Importação inicial dos códigos da notebook (jupyter) em https://github.com/petroniocandido/FuzzyTimeSeries
This commit is contained in:
Petrônio Cândido de Lima e Silva
parent
c96f4b69d7
commit
cf9fbbf5a7
35
FTS.py
Normal file
35
FTS.py
Normal file
@ -0,0 +1,35 @@
|
||||
class FTS:
|
||||
def __init__(self,order,name):
|
||||
self.sets = {}
|
||||
self.flrgs = {}
|
||||
self.order = order
|
||||
self.name = name
|
||||
|
||||
def fuzzy(self,data):
|
||||
best = {"fuzzyset":"", "membership":0.0}
|
||||
|
||||
for f in self.sets:
|
||||
fset = self.sets[f]
|
||||
if best["membership"] <= fset.membership(data):
|
||||
best["fuzzyset"] = fset.name
|
||||
best["membership"] = fset.membership(data)
|
||||
|
||||
return best
|
||||
|
||||
def defuzzy(self,data):
|
||||
pass
|
||||
|
||||
def learn(self, data, sets):
|
||||
pass
|
||||
|
||||
def predict(self,data):
|
||||
return self.defuzzy(data)
|
||||
|
||||
def predictDiff(self,data,t):
|
||||
return data[t] + self.defuzzy(data[t-1]-data[t])
|
||||
|
||||
def __str__(self):
|
||||
tmp = self.name + ":\n"
|
||||
for r in self.flrgs.keys():
|
||||
tmp = tmp + str(self.flrgs[r]) + "\n"
|
||||
return tmp
|
||||
390
benchmarks.py
Normal file
390
benchmarks.py
Normal file
@ -0,0 +1,390 @@
|
||||
# Erro quadrático médio
|
||||
def rmse(predictions,targets):
|
||||
return np.sqrt(np.mean((predictions-targets)**2))
|
||||
|
||||
# Erro Percentual médio
|
||||
def mape(predictions,targets):
|
||||
return np.mean(abs(predictions-targets)/predictions)
|
||||
|
||||
def plotComparedSeries(original,fts,title):
|
||||
fig = plt.figure(figsize=[20,6])
|
||||
ax = fig.add_subplot(111)
|
||||
predicted = [fts.predict(xx) for xx in original]
|
||||
error = rmse(original,predicted)
|
||||
ax.plot(original,color='b',label="Original")
|
||||
ax.plot(predicted,color='r',label="Predicted")
|
||||
handles0, labels0 = ax.get_legend_handles_labels()
|
||||
ax.legend(handles0,labels0)
|
||||
ax.set_title(title)
|
||||
ax.set_ylabel('F(T)')
|
||||
ax.set_xlabel('T')
|
||||
ax.set_xlim([0,len(original)])
|
||||
ax.set_ylim([min(original),max(original)])
|
||||
|
||||
def plotCompared(original,predicted,labels,title):
|
||||
fig = plt.figure(figsize=[13,6])
|
||||
ax = fig.add_subplot(111)
|
||||
ax.plot(original,color='k',label="Original")
|
||||
for c in range(0,len(predicted)):
|
||||
ax.plot(predicted[c],label=labels[c])
|
||||
handles0, labels0 = ax.get_legend_handles_labels()
|
||||
ax.legend(handles0,labels0)
|
||||
ax.set_title(title)
|
||||
ax.set_ylabel('F(T)')
|
||||
ax.set_xlabel('T')
|
||||
ax.set_xlim([0,len(original)])
|
||||
ax.set_ylim([min(original),max(original)])
|
||||
|
||||
def SelecaoKFold_MenorRMSE(original,parameters,modelo):
|
||||
nfolds = 5
|
||||
ret = []
|
||||
errors = np.array([[0 for k in parameters] for z in np.arange(0,nfolds)])
|
||||
predicted_best = []
|
||||
print("Série Original")
|
||||
fig = plt.figure(figsize=[18,10])
|
||||
fig.suptitle("Comparação de modelos ")
|
||||
ax0 = fig.add_axes([0, 0.5, 0.65, 0.45]) #left, bottom, width, height
|
||||
ax0.set_xlim([0,len(original)])
|
||||
ax0.set_ylim([min(original),max(original)])
|
||||
ax0.set_title('Série Temporal')
|
||||
ax0.set_ylabel('F(T)')
|
||||
ax0.set_xlabel('T')
|
||||
ax0.plot(original,label="Original")
|
||||
min_rmse_fold = 100000.0
|
||||
best = None
|
||||
fc = 0 #Fold count
|
||||
