2 = np.array(
+                            [density[density.columns[q]][k] + 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)
 
         if fts.hasIntervalForecasting:
             forecasts = fts.forecastAheadInterval(original[time_from - fts.order:time_from], time_to)
@@ -508,8 +278,6 @@ def plotComparedIntervalsAhead(original, models, colors, distributions, time_fro
     ax.set_xlabel('T')
     ax.set_xlim([0, len(original)])
 
-    #plt.colorbar()
-
     Util.showAndSaveImage(fig, file, save)
 
 
@@ -795,8 +563,7 @@ def compareModelsTable(original, models_fo, models_ho):
 
 
 def simpleSearch_RMSE(train, test, model, partitions, orders, save=False, file=None, tam=[10, 15],
-                      plotforecasts=False, elev=30, azim=144, intervals=False,parameters=None):
-    _3d = len(orders) > 1
+                      plotforecasts=False, elev=30, azim=144, intervals=False):
     ret = []
     errors = np.array([[0 for k in range(len(partitions))] for kk in range(len(orders))])
     forecasted_best = []
@@ -817,13 +584,10 @@ def simpleSearch_RMSE(train, test, model, partitions, orders, save=False, file=N
         sets = Grid.GridPartitionerTrimf(train, p)
         for oc, o in enumerate(orders, start=0):
             fts = model("q = " + str(p) + " n = " + str(o))
-            fts.train(train, sets, o,parameters=parameters)
+            fts.train(train, sets, o)
             if not intervals:
                 forecasted = fts.forecast(test)
-                if not fts.hasSeasonality:
                 error = Measures.rmse(np.array(test[o:]), np.array(forecasted[:-1]))
-                else:
-                    error = Measures.rmse(np.array(test[o:]), np.array(forecasted))
                 for kk in range(o):
                     forecasted.insert(0, None)
                     if plotforecasts: ax0.plot(forecasted, label=fts.name)
@@ -841,22 +605,15 @@ def simpleSearch_RMSE(train, test, model, partitions, orders, save=False, file=N
         # handles0, labels0 = ax0.get_legend_handles_labels()
         # ax0.legend(handles0, labels0)
         ax0.plot(test, label="Original", linewidth=3.0, color="black")
-        if _3d: ax1 = Axes3D(fig, rect=[0, 1, 0.9, 0.9], elev=elev, azim=azim)
+        ax1 = Axes3D(fig, rect=[0, 1, 0.9, 0.9], elev=elev, azim=azim)
     if not plotforecasts: ax1 = Axes3D(fig, rect=[0, 1, 0.9, 0.9], elev=elev, azim=azim)
     # ax1 = fig.add_axes([0.6, 0.5, 0.45, 0.45], projection='3d')
-    if _3d:
     ax1.set_title('Error Surface')
     ax1.set_ylabel('Model order')
     ax1.set_xlabel('Number of partitions')
     ax1.set_zlabel('RMSE')
     X, Y = np.meshgrid(partitions, orders)
     surf = ax1.plot_surface(X, Y, errors, rstride=1, cstride=1, antialiased=True)
-    else:
-        ax1 = fig.add_axes([0, 1, 0.9, 0.9])
-        ax1.set_title('Error Curve')
-        ax1.set_ylabel('Number of partitions')
-        ax1.set_xlabel('RMSE')
-        ax0.plot(errors,partitions)
     ret.append(best)
     ret.append(forecasted_best)
 
@@ -877,7 +634,7 @@ def pftsExploreOrderAndPartitions(data,save=False, file=None):
     axes[2].set_title('Interval Forecasts by Order')
 
     for order in np.arange(1, 6):
-        fts = pwfts.ProbabilisticWeightedFTS("")
+        fts = pfts.ProbabilisticFTS("")
         fts.shortname = "n = " + str(order)
         fts.train(data, data_fs1, order=order)
         point_forecasts = fts.forecast(data)
@@ -899,7 +656,7 @@ def pftsExploreOrderAndPartitions(data,save=False, file=None):
 
     for partitions in np.arange(5, 11):
         data_fs = Grid.GridPartitionerTrimf(data, partitions)
-        fts = pwfts.ProbabilisticWeightedFTS("")
+        fts = pfts.ProbabilisticFTS("")
         fts.shortname = "q = " + str(partitions)
         fts.train(data, data_fs, 1)
         point_forecasts = fts.forecast(data)
@@ -927,4 +684,3 @@ def pftsExploreOrderAndPartitions(data,save=False, file=None):
     plt.tight_layout()
 
