Fixed tree

main.py

@@ -30,13 +30,23 @@ EAT_TIME = 15
 
 #### Menu
 menu = Context.fromstring(''' |meat|salad|meal|drink|cold|hot |
-                   Pork       |  X |     |  X |     |    |  X |
+                   Pork       |  X |     |    |     |    |  X |
                    Espresso   |    |     |    |  X  |    |  X |
                    Green Tea  |    |     |    |  X  | X  |    |
-                   Greek Salad|    |  X  |  X |     | X  |    |
+                   Greek Salad|    |  X  |    |     | X  |    |
                    Pizza      |    |     |  X |     |    |  X |''')
 
 
+training_data = [
+['meat','hot','Pork'],
+['salad','cold','Greek Salad'],
+['drink','hot','Espresso'],
+['drink','cold','Green Tea'],
+['meal','hot','Pizza'],
+]
+
+tree_format = ["dish", "temperature", "label"]
+
 #menu.lattice.graphviz()
 #Digraph.render('Lattice.gv', view=True)
 
@@ -46,7 +56,6 @@ menu = Context.fromstring(''' |meat|salad|meal|drink|cold|hot |
 
 #print(func_output)
 
-'''
 def uniq_val_from_data(rows, col):
     return set([row[col] for row in rows])
 
@@ -65,16 +74,14 @@ def isnumer(value):
     return isinstance(value, int) or isinstance(value, float)
 
 
-header = ...
-
 class Question():
 
-    def __init__(self, column, value):
-        self.column = column
+    def __init__(self, col, value):
+        self.col = col
         self.value = value
         
     def compare(self, example):
-        val = example[self.column]
+        val = example[self.col]
         if isnumer(val):
             return val >= self.value
         else:
@@ -83,14 +90,14 @@ class Question():
     def __repr__(self):
         condition = "=="
         if isnumer(self.value):
-        condition = ">="
-        return "Is %s %s %s?" % (header[self.column], condition, str(self.value))
+            condition = ">="
+        return "Is %s %s %s?" % (tree_format[self.col], condition, str(self.value))
     
 
 def partition(rows, quest):
     t_rows, f_rows = [], []
-    for rows in rows:
-        if quest.compare(row)
+    for row in rows:
+        if quest.compare(row):
             t_rows.append(row)
         else:
             f_rows.append(row)
@@ -101,12 +108,12 @@ def gini(rows):
     counts = class_counts(rows)    
     impurity = 1
     for lbl in counts:
-        prob_of_lbl = counts[lbl] / float(lem(rows))
+        prob_of_lbl = counts[lbl] / float(len(rows))
         impurity -= prob_of_lbl**2
     return impurity
   
     
-def info_gain(l,r, current_uncertainty):
+def info_gain(l, r, current_uncertainty):
     p = float(len(l)) / (len(l) + len(r))    
     return current_uncertainty - p*gini(l) - (1-p)*gini(r)
 
@@ -115,29 +122,29 @@ def find_best_q(rows):
     best_gain = 0
     best_quest = None
     current_uncertainty = gini(rows)
-    n_features = len(rows[0]) - 1
+    n_feat = len(rows[0]) - 1
     
     for col in range(n_feat):
-        values = set([row[col] for row in rows])
+        vals = set([row[col] for row in rows])
         
-        for cal in values:
+        for val in vals:
             quest = Question(col, val)
             
             t_rows, f_rows = partition(rows, quest)
             
-            if len(t_rows) == 0 or len(f_rows) == 0Ж
+            if len(t_rows) == 0 or len(f_rows) == 0:
                 continue
                 
-            fain = info_gain(t_rows, f_rows, current_uncertainty)
+            gain = info_gain(t_rows, f_rows, current_uncertainty)
             
-            if gain >= best gain:
+            if gain >= best_gain:
                 best_gain, best_quest = gain, quest
                 
     return best_gain, best_quest
     
     
 class Leaf:
-    def __init__(self,rows):
+    def __init__(self, rows):
         self.predicts = class_counts(rows)    
     
     
@@ -148,7 +155,7 @@ class Decision_Node():
         self.f_branch = f_branch
    
 
-def build_tree():   
+def build_tree(rows):
     gain, quest = find_best_q(rows)
     
     if gain == 0:
@@ -162,22 +169,22 @@ def build_tree():
     return Decision_Node(quest, t_branch, f_branch)
     
 
-def print_tree(node):
+def print_tree(node, spc=""):
     
-    if isinstance(node, leaf):
-        print("" + "Predict", node.predictions)
+    if isinstance(node, Leaf):
+        print("    " + "Predict", node.predicts)
         return
     
     print("" + str(node.quest))
     
     print("" + '--> True:')
-    print_tree(node.t_branch, ""+ "  ")
+    print_tree(node.t_branch, spc + "  ")
     
     print("" + '--> False:')
-    print_tree(node.f_branch,"" + "  ")
+    print_tree(node.f_branch, spc + "  ")
     
 def classify(row, node):
-    if isinstance(node, leaf):
+    if isinstance(node, Leaf):
         return node.predictions
         
     if node.quest.compare(row):
@@ -194,7 +201,12 @@ def print_leaf(counts):
     return probs
 
 
-'''
+
+#print(menu.extension(['meal',]))
+
+tree = build_tree(training_data)
+print_tree(tree)
+
 
 ###
 class Node: