Conditional Variance FTS

pyFTS/common/FLR.py

@@ -70,6 +70,14 @@ def generateRecurrentFLRs(fuzzyData):
                 for r in rhs:
                     tmp = FLR(l, r)
                     flrs.append(tmp)
+        elif isinstance(lhs, list) and not isinstance(rhs, list):
+            for l in lhs:
+                tmp = FLR(l, rhs)
+                flrs.append(tmp)
+        elif not isinstance(lhs, list) and isinstance(rhs, list):
+            for r in rhs:
+                tmp = FLR(lhs, r)
+                flrs.append(tmp)
         else:
             tmp = FLR(lhs,rhs)
             flrs.append(tmp)

pyFTS/common/FuzzySet.py

@@ -102,8 +102,40 @@ def getMaxMembershipFuzzySetIndex(inst, fuzzySets):
     return np.argwhere(mv == max(mv))[0, 0]
 
 
-def fuzzySeries(data, fuzzySets):
+def fuzzySeries(data, fuzzySets, method='maximum'):
     fts = []
     for item in data:
         fts.append(getMaxMembershipFuzzySet(item, fuzzySets))
     return fts
+
+
+def fuzzifySeries(data, fuzzySets, method='maximum'):
+    fts = []
+    for t, i in enumerate(data):
+        mv = np.array([fs.membership(i) for fs in fuzzySets])
+        if len(mv) == 0:
+            sets = check_bounds(i, fuzzySets)
+        else:
+            if method == 'fuzzy':
+                ix = np.ravel(np.argwhere(mv > 0.0))
+                sets = [fuzzySets[i] for i in ix]
+            elif method == 'maximum':
+                mx = max(mv)
+                ix = np.ravel(np.argwhere(mv == mx))
+                sets = fuzzySets[ix[0]]
+        fts.append(sets)
+    return fts
+
+
+def check_bounds(data, sets):
+    if data < sets[0].get_lower():
+        return sets[0]
+    elif data > sets[-1].get_upper():
+        return sets[-1]
+
+
+def check_bounds_index(data, sets):
+    if data < sets[0].get_lower():
+        return 0
+    elif data > sets[-1].get_upper():
+        return len(sets) -1

pyFTS/nonstationary/common.py

@@ -107,7 +107,7 @@ class FuzzySet(FS.FuzzySet):
     
         self.perturbate_parameters(t)
     
-        tmp = self.mf(x, self.perturbated_parameters[t])
+        tmp = self.mf(x, self.perturbated_parameters[str(t)])
     
         if self.noise is not None:
             tmp += self.noise(t, self.noise_params)
@@ -115,16 +115,20 @@ class FuzzySet(FS.FuzzySet):
         return tmp
     
     def perturbate_parameters(self, t):
-        if t not in self.perturbated_parameters:
+        if str(t) not in self.perturbated_parameters:
             param = self.parameters
+            if isinstance(t, (list, set)):
+                param = self.perform_location(t[0], param)
+                param = self.perform_width(t[1], param)
+            else:
                 param = self.perform_location(t, param)
                 param = self.perform_width(t, param)
-            self.perturbated_parameters[t] = param
+            self.perturbated_parameters[str(t)] = param
             
     def get_midpoint(self, t):
 
         self.perturbate_parameters(t)
-        param = self.perturbated_parameters[t]
+        param = self.perturbated_parameters[str(t)]
 
         if self.mf == Membership.gaussmf:
             return param[0]
@@ -140,7 +144,7 @@ class FuzzySet(FS.FuzzySet):
     def get_lower(self, t):
 
         self.perturbate_parameters(t)
-        param = self.perturbated_parameters[t]
+        param = self.perturbated_parameters[str(t)]
 
         if self.mf == Membership.gaussmf:
             return param[0] - 3*param[1]
@@ -156,7 +160,7 @@ class FuzzySet(FS.FuzzySet):
     def get_upper(self, t):
 
         self.perturbate_parameters(t)
-        param = self.perturbated_parameters[t]
+        param = self.perturbated_parameters[str(t)]
 
         if self.mf == Membership.gaussmf:
             return param[0] + 3*param[1]
@@ -183,105 +187,6 @@ class FuzzySet(FS.FuzzySet):
         return self.name + ": " + str(self.mf.__name__) + tmp
 
