for bone in range(0, 2):
for joint in range(0, 3):
X[row, col] = set1[finger, bone, joint, row]
X[row + 1000, col] = set2[finger, bone, joint, row]
col = col + 1
return X, y
trainM = ReduceData(trainM)
trainN = ReduceData(trainN)
testM = ReduceData(testM)
testN = ReduceData(testN)
trainM = CenterData(trainM)
trainN = CenterData(trainN)
testM = CenterData(testM)
testN = CenterData(testN)
trainX, trainy = ReshapeData(trainM, trainN)
testX, testy = ReshapeData(testM, testN)
knn.Use_K_Of(15)
knn.Fit(trainX, trainy)
counter = 0
for row in range(2000):
prediction = knn.Predict(testX[row, :])
if prediction == testy[row]:
counter = counter + 1
print((counter / 2000) * 100)
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]
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, :]))
if prediction == int(testy[row, 0]):
counter += 1
print("Number correct is %s" % str(counter))
