commit dfb26e79502d330ad588c74c42ee8a71eb325409
Author: s452726 <aleros8@st.amu.edu.pl>
Date:   Tue Jun 22 19:02:44 2021 +0200

    Dodanie projektu

diff --git a/.idea/inspectionProfiles/profiles_settings.xml b/.idea/inspectionProfiles/profiles_settings.xml
new file mode 100644
index 0000000..105ce2d
--- /dev/null
+++ b/.idea/inspectionProfiles/profiles_settings.xml
@@ -0,0 +1,6 @@
+<component name="InspectionProjectProfileManager">
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+    <option name="USE_PROJECT_PROFILE" value="false" />
+    <version value="1.0" />
+  </settings>
+</component>
\ No newline at end of file
diff --git a/.idea/misc.xml b/.idea/misc.xml
new file mode 100644
index 0000000..ea01f0b
--- /dev/null
+++ b/.idea/misc.xml
@@ -0,0 +1,4 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="ProjectRootManager" version="2" project-jdk-name="Python 3.7 (untitled2)" project-jdk-type="Python SDK" />
+</project>
\ No newline at end of file
diff --git a/.idea/modules.xml b/.idea/modules.xml
new file mode 100644
index 0000000..f798539
--- /dev/null
+++ b/.idea/modules.xml
@@ -0,0 +1,8 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="ProjectModuleManager">
+    <modules>
+      <module fileurl="file://$PROJECT_DIR$/.idea/smieciara.iml" filepath="$PROJECT_DIR$/.idea/smieciara.iml" />
+    </modules>
+  </component>
+</project>
\ No newline at end of file
diff --git a/.idea/smieciara.iml b/.idea/smieciara.iml
new file mode 100644
index 0000000..c67b363
--- /dev/null
+++ b/.idea/smieciara.iml
@@ -0,0 +1,8 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<module type="PYTHON_MODULE" version="4">
+  <component name="NewModuleRootManager">
+    <content url="file://$MODULE_DIR$" />
+    <orderEntry type="jdk" jdkName="Python 3.7 (untitled2)" jdkType="Python SDK" />
+    <orderEntry type="sourceFolder" forTests="false" />
+  </component>
+</module>
\ No newline at end of file
diff --git a/.idea/vcs.xml b/.idea/vcs.xml
new file mode 100644
index 0000000..94a25f7
--- /dev/null
+++ b/.idea/vcs.xml
@@ -0,0 +1,6 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="VcsDirectoryMappings">
+    <mapping directory="$PROJECT_DIR$" vcs="Git" />
+  </component>
+</project>
\ No newline at end of file
diff --git a/.idea/workspace.xml b/.idea/workspace.xml
new file mode 100644
index 0000000..686b5de
--- /dev/null
+++ b/.idea/workspace.xml
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+<?xml version="1.0" encoding="UTF-8"?>
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+      <filtered-out-file-type name="TAG" />
+      <filtered-out-file-type name="COMMIT_BY_MESSAGE" />
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+  <component name="ProjectLevelVcsManager" settingsEditedManually="true" />
+  <component name="ProjectViewState">
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+    <option name="showLibraryContents" value="true" />
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+  <component name="PropertiesComponent">
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+    <property name="run.code.analysis.last.selected.profile" value="aDefault" />
+    <property name="settings.editor.selected.configurable" value="com.jetbrains.python.configuration.PyActiveSdkModuleConfigurable" />
+  </component>
+  <component name="RecentsManager">
+    <key name="CopyFile.RECENT_KEYS">
+      <recent name="C:\Users\micha\Desktop\smieciara" />
+    </key>
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+  <component name="RunManager" selected="Python.genetic_algorithm (1)">
+    <configuration name="genetic_algorithm (1)" type="PythonConfigurationType" factoryName="Python" temporary="true">
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+      <option name="INTERPRETER_OPTIONS" value="" />
+      <option name="PARENT_ENVS" value="true" />
+      <envs>
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+    <configuration name="genetic_algorithm" type="PythonConfigurationType" factoryName="Python" temporary="true">
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+      <option name="INTERPRETER_OPTIONS" value="" />
+      <option name="PARENT_ENVS" value="true" />
+      <envs>
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+      <option name="INTERPRETER_OPTIONS" value="" />
+      <option name="PARENT_ENVS" value="true" />
+      <envs>
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+      <option name="EMULATE_TERMINAL" value="false" />
+      <option name="MODULE_MODE" value="false" />
+      <option name="REDIRECT_INPUT" value="false" />
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+  <component name="SvnConfiguration">
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+  <component name="TaskManager">
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diff --git a/X_test.csv b/X_test.csv
new file mode 100644
index 0000000..bb91628
--- /dev/null
+++ b/X_test.csv
@@ -0,0 +1,801 @@
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diff --git a/astar.py b/astar.py
new file mode 100644
index 0000000..c9cadde
--- /dev/null
+++ b/astar.py
@@ -0,0 +1,148 @@
+import heapq
+
+from world import set_world
+
+
+class PriorityQueue:
+    def __init__(self, order='min', f=lambda x: x):
+        self.queue = []
+        if order == 'min':
+            self.f = f
+
+    def insert(self, item):
+        heapq.heappush(self.queue, (self.f(item), item))
+
+    def pop(self):
+        if self.queue:
+            return heapq.heappop(self.queue)[1]
+
+    def __len__(self):
+        return len(self.queue)
+
+
+    def __contains__(self, item):
+        return (self.f(item), item) in self.queue
+
+
+class Problem(object):
+    def __init__(self, initial, goal=None):
+        self.initial = initial
+        self.goal = goal
+
+    def path_cost(self, c, state1, action, state2):
+        world_map_file = set_world('worldmap.txt')
+        l = state2[1]
+        k = state2[0]
+        return int(world_map_file[l][k])
+
+
+class Node:
+    def __init__(self, state, parent=None, action=None, path_cost=0):
+        self.state = state
+        self.parent = parent
+        self.action = action
+        self.path_cost = path_cost
+        self.depth = 0
+        if parent:
+            self.depth = parent.depth + 1
+
+    def __lt__(self, node):
+        return self.state < node.state
+
+    def child_node(self, problem, action):
+        next = problem.result(self.state, action)
+        return Node(next, self, action, problem.path_cost(self.path_cost, self.state, action, next))
+
+    def expand(self, route_planning):
+        return [self.child_node(route_planning, action) for action in route_planning.actions(self.state)]
+
+    def path(self):
+        node, parents_path = self, []
+        while node:
+            parents_path.append(node)
+            node = node.parent
+        return list(reversed(parents_path))
+
+    def reconstruct_path(self):
+        return [node.action for node in self.path()[1:]]
+
+
+class RoutePlanning(Problem):
+    def __init__(self, initial, goal, paths):
+        Problem.__init__(self, initial, goal)
+        self.x_size = 20
+        self.y_size = 14
+        self.paths = paths
+
+    def goaltest(self, state):
+        return state == self.goal
+
+    def actions(self, state):
+        actions = ["Go up", "Go left", "Go down", "Go right"]
+        x, y = state
+        if y == 0:
+            if "Go up" in actions:
+                actions.remove("Go up")
+        if x == 0:
+            if "Go left" in actions:
+                actions.remove("Go left")
+        if y == (self.y_size - 1):
+            if "Go down" in actions:
+                actions.remove("Go down")
+        if x == (self.x_size - 1):
+            if "Go right" in actions:
+                actions.remove("Go right")
+        return actions
+
+    def result(self, state, action):
+        x, y = state
+        location = tuple()
+
+        if action == "Go up":
+            location = (x, y - 1)
+        elif action == "Go down":
+            location = (x, y + 1)
+        elif action == "Go left":
+            location = (x - 1, y)
+        elif action == "Go right":
+            location = (x + 1, y)
+        if location in self.paths:
+            state = location
+        return state
+
+    def heuristic(self, node):
+        pos_x1, pos_y1 = node.state
+        pos_x2, pos_y2 = self.goal
+        return abs(pos_x2 - pos_x1) + abs(pos_y2 - pos_y1)
+
+
+def graphsearch(problem, f):
+    fringe = PriorityQueue('min', f)
+    explored = set()
+
+    node = Node(problem.initial)
+    fringe.insert(node)
+    while fringe:
+        node = fringe.pop()
+        if problem.goaltest(node.state):
+            return node
+        explored.add(node.state)
+        for child in node.expand(problem):
+            if child.state not in explored and child not in fringe: #cost2 to estimated cost
+                fringe.insert(child)
+            elif child in fringe:
+                current_path = fringe[child]
+                cost2 = f(current_path)
+                cost1 = f(child)
+                if cost2 >= cost1:
+                    del fringe[current_path]
+                    fringe.insert(child)
+
+    return None
+
+
+def astar(route_planning):
+    heuristic = route_planning.heuristic
+    fun = lambda x: x.path_cost + heuristic(x)
+    return graphsearch(route_planning, fun)
+
diff --git a/data.csv b/data.csv
new file mode 100644
index 0000000..bc9bdc7
--- /dev/null
+++ b/data.csv
@@ -0,0 +1,4001 @@
+,color,transparency,smell,elastic,dirt,weight,sound,reflectiveness,label
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diff --git a/data.py b/data.py
new file mode 100644
index 0000000..e79925b
--- /dev/null
+++ b/data.py
@@ -0,0 +1,47 @@
+from pyDatalog import pyDatalog
+
+pyDatalog.create_terms('X,Y,trash')
+pyDatalog.create_terms('plasticContainer,paperContainer,glassContainer')
+pyDatalog.create_terms('plastic,glass,paper')
+pyDatalog.create_terms('color,elastic,fragile,white')
+
+# example trash:
+# glass
+trash['glass_trash1'] = 1
+color['glass_trash1'] = 0
+fragile['glass_trash1'] = 8
+
+# plastic
+trash['plastic_trash1'] = 1
+color['plastic_trash1'] = 1
+elastic['plastic_trash1'] = 1
+
+# paper
+trash['paper_trash1']=1
+white['paper_trash1']=8
+
+#glass
+trash['glass_trash2'] = 1
+color['glass_trash2'] = 0
+fragile['glass_trash2'] = 9
+
+
+#glass
+trash['glass_trash3'] = 1
+color['glass_trash3'] = 0
+fragile['glass_trash3'] = 9
+
+glass(X) <= (trash[X]==1) & (color[X]==0) & (fragile[X]>5)
+plastic(X) <= (trash[X]==1) & (color[X]==1) & (elastic[X]>8)
+paper(X) <= (trash[X]==1) & (white[X]>5)
+
+
+glassContainer(X) <= glass(X)
+plasticContainer(X) <= plastic(X)
+paperContainer(X) <= paper(X)
+
+print(glass(X))
+
+print(glassContainer(X))
+
+print (glassContainer(X) & (fragile[X]==9))
diff --git a/decision_tree.py b/decision_tree.py
new file mode 100644
index 0000000..1dea163
--- /dev/null
+++ b/decision_tree.py
@@ -0,0 +1,1026 @@
+from sklearn import tree
+import pandas as pd
+
+
+def buildTree():
+    data = pd.read_csv("TreeCsv.csv")
+    properties= data.loc[:, data.columns != 'Unnamed: 0']
+
+
+    types = [
+            'glass',
+            'glass',
+            'glass',
+            'glass',
+            'glass',
+            'glass',
+            'glass',
+            'glass',
+            'glass',
+            'glass',
+            'glass',
+            'glass',
+            'glass',
+            'glass',
+            'glass',
+            'glass',
+            'glass',
+            'glass',
+            'glass',
+            'glass',
+            'glass',
+            'glass',
+            'glass',
+            'glass',
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+            'glass',
+            'glass',
+            'glass',
+            'glass',
+            'glass',
+            'glass',
+             'mixed',
+             'mixed',
+             'mixed',
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+             'metal',
+             'metal',
+             'metal',
+             'metal',
+             'metal',
+             'metal',
+             'metal',
+             'metal',