 
-class PolynomialNonStationaryPartitioner(partitioner.Partitioner):
-    """
-    Non Stationary Universe of Discourse Partitioner
-    """
-
-    def __init__(self, data, part, **kwargs):
-        """"""
-        super(PolynomialNonStationaryPartitioner, self).__init__(name=part.name, data=data, npart=part.partitions,
-                                                                 func=part.membership_function, names=part.setnames,
-                                                                 prefix=part.prefix, transformation=part.transformation,
-                                                                 indexer=part.indexer)
-
-        self.sets = []
-
-        loc_params, wid_params = self.get_polynomial_perturbations(data, **kwargs)
-
-        for ct, set in enumerate(part.sets):
-            loc_roots = np.roots(loc_params[ct])[0]
-            wid_roots = np.roots(wid_params[ct])[0]
-            tmp = FuzzySet(set.name, set.mf, set.parameters,
-                           location=perturbation.polynomial,
-                           location_params=loc_params[ct],
-                           location_roots=loc_roots, #**kwargs)
-                           width=perturbation.polynomial,
-                           width_params=wid_params[ct],
-                           width_roots=wid_roots, **kwargs)
-
-            self.sets.append(tmp)
-
-    def poly_width(self, par1, par2, rng, deg):
-        a = np.polyval(par1, rng)
-        b = np.polyval(par2, rng)
-        diff = [b[k] - a[k] for k in rng]
-        tmp = np.polyfit(rng, diff, deg=deg)
-        return tmp
-
-    def scale_up(self,x,pct):
-        if x > 0: return x*(1+pct)
-        else: return x*pct
-
-    def scale_down(self,x,pct):
-        if x > 0: return x*pct
-        else: return x*(1+pct)
-
-    def get_polynomial_perturbations(self, data, **kwargs):
-        w = kwargs.get("window_size", int(len(data) / 5))
-        deg = kwargs.get("degree", 2)
-        xmax = [data[0]]
-        tmax = [0]
-        xmin = [data[0]]
-        tmin = [0]
-
-        l = len(data)
-
-        for i in np.arange(0, l, w):
-            sample = data[i:i + w]
-            tx = max(sample)
-            xmax.append(tx)
-            tmax.append(np.ravel(np.argwhere(data == tx)).tolist()[0])
-            tn = min(sample)
-            xmin.append(tn)
-            tmin.append(np.ravel(np.argwhere(data == tn)).tolist()[0])
-
-        cmax = np.polyfit(tmax, xmax, deg=deg)
-        cmin = np.polyfit(tmin, xmin, deg=deg)
-
-        cmed = []
-
-        for d in np.arange(0, deg + 1):
-            cmed.append(np.linspace(cmin[d], cmax[d], self.partitions)[1:self.partitions - 1])
-
-        loc_params = [cmin.tolist()]
-        for i in np.arange(0, self.partitions - 2):
-            tmp = [cmed[k][i] for k in np.arange(0, deg + 1)]
-            loc_params.append(tmp)
-        loc_params.append(cmax.tolist())
-
-        rng = np.arange(0, l)
-
-        clen = []
-
-        for i in np.arange(1, self.partitions-1):
-            tmp = self.poly_width(loc_params[i - 1], loc_params[i + 1], rng, deg)
-            clen.append(tmp)
-
-        tmp = self.poly_width(loc_params[0], loc_params[1], rng, deg)
-        clen.insert(0, tmp)
-
-        tmp = self.poly_width(loc_params[self.partitions-2], loc_params[self.partitions-1], rng, deg)
-        clen.append(tmp)
-
-        tmp = (loc_params, clen)
-
-        return tmp
-
-    def build(self, data):
-        pass
-
-
 def fuzzify(inst, t, fuzzySets):
     """
     Calculate the membership values for a data point given nonstationary fuzzy sets
@@ -299,10 +204,10 @@ def fuzzify(inst, t, fuzzySets):
     return ret
 