     Util.showAndSaveImage(fig, file, save)
-

common/Util.py

@@ -30,11 +30,16 @@ def enumerate2(xs, start=0, step=1):
         yield (start, x)
         start += step
 
-def sliding_window(data, windowsize, train=0.8):
+def sliding_window(data, windowsize, train=0.8, inc=0.1):
     l = len(data)
     ttrain = int(round(windowsize * train, 0))
-    for count in np.arange(0,l,windowsize):
-        yield ( data[count : count + ttrain], data[count + ttrain : count + windowsize]  )
+    ic = int(round(windowsize * inc, 0))
+    for count in np.arange(0,l-windowsize+ic,ic):
+        if count + windowsize > l:
+            _end = l
+        else:
+            _end = count + windowsize
+        yield (count,  data[count : count + ttrain], data[count + ttrain : _end]  )
 
 
 def persist_obj(obj, file):

partitioners/CMeans.py

@@ -77,18 +77,14 @@ def c_means(k, dados, tam):
     return centroides
 
 class CMeansPartitioner(partitioner.Partitioner):
-    def __init__(self, npart,data,func = Membership.trimf):
-        super(CMeansPartitioner, self).__init__("CMeans ",data,npart,func)
+    def __init__(self, data, npart, func = Membership.trimf, transformation=None):
+        super(CMeansPartitioner, self).__init__("CMeans", data, npart, func=func, transformation=transformation)
 
     def build(self, data):
         sets = []
-        dmax = max(data)
-        dmax += dmax * 0.10
-        dmin = min(data)
-        dmin -= dmin * 0.10
         centroides = c_means(self.partitions, data, 1)
-        centroides.append(dmax)
-        centroides.append(dmin)
+        centroides.append(self.max)
+        centroides.append(self.min)
         centroides = list(set(centroides))
         centroides.sort()
         for c in np.arange(1, len(centroides) - 1):

partitioners/Entropy.py

@@ -77,19 +77,15 @@ def bestSplit(data, npart):
         return [threshold]
 
 class EntropyPartitioner(partitioner.Partitioner):
-    def __init__(self, data,npart,func = Membership.trimf):
-        super(EntropyPartitioner, self).__init__("Entropy" ,data,npart,func)
+    def __init__(self, data, npart, func = Membership.trimf, transformation=None):
+        super(EntropyPartitioner, self).__init__("Entropy", data, npart, func=func, transformation=transformation)
 
     def build(self, data):
         sets = []
-        dmax = max(data)
-        dmax += dmax * 0.10
-        dmin = min(data)
-        dmin -= dmin * 0.10
 
         partitions = bestSplit(data, self.partitions)
-        partitions.append(dmin)
-        partitions.append(dmax)
+        partitions.append(self.min)
+        partitions.append(self.max)
         partitions = list(set(partitions))
         partitions.sort()
         for c in np.arange(1, len(partitions) - 1):

partitioners/FCM.py

@@ -101,18 +101,15 @@ def fuzzy_cmeans(k, dados, tam, m, deltadist=0.001):
 
 
 class FCMPartitioner(partitioner.Partitioner):
-    def __init__(self, data,npart,func = Membership.trimf):
-        super(FCMPartitioner, self).__init__("FCM ",data,npart,func)
+    def __init__(self, data,npart,func = Membership.trimf, transformation=None):
+        super(FCMPartitioner, self).__init__("FCM", data, npart, func=func, transformation=transformation)
 