+             'metal',
+             'paper',
+             'paper',
+             'paper',
+             'paper',
+             'paper',
+             'paper',
+             'paper',
+             'paper',
+             'paper',
+             'paper',
+             'paper',
+             'paper',
+             'paper',
+             'paper',
+             'paper',
+             'paper',
+             'paper',
+             'paper',
+             'paper',
+             'paper',
+             'paper',
+             'paper',
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+             'paper',
+             'paper',
+             'paper',
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+             'paper',
+             'paper',
+             'paper',
+             'paper',
+             'paper',
+             'paper',
+             'paper',
+             'paper',
+             'paper',
+             'paper',
+             'paper',
+             'paper',
+             'paper',
+             'paper',
+             'paper',
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+             'paper',
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+             'paper',
+             'paper',
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+             'paper',
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+             'paper',
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+             'paper',
+             'paper',
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+             'paper',
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+             'paper',
+             'paper',
+             'paper',
+             'paper',
+             'paper',
+             'paper',
+             'paper',
+             'paper',
+             'paper',
+             'paper',
+             'paper',
+             'paper',
+             'paper',
+             'paper',
+             'paper',
+             'paper',
+             'paper',
+             'paper',
+             'paper',
+             'paper',
+             'paper',
+             'paper',
+             'paper',
+             'paper',
+             'paper',
+             'paper',
+             'paper',
+             'paper',
+             'paper',
+             'paper',
+             'paper',
+             'paper',
+             'paper',
+             'paper',
+             'paper',
+             'paper',
+             'paper',
+             'paper',
+             'paper',
+             'paper',
+             'paper',
+             'paper',
+             'paper',
+             'paper',
+             'paper',
+             'paper',
+             'paper',
+             'paper',
+             'paper',
+             'paper',
+             'paper',
+             'paper',
+             'paper',
+             'paper',
+             'paper',
+             'paper',
+             'paper',
+             'paper',
+             'paper',
+             'paper',
+             'paper',
+             'paper',
+             'paper',
+             'paper',
+             'paper',
+             'paper',
+             'paper',
+             'paper',
+             'paper',
+             'paper',
+             'paper',
+             'paper',
+             'paper',
+             'paper',
+             'paper',
+             'paper',
+             'paper',
+             'paper',
+             'paper',
+             'paper',
+             'paper',
+             'paper',
+             'paper',
+             'paper',
+             'paper',
+             'paper',
+             'paper',
+             'paper',
+             'paper',
+             'paper',
+             'paper',
+             'paper',
+             'paper',
+             'paper',
+             'paper',
+             'paper',
+             'paper',
+             'paper',
+             'paper',
+             'paper',
+             'paper',
+             'paper',
+             'paper',
+             'paper',
+             'paper',
+             'paper',
+             'paper',