 
-def fuzzySeries(data, fuzzySets, window_size=1, method='fuzzy'):
+def fuzzySeries(data, fuzzySets, window_size=1, method='fuzzy', const_t= None):
     fts = []
     for t, i in enumerate(data):
-        tdisp = window_index(t, window_size)
+        tdisp = window_index(t, window_size) if const_t is None else const_t
         mv = np.array([fs.membership(i, tdisp) for fs in fuzzySets])
         if len(mv) == 0:
             sets = [check_bounds(i, fuzzySets, tdisp)]
@@ -318,6 +223,8 @@ def fuzzySeries(data, fuzzySets, window_size=1, method='fuzzy'):
 
 
 def window_index(t, window_size):
+    if isinstance(t, (list, set)):
+        return t
     return t - (t % window_size)
 
 

pyFTS/nonstationary/cvfts.py

@@ -0,0 +1,180 @@
+import numpy as np
+from pyFTS import fts, flrg, chen
+from pyFTS.nonstationary import common, perturbation, nsfts
+from pyFTS.common import Transformations, FuzzySet, FLR
+
+
+class ConditionalVarianceFTS(chen.ConventionalFTS):
+    def __init__(self, name, **kwargs):
+        super(ConditionalVarianceFTS, self).__init__("CVFTS " + name, **kwargs)
+        self.name = "Conditional Variance FTS"
+        self.detail = ""
+        self.flrgs = {}
+        #self.appendTransformation(Transformations.Differential(1))
+        if self.partitioner is None:
+            self.min_tx = None
+            self.max_tx = None
+        else:
+            self.min_tx = self.partitioner.min
+            self.max_tx = self.partitioner.max
+            self.appendTransformation(self.partitioner.transformation)
+
+        self.min_stack = [0,0,0]
+        self.max_stack = [0,0,0]
+
+    def train(self, data, sets = None, order=1,parameters=None):
+        if sets is not None:
+            self.sets = sets
+        else:
+            self.sets = self.partitioner.sets
+
+        ndata = self.doTransformations(data)
+
+        self.min_tx = min(ndata)
+        self.max_tx = max(ndata)
+
+        tmpdata = common.fuzzySeries(ndata, self.sets, method='fuzzy', const_t=0)
+        flrs = FLR.generateNonRecurrentFLRs(tmpdata)
+        self.flrgs = self.generate_flrg(flrs)
+
+    def generate_flrg(self, flrs, **kwargs):
+        flrgs = {}
+        for flr in flrs:
+            if flr.LHS.name in flrgs:
+                flrgs[flr.LHS.name].append(flr.RHS)
+            else:
+                flrgs[flr.LHS.name] = nsfts.ConventionalNonStationaryFLRG(flr.LHS)
+                flrgs[flr.LHS.name].append(flr.RHS)
+        return flrgs
+
+    def _smooth(self, a):
+        return .1 * a[0] + .3 * a[1] + .6 * a[2]
+
+    def perturbation_factors(self, data):
+        _max = 0
+        _min = 0
+        if data < self.min_tx:
+            _min = data - self.min_tx if data < 0 else self.min_tx - data
+        elif data > self.max_tx:
+            _max = data - self.max_tx if data > 0 else self.max_tx - data
+        self.min_stack.pop(2)
+        self.min_stack.insert(0,_min)
+        _min = min(self.min_stack)
+        self.max_stack.pop(2)
+        self.max_stack.insert(0, _max)
+        _max = max(self.max_stack)
+
+        location = np.linspace(_min, _max, self.partitioner.partitions)
+        scale = [abs(location[0] - location[2])]
+        scale.extend([abs(location[k-1] - location[k+1]) for k in np.arange(1,self.partitioner.partitions-1)])
+        scale.append(abs(location[-1] - location[-3]))
+
+        perturb = [[location[k], scale[k]] for k in np.arange(0, self.partitioner.partitions)]
+
+        return perturb
+
