upgrade

Kamila.py

@@ -4,33 +4,57 @@ from sklearn.model_selection import train_test_split
 from sklearn import metrics
 import numpy
 
-
+header = ["ready", "hydration", "weeds", "empty", "TODO"]
-header = ["ready", "hydration", "weeds", "planted"]
+work = ["Zebrac","Podlac","Odchwascic","Zasadzic"]
+#0 - 3
+#1 - 0
+#2 - 1
+#3 - 2
+def check_p(field):
+    if field == 0:
+        return [0, 0, 0, 0, "Zasadzic"]
+    elif field == 1:
+        return [0, 0, 1, 0, "Odchwascic"]
+    elif field == 2:
+        return [0, 0, 0, 1, "Podlac"]
+    elif field == 3:
+        return [0, 0, 1, 1, "Odchwascic"]
+    elif field == 4:
+        return [0, 1, 0, 0, "Zasadzic"]
+    elif field == 5:
+        return [0, 1, 1, 0, "Odchwascic"]
+    elif field == 6:
+        return [0, 1, 0, 1, "Ignoruj"]
+    elif field == 7:
+        return [0, 1, 1, 1, "Odchwascic"]
+    elif field == 8:
+        return [1, 0, 0, 1, "Zebrac"]
+    else:
+        print("wrong field number")
 
 
 def check(field):
     if field == 0:
-        return [0, 0, 0, 'N']
+        return [[0, 0, 0, 1, "Zasadzic"],[0,0,0,1,"Podlac"]]
     elif field == 1:
-        return [0, 0, 1, 'N']
+        return [[0, 0, 1, 1, "Odchwascic"], [0,0,1,1,"Podlac"], [0,0,1,1,"Zasadzic"]]
     elif field == 2:
-        return [0, 0, 0, 'Y']
+        return [[0, 0, 0, 0, "Podlac"]]
     elif field == 3:
-        return [0, 0, 1, 'Y']
+        return [[0, 0, 1, 0, "Odchwascic"],[0,0,1,0,"Podlac"]]
     elif field == 4:
-        return [0, 1, 0, 'N']
+        return [[0, 1, 0, 1, "Zasadzic"]]
     elif field == 5:
-        return [0, 1, 1, 'N']
+        return [[0, 1, 1, 1, "Odchwascic"],[0,1,1,1,"Zasadzic"]]
     elif field == 6:
-        return [0, 1, 0, 'Y']
+        return []
     elif field == 7:
-        return [0, 1, 1, 'Y']
+        return [[0, 1, 1, 0, "Odchwascic"]]
     elif field == 8:
-        return [1, 0, 0, 'N']
+        return [[1, 0, 0, 0, "Zebrac"],[1, 0, 0, 0, "Potem podlac"],[1, 0, 0, 0, "Potem zasadzic"]]
     else:
         print("wrong field number")
 
-
 def un_values(rows, col):
     return set([row[col] for row in rows])
 
@@ -177,8 +201,8 @@ class main():
         self.field = field
         self.ui = ui
         self.path = path
-
+        self.best_action = 0
-    def tree(field):
+    def main(self):
         array = ([[8, 8, 8, 8, 8, 8, 8, 8, 8, 8],
                   [7, 7, 7, 7, 7, 7, 7, 7, 7, 7],
                   [6, 6, 6, 6, 6, 6, 6, 6, 6, 6],
@@ -189,15 +213,58 @@ class main():
                   [1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
                   [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
                   [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]])
+        while (self.best_action != -1):
+            self.find_best_action()
+            self.do_best_action()
+        print("Koniec roboty")
 
+
+    def find_best_action(self):
         testing_data = []
-
+        matrix = self.field.get_matrix()
+        matrix_todo = []
+        #print(self.field)
         for i in range(10):
-            verse = field[i]
+            matrix_todo.append([])
-            for j in verse:
+            verse = matrix[i]
+            for j in range(len(verse)):
                 coord = (i, j)
-                current_field = check(verse[j])
+                current_field = check(verse[j]) #czynnosci ktore trzeba jeszcze zrobic na kazdym polu
-                testing_data.append(current_field)
+                matrix_todo[i].append([])
-
+                for action in current_field:
+                    matrix_todo[i][j].append(action[-1])
+                testing_data.extend(current_field)
+                #testing_data.append(current_field)
+        if len(testing_data) > 0:
             x = build_tree(testing_data)
             print_tree(x)
+            if isinstance(x, Leaf):
+                self.best_action = self.find_remaining_action(matrix_todo)
+                return
+            self.best_action = x.question.column
+            print(header[x.question.column])
+            print(x.question.value)
+        else:
+            self.best_action = self.find_remaining_action(matrix_todo)
+            return
+        #for row in testing_data:
+         #   print("Actual: %s. Predicted %s" %
+          #        (row[-1], print_leaf(classify(row, x))))
+        #for row in matrix_todo:
+        #   print(row)
+
+    def do_best_action(self):
+        self.traktor.set_mode((self.best_action+3) % 4)
+        while self.path.pathfinding(self.traktor,self.field,self.ui) != 0:
+            pass
+# 0 - 3
+# 1 - 0
+# 2 - 1
+# 3 - 2
+    def find_remaining_action(self, matrix_todo):
+        for row in matrix_todo:
+            for field in row:
+                for action in field:
+                    print(action)
+                    return work.index(action)
+        return -1