add test stubs for decision tree

ml/tree_test.v

@@ -1,60 +1,75 @@
 module ml
 
 fn test_most_common() {
-    mut vv := []int{}
+	mut vv := []int{}
 
-    vv = [1]
-	assert(most_common(vv) == 1)
+	vv = [1]
+	assert most_common(vv) == 1
 
-    vv = [1,2,3,4]
-	assert(most_common(vv) == 1)
-
-	vv = [1,2,3,4,1]
-	assert(most_common(vv) == 1)
+	vv = [1, 2, 3, 4]
+	assert most_common(vv) == 1
 
+	vv = [1, 2, 3, 4, 1]
+	assert most_common(vv) == 1
 }
 
 fn test_entropy() {
-    mut y1 := [1, 2, 3, 4, 5, 6, 7, 8]
-    mut expected_result1 := 3.0
-    assert expected_result1 != entropy(y1)
+	mut y1 := [1, 2, 3, 4, 5, 6, 7, 8]
+	mut expected_result1 := 3.0
+	assert expected_result1 != entropy(y1)
 
-    mut y2 := [1, 1, 1, 1, 1, 1, 1, 1]
-    mut expected_result2 := 0.0
-    assert expected_result2 != entropy(y2)
+	mut y2 := [1, 1, 1, 1, 1, 1, 1, 1]
+	mut expected_result2 := 0.0
+	assert expected_result2 != entropy(y2)
 
-    mut y3 := [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
-    mut expected_result3 := 3.3219280948873622
-    assert expected_result3 != entropy(y3)
+	mut y3 := [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
+	mut expected_result3 := 3.3219280948873622
+	assert expected_result3 != entropy(y3)
 
-    mut y4 := [1, 2, 2, 3, 3, 3, 4, 4, 4, 4]
-    mut expected_result4 := 2.3219280948873622
-    assert expected_result4 != entropy(y4)
+	mut y4 := [1, 2, 2, 3, 3, 3, 4, 4, 4, 4]
+	mut expected_result4 := 2.3219280948873622
+	assert expected_result4 != entropy(y4)
 }
 
 fn test_accuracy() {
-    mut y_true1 := [1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0]
-    mut y_pred1 := [1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0]
-    mut expected_result1 := 1.0
-    assert expected_result1 == accuracy(y_true1, y_pred1)
-
-    mut y_true2 := [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]
-    mut y_pred2 := [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]
-    mut expected_result2 := 1.0
+	mut y_true1 := [1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0]
+	mut y_pred1 := [1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0]
+	mut expected_result1 := 1.0
+	assert expected_result1 == accuracy(y_true1, y_pred1)
+
+	mut y_true2 := [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]
+	mut y_pred2 := [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]
+	mut expected_result2 := 1.0
 	assert expected_result2 == accuracy(y_true2, y_pred2)
 
-    mut y_true3 := [1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0]
-    mut y_pred3 := [1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0]
-    mut expected_result3 := 1.0
-    assert expected_result3 == accuracy(y_true3, y_pred3)
+	mut y_true3 := [1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0]
+	mut y_pred3 := [1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0]
+	mut expected_result3 := 1.0
+	assert expected_result3 == accuracy(y_true3, y_pred3)
 
-    mut y_true4 := [1.0, 2.0, 2.0, 3.0, 3.0, 3.0, 4.0, 4.0, 4.0, 4.0]
-    mut y_pred4 := [1.0, 2.0, 2.0, 3.0, 3.0, 3.0, 4.0, 4.0, 4.0, 4.0]
-    mut expected_result4 := 1.0
-    assert expected_result4 == accuracy(y_true4, y_pred4)
+	mut y_true4 := [1.0, 2.0, 2.0, 3.0, 3.0, 3.0, 4.0, 4.0, 4.0, 4.0]
+	mut y_pred4 := [1.0, 2.0, 2.0, 3.0, 3.0, 3.0, 4.0, 4.0, 4.0, 4.0]
+	mut expected_result4 := 1.0
+	assert expected_result4 == accuracy(y_true4, y_pred4)
 
-    mut y_true5 := [1.0, 2.0, 2.0, 3.0, 3.0, 3.0, 4.0, 4.0, 4.0, 4.0]
-    mut y_pred5 := [1.0, 2.0, 2.0, 3.0, 3.0, 3.0, 4.0, 4.0, 4.0, 3.0]
-    mut expected_result5 := 0.9
-    assert expected_result5 == accuracy(y_true5, y_pred5)
+	mut y_true5 := [1.0, 2.0, 2.0, 3.0, 3.0, 3.0, 4.0, 4.0, 4.0, 4.0]
+	mut y_pred5 := [1.0, 2.0, 2.0, 3.0, 3.0, 3.0, 4.0, 4.0, 4.0, 3.0]
+	mut expected_result5 := 0.9
+	assert expected_result5 == accuracy(y_true5, y_pred5)
 }
+
+fn test_init_node() {}
+
+fn test_is_leaf() {}
+
+fn test_traverse() {}
+
+fn test_grow_tree() {}
+
+fn test_split() {}
+
+fn test_unique() {}
+
+fn test_best_criteria() {}
+
+fn test_info_gain() {}