+    def _affected_sets(self, sample, perturb):
+
+        affected_sets = [[ct, set.membership(sample, perturb[ct])]
+                         for ct, set in enumerate(self.sets)
+                         if set.membership(sample, perturb[ct]) > 0.0]
+
+        if len(affected_sets) == 0:
+            if sample < self.sets[0].get_lower(perturb[0]):
+                affected_sets.append([0, 1])
+            elif sample < self.sets[-1].get_lower(perturb[-1]):
+                affected_sets.append([len(self.sets) - 1, 1])
+
+
+        return affected_sets
+
+    def forecast(self, data, **kwargs):
+        ndata = np.array(self.doTransformations(data))
+
+        l = len(ndata)
+
+        ret = []
+
+        for k in np.arange(0, l):
+
+            sample = ndata[k]
+
+            perturb = self.perturbation_factors(sample)
+
+            affected_sets = self._affected_sets(sample, perturb)
+
+            tmp = []
+
+            if len(affected_sets) == 1:
+                ix = affected_sets[0][0]
+                aset = self.sets[ix]
+                if aset.name in self.flrgs:
+                    tmp.append(self.flrgs[aset.name].get_midpoint(perturb[ix]))
+                else:
+                    print('naive')
+                    tmp.append(aset.get_midpoint(perturb[ix]))
+            else:
+                for aset in affected_sets:
+                    ix = aset[0]
+                    fs = self.sets[ix]
+                    tdisp = perturb[ix]
+                    if fs.name in self.flrgs:
+                        tmp.append(self.flrgs[fs.name].get_midpoint(tdisp) * aset[1])
+                    else:
+                        tmp.append(fs.get_midpoint(tdisp) * aset[1])
+
+            pto = sum(tmp)
+
+            ret.append(pto)
+
+        ret = self.doInverseTransformations(ret, params=[data[self.order - 1:]])
+
+        return ret
+
+
+    def forecastInterval(self, data, **kwargs):
+        ndata = np.array(self.doTransformations(data))
+
+        l = len(ndata)
+
+        ret = []
+
+        for k in np.arange(0, l):
+
+            sample = ndata[k]
+
+            perturb = self.perturbation_factors(sample)
+
+            affected_sets = self._affected_sets(sample, perturb)
+
+            upper = []
+            lower = []
+
+            if len(affected_sets) == 1:
+                ix = affected_sets[0][0]
+                aset = self.sets[ix]
+                if aset.name in self.flrgs:
+                    lower.append(self.flrgs[aset.name].get_lower(perturb[ix]))
+                    upper.append(self.flrgs[aset.name].get_upper(perturb[ix]))
+                else:
+                    lower.append(aset.get_lower(perturb[ix]))
+                    upper.append(aset.get_upper(perturb[ix]))
+            else:
+                for aset in affected_sets:
+                    ix = aset[0]
+                    fs = self.sets[ix]
+                    tdisp = perturb[ix]
+                    if fs.name in self.flrgs:
+                        lower.append(self.flrgs[fs.name].get_lower(tdisp) * aset[1])
+                        upper.append(self.flrgs[fs.name].get_upper(tdisp) * aset[1])
+                    else:
+                        lower.append(fs.get_lower(tdisp) * aset[1])
+                        upper.append(fs.get_upper(tdisp) * aset[1])
+
+            itvl = [sum(lower), sum(upper)]
+
+            ret.append(itvl)
+
+        ret = self.doInverseTransformations(ret, params=[data[self.order - 1:]])
+
+        return ret

