Your tree output should be of the form:

	feature 0 == x
       		feature 1 == y
         		feature 2 == z
                  		class = +
         		feature 2 == k
                  		class = -
		feature 1 != y
			class = +
	feature 0 != x
		feature 1 == r:
			class = +
		feature 1 != r:
			class = -

In addition to this display function, your code should include a function
that reads this format, applies it to the data, and prints an output file
such as:

	Test 	Cases	True	False
	+	100	70	5 
	-	50	45	30  


Record your output for Part 1 as:
	part-1-solution.{c|cpp|java}	Source code of your program
	part-1-solution.txt		1-paragraph report/explanation
        part-1-train-results.txt	Table for full training set (training)
        part-1-test-results.txt		Table for full training set (test)
        part-1-train-160-results.txt	Table for 160 examples (training)
        part-1-test-160-results.txt	Table for 160 examples (test)
        part-1-train-80-results.txt	Table for 80 examples (training)
        part-1-test-80-results.txt	Table for 80 examples (test)
        part-1-train-40-results.txt	Table for 40 examples (training)
        part-1-test-40-results.txt	Table for 40 examples (test)
        part-1-train-20-results.txt	Table for 20 examples (training)
        part-1-test-20-results.txt	Table for 20 examples (test)
        part-1-train-d3-results.txt	Table for max depth 3 (training)
        part-1-test-d3-results.txt	Table for max depth 3 (test)
        part-1-train-d5-results.txt	Table for max depth 5 (training)
        part-1-test-d5-results.txt	Table for max depth 5 (test)
        part-1-train-d7-results.txt	Table for max depth 7 (training)
        part-1-test-d7-results.txt	Table for max depth 7 (test)
	part-1-curve.txt		Learning curve plot