kf = KFold(len(original), n_folds=nfolds)
|
||||
for train_ix, test_ix in kf:
|
||||
train = original[train_ix]
|
||||
test = original[test_ix]
|
||||
min_rmse = 100000.0
|
||||
best_fold = None
|
||||
predicted_best_fold = []
|
||||
errors_fold = []
|
||||
pc = 0 #Parameter count
|
||||
for p in parameters:
|
||||
sets = GridPartitionerTrimf(train,p)
|
||||
fts = modelo(str(p)+ " particoes")
|
||||
fts.learn(train,sets)
|
||||
predicted = [fts.predict(xx) for xx in test]
|
||||
error = rmse(np.array(predicted),np.array(test))
|
||||
errors_fold.append(error)
|
||||
print(fc, p, error)
|
||||
errors[fc,pc] = error
|
||||
if error < min_rmse:
|
||||
min_rmse = error
|
||||
best_fold = fts
|
||||
predicted_best_fold = predicted
|
||||
pc = pc + 1
|
||||
predicted_best_fold = [best_fold.predict(xx) for xx in original]
|
||||
ax0.plot(predicted_best_fold,label=best_fold.name)
|
||||
if np.mean(errors_fold) < min_rmse_fold:
|
||||
min_rmse_fold = np.mean(errors)
|
||||
best = best_fold
|
||||
predicted_best = predicted_best_fold
|
||||
fc = fc + 1
|
||||
handles0, labels0 = ax0.get_legend_handles_labels()
|
||||
ax0.legend(handles0, labels0)
|
||||
ax1 = Axes3D(fig, rect=[0.7, 0.5, 0.3, 0.45], elev=30, azim=144)
|
||||
#ax1 = fig.add_axes([0.6, 0.0, 0.45, 0.45], projection='3d')
|
||||
ax1.set_title('Comparação dos Erros Quadráticos Médios')
|
||||
ax1.set_zlabel('RMSE')
|
||||
ax1.set_xlabel('K-fold')
|
||||
ax1.set_ylabel('Partições')
|
||||
X,Y = np.meshgrid(np.arange(0,nfolds),parameters)
|
||||
surf = ax1.plot_surface(X, Y, errors.T, rstride=1, cstride=1, antialiased=True)
|
||||
ret.append(best)
|
||||
ret.append(predicted_best)
|
||||
|
||||
# Modelo diferencial
|
||||
print("\nSérie Diferencial")
|
||||
errors = np.array([[0 for k in parameters] for z in np.arange(0,nfolds)])
|
||||
predictedd_best = []
|
||||
ax2 = fig.add_axes([0, 0, 0.65, 0.45]) #left, bottom, width, height
|
||||
ax2.set_xlim([0,len(original)])
|
||||
ax2.set_ylim([min(original),max(original)])
|
||||
ax2.set_title('Série Temporal')
|
||||
ax2.set_ylabel('F(T)')
|
||||
ax2.set_xlabel('T')
|
||||
ax2.plot(original,label="Original")
|
||||
min_rmse = 100000.0
|
||||
min_rmse_fold = 100000.0
|
||||
bestd = None
|
||||
fc = 0
|
||||
diff = diferencas(original)
|
||||
kf = KFold(len(original), n_folds=nfolds)
|
||||
for train_ix, test_ix in kf:
|
||||
train = diff[train_ix]
|
||||
test = diff[test_ix]
|
||||
min_rmse = 100000.0
|
||||
best_fold = None
|
||||
predicted_best_fold = []
|
||||
errors_fold = []
|
||||
pc = 0
|
||||
for p in parameters:
|
||||
sets = GridPartitionerTrimf(train,p)
|
||||
fts = modelo(str(p)+ " particoes")
|
||||
fts.learn(train,sets)
|
||||
predicted = [fts.predictDiff(test,xx) for xx in np.arange(len(test))]
|
||||
error = rmse(np.array(predicted),np.array(test))
|
||||
print(fc, p,error)
|
||||
errors[fc,pc] = error
|
||||
errors_fold.append(error)
|
||||
if error < min_rmse:
|
||||
min_rmse = error
|
||||
best_fold = fts
|
||||
pc = pc + 1
|
||||
predicted_best_fold = [best_fold.predictDiff(original, xx) for xx in np.arange(len(original))]
|
||||
ax2.plot(predicted_best_fold,label=best_fold.name)
|
||||
if np.mean(errors_fold) < min_rmse_fold:
|
||||
min_rmse_fold = np.mean(errors)
|
||||
best = best_fold
|
||||
predicted_best = predicted_best_fold
|
||||
fc = fc + 1
|
||||
handles0, labels0 = ax2.get_legend_handles_labels()