pyFTS/nonstationary/flrg.py

@@ -23,7 +23,7 @@ class NonStationaryFLRG(flrg.FLRG):
 
     def get_midpoint(self, t, window_size=1):
         if len(self.RHS) > 0:
-            if isinstance(self.RHS, (list,set)):
+            if isinstance(self.RHS, (list, set)):
                 tmp = [r.get_midpoint(common.window_index(t, window_size)) for r in self.RHS]
             elif isinstance(self.RHS, dict):
                 tmp = [self.RHS[r].get_midpoint(common.window_index(t, window_size)) for r in self.RHS.keys()]

pyFTS/nonstationary/partitioners.py

@@ -0,0 +1,122 @@
+import numpy as np
+from pyFTS.partitioners import partitioner
+from pyFTS.nonstationary import common, perturbation
+
+
+class PolynomialNonStationaryPartitioner(partitioner.Partitioner):
+    """
+    Non Stationary Universe of Discourse Partitioner
+    """
+
+    def __init__(self, data, part, **kwargs):
+        """"""
+        super(PolynomialNonStationaryPartitioner, self).__init__(name=part.name, data=data, npart=part.partitions,
+                                                                 func=part.membership_function, names=part.setnames,
+                                                                 prefix=part.prefix, transformation=part.transformation,
+                                                                 indexer=part.indexer)
+
+        self.sets = []
+
+        loc_params, wid_params = self.get_polynomial_perturbations(data, **kwargs)
+
+        for ct, set in enumerate(part.sets):
+            loc_roots = np.roots(loc_params[ct])[0]
+            wid_roots = np.roots(wid_params[ct])[0]
+            tmp = common.FuzzySet(set.name, set.mf, set.parameters,
+                           location=perturbation.polynomial,
+                           location_params=loc_params[ct],
+                           location_roots=loc_roots, #**kwargs)
+                           width=perturbation.polynomial,
+                           width_params=wid_params[ct],
+                           width_roots=wid_roots, **kwargs)
+
+            self.sets.append(tmp)
+
+    def poly_width(self, par1, par2, rng, deg):
+        a = np.polyval(par1, rng)
+        b = np.polyval(par2, rng)
+        diff = [b[k] - a[k] for k in rng]
+        tmp = np.polyfit(rng, diff, deg=deg)
+        return tmp
+
+    def scale_up(self,x,pct):
+        if x > 0: return x*(1+pct)
+        else: return x*pct
+
+    def scale_down(self,x,pct):
+        if x > 0: return x*pct
+        else: return x*(1+pct)
+
+    def get_polynomial_perturbations(self, data, **kwargs):
+        w = kwargs.get("window_size", int(len(data) / 5))
+        deg = kwargs.get("degree", 2)
+        xmax = [data[0]]
+        tmax = [0]
+        xmin = [data[0]]
+        tmin = [0]
+
+        l = len(data)
+
+        for i in np.arange(0, l, w):
+            sample = data[i:i + w]
+            tx = max(sample)
+            xmax.append(tx)
+            tmax.append(np.ravel(np.argwhere(data == tx)).tolist()[0])
+            tn = min(sample)
+            xmin.append(tn)
+            tmin.append(np.ravel(np.argwhere(data == tn)).tolist()[0])
+
+        cmax = np.polyfit(tmax, xmax, deg=deg)
+        cmin = np.polyfit(tmin, xmin, deg=deg)
+
+        cmed = []
+
+        for d in np.arange(0, deg + 1):
+            cmed.append(np.linspace(cmin[d], cmax[d], self.partitions)[1:self.partitions - 1])
+
+        loc_params = [cmin.tolist()]
+        for i in np.arange(0, self.partitions - 2):
+            tmp = [cmed[k][i] for k in np.arange(0, deg + 1)]
+            loc_params.append(tmp)
+        loc_params.append(cmax.tolist())
+
+        rng = np.arange(0, l)
+
+        clen = []
+
+        for i in np.arange(1, self.partitions-1):
+            tmp = self.poly_width(loc_params[i - 1], loc_params[i + 1], rng, deg)
+            clen.append(tmp)
+
+        tmp = self.poly_width(loc_params[0], loc_params[1], rng, deg)
+        clen.insert(0, tmp)
+
+        tmp = self.poly_width(loc_params[self.partitions-2], loc_params[self.partitions-1], rng, deg)
+        clen.append(tmp)
+
+        tmp = (loc_params, clen)
+
+        return tmp
+
+    def build(self, data):
+        pass
+
+
+class ConstantNonStationaryPartitioner(partitioner.Partitioner):
+    """
+    Non Stationary Universe of Discourse Partitioner
+    """
+
+    def __init__(self, data, part, **kwargs):
+        """"""
+        super(ConstantNonStationaryPartitioner, self).__init__(name=part.name, data=data, npart=part.partitions,
+                                                                 func=part.membership_function, names=part.setnames,
+                                                                 prefix=part.prefix, transformation=part.transformation,
+                                                                 indexer=part.indexer)
+
+        self.sets = []
+
+        for set in part.sets:
+            tmp = common.FuzzySet(set.name, set.mf, set.parameters, **kwargs)
+
+            self.sets.append(tmp)

