From 3c8dc84f7b8409ca1a7171e58311d2bb22340a05 Mon Sep 17 00:00:00 2001
From: Serhii Hromov <serhro@st.amu.edu.pl>
Date: Mon, 18 May 2020 14:38:44 +0000
Subject: [PATCH] Update

---
 raport.md | 57 +++++++++++++++++++++++++++++++++++++++++++------------
 1 file changed, 45 insertions(+), 12 deletions(-)

diff --git a/raport.md b/raport.md
index 51782e6..0d195e9 100644
--- a/raport.md
+++ b/raport.md
@@ -23,6 +23,7 @@ Potrawy, ich nazwa, rodzaj oraz charakterystyka.
 
 
 Dane uczące:
+    
     training_data = [
         ['meat','hot','Pork'],
         ['salad','cold','Greek Salad'],
@@ -30,30 +31,62 @@ Dane uczące:
         ['drink','hot','Latte'],
         ['drink','cold','Green Tea'],
         ['meal','hot','Pizza'],
+        ['meal','cold','Wheat Pita'],
     ]
 
 
 
-Dane testowe: 
-    test_data = [
-        ['meat','hot','Latte'],
-        ['salad','hot','Greek Salad'],
-        ['drink','hot','Pork'],
-        ['drink','cold','Green Tea'],
-        ['drink','hot','Greek Salad'],
-    ]
+Dane testowe jest tworzone losowo w funkcji:  
+ 
+    def client_ordering():
+        order = []
+
+        dish = uniq_val_from_data(training_data, 0)
+        temperature = uniq_val_from_data(training_data, 1)
+
+        tmpr = random.sample(dish, 1)
+        order.append(tmpr[0])
+
+        tmpr = random.sample(temperature, 1)
+        order.append(tmpr[0])
+        order.append('order')
+        return order
+   
 
 
 
 
 ### Implementacja
 
-Główna część:
+####Drzewo:
 
-    In process
+Klasy: 
 
-    ...
-    
+#####Question
+    class Queestion:
+        def __init__(self, col, value):
+            self.col = col      #column
+            self.value = value  #value of column
+     
+        def compare(self, example):
+        #compare val in example with val in the question
+            
+        def __repr__(self):
+        #just to print
+        
+#####Node
+    class Decision_Node():
+    #contain the question and child nodes
+    def __init__(self, quest, t_branch, f_branch):
+        self.quest = quest
+        self.t_branch = t_branch
+        self.f_branch = f_branch
+        
+#####Leaf
+    class Leaf:
+    #contain a number of how many times the label has appeared in dataset
+    def __init__(self, rows):
+        self.predicts = uniq_count(rows)
 
  
 ### Biblioteki