|
||||
ax2.legend(handles0, labels0)
|
||||
ax3 = Axes3D(fig, rect=[0.7, 0, 0.3, 0.45], elev=30, azim=144)
|
||||
#ax1 = fig.add_axes([0.6, 0.0, 0.45, 0.45], projection='3d')
|
||||
ax3.set_title('Comparação dos Erros Quadráticos Médios')
|
||||
ax3.set_zlabel('RMSE')
|
||||
ax3.set_xlabel('K-fold')
|
||||
ax3.set_ylabel('Partições')
|
||||
X,Y = np.meshgrid(np.arange(0,nfolds),parameters)
|
||||
surf = ax3.plot_surface(X, Y, errors.T, rstride=1, cstride=1, antialiased=True)
|
||||
ret.append(best)
|
||||
ret.append(predicted_best)
|
||||
return ret
|
||||
|
||||
def SelecaoSimples_MenorRMSE(original,parameters,modelo):
|
||||
ret = []
|
||||
errors = []
|
||||
predicted_best = []
|
||||
print("Série Original")
|
||||
fig = plt.figure(figsize=[20,12])
|
||||
fig.suptitle("Comparação de modelos ")
|
||||
ax0 = fig.add_axes([0, 0.5, 0.65, 0.45]) #left, bottom, width, height
|
||||
ax0.set_xlim([0,len(original)])
|
||||
ax0.set_ylim([min(original),max(original)])
|
||||
ax0.set_title('Série Temporal')
|
||||
ax0.set_ylabel('F(T)')
|
||||
ax0.set_xlabel('T')
|
||||
ax0.plot(original,label="Original")
|
||||
min_rmse = 100000.0
|
||||
best = None
|
||||
for p in parameters:
|
||||
sets = GridPartitionerTrimf(original,p)
|
||||
fts = modelo(str(p)+ " particoes")
|
||||
fts.learn(original,sets)
|
||||
predicted = [fts.predict(xx) for xx in original]
|
||||
ax0.plot(predicted,label=fts.name)
|
||||
error = rmse(np.array(predicted),np.array(original))
|
||||
print(p,error)
|
||||
errors.append(error)
|
||||
if error < min_rmse:
|
||||
min_rmse = error
|
||||
best = fts
|
||||
predicted_best = predicted
|
||||
handles0, labels0 = ax0.get_legend_handles_labels()
|
||||
ax0.legend(handles0, labels0)
|
||||
ax1 = fig.add_axes([0.7, 0.5, 0.3, 0.45]) #left, bottom, width, height
|
||||
ax1.set_title('Comparação dos Erros Quadráticos Médios')
|
||||
ax1.set_ylabel('RMSE')
|
||||
ax1.set_xlabel('Quantidade de Partições')
|
||||
ax1.set_xlim([min(parameters),max(parameters)])
|
||||
ax1.plot(parameters,errors)
|
||||
ret.append(best)
|
||||
ret.append(predicted_best)
|
||||
# Modelo diferencial
|
||||
print("\nSérie Diferencial")
|
||||
errors = []
|
||||
predictedd_best = []
|
||||
ax2 = fig.add_axes([0, 0, 0.65, 0.45]) #left, bottom, width, height
|
||||
ax2.set_xlim([0,len(original)])
|
||||
ax2.set_ylim([min(original),max(original)])
|
||||
ax2.set_title('Série Temporal')
|
||||
ax2.set_ylabel('F(T)')
|
||||
ax2.set_xlabel('T')
|
||||
ax2.plot(original,label="Original")
|
||||
min_rmse = 100000.0
|
||||
bestd = None
|
||||
for p in parameters:
|
||||
sets = GridPartitionerTrimf(diferencas(original),p)
|
||||
fts = modelo(str(p)+ " particoes")
|
||||
fts.learn(diferencas(original),sets)
|
||||
predicted = [fts.predictDiff(original, xx) for xx in range(1,len(original))]
|
||||
predicted.insert(0,original[0])
|
||||
ax2.plot(predicted,label=fts.name)
|
||||
error = rmse(np.array(predicted),np.array(original))
|
||||
print(p,error)
|
||||
errors.append(error)
|
||||
if error < min_rmse:
|
||||
min_rmse = error
|
||||
bestd = fts
|
||||
predictedd_best = predicted
|
||||
handles0, labels0 = ax2.get_legend_handles_labels()
|
||||
ax2.legend(handles0, labels0)
|
||||
ax3 = fig.add_axes([0.7, 0, 0.3, 0.45]) #left, bottom, width, height
|
||||
ax3.set_title('Comparação dos Erros Quadráticos Médios')
|
||||
ax3.set_ylabel('RMSE')
|
