def _LVQ3(relPath, columns, resultColumn):
dataSet = r.readDataSet(relPath, columns)
trainingSets = []
avaliationSets = []
kfold = kc(dataSet, 10, resultColumn, True)
kfold.run(trainingSets, avaliationSets, stratified=True)
dataSet = dataSet.apply(pd.to_numeric)
tset = []
aset = []
for i in range(len(trainingSets)):
print("\n")
print(" --------- FOLD " + str(i + 1) + " ----------------")
tset = []
aset = []
for index, row in dataSet.iterrows():
tupla = (dataSet.iloc[index][resultColumn], index)
if tupla in trainingSets[i]:
tset.append(row.tolist())
if tupla in avaliationSets[i]:
aset.append(row.tolist())
print("------------- SIMPLE KNN ----------------")
k = Knn(tset, 3)
k.test(aset)
lvq = LVQ3(tset, resultColumn)
newtset = lvq.run()
print("-------------- LVQ3 ----------------------")
k = Knn(newtset, 3)
k.test(aset)
def makeGraph(relPath, columns, resultColumn,k ,tp):
dataSet = r.readDataSet(relPath, columns)
trainingSets = []
avaliationSets = []
kfold = kc(dataSet, k, resultColumn, True)
kfold.run(trainingSets, avaliationSets, stratified = True)
dataSet = dataSet.apply(pd.to_numeric)
ks = [1,2,3,5,7,9,11,13,15]
means = []
for j in ks:
print("Using k = " + str(j))
correctPercentage = 0
for i in range(len(trainingSets)):
tset=[]
aset=[]
for index, row in dataSet.iterrows():
tupla = (dataSet.iloc[index][resultColumn], index)
if tupla in trainingSets[i]:
tset.append(row.tolist())
if tupla in avaliationSets[i]:
aset.append(row.tolist())
k = Knn(tset, j, tp = tp)
correctPercentage += k.test(aset)
generalMean = correctPercentage / len(trainingSets)
means.append(generalMean)
matplotlib.pyplot.plot(ks, means)
matplotlib.pyplot.show()
def simpleKnn(relPath, columns, resultColumn,k ,tp):
dataSet = r.readDataSet(relPath, columns)
trainingSets = []
avaliationSets = []
kfold = kc(dataSet, k, resultColumn, True)
kfold.run(trainingSets, avaliationSets, stratified = True)
dataSet = dataSet.apply(pd.to_numeric)
for i in range(len(trainingSets)):
tset=[]
aset=[]
for index, row in dataSet.iterrows():
tupla = (dataSet.iloc[index][resultColumn], index)
if tupla in trainingSets[i]:
tset.append(row.tolist())
if tupla in avaliationSets[i]:
aset.append(row.tolist())
k = Knn(tset, 1, tp = tp)
k.test(aset)
def _makeGraph(relPath, columns, resultColumn):
dataSet = r.readDataSet(relPath, columns)
trainingSets = []
avaliationSets = []
kfold = kc(dataSet, 10, resultColumn, True)
kfold.run(trainingSets, avaliationSets, stratified=True)
dataSet = dataSet.apply(pd.to_numeric)
ks = [1, 3]
nPrototypes = [3, 5, 10, 20]
for k in ks:
meansGeral = []
meansFalse = []
meansTrue = []
for j in nPrototypes:
correctnessPercentage = 0
correctTrue = 0
correctFalse = 0
for i in range(len(trainingSets)):
print("\n")
print(" --------- FOLD " + str(i + 1) + " ----------------")
tset = []
aset = []
for index, row in dataSet.iterrows():
tupla = (dataSet.iloc[index][resultColumn], index)
if tupla in trainingSets[i]:
tset.append(row.tolist())
if tupla in avaliationSets[i]:
aset.append(row.tolist())
lvq = LVQ3(tset, resultColumn)
newtset = lvq.run(nPrototypes=j)
kn = Knn(newtset, k)
result = kn.test(aset)
correctnessPercentage += result[0]
classErrors = result[1]
classNumbers = result[2]
correctFalse += (
classErrors[False] /
classNumbers[False]) if False in classErrors.keys() else 0
correctTrue += (
classErrors[True] /
classNumbers[True]) if True in classErrors.keys() else 0
meansGeral.append(correctnessPercentage / len(trainingSets))
meansFalse.append(correctFalse / len(trainingSets))
meansTrue.append(correctTrue / len(trainingSets))
plt.ylim(0, 1)
plt.plot(nPrototypes, meansGeral, 'r', label='general')
plt.plot(nPrototypes, meansFalse, 'g', label='false')
plt.plot(nPrototypes, meansTrue, 'b', label='true')
plt.legend(loc='upper left')
plt.show()