     def build(self,data):
         sets = []
-        dmax = max(data)
-        dmax = dmax + dmax*0.10
-        dmin = min(data)
-        dmin = dmin - dmin*0.10
+
         centroides = fuzzy_cmeans(self.partitions, data, 1, 2)
-        centroides.append(dmax)
-        centroides.append(dmin)
+        centroides.append(self.max)
+        centroides.append(self.min)
         centroides = list(set(centroides))
         centroides.sort()
         for c in np.arange(1,len(centroides)-1):

partitioners/Grid.py

@@ -7,28 +7,17 @@ from pyFTS.partitioners import partitioner
 
 
 class GridPartitioner(partitioner.Partitioner):
-    def __init__(self, data,npart,func = Membership.trimf):
-        super(GridPartitioner, self).__init__("Grid",data,npart,func)
+    def __init__(self, data,npart,func = Membership.trimf, transformation=None):
+        super(GridPartitioner, self).__init__("Grid", data, npart, func=func, transformation=transformation)
 
     def build(self, data):
         sets = []
-        _min = min(data)
-        if _min < 0:
-            dmin = _min * 1.1
-        else:
-            dmin = _min * 0.9
 
-        _max = max(data)
-        if _max > 0:
-            dmax = _max * 1.1
-        else:
-            dmax = _max * 0.9
-
-        dlen = dmax - dmin
+        dlen = self.max - self.min
         partlen = dlen / self.partitions
 
         count = 0
-        for c in np.arange(dmin, dmax, partlen):
+        for c in np.arange(self.min, self.max, partlen):
             if self.membership_function == Membership.trimf:
                 sets.append(
                     FuzzySet.FuzzySet(self.prefix + str(count), Membership.trimf, [c - partlen, c, c + partlen],c))

partitioners/Huarng.py

@@ -11,11 +11,12 @@ from pyFTS.partitioners import partitioner
 
 
 class HuarngPartitioner(partitioner.Partitioner):
-    def __init__(self, npart,data,func = Membership.trimf):
-        super(HuarngPartitioner, self).__init__("Huarng",data,npart,func)
+    def __init__(self, data,npart,func = Membership.trimf, transformation=None):
+        super(HuarngPartitioner, self).__init__("Huarng", data, npart, func=func, transformation=transformation)
 
     def build(self, data):
-        data2 = Transformations.differential(data)
+        diff = Transformations.Differential(1)
+        data2 = diff.apply(data)
         davg = np.abs( np.mean(data2) / 2 )
 
         if davg <= 1.0:
@@ -28,13 +29,10 @@ class HuarngPartitioner(partitioner.Partitioner):
             base = 100
 
         sets = []
-        dmax = max(data)
-        dmax += dmax * 0.10
-        dmin = min(data)
-        dmin -= dmin * 0.10
-        dlen = dmax - dmin
+
+        dlen = self.max - self.min
         npart = math.ceil(dlen / base)
-        partition = math.ceil(dmin)
+        partition = math.ceil(self.min)
         for c in range(npart):
             sets.append(
                 FuzzySet.FuzzySet(self.prefix + str(c), Membership.trimf, [partition - base, partition, partition + base], partition))

partitioners/partitioner.py

@@ -2,27 +2,34 @@ from pyFTS.common import FuzzySet, Membership
 import  numpy as np
 
 class Partitioner(object):
-    def __init__(self,name,data,npart,func = Membership.trimf, names=None, prefix="A"):
+    def __init__(self,name,data,npart,func = Membership.trimf, names=None, prefix="A", transformation=None):
         self.name = name
         self.partitions = npart
         self.sets = []
         self.membership_function = func
         self.setnames = names
         self.prefix = prefix
-        _min = min(data)
+        self.transformation = transformation
+
+        if transformation is not None:
+            ndata = transformation.apply(data)
+        else:
+            ndata = data
+
+        _min = min(ndata)
         if _min < 0:
             self.min = _min * 1.1
         else:
             self.min = _min * 0.9
 
-        _max = max(data)
+        _max = max(ndata)
         if _max > 0:
             self.max = _max * 1.1
         else:
             self.max = _max * 0.9
-        self.sets = self.build(data)
+        self.sets = self.build(ndata)
 
