def call_knn(data):
print("KNN-----------------")
print("accuracy: " , knn.calculate_accuracy( data ) )
#calculate the best k in [3,15]
accuracies = []
ks = []
posCounter=0
for i in range(3,16):
accuracies.append(knn.calculate_accuracy( data, i))
ks.append(i)
#calculate the best accuracy and print the k
maxAc = accuracies[0]
maxK = ks[0]
for i in range(1,len(accuracies)):
if (accuracies[i] > maxAc):
maxAc=accuracies[i]
maxK=ks[i]
bestk = maxK
print("The best k is: ", bestk)
def flores_data_set_run():
data_set = arff.load(open('./data/flores.arff'))
test_set, train_set = list_helper.separate_list(data_set['data'], 10)
results = []
for x in range(len(test_set)):
result = knn.classify(train_set, test_set[x][:-1])
results.append(result)
print "Real: " + test_set[x][-1] + " - Predicted: " + result
accuracy = knn.calculate_accuracy(test_set, results)
print "Accuracy: " + str(accuracy) + "%"
def prediccion_data_set_run():
data_set = arff.load(open('./data/prediccion.arff'))
cleaned_data_set = [list_helper.unicode_to_int(x) for x in data_set['data']]
test_set, train_set = list_helper.separate_list(cleaned_data_set, 1)
results = []
for x in range(len(test_set)):
result = knn.classify(train_set, test_set[x][:-1])
results.append(result)
print "Real: " + str(test_set[x][-1]) + " - Predicted: " + str(result)
accuracy = knn.calculate_accuracy(test_set, results)
print "Accuracy: " + str(accuracy) + "%"