In Naive Bayes, we use probabilities of particular features values rather than calculating the probability of the Evidence as a whole. We do this because we assume that there will be many occurrences of each e_i. However, if those occurrence counts are small (or zero!) our probabilities are likely to be underestimated. We've seen this problem before in other methods for class probability estimation. Use the same correction method to calculate an estimated p(e_i) given that the count of e_i in your training data is just 1 out of the 1000 training examples.

Write a program in plp assembly that counts up by one starting from zero (or one) inside a loop and writes this value to the leds every time the value is increased. the memory address of the leds is 0xf0200000. the table below shows the meaning and an example usage of the instructions covered in the video, plp instructions for project 1. instruction example usage meaning load immediate li $t0, 8 register $t0 is set to the value, 8. store word sw $t2, 0($t1) the value in register $t1 is used as the memory address. the value in register $t2 is copied into this memory address. add addiu $t4, $t3, 29 register $t4 is assigned the sum of 29 and the value in register $t3. jump j your_label_name the program jumps to the line following the label, "your_label_name: ". label your label name: defines a label called "your_label_name: " that can be jumped to