test2

num = len(X_train) # Get the length of our training data
no_errors = 0 # Keep track of the number of errors
distance = np.zeros(num) # Create an array the length of X_trains, filled with zeros
for j in range(1697, 1797):
    X_test = digits.data[j] # Test values in the range [1697, 1797)
    for i in range(num):
        distance[i] = dist(X_train[i], X_test) # Compute distance from X_train[i] to X_test
    min_index = np.argmin(distance) # Get the index of the minimum distance
    if Y_train[min_index] != digits.target[j]: # If the actual label is not the same as the nearest neighbor, add a count to the number of errors
        no_errors += 1
print(no_errors)
37