pyFTS/nonstationary/util.py

@@ -19,7 +19,7 @@ def plot_sets(sets, start=0, end=10, step=1, tam=[5, 5], colors=None,
             for t in range:
                 tdisp = t - (t % window_size)
                 set.membership(0, tdisp)
-                param = set.perturbated_parameters[tdisp]
+                param = set.perturbated_parameters[str(tdisp)]
 
                 if set.mf == Membership.trimf:
                     if t == start:
@@ -34,7 +34,7 @@ def plot_sets(sets, start=0, end=10, step=1, tam=[5, 5], colors=None,
             for t in range:
                 tdisp = t - (t % window_size)
                 set.membership(0, tdisp)
-                param = set.perturbated_parameters[tdisp]
+                param = set.perturbated_parameters[str(tdisp)]
                 tmp.append(np.polyval(param, tdisp))
             axes.plot(range, tmp, ls="--", c="blue")
 
@@ -51,3 +51,41 @@ def plot_sets(sets, start=0, end=10, step=1, tam=[5, 5], colors=None,
     plt.tight_layout()
 
     Util.showAndSaveImage(fig, file, save)
+
+
+def plot_sets_conditional(model, data, start=0, end=10, step=1, tam=[5, 5], colors=None,
+                          save=False, file=None, axes=None):
+
+    range = np.arange(start,end,step)
+    ticks = []
+    if axes is None:
+        fig, axes = plt.subplots(nrows=1, ncols=1, figsize=tam)
+
+    for ct, set in enumerate(model.sets):
+            for t in range:
+                tdisp = model.perturbation_factors(data[t])
+                set.perturbate_parameters(tdisp[ct])
+                param = set.perturbated_parameters[str(tdisp[ct])]
+
+                if set.mf == Membership.trimf:
+                    if t == start:
+                        line = axes.plot([t, t+1, t], param, label=set.name)
+                        set.metadata['color'] = line[0].get_color()
+                    else:
+                        axes.plot([t, t + 1, t], param,c=set.metadata['color'])
+
+                ticks.extend(["t+"+str(t),""])
+
+    axes.set_ylabel("Universe of Discourse")
+    axes.set_xlabel("Time")
+    plt.xticks([k for k in range], ticks, rotation='vertical')
+
+    handles0, labels0 = axes.get_legend_handles_labels()
+    lgd = axes.legend(handles0, labels0, loc=2, bbox_to_anchor=(1, 1))
+
+    if data is not None:
+        axes.plot(np.arange(start, start + len(data), 1), data,c="black")
+
+    plt.tight_layout()
+
+    Util.showAndSaveImage(fig, file, save)

pyFTS/tests/nonstationary.py

@@ -1,13 +1,14 @@
 import os
 import numpy as np
-from pyFTS.common import Membership
+from pyFTS.common import Membership, Transformations
-from pyFTS.nonstationary import common,perturbation,util,nsfts, honsfts
+from pyFTS.nonstationary import common,perturbation, partitioners, util,nsfts, honsfts, cvfts
 from pyFTS.partitioners import Grid
 import matplotlib.pyplot as plt
 import pandas as pd
 os.chdir("/home/petronio/Dropbox/Doutorado/Codigos/")
 