-    def build(self,data):
+    def build(self, data):
         pass
 
     def plot(self,ax):

pfts.py

@@ -0,0 +1,487 @@
+#!/usr/bin/python
+# -*- coding: utf8 -*-
+
+import numpy as np
+import pandas as pd
+import math
+from operator import itemgetter
+from pyFTS.common import FuzzySet, SortedCollection
+from pyFTS import hofts, ifts, tree
+
+
+class ProbabilisticFLRG(hofts.HighOrderFLRG):
+    def __init__(self, order):
+        super(ProbabilisticFLRG, self).__init__(order)
+        self.RHS = {}
+        self.frequencyCount = 0.0
+
+    def appendRHS(self, c):
+        self.frequencyCount += 1.0
+        if c.name in self.RHS:
+            self.RHS[c.name] += 1.0
+        else:
+            self.RHS[c.name] = 1.0
+
+    def getProbability(self, c):
+        return self.RHS[c] / self.frequencyCount
+
+    def __str__(self):
+        tmp2 = ""
+        for c in sorted(self.RHS):
+            if len(tmp2) > 0:
+                tmp2 = tmp2 + ", "
+            tmp2 = tmp2 + "(" + str(round(self.RHS[c] / self.frequencyCount, 3)) + ")" + c
+        return self.strLHS() + " -> " + tmp2
+
+
+class ProbabilisticFTS(ifts.IntervalFTS):
+    def __init__(self, name):
+        super(ProbabilisticFTS, self).__init__("PFTS")
+        self.shortname = "PFTS " + name
+        self.name = "Probabilistic FTS"
+        self.detail = "Silva, P.; Guimarães, F.; Sadaei, H."
+        self.flrgs = {}
+        self.globalFrequency = 0
+        self.hasPointForecasting = True
+        self.hasIntervalForecasting = True
+        self.hasDistributionForecasting = True
+        self.isHighOrder = True
+
+    def generateFLRG(self, flrs):
+        flrgs = {}
+        l = len(flrs)
+        for k in np.arange(self.order, l+1):
+            if self.dump: print("FLR: " + str(k))
+            flrg = ProbabilisticFLRG(self.order)
+
+            for kk in np.arange(k - self.order, k):
+                flrg.appendLHS(flrs[kk].LHS)
+                if self.dump: print("LHS: " + str(flrs[kk]))
+
+            if flrg.strLHS() in flrgs:
+                flrgs[flrg.strLHS()].appendRHS(flrs[k-1].RHS)
+            else:
+                flrgs[flrg.strLHS()] = flrg;
+                flrgs[flrg.strLHS()].appendRHS(flrs[k-1].RHS)
+            if self.dump: print("RHS: " + str(flrs[k-1]))
+
+            self.globalFrequency += 1
+        return (flrgs)
+
+    def addNewPFLGR(self,flrg):
+        if flrg.strLHS() not in self.flrgs:
+            tmp = ProbabilisticFLRG(self.order)
+            for fs in flrg.LHS: tmp.appendLHS(fs)
+            tmp.appendRHS(flrg.LHS[-1])
+            self.flrgs[tmp.strLHS()] = tmp;
+            self.globalFrequency += 1
+
+    def getProbability(self, flrg):
+        if flrg.strLHS() in self.flrgs:
+            return self.flrgs[flrg.strLHS()].frequencyCount / self.globalFrequency
+        else:
+            self.addNewPFLGR(flrg)
+            return self.getProbability(flrg)
+
+    def getMidpoints(self, flrg):
+        if flrg.strLHS() in self.flrgs:
+            tmp = self.flrgs[flrg.strLHS()]
+            ret = sum(np.array([tmp.getProbability(s) * self.setsDict[s].centroid for s in tmp.RHS]))
+        else:
+            pi = 1 / len(flrg.LHS)
+            ret = sum(np.array([pi * s.centroid for s in flrg.LHS]))
+        return ret
+
+    def getUpper(self, flrg):
+        if flrg.strLHS() in self.flrgs:
+            tmp = self.flrgs[flrg.strLHS()]
+            ret = sum(np.array([tmp.getProbability(s) * self.setsDict[s].upper for s in tmp.RHS]))
+        else:
+            pi = 1 / len(flrg.LHS)
+            ret = sum(np.array([pi * s.upper for s in flrg.LHS]))
+        return ret
+
+    def getLower(self, flrg):
+        if flrg.strLHS() in self.flrgs:
+            tmp = self.flrgs[flrg.strLHS()]
+            ret = sum(np.array([tmp.getProbability(s) * self.setsDict[s].lower for s in tmp.RHS]))
+        else:
+            pi = 1 / len(flrg.LHS)
+            ret = sum(np.array([pi * s.lower for s in flrg.LHS]))
+        return ret
+
+    def forecast(self, data):
+
+        ndata = np.array(self.doTransformations(data))
+
+        l = len(ndata)
+
+        ret = []
+
+        for k in np.arange(self.order - 1, l):
+