||||
ax3.set_xlabel('Quantidade de Partições')
|
||||
ax3.set_xlim([min(parameters),max(parameters)])
|
||||
ax3.plot(parameters,errors)
|
||||
ret.append(bestd)
|
||||
ret.append(predictedd_best)
|
||||
return ret
|
||||
|
||||
def compareModelsPlot(original,models_fo,models_ho):
|
||||
fig = plt.figure(figsize=[13,6])
|
||||
fig.suptitle("Comparação de modelos ")
|
||||
ax0 = fig.add_axes([0, 0, 1, 1]) #left, bottom, width, height
|
||||
rows = []
|
||||
for model in models_fo:
|
||||
fts = model["model"]
|
||||
ax0.plot(model["predicted"], label=model["name"])
|
||||
for model in models_ho:
|
||||
fts = model["model"]
|
||||
ax0.plot(model["predicted"], label=model["name"])
|
||||
handles0, labels0 = ax0.get_legend_handles_labels()
|
||||
ax0.legend(handles0, labels0)
|
||||
|
||||
def compareModelsTable(original,models_fo,models_ho):
|
||||
fig = plt.figure(figsize=[12,4])
|
||||
fig.suptitle("Comparação de modelos ")
|
||||
columns = ['Modelo','Ordem','Partições','RMSE','MAPE (%)']
|
||||
rows = []
|
||||
for model in models_fo:
|
||||
fts = model["model"]
|
||||
error_r = rmse(model["predicted"],original)
|
||||
error_m = round(mape(model["predicted"],original)*100,2)
|
||||
rows.append([model["name"],fts.order,len(fts.sets),error_r,error_m])
|
||||
for model in models_ho:
|
||||
fts = model["model"]
|
||||
error_r = rmse(model["predicted"][fts.order:],original[fts.order:])
|
||||
error_m = round(mape(model["predicted"][fts.order:],original[fts.order:])*100,2)
|
||||
rows.append([model["name"],fts.order,len(fts.sets),error_r,error_m])
|
||||
ax1 = fig.add_axes([0, 0, 1, 1]) #left, bottom, width, height
|
||||
ax1.set_xticks([])
|
||||
ax1.set_yticks([])
|
||||
ax1.table(cellText=rows,
|
||||
colLabels=columns,
|
||||
cellLoc='center',
|
||||
bbox=[0,0,1,1])
|
||||
sup = "\\begin{tabular}{"
|
||||
header = ""
|
||||
body = ""
|
||||
footer = ""
|
||||
|
||||
for c in columns:
|
||||
sup = sup + "|c"
|
||||
if len(header) > 0:
|
||||
header = header + " & "
|
||||
header = header + "\\textbf{" + c + "} "
|
||||
sup = sup + "|} \\hline\n"
|
||||
header = header + "\\\\ \\hline \n"
|
||||
|
||||
for r in rows:
|
||||
lin = ""
|
||||
for c in r:
|
||||
if len(lin) > 0:
|
||||
lin = lin + " & "
|
||||
lin = lin + str(c)
|
||||
|
||||
body = body + lin + "\\\\ \\hline \n"
|
||||
|
||||
return sup + header + body + "\\end{tabular}"
|
||||
|
||||
def HOSelecaoSimples_MenorRMSE(original,parameters,orders):
|
||||
ret = []
|
||||
errors = np.array([[0 for k in range(len(parameters))] for kk in range(len(orders))])
|
||||
predicted_best = []
|
||||
print("Série Original")
|
||||
fig = plt.figure(figsize=[20,12])
|
||||
fig.suptitle("Comparação de modelos ")
|
||||
ax0 = fig.add_axes([0, 0.5, 0.6, 0.45]) #left, bottom, width, height
|
||||
ax0.set_xlim([0,len(original)])
|
||||
ax0.set_ylim([min(original),max(original)])
|
||||
ax0.set_title('Série Temporal')
|
||||
ax0.set_ylabel('F(T)')
|
||||
ax0.set_xlabel('T')
|
||||
ax0.plot(original,label="Original")
|
||||
min_rmse = 100000.0
|
||||
best = None
|
||||
pc = 0
|
||||
for p in parameters:
|
||||
oc = 0
|
||||
for o in orders:
|
||||
sets = GridPartitionerTrimf(original,p)
|
||||
fts = HighOrderFTS(o,"k = " + str(p)+ " w = " + str(o))
|
||||
fts.learn(original,sets)
|
||||
predicted = [fts.predict(original, xx) for xx in range(o,len(original))]
|
||||