-"""
+diff = Transformations.Differential(1)
+
 def generate_heteroskedastic_linear(mu_ini, sigma_ini, mu_inc, sigma_inc, it=10, num=35):
     mu = mu_ini
     sigma = sigma_ini
@@ -19,34 +20,55 @@ def generate_heteroskedastic_linear(mu_ini, sigma_ini, mu_inc, sigma_inc, it=10,
     return ret
 
 
-lmv1 = generate_heteroskedastic_linear(1,0.1,1,0.3)
+#lmv1 = generate_heteroskedastic_linear(1,0.1,1,0.3)
-#lmv1 = generate_heteroskedastic_linear(5,0.1,0,0.2)
+lmv1 = generate_heteroskedastic_linear(5,0.1,0,0.2)
 #lmv1 = generate_heteroskedastic_linear(1,0.3,1,0)
 
-ns = 5 #number of fuzzy sets
+lmv1 = diff.apply(lmv1)
+
+ns = 10 #number of fuzzy sets
 ts = 200
 train = lmv1[:ts]
 test = lmv1[ts:]
 w = 25
 deg = 4
 
-tmp_fs = Grid.GridPartitioner(train[:35], 10)
+tmp_fs = Grid.GridPartitioner(train, 10)
 
-fs = common.PolynomialNonStationaryPartitioner(train, tmp_fs, window_size=35, degree=1)
+#fs = partitioners.PolynomialNonStationaryPartitioner(train, tmp_fs, window_size=35, degree=1)
-
+fs = partitioners.ConstantNonStationaryPartitioner(train, tmp_fs,
-nsfts1 = nsfts.NonStationaryFTS("", partitioner=fs)
+                                                   location=perturbation.polynomial,
-
+                                                   location_params=[1,0],
-nsfts1.train(train[:100])
+                                                   location_roots=0,
-
+                                                   width=perturbation.polynomial,
-print(fs)
+                                                   width_params=[1,0],
-
+                                                   width_roots=0)
-print(nsfts1)
-
-tmp = nsfts1.forecast(test[:10], time_displacement=200)
-
-print(tmp)
 """
+perturb = [0.5, 0.25]
+for i in [0,1]:
+    print(fs.sets[i].parameters)
+    fs.sets[i].perturbate_parameters(perturb[i])
+for i in [0,1]:
+    print(fs.sets[i].perturbated_parameters[perturb[i]])
+"""
+#nsfts1 = nsfts.NonStationaryFTS("", partitioner=fs)
 
+nsfts1 = cvfts.ConditionalVarianceFTS("", partitioner=fs)
+
+nsfts1.train(train)
+
+#print(fs)
+
+#print(nsfts1)
+
+#tmp = nsfts1.forecast(test[50:60])
+
+#print(tmp)
+#print(test[50:60])
+
+util.plot_sets_conditional(nsfts1, test, end=150, step=1,tam=[10, 5])
+print('')
+"""
 passengers = pd.read_csv("DataSets/AirPassengers.csv", sep=",")
 passengers = np.array(passengers["Passengers"])
 
@@ -83,6 +105,7 @@ print([k[1] for k in tmpi])
 #               window_size=ws, only_lines=False)
 
 #fig, axes = plt.subplots(nrows=1, ncols=1, figsize=[15,5])
+"""
 
 """
 axes.plot(testp, label="Original")