X[:, :, 2, :] = allZCoordinates - meanValue
return X
train3 = ReduceData(train3)
train4 = ReduceData(train4)
test3 = ReduceData(test3)
test4 = ReduceData(test4)
train3 = CenterData(train3)
train4 = CenterData(train4)
test3 = CenterData(test3)
test4 = CenterData(test4)
trainX, trainy = ReshapeData(train3, train4)
testX, testy = ReshapeData(test3, test4)
knn = knn.KNN()
knn.Use_K_Of(15)
knn.Fit(trainX, trainy)
correctPredictions = 0
for row in range(0, 2000):
actualClass = testy[row]
prediction = knn.Predict(testX[row])
if (actualClass == prediction):
correctPredictions = correctPredictions + 1
print(correctPredictions)
print((correctPredictions / 2000) * 100)
plt.figure()
[numItems, numFeatures] = knn.data.shape
for i in range(0, numItems / 2):
itemClass = int(trainy[i])
currColor = colors[itemClass, :]
plt.scatter(trainX[i, 0],
trainX[i, 1],
facecolor=currColor,
s=50,
lw=2,
edgecolor=[0, 0, 0])
numCorrect = 0
for i in range(0, numItems / 2):
itemClass = int(testy[i])
currColor = colors[itemClass, :]
prediction = int(knn.Predict(testX[i, :]))
if itemClass == prediction:
numCorrect = numCorrect + 1
edgeColor = colors[prediction, :]
plt.scatter(testX[i, 0],
testX[i, 1],
facecolor=currColor,
s=50,
lw=2,
edgecolor=edgeColor)
print(numCorrect)
print(str((float(numCorrect) / float(numItems / 2)) * 100.0) + '%')
# plt.scatter(x, y, c=trainy)
# plt.scatter(xTest, yTest, c=trainy)
plt.show()
print(testX.shape, testy.shape)
count0 = 0
count1 = 0
count2 = 0
count3 = 0
count4 = 0
count5 = 0
count6 = 0
count7 = 0
count8 = 0
count9 = 0
for row in range(0, 15000): # normally 0:20000
itemClass = int(testy[row])
prediction = int(knn.Predict(testX[row, :]))
actualClass = testy[row]
print(prediction, itemClass)
if actualClass == prediction:
counter += 1
if actualClass == 0:
count0 = count0 + 1
if actualClass == 1:
count1 = count1 + 1
if actualClass == 2:
count2 = count2 + 1
if actualClass == 3:
count3 = count3 + 1
if actualClass == 4:
count4 = count4 + 1
if actualClass == 5:
x = knn.data[:, 0]
y = knn.data[:, 1]
trainX = knn.data[::2, 1:3]
trainy = knn.target[::2]
testX = knn.data[1::2, 1:3]
testy = knn.target[1::2]
knn.Use_K_Of(15)
knn.Fit(trainX, trainy)
correct = 0
for i in range(0, 75):
actualClass = testy[i]
prediction = knn.Predict(testX[i, 0:2])
if (actualClass == prediction):
correct = correct + 1
print((correct / float(len(testX))) * 100)
colors = np.zeros((3, 3), dtype='f')
colors[0, :] = [1, 0.5, 0.5]
colors[1, :] = [0.5, 1, 0.5]
colors[2, :] = [0.5, 0.5, 1]
plt.figure()
# plt.scatter(trainX[:,0],trainX[:,1],c=trainy)
# plt.scatter(testX[:,0],testX[:,1],c=testy)
[numItems, numFeatures] = knn.data.shape
for i in range(0, numItems / 2):
itemClass = int(trainy[i])