error = rmse(np.array(predicted),np.array(original[o:]))
|
||||
for kk in range(o):
|
||||
predicted.insert(0,None)
|
||||
ax0.plot(predicted,label=fts.name)
|
||||
print(o,p,error)
|
||||
errors[oc,pc] = error
|
||||
if error < min_rmse:
|
||||
min_rmse = error
|
||||
best = fts
|
||||
predicted_best = predicted
|
||||
oc = oc + 1
|
||||
pc = pc + 1
|
||||
handles0, labels0 = ax0.get_legend_handles_labels()
|
||||
ax0.legend(handles0, labels0)
|
||||
ax1 = Axes3D(fig, rect=[0.6, 0.5, 0.45, 0.45], elev=30, azim=144)
|
||||
#ax1 = fig.add_axes([0.6, 0.5, 0.45, 0.45], projection='3d')
|
||||
ax1.set_title('Comparação dos Erros Quadráticos Médios por tamanho da janela')
|
||||
ax1.set_ylabel('RMSE')
|
||||
ax1.set_xlabel('Quantidade de Partições')
|
||||
ax1.set_zlabel('W')
|
||||
X,Y = np.meshgrid(parameters,orders)
|
||||
surf = ax1.plot_surface(X, Y, errors, rstride=1, cstride=1, antialiased=True)
|
||||
ret.append(best)
|
||||
ret.append(predicted_best)
|
||||
|
||||
# Modelo diferencial
|
||||
print("\nSérie Diferencial")
|
||||
errors = np.array([[0 for k in range(len(parameters))] for kk in range(len(orders))])
|
||||
predictedd_best = []
|
||||
ax2 = fig.add_axes([0, 0, 0.6, 0.45]) #left, bottom, width, height
|
||||
ax2.set_xlim([0,len(original)])
|
||||
ax2.set_ylim([min(original),max(original)])
|
||||
ax2.set_title('Série Temporal')
|
||||
ax2.set_ylabel('F(T)')
|
||||
ax2.set_xlabel('T')
|
||||
ax2.plot(original,label="Original")
|
||||
min_rmse = 100000.0
|
||||
bestd = None
|
||||
pc = 0
|
||||
for p in parameters:
|
||||
oc = 0
|
||||
for o in orders:
|
||||
sets = GridPartitionerTrimf(diferencas(original),p)
|
||||
fts = HighOrderFTS(o,"k = " + str(p)+ " w = " + str(o))
|
||||
fts.learn(original,sets)
|
||||
predicted = [fts.predictDiff(original, xx) for xx in range(o,len(original))]
|
||||
error = rmse(np.array(predicted),np.array(original[o:]))
|
||||
for kk in range(o):
|
||||
predicted.insert(0,None)
|
||||
ax2.plot(predicted,label=fts.name)
|
||||
print(o,p,error)
|
||||
errors[oc,pc] = error
|
||||
if error < min_rmse:
|
||||
min_rmse = error
|
||||
bestd = fts
|
||||
predictedd_best = predicted
|
||||
oc = oc + 1
|
||||
pc = pc + 1
|
||||
handles0, labels0 = ax2.get_legend_handles_labels()
|
||||
ax2.legend(handles0, labels0)
|
||||
ax3 = Axes3D(fig, rect=[0.6, 0.0, 0.45, 0.45], elev=30, azim=144)
|
||||
#ax3 = fig.add_axes([0.6, 0.0, 0.45, 0.45], projection='3d')
|
||||
ax3.set_title('Comparação dos Erros Quadráticos Médios')
|
||||
ax3.set_ylabel('RMSE')
|
||||
ax3.set_xlabel('Quantidade de Partições')
|
||||
ax3.set_zlabel('W')
|
||||
X,Y = np.meshgrid(parameters,orders)
|
||||
surf = ax3.plot_surface(X, Y, errors, rstride=1, cstride=1, antialiased=True)
|
||||
ret.append(bestd)
|
||||
ret.append(predictedd_best)
|
||||
return ret
|
||||
60
chen.py
Normal file
60
chen.py
Normal file
@ -0,0 +1,60 @@
|
||||
class FirstOrderFLRG:
|
||||
def __init__(self,premiss):
|
||||
self.premiss = premiss
|
||||
self.consequent = set()
|
||||
|
||||
def append(self,c):
|
||||
self.consequent.add(c)
|
||||
|
||||
def __str__(self):
|
||||
tmp = self.premiss + " -> "
|
||||
tmp2 = ""
|
||||
for c in self.consequent:
|
||||
if len(tmp2) > 0:
|
||||
tmp2 = tmp2 + ","
|
||||
tmp2 = tmp2 + c
|
||||
return tmp + tmp2
|
||||
|
||||
|
||||
class FirstOrderFTS(FTS):
|
||||
def __init__(self,name):
|
||||
super(FirstOrderFTS, self).__init__(1,name)
