From 9455e783a4d50b1fed4e670a00fa6ce5868b039c Mon Sep 17 00:00:00 2001
From: Serhii Hromov <serhro@st.amu.edu.pl>
Date: Mon, 18 May 2020 12:50:27 +0000
Subject: [PATCH] =?UTF-8?q?=D0=9E=D0=BD=D0=BE=D0=B2=D0=B8=D1=82=D0=B8=20'r?=
 =?UTF-8?q?aport.md'?=
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---
 raport.md | 74 +++++++++++++++++++------------------------------------
 1 file changed, 25 insertions(+), 49 deletions(-)

diff --git a/raport.md b/raport.md
index e95c54a..51782e6 100644
--- a/raport.md
+++ b/raport.md
@@ -2,79 +2,56 @@
 
 ### Opis
 
-Tematem podprojektu jest rozpoznawanie posiłków.
-Użyty jest algorytm genetyczny.
+Tematem podprojektu jest rozpoznawanie zamówień na podstawie historii zamówień.
+Użyłem drzew decyzyjnych.
 
 ### Dane
 
-Posiłki, ich nazwa oraz rodzaj.
+Potrawy, ich nazwa, rodzaj oraz charakterystyka.
 
 
 
     menu = Context.fromstring(''' |meat|salad|meal|drink|cold|hot |
-                       Pork       |  X |     |  X |     |    |  X |
+                       Pork       |  X |     |    |     |    |  X |
                        Espresso   |    |     |    |  X  |    |  X |
+                       Latte      |    |     |    |  X  |    |  X |
                        Green Tea  |    |     |    |  X  | X  |    |
-                       Greek Salad|    |  X  |  X |     | X  |    |
+                       Greek Salad|    |  X  |    |     | X  |    |
                        Pizza      |    |     |  X |     |    |  X |''')
 
 
-Za pomocy graphviz możemy narysować grafy z poniższego kodu:  
 
 
-    digraph Lattice {
-	node [label="" shape=circle style=filled width=.25]
-	edge [dir=none labeldistance=1.5 minlen=2]
-	c0
-	c1
-	c1 -> c1 [color=transparent headlabel=Pork labelangle=270]
-	c1 -> c1 [color=transparent labelangle=90 taillabel=meat]
-	c1 -> c0
-	c2
-	c2 -> c2 [color=transparent headlabel="Green Tea" labelangle=270]
-	c2 -> c2 [color=transparent labelangle=90 taillabel=cold]
-	c2 -> c0
-	c3
-	c3 -> c3 [color=transparent headlabel="Greek Salad" labelangle=270]
-	c3 -> c3 [color=transparent labelangle=90 taillabel=salad]
-	c3 -> c0
-	c4
-	c4 -> c4 [color=transparent headlabel=Espresso labelangle=270]
-	c4 -> c4 [color=transparent labelangle=90 taillabel=drink]
-	c4 -> c2
-	c5
-	c5 -> c1
-	c5 -> c3
-	c5 -> c4
-    }
+Dane uczące:
+    training_data = [
+        ['meat','hot','Pork'],
+        ['salad','cold','Greek Salad'],
+        ['drink','hot','Espresso'],
+        ['drink','hot','Latte'],
+        ['drink','cold','Green Tea'],
+        ['meal','hot','Pizza'],
+    ]
+
+
 
 Dane testowe: 
-    func_input = ['meal']
+    test_data = [
+        ['meat','hot','Latte'],
+        ['salad','hot','Greek Salad'],
+        ['drink','hot','Pork'],
+        ['drink','cold','Green Tea'],
+        ['drink','hot','Greek Salad'],
+    ]
 
-Dane wyjściowe: 
 
-    ['Pork', 'Greek Salad', 'Pizza']
 
 
 ### Implementacja
 
 Główna część:
 
+    In process
 
-    gen_num = 20   #generations
-    gen_sol = 6     #solutions
-    gen_par_mating = 2  #how many solutions we select
-    
-    mut_per_gen = 10
-    mut_num_gen = None
-    
-    par_selc_type = "tournament"
-    crossover = "two_points"
-    muta_type = "scramble"
-    par_keep = 1 #keep only one parent
-    
-    init_range_l = -2 #low
-    init_range_h = -5 #high
     ...
     
 
@@ -82,7 +59,6 @@ Główna część:
 ### Biblioteki
 
 * concepts
-* pygad     (algorytm genetyczny)
 * random
 * numpy