+            # print(k)
+
+            affected_flrgs = []
+            affected_flrgs_memberships = []
+            norms = []
+
+            mp = []
+
+            # Find the sets which membership > 0 for each lag
+            count = 0
+            lags = {}
+            if self.order > 1:
+                subset = ndata[k - (self.order - 1): k + 1]
+
+                for instance in subset:
+                    mb = FuzzySet.fuzzyInstance(instance, self.sets)
+                    tmp = np.argwhere(mb)
+                    idx = np.ravel(tmp)  # flatten the array
+
+                    if idx.size == 0:  # the element is out of the bounds of the Universe of Discourse
+                        if instance <= self.sets[0].lower:
+                            idx = [0]
+                        elif instance >= self.sets[-1].upper:
+                            idx = [len(self.sets) - 1]
+                        else:
+                            raise Exception(instance)
+
+                    lags[count] = idx
+                    count = count + 1
+
+                # Build the tree with all possible paths
+
+                root = tree.FLRGTreeNode(None)
+
+                self.buildTree(root, lags, 0)
+
+                # Trace the possible paths and build the PFLRG's
+
+                for p in root.paths():
+                    path = list(reversed(list(filter(None.__ne__, p))))
+                    flrg = hofts.HighOrderFLRG(self.order)
+                    for kk in path: flrg.appendLHS(self.sets[kk])
+
+                    assert len(flrg.LHS) == subset.size, str(subset) + " -> " + str([s.name for s in flrg.LHS])
+
+                    ##
+                    affected_flrgs.append(flrg)
+
+                    # Find the general membership of FLRG
+                    affected_flrgs_memberships.append(min(self.getSequenceMembership(subset, flrg.LHS)))
+
+            else:
+
+                mv = FuzzySet.fuzzyInstance(ndata[k], self.sets)  # get all membership values
+                tmp = np.argwhere(mv)  # get the indices of values > 0
+                idx = np.ravel(tmp)  # flatten the array
+
+                if idx.size == 0:  # the element is out of the bounds of the Universe of Discourse
+                    if ndata[k] <= self.sets[0].lower:
+                        idx = [0]
+                    elif ndata[k] >= self.sets[-1].upper:
+                        idx = [len(self.sets) - 1]
+                    else:
+                        raise Exception(ndata[k])
+
+                for kk in idx:
+                    flrg = hofts.HighOrderFLRG(self.order)
+                    flrg.appendLHS(self.sets[kk])
+                    affected_flrgs.append(flrg)
+                    affected_flrgs_memberships.append(mv[kk])
+
+            count = 0
+            for flrg in affected_flrgs:
+                # achar o os bounds de cada FLRG, ponderados pela probabilidade e pertinência
+                norm = self.getProbability(flrg) * affected_flrgs_memberships[count]
+                if norm == 0:
+                    norm = self.getProbability(flrg)  # * 0.001
+                mp.append(norm * self.getMidpoints(flrg))
+                norms.append(norm)
+                count = count + 1
+
+            # gerar o intervalo
+            norm = sum(norms)
+            if norm == 0:
+                ret.append(0)
+            else:
+                ret.append(sum(mp) / norm)
+
+        ret = self.doInverseTransformations(ret, params=[data[self.order - 1:]])
+
+        return ret
+
+    def forecastInterval(self, data):
+
+        ndata = np.array(self.doTransformations(data))
+
+        l = len(ndata)
+
+        ret = []
+
+        for k in np.arange(self.order - 1, l):
+
+            # print(k)
+
+            affected_flrgs = []
+            affected_flrgs_memberships = []
+            norms = []
+
+            up = []
+            lo = []
+
+            # Find the sets which membership > 0 for each lag
+            count = 0
+            lags = {}
+            if self.order > 1:
+                subset = ndata[k - (self.order - 1): k + 1]
+
+                for instance in subset:
+                    mb = FuzzySet.fuzzyInstance(instance, self.sets)