|
||||
|
||||
def defuzzy(self,data):
|
||||
|
||||
actual = self.fuzzy(data)
|
||||
|
||||
if actual["fuzzyset"] not in self.flrgs:
|
||||
return self.sets[actual["fuzzyset"]].centroid
|
||||
|
||||
flrg = self.flrgs[actual["fuzzyset"]]
|
||||
|
||||
count = 0.0
|
||||
denom = 0.0
|
||||
|
||||
for s in flrg.consequent:
|
||||
denom = denom + self.sets[s].centroid
|
||||
count = count + 1.0
|
||||
|
||||
return denom/count
|
||||
|
||||
def learn(self, data, sets):
|
||||
last = {"fuzzyset":"", "membership":0.0}
|
||||
actual = {"fuzzyset":"", "membership":0.0}
|
||||
|
||||
for s in sets:
|
||||
self.sets[s.name] = s
|
||||
|
||||
self.flrgs = {}
|
||||
count = 1
|
||||
for inst in data:
|
||||
actual = self.fuzzy(inst)
|
||||
|
||||
if count > self.order:
|
||||
if last["fuzzyset"] not in self.flrgs:
|
||||
self.flrgs[last["fuzzyset"]] = FirstOrderFLRG(last["fuzzyset"])
|
||||
|
||||
self.flrgs[last["fuzzyset"]].append(actual["fuzzyset"])
|
||||
count = count + 1
|
||||
last = actual
|
||||
|
||||
61
common.py
Normal file
61
common.py
Normal file
@ -0,0 +1,61 @@
|
||||
def trimf(x,parameters):
|
||||
if(x < parameters[0]):
|
||||
return 0
|
||||
elif(x >= parameters[0] and x < parameters[1]):
|
||||
return (x-parameters[0])/(parameters[1]-parameters[0])
|
||||
elif(x >= parameters[1] and x <= parameters[2]):
|
||||
return (parameters[2]-x)/(parameters[2]-parameters[1])
|
||||
else:
|
||||
return 0
|
||||
|
||||
def trapmf(x, parameters):
|
||||
if(x < parameters[0]):
|
||||
return 0
|
||||
elif(x >= parameters[0] and x < parameters[1]):
|
||||
return (x-parameters[0])/(parameters[1]-parameters[0])
|
||||
elif(x >= parameters[1] and x <= parameters[2]):
|
||||
return 1
|
||||
elif(x >= parameters[2] and x <= parameters[3]):
|
||||
return (parameters[3]-x)/(parameters[3]-parameters[2])
|
||||
else:
|
||||
return 0
|
||||
|
||||
def gaussmf(x,parameters):
|
||||
return math.exp(-0.5*((x-parameters[0]) / parameters[1] )**2)
|
||||
|
||||
|
||||
def bellmf(x,parameters):
|
||||
return 1 / (1 + abs((xx - parameters[2])/parameters[0])**(2*parameters[1]))
|
||||
|
||||
|
||||
def sigmf(x,parameters):
|
||||
return 1 / (1 + math.exp(-parameters[0] * (x - parameters[1])))
|
||||
|
||||
|
||||
class FuzzySet:
|
||||
|
||||
def __init__(self,name,mf,parameters,centroid):
|
||||
self.name = name
|
||||
self.mf = mf
|
||||
self.parameters = parameters
|
||||
self.centroid = centroid
|
||||
|
||||
def membership(self,x):
|
||||
return self.mf(x,self.parameters)
|
||||
|
||||
def __str__(self):
|
||||
return self.name + ": " + str(self.mf) + "(" + str(self.parameters) + ")"
|
||||
|
||||
|
||||
def GridPartitionerTrimf(data,npart,names = None,prefix = "A"):
|
||||
sets = []
|
||||
dmax = max(data)
|
||||
dmin = min(data)
|
||||
dlen = dmax - dmin
|
||||
partlen = dlen / npart
|
||||
partition = dmin
|
||||
for c in range(npart):
|
||||
sets.append( FuzzySet(prefix+str(c),trimf,[partition-partlen, partition, partition+partlen], partition ) )
|
||||
partition = partition + partlen
|
||||
|
||||
return sets
|
||||
34
hwang.py
Normal file
34
hwang.py
Normal file
@ -0,0 +1,34 @@
|
||||
class HighOrderFTS(FTS):
|
||||
def __init__(self,order,name):
|
||||
super(HighOrderFTS, self).__init__(order,name)
|
||||
|
||||
def defuzzy(self,data,t):
|
||||
cn = np.array([0.0 for k in range(len(self.sets))])
|
||||
