test_3b,
test_4,
test_5,
test_5b,
test_6,
test_6b,
test_7, #test_7b,
test_8, #test_8b,
test_9
) #test_9b) #, test_7b) #test2_a, train3_a, test4_a, test5_a, test6_a, test7_a, test8_a, test9_a)
# for when each digit has 3 sets
# trainX, trainy = ReshapeData(train0_a, train0_b, train0_c, train1_a, train1_b, train1_c, train2_a, train2_b, train2_c, train3_a, train3_b, train3_c, train4_a, train4_b, train4_c, train5_a, train5_b, train5_c, train6_a, train6_b, train6_c, train7_a, train7_b, train7_c, train8_a, train8_b, train8_c, train9_a, train9_b, train9_c)
# testX, testy = ReshapeData(test0_a, test0_b, test0_c, test1_a, test1_b, test1_c, test2_a, test2_b, test2_c, test3_a, test3_b, test3_c, test4_a, test4_b, test4_c, test5_a, test5_b, test5_c, test6_a, test6_b, test6_c, test7_a, test7_b, test7_c, test8_a, test8_b, test8_c, test9_a, test9_b, test9_c)
knn.Use_K_Of(100) # originally 15
knn.Fit(trainX, trainy)
counter = 0
print(testX)
print(testy)
print(testX.shape, testy.shape)
count0 = 0
count1 = 0
count2 = 0
count3 = 0
count4 = 0
count5 = 0
count6 = 0
count7 = 0
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)
testX, testy = ReshapeData(test0_a, test0_b, test0_c,
test1_a, test1_b, test1_c,
test2_a, test2_b, test2_c,
#test3_a,
test4_a, test4_b, test4_c,
test5_a, test5_b, test5_c,
test6_a, test6_b, test6_c,
test7_a, test7_b, test7_c,
test8_a, test8_b, test8_c,
test9_a, test9_b, test9_c)
# for when each digit except for 3 gets 3 sets, because 3 is being a little shit
knn.Use_K_Of(175) # originally 15
knn.Fit(trainX, trainy)
counter = 0
print(testX)
print(testy)
print(testX.shape, testy.shape)
count0 = 0
count1 = 0
count2 = 0
count3 = 0
count4 = 0
count5 = 0
count6 = 0
count7 = 0