+             'paper',
+             'paper',
+             'paper',
+             'paper',
+             'paper',
+             'paper',
+             'paper',
+             'paper',
+             'paper',
+             'paper',
+             'paper',
+             'paper',
+             'paper',
+             'paper',
+             'paper',
+             'paper',
+             'paper',
+             'paper',
+             'paper',
+             'paper',
+             'paper',
+             'paper',
+             'paper',
+             'paper',
+             'paper',
+             'paper',
+             'paper',
+             'paper',
+             'paper',
+             'paper',
+             'paper',
+             'paper',
+             'paper',
+             'paper',
+             'paper',
+             'paper',
+             'paper',
+             'paper',
+             'paper',
+             'paper',
+             'paper',
+             'paper',
+             'paper'
+
+    ]
+    #'color', 'transparency', 'smell', 'elastic', 'dirt', 'weight', 'sound', 'reflectiveness'
+
+    cfl = tree.DecisionTreeClassifier()
+    cfl = cfl.fit(properties,types)
+
+    print('built the tree')
+    return cfl
+
+
+def predictTypeOfTrash(t, a):
+    prediction = t.predict(a)
+    text_representation = tree.export_text(t)
+    print(text_representation)
+    return prediction
diff --git a/drzewo_decyzyjne.png b/drzewo_decyzyjne.png
new file mode 100644
index 0000000..42c9c57
Binary files /dev/null and b/drzewo_decyzyjne.png differ
diff --git a/generator.py b/generator.py
new file mode 100644
index 0000000..099d946
--- /dev/null
+++ b/generator.py
@@ -0,0 +1,43 @@
+import numpy as np
+import pandas as pd
+
+etykiety = list(range(4))
+
+features = ['color', 'transparency', 'smell', 'elastic', 'dirt', 'weight', 'sound', 'reflectiveness']
+n_features = len(features)
+
+products = ['glass', 'mixed', 'metal', 'paper']
+n_products = len(products)
+
+glass_mean = [0.5, 0.8, 0.1, 0.01, 0.1, 0.9, 0.95, 0.5 ]
+glass_var = [1, 0.4, 0.1, 0.1, 0.4, 0.2, 0.2, 0.3]
+
+mixed_mean = [0.5, 0.3, 0.8, 0.5, 0.7, 0.6, 0.5, 0.3]
+mixed_var = [1, 0.2, 0.6, 1, 0.5, 0.5, 0.4, 0.6]
+
+metal_mean = [0.8, 0.01, 0.1, 0.3 , 0.5, 0.7, 0.6, 0.4]
+metal_var = [0.1,0.1, 0.2, 0.1, 0.3, 0.3, 0.3, 0.3]
+
+paper_mean = [0.5, 0.2, 0.4, 0.9, 0.2, 0.4, 0.2, 0.1]
+paper_var = [1, 0.1, 0.3, 0.4, 0.5, 0.3, 0.3, 0.4]
+
+
+
+
+n_samples = 1000
+
+data = np.zeros((n_samples * n_products, n_features + 1))
+
+for product, means, vars in zip(range(4), [glass_mean, mixed_mean, metal_mean, paper_mean ], [glass_var, mixed_var, metal_var, paper_var]):
+  for index, feature in enumerate(features):
+    data[n_samples*product:n_samples *(product+1),index] = np.random.normal(means[index], vars[index]/3, 1000)
+
+  data[n_samples*product:n_samples *(product+1),-1] = product
+
+
+
+data[:,:-1][data[:,:-1]<0] = 0
+data[:,:-1][data[:,:-1]>1] = 1
+
+df = pd.DataFrame(data, columns=[*features, "label"])
+df.to_csv("data.csv")
\ No newline at end of file
diff --git a/genetic_algorithm.py b/genetic_algorithm.py
new file mode 100644
index 0000000..fcd7401
--- /dev/null
+++ b/genetic_algorithm.py
@@ -0,0 +1,335 @@
+import random
+import tqdm
+from copy import deepcopy
+from functools import reduce
+
+def genetic_algoritm(world_map: list, START: tuple, houses: list, pop_size = 200, rounds = 1000, tournament_size=20, verbose=0):
+
+    population = [Chromosome(START, world_map, houses) for _ in range(pop_size)]
+
+    for _ in tqdm.tqdm(range(rounds), position=0, leave=True):
+
+        candidates = random.sample(population, tournament_size)
+        parent_1 = get_best_parent(candidates)
+
+        candidates = random.sample(population, tournament_size)
+        parent_2 = get_best_parent(candidates)
+
+        offspring_1, offspring_2 = parent_1.cross_over(parent_2,verbose=verbose)
+
+        offspring_1.mutate(verbose=verbose)
+        offspring_2.mutate(verbose=verbose)
+        #Cykle czy nie
+        offspring_1.remove_cycles()
+        offspring_2.remove_cycles()