ow = np.array([[0.0 for k in range(len(self.sets))] for z in range(self.order-1)])
|
||||
rn = np.array([[0.0 for k in range(len(self.sets))] for z in range(self.order-1)])
|
||||
ft = np.array([0.0 for k in range(len(self.sets))])
|
||||
|
||||
for s in range(len(self.sets)):
|
||||
cn[s] = self.sets[s].membership(data[t])
|
||||
for w in range(self.order-1):
|
||||
ow[w,s] = self.sets[s].membership(data[t-w])
|
||||
rn[w,s] = ow[w,s] * cn[s]
|
||||
ft[s] = max(ft[s],rn[w,s])
|
||||
mft = max(ft)
|
||||
out = 0.0
|
||||
count = 0.0
|
||||
for s in range(len(self.sets)):
|
||||
if ft[s] == mft:
|
||||
out = out + self.sets[s].centroid
|
||||
count = count + 1.0
|
||||
return out / count
|
||||
|
||||
|
||||
def learn(self, data, sets):
|
||||
self.sets = sets
|
||||
|
||||
def predict(self,data,t):
|
||||
return self.defuzzy(data,t)
|
||||
|
||||
def predictDiff(self,data,t):
|
||||
return data[t] + self.defuzzy(diferencas(data),t)
|
||||
57
ismailefendi.py
Normal file
57
ismailefendi.py
Normal file
@ -0,0 +1,57 @@
|
||||
class ImprovedWeightedFLRG:
|
||||
def __init__(self,premiss):
|
||||
self.premiss = premiss
|
||||
self.consequent = {}
|
||||
self.count = 0.0
|
||||
|
||||
def append(self,c):
|
||||
if c not in self.consequent:
|
||||
self.consequent[c] = 1.0
|
||||
else:
|
||||
self.consequent[c] = self.consequent[c] + 1.0
|
||||
self.count = self.count + 1.0
|
||||
|
||||
def weights(self):
|
||||
return np.array([ self.consequent[c]/self.count for c in self.consequent.keys() ])
|
||||
|
||||
def __str__(self):
|
||||
tmp = self.premiss + " -> "
|
||||
tmp2 = ""
|
||||
for c in self.consequent.keys():
|
||||
if len(tmp2) > 0:
|
||||
tmp2 = tmp2 + ","
|
||||
tmp2 = tmp2 + c + "(" + str(round(self.consequent[c]/self.count,3)) + ")"
|
||||
return tmp + tmp2
|
||||
|
||||
|
||||
class ImprovedWeightedFTS(FTS):
|
||||
def __init__(self,name):
|
||||
super(ImprovedWeightedFTS, self).__init__(1,name)
|
||||
|
||||
def defuzzy(self,data):
|
||||
actual = self.fuzzy(data)
|
||||
if actual["fuzzyset"] not in self.flrgs:
|
||||
return self.sets[actual["fuzzyset"]].centroid
|
||||
flrg = self.flrgs[actual["fuzzyset"]]
|
||||
mi = np.array([self.sets[s].centroid for s in flrg.consequent.keys()])
|
||||
return mi.dot( flrg.weights() )
|
||||
|
||||
def learn(self, data, sets):
|
||||
last = {"fuzzyset":"", "membership":0.0}
|
||||
actual = {"fuzzyset":"", "membership":0.0}
|
||||
|
||||
for s in sets:
|
||||
self.sets[s.name] = s
|
||||
|
||||
self.flrgs = {}
|
||||
count = 1
|
||||
for inst in data:
|
||||
actual = self.fuzzy(inst)
|
||||
|
||||
if count > self.order:
|
||||
if last["fuzzyset"] not in self.flrgs:
|
||||
self.flrgs[last["fuzzyset"]] = ImprovedWeightedFLRG(last["fuzzyset"])
|
||||
|
||||
self.flrgs[last["fuzzyset"]].append(actual["fuzzyset"])
|
||||
count = count + 1
|
||||
last = actual
|
||||
65
sadaei.py
Normal file
65
sadaei.py
Normal file
@ -0,0 +1,65 @@
|
||||
class ExponentialyWeightedFLRG:
|
||||
def __init__(self,premiss,c):
|
||||
self.premiss = premiss
|
||||
self.consequent = []
|
||||
self.count = 0.0
|
||||
self.c = c
|
||||
|
||||
def append(self,c):
|
||||
self.consequent.append(c)
|
||||
self.count = self.count + 1.0
|
||||
|
||||
def weights(self):
|
||||
wei = [ self.c**k for k in np.arange(0.0,self.count,1.0)]
|
||||
tot = sum( wei )
|
||||
return np.array([ k/tot for k in wei ])
|
||||
|