+                    tmp = np.argwhere(mb)
+                    idx = np.ravel(tmp)  # flatten the array
+
+                    if idx.size == 0:  # the element is out of the bounds of the Universe of Discourse
+                        if instance <= self.sets[0].lower:
+                            idx = [0]
+                        elif instance >= self.sets[-1].upper:
+                            idx = [len(self.sets) - 1]
+                        else:
+                            raise Exception(instance)
+
+                    lags[count] = idx
+                    count = count + 1
+
+                # Build the tree with all possible paths
+
+                root = tree.FLRGTreeNode(None)
+
+                self.buildTree(root, lags, 0)
+
+                # Trace the possible paths and build the PFLRG's
+
+                for p in root.paths():
+                    path = list(reversed(list(filter(None.__ne__, p))))
+                    flrg = hofts.HighOrderFLRG(self.order)
+                    for kk in path: flrg.appendLHS(self.sets[kk])
+
+                    assert len(flrg.LHS) == subset.size, str(subset) + " -> " + str([s.name for s in flrg.LHS])
+
+                    ##
+                    affected_flrgs.append(flrg)
+
+                    # Find the general membership of FLRG
+                    affected_flrgs_memberships.append(min(self.getSequenceMembership(subset, flrg.LHS)))
+
+            else:
+
+                mv = FuzzySet.fuzzyInstance(ndata[k], self.sets)  # get all membership values
+                tmp = np.argwhere(mv)  # get the indices of values > 0
+                idx = np.ravel(tmp)  # flatten the array
+
+                if idx.size == 0:  # the element is out of the bounds of the Universe of Discourse
+                    if ndata[k] <= self.sets[0].lower:
+                        idx = [0]
+                    elif ndata[k] >= self.sets[-1].upper:
+                        idx = [len(self.sets) - 1]
+                    else:
+                        raise Exception(ndata[k])
+
+                for kk in idx:
+                    flrg = hofts.HighOrderFLRG(self.order)
+                    flrg.appendLHS(self.sets[kk])
+                    affected_flrgs.append(flrg)
+                    affected_flrgs_memberships.append(mv[kk])
+
+            count = 0
+            for flrg in affected_flrgs:
+                # achar o os bounds de cada FLRG, ponderados pela probabilidade e pertinência
+                norm = self.getProbability(flrg) * affected_flrgs_memberships[count]
+                if norm == 0:
+                    norm = self.getProbability(flrg)  # * 0.001
+                up.append(norm * self.getUpper(flrg))
+                lo.append(norm * self.getLower(flrg))
+                norms.append(norm)
+                count = count + 1
+
+            # gerar o intervalo
+            norm = sum(norms)
+            if norm == 0:
+                ret.append([0, 0])
+            else:
+                lo_ = self.doInverseTransformations(sum(lo) / norm, params=[data[k - (self.order - 1): k + 1]])
+                up_ = self.doInverseTransformations(sum(up) / norm, params=[data[k - (self.order - 1): k + 1]])
+                ret.append([lo_, up_])
+
+        return ret
+
+    def forecastAhead(self, data, steps):
+        ret = [data[k] for k in np.arange(len(data) - self.order, len(data))]
+
+        for k in np.arange(self.order - 1, steps):
+
+            if ret[-1] <= self.sets[0].lower or ret[-1] >= self.sets[-1].upper:
+                ret.append(ret[-1])
+            else:
+                mp = self.forecast([ret[x] for x in np.arange(k - self.order, k)])
+
+                ret.append(mp)
+
+        return ret
+
+    def forecastAheadInterval(self, data, steps):
+        ret = [[data[k], data[k]] for k in np.arange(len(data) - self.order, len(data))]
+
+        for k in np.arange(self.order, steps+self.order):
+
+            if ret[-1][0] <= self.sets[0].lower and ret[-1][1] >= self.sets[-1].upper:
+                ret.append(ret[-1])
+            else:
+                lower = self.forecastInterval([ret[x][0] for x in np.arange(k - self.order, k)])