+
+        worst, worst_index = get_worst_chromosome(population)
+        if offspring_1.fitting_function() < worst.fitting_function():
+            # print(offspring_1.fitting_function())
+
+            population[worst_index] = offspring_1
+
+        worst, worst_index = get_worst_chromosome(population)
+        if offspring_2.fitting_function() < worst.fitting_function():
+            # print(offspring_2.fitting_function())
+            population[worst_index] = offspring_2
+
+        if verbose > 0:
+            print( get_best_parent(population).fitting_function(), get_worst_chromosome(population)[0].fitting_function())
+
+
+    # optymalne -  najkrotsze z poprawnych
+    rozwiazanie = get_best_parent(population)
+
+
+    return rozwiazanie
+
+
+class Chromosome():
+    def __init__(self, START: tuple, world_map: list, houses: list):
+        self.houses = houses
+        self.max_x = len(world_map)
+        self.max_y = len(world_map[0])
+        self.solution = [START]
+        self.world_map = world_map
+
+        visited_houses = set()
+        n_houses_to_visit = len(houses)
+        curr_x, curr_y = START
+
+        while len(visited_houses) < n_houses_to_visit:
+            #     print(self.solution)
+            moved = False
+            while not moved:
+                move = random.randint(0, 3)
+                # 0 - R, 2  - L, 2 - D, 3 - U
+                if move == 0 and curr_x + 1 < self.max_x and self.world_map[curr_x + 1][curr_y] != '0':
+                    self.solution.append((curr_x + 1, curr_y))
+                    moved = True
+                elif move == 1 and curr_x - 1 >= 0 and self.world_map[curr_x - 1][curr_y] != '0':
+                    self.solution.append((curr_x - 1, curr_y))
+                    moved = True
+                elif move == 2 and curr_y + 1 < self.max_y and self.world_map[curr_x][curr_y + 1] != '0':
+                    self.solution.append((curr_x, curr_y + 1))
+                    moved = True
+                elif move == 3 and curr_y - 1 >= 0 and self.world_map[curr_x][curr_y - 1] != '0':
+                    self.solution.append((curr_x, curr_y - 1))
+                    moved = True
+            curr_x, curr_y = self.solution[-1]
+            if self.solution[-1] in houses and self.solution[-1] not in visited_houses:
+                visited_houses.add(self.solution[-1])
+
+        # print("Przed", self.solution)
+        # print("FF", self.fitting_function(verbose=1))
+        self.remove_cycles()
+        # print("Po", self.solution)
+        # print("FF", self.fitting_function(verbose=1))
+
+
+
+
+
+    def remove_cycles(self):
+        #modyfikacja orgfinalnego rozwiazania poprzez usuniecie cyklu bez nieodwiedzonego domku
+        #Droga podzielona na frafgmenty od domku do domku
+        try:
+            checkpoints = sorted([self.solution.index(house) for house in self.houses])
+            subsolutions = [self.solution[indx_1:indx_2] for indx_1,indx_2 in zip([0,*checkpoints], [*checkpoints, len(self.solution)])]
+            self.solution = reduce(lambda z, y :z + y, self._remove_cycles(subsolutions))
+        except:
+            pass
+
+    def fitting_function(self, verbose=0):
+        # Rozwiazania nieoprawne
+        # nie odwiedza wszystkich domow
+        for house in self.houses:
+            if house not in self.solution:
+                if verbose > 0:
+                    print("Not visiting all the houses!")
+                return 100 * self.max_x * self.max_y
+
+        # Odwiedza miejsca poza mapą albo niedozwolone
+        for x, y in self.solution:
+            if x >= self.max_x or x < 0 or y >= self.max_y or y < 0 or self.world_map[x][y] == 0:
+                if verbose > 0:
+                    print("Wrong!")
+                return 100 * self.max_x * self.max_y
+
+        #Sprawdzamy czy droga jest spojna, czy mozna ja wykonac krok po kroku
+        for index in range(len(self.solution)-1):
+            if abs(self.solution[index][0] - self.solution[index + 1][0]) + abs(self.solution[index][1] - self