||||
def __str__(self):
|
||||
tmp = self.premiss + " -> "
|
||||
tmp2 = ""
|
||||
cc = 0
|
||||
wei = [ self.c**k for k in np.arange(0.0,self.count,1.0)]
|
||||
tot = sum( wei )
|
||||
for c in self.consequent:
|
||||
if len(tmp2) > 0:
|
||||
tmp2 = tmp2 + ","
|
||||
tmp2 = tmp2 + c + "(" + str(wei[cc]/tot) + ")"
|
||||
cc = cc + 1
|
||||
return tmp + tmp2
|
||||
|
||||
class ExponentialyWeightedFTS(FTS):
|
||||
def __init__(self,name):
|
||||
super(ExponentialyWeightedFTS, self).__init__(1,name)
|
||||
|
||||
def defuzzy(self,data):
|
||||
|
||||
actual = self.fuzzy(data)
|
||||
|
||||
if actual["fuzzyset"] not in self.flrgs:
|
||||
return self.sets[actual["fuzzyset"]].centroid
|
||||
|
||||
flrg = self.flrgs[actual["fuzzyset"]]
|
||||
|
||||
mi = np.array([self.sets[s].centroid for s in flrg.consequent])
|
||||
|
||||
return mi.dot( flrg.weights() )
|
||||
|
||||
def learn(self, data, sets):
|
||||
last = {"fuzzyset":"", "membership":0.0}
|
||||
actual = {"fuzzyset":"", "membership":0.0}
|
||||
|
||||
for s in sets:
|
||||
self.sets[s.name] = s
|
||||
|
||||
self.flrgs = {}
|
||||
count = 1
|
||||
for inst in data:
|
||||
actual = self.fuzzy(inst)
|
||||
|
||||
if count > self.order:
|
||||
if last["fuzzyset"] not in self.flrgs:
|
||||
self.flrgs[last["fuzzyset"]] = ExponentialyWeightedFLRG(last["fuzzyset"],2)
|
||||
|
||||
self.flrgs[last["fuzzyset"]].append(actual["fuzzyset"])
|
||||
count = count + 1
|
||||
last = actual
|
||||
63
yu.py
Normal file
63
yu.py
Normal file
@ -0,0 +1,63 @@
|
||||
class WeightedFLRG(FTS):
|
||||
def __init__(self,premiss):
|
||||
self.premiss = premiss
|
||||
self.consequent = []
|
||||
self.count = 1.0
|
||||
|
||||
def append(self,c):
|
||||
self.consequent.append(c)
|
||||
self.count = self.count + 1.0
|
||||
|
||||
def weights(self):
|
||||
tot = sum( np.arange(1.0,self.count,1.0) )
|
||||
return np.array([ k/tot for k in np.arange(1.0,self.count,1.0) ])
|
||||
|
||||
def __str__(self):
|
||||
tmp = self.premiss + " -> "
|
||||
tmp2 = ""
|
||||
cc = 1.0
|
||||
tot = sum( np.arange(1.0,self.count,1.0) )
|
||||
for c in self.consequent:
|
||||
if len(tmp2) > 0:
|
||||
tmp2 = tmp2 + ","
|
||||
tmp2 = tmp2 + c + "(" + str(round(cc/tot,3)) + ")"
|
||||
cc = cc + 1.0
|
||||
return tmp + tmp2
|
||||
|
||||
|
||||
class WeightedFTS(FTS):
|
||||
def __init__(self,name):
|
||||
super(WeightedFTS, self).__init__(1,name)
|
||||
|
||||
def defuzzy(self,data):
|
||||
|
||||
actual = self.fuzzy(data)
|
||||
|
||||
if actual["fuzzyset"] not in self.flrgs:
|
||||
return self.sets[actual["fuzzyset"]].centroid
|
||||
|
||||
flrg = self.flrgs[actual["fuzzyset"]]
|
||||
|
||||
mi = np.array([self.sets[s].centroid for s in flrg.consequent])
|
||||
|
||||
return mi.dot( flrg.weights() )
|
||||
|
||||
def learn(self, data, sets):
|
||||
last = {"fuzzyset":"", "membership":0.0}
|
||||
actual = {"fuzzyset":"", "membership":0.0}
|
||||
|
||||
for s in sets:
|
||||
self.sets[s.name] = s
|
||||
|
||||
self.flrgs = {}
|
||||
count = 1
|
||||
for inst in data:
|
||||
actual = self.fuzzy(inst)
|
||||
|
||||
if count > self.order:
|
||||
if last["fuzzyset"] not in self.flrgs:
|
||||
self.flrgs[last["fuzzyset"]] = WeightedFLRG(last["fuzzyset"])
|
||||
|
||||
self.flrgs[last["fuzzyset"]].append(actual["fuzzyset"])
|
||||
count = count + 1
|
||||
last = actual
|
||||
Loading…
Reference in New Issue
Block a user