+                upper = self.forecastInterval([ret[x][1] for x in np.arange(k - self.order, k)])
+
+                ret.append([np.min(lower), np.max(upper)])
+
+        return ret
+
+    def getGridClean(self, resolution):
+        grid = {}
+
+        if len(self.transformations) == 0:
+            _min = self.sets[0].lower
+            _max = self.sets[-1].upper
+        else:
+            _min = self.original_min
+            _max = self.original_max
+
+        for sbin in np.arange(_min,_max, resolution):
+            grid[sbin] = 0
+
+        return grid
+
+    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:
+            return
+
+        for s in lags[level]:
+            node.appendChild(tree.FLRGTreeNode(s))
+
+        for child in node.getChildren():
+            self.buildTreeWithoutOrder(child, lags, level + 1)
+
+    def forecastAheadDistribution(self, data, steps, resolution, parameters=None):
+
+        ret = []
+
+        intervals = self.forecastAheadInterval(data, steps)
+
+        grid = self.getGridClean(resolution)
+
+        index = SortedCollection.SortedCollection(iterable=grid.keys())
+
+        if parameters is None:
+
+            grids = []
+            for k in np.arange(0, steps):
+                grids.append(self.getGridClean(resolution))
+
+            for k in np.arange(self.order, steps + self.order):
+
+                lags = {}
+
+                cc = 0
+
+                for i in intervals[k - self.order : k]:
+
+                    quantiles = []
+
+                    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))
+
+                    quantiles = list(set(quantiles))
+
+                    quantiles.sort()
+
+                    lags[cc] = quantiles
+
+                    cc += 1
+
+                # Build the tree with all possible paths
+
+                root = tree.FLRGTreeNode(None)
+
+                self.buildTreeWithoutOrder(root, lags, 0)
+
+                # Trace the possible paths
+
+                for p in root.paths():
+                    path = list(reversed(list(filter(None.__ne__, p))))
+
+                    qtle = self.forecastInterval(path)
+
+                    grids[k - self.order] = self.gridCount(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])])
+                ret.append(tmp / sum(tmp))
+
+            grid = self.getGridClean(resolution)
+            df = pd.DataFrame(ret, columns=sorted(grid))
+            return df
+        else:
+
+            print("novo")
+
+            ret = []
+
+            for k in np.arange(self.order, steps + self.order):
+
+                grid = self.getGridClean(resolution)
+                grid = self.gridCount(grid, resolution, index, intervals[k])
+
+                for qt in np.arange(0, 50, 1):
+                    # print(qt)
+                    qtle_lower = self.forecastInterval(
+                        [intervals[x][0] + qt * ((intervals[x][1] - intervals[x][0]) / 100) for x in
+                         np.arange(k - self.order, k)])
+                    grid = self.gridCount(grid, resolution, index, np.ravel(qtle_lower))
+                    qtle_upper = self.forecastInterval(
+                        [intervals[x][1] - qt * ((intervals[x][1] - intervals[x][0]) / 100) for x in
+                         np.arange(k - self.order, k)])
+                    grid = self.gridCount(grid, resolution, index, np.ravel(qtle_upper))
+                qtle_mid = self.forecastInterval(
+                    [intervals[x][0] + (intervals[x][1] - intervals[x][0]) / 2 for x in np.arange(k - self.order, k)])
+                grid = self.gridCount(grid, resolution, index, np.ravel(qtle_mid))
+
+                tmp = np.array([grid[k] for k in sorted(grid)])
+
+                ret.append(tmp / sum(tmp))
+
+            grid = self.getGridClean(resolution)
+            df = pd.DataFrame(ret, columns=sorted(grid))
+            return df
+
+
+    def __str__(self):
+        tmp = self.name + ":\n"
+        for r in sorted(self.flrgs):
+            p = round(self.flrgs[r].frequencyCount / self.globalFrequency, 3)
+            tmp = tmp + "(" + str(p) + ") " + str(self.flrgs[r]) + "\n"
+        return tmp

tests/general.py

@@ -9,23 +9,61 @@ import matplotlib.pyplot as plt
 from mpl_toolkits.mplot3d import Axes3D
 
 import pandas as pd
-from pyFTS.partitioners import Grid, Entropy, FCM
+from pyFTS.partitioners import Grid, Entropy, FCM, Huarng
 from pyFTS.common import FLR,FuzzySet,Membership,Transformations
-from pyFTS import fts,hofts,ifts,pwfts,tree, chen
+from pyFTS import fts,hofts,ifts,pwfts,tree, chen, pfts
 from pyFTS.benchmarks import benchmarks as bchmk
+from pyFTS.benchmarks import naive
 from pyFTS.benchmarks import Measures
 from numpy import random
 
 #print(FCM.FCMPartitionerTrimf.__module__)
 
-gauss = random.normal(0,1.0,2000)
+#gauss = random.normal(0,1.0,2000)
 #gauss_teste = random.normal(0,1.0,400)
 
 
 os.chdir("/home/petronio/dados/Dropbox/Doutorado/Disciplinas/AdvancedFuzzyTimeSeriesModels/")
 
+#taiexpd = pd.read_csv("DataSets/TAIEX.csv", sep=",")
+#taiex = np.array(taiexpd["avg"][:5000])
+
+taiex = pd.read_csv("DataSets/TAIEX.csv", sep=",")
+taiex_treino = np.array(taiex["avg"][2500:3900])
+taiex_teste = np.array(taiex["avg"][3901:4500])
+
+#print(len(taiex))
+
 #from pyFTS.common import Util
 
+#, ,
 
-bchmk.sliding_window(gauss,500,train=0.7,partitioners=[Grid.GridPartitioner, FCM.FCMPartitioner, Entropy.EntropyPartitioner])
+diff = Transformations.Differential(1)
 
+#bchmk.sliding_window(taiex,2000,train=0.8, #transformation=diff, #models=[pwfts.ProbabilisticWeightedFTS],
+#                     partitioners=[Grid.GridPartitioner, FCM.FCMPartitioner, Entropy.EntropyPartitioner],
+#                     partitions=[10, 15, 20, 25, 30, 35, 40], dump=True, save=True, file="experiments/points.csv")
+
+
+bchmk.allPointForecasters(taiex_treino, taiex_treino, 7, transformation=diff,
+                          models=[ naive.Naive, pfts.ProbabilisticFTS, pwfts.ProbabilisticWeightedFTS],
+                         statistics=True, residuals=False, series=False)
+
+data_train_fs = Grid.GridPartitioner(taiex_treino, 10, transformation=diff).sets
+
+fts1 = pfts.ProbabilisticFTS("")
+fts1.appendTransformation(diff)
+fts1.train(taiex_treino, data_train_fs, order=1)
+
+print(fts1.forecast([5000, 5000]))
+
+fts2 = pwfts.ProbabilisticWeightedFTS("")
+fts2.appendTransformation(diff)
+fts2.train(taiex_treino, data_train_fs, order=1)
+
+print(fts2.forecast([5000, 5000]))
+
+
+#tmp = Grid.GridPartitioner(taiex_treino,7,transformation=diff)
+
+#for s in tmp.sets: print(s)