test9 = CenterData(test9)
trainX, trainY = ReshapeData(train0, train1, train2, train3, train4, train5,
train6, train7, train8, train9)
testX, testY = ReshapeData(test0, test1, test2, test3, test4, test5, test6,
test7, test8, test9)
#print(trainX)
#print(trainY)
#print(trainX.shape)
#print(trainY.shape)
#print(testX)
#print(testY)
#print(testX.shape)
#print(testY.shape)
knn.Use_K_Of(50)
knn.Fit(trainX, trainY)
wrongPrediction = 0
for row in range(0, 1000):
prediction = knn.Predict(testX[row])
actual = testY[row]
if (prediction != actual):
wrongPrediction = wrongPrediction + 1
print(str(actual) + " " + str(prediction))
print("Wrong Predictions: ", wrongPrediction)
pickle.dump(knn, open('userData/classifier.p', 'wb'))
#[numItemsTrain, numFeatures] = knn.data.shape
trainX = knn.data[::2, 1:3] trainY = knn.target[::2] #print(trainX[0]) #print(trainY) #x = [0,1,2,3,4,5] #y = [0,1,2,3,4,5] #co = [0,0,0,1,1,1] #print(x) #0:2 testX = knn.data[1::2, 1:3] testY = knn.target[1::2] #print(testX) #print(testY) knn.Use_K_Of(15) knn.Fit(trainX, trainY) [numItemsTrain, numFeatures] = knn.data.shape #for i in range(0,numItems/2): # actualClass = testY[i] # prediction = knn.Predict(testX[i,0:2]) # print(actualClass, prediction) #plt.figure() #plt.scatter(x,y,c=knn.target) #plt.show() 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]
test3 = CenterData(test3)
test4 = CenterData(test4)
test5 = CenterData(test5)
test6 = CenterData(test6)
test7 = CenterData(test7)
test8 = CenterData(test8)
test9 = CenterData(test9)
# add new one
trainX, trainy = ReshapeData(train0, train1, train2, train3, train4, train5,
train6, train7, train8, train9)
testX, testy = ReshapeData(test0, test1, test2, test3, test4, test5, test6,
test7, test8, test9)
knn = KNN()
knn.Use_K_Of(25)
trainX = trainX.astype(int)
trainy = trainy.astype(int)
knn.Fit(trainX, trainy[:, 0])
actualClass = testy[0]
prediction = knn.Predict(testX[0, :])
#print(actualClass, prediction)
#prediction = knn.Predict(testX[1])
#print(prediction)
counter = 0
# add 1000
for row in range(0, 10000):
prediction = int(knn.Predict(testX[row, :]))
