def test(data, k):
random.shuffle(data)
pts, labels = column(data, 0), column(data, 1)
trainingData = pts[:800]
trainingLabels = labels[:800]
testData = pts[800:]
testLabels = labels[800:]
f = knn.makeKNNClassifier(trainingData, trainingLabels, k, knn.euclideanDistance)
correct = 0.0
total = len(testLabels)
for (point, label) in zip(testData, testLabels):
if f(point) == label:
correct += 1
return correct/total
def test(data, k):
random.shuffle(data)
pts, labels = column(data, 0), column(data, 1)
trainingData = pts[:800]
trainingLabels = labels[:800]
testData = pts[800:]
testLabels = labels[800:]
f = knn.makeKNNClassifier(trainingData, trainingLabels, k,
knn.euclideanDistance)
correct = 0.0
total = len(testLabels)
for (point, label) in zip(testData, testLabels):
if f(point) == label:
correct += 1
return correct / total
def test(data, k):
random.seed(2003892049)
random.shuffle(data)
pts, labels = column(data, 0), column(data, 1)
trainingData = pts[:800]
trainingLabels = labels[:800]
testData = pts[800:]
testLabels = labels[800:]
f = knn.makeKNNClassifier(trainingData, trainingLabels, k, knn.mEp4)
correct = 0.0
total = len(testLabels)
t1 = time.time()
for (point, label) in zip(testData, testLabels):
if f(point) == label:
correct += 1
print "Total time: {0} s\r".format(round(time.time() - t1, 2)),
sys.stdout.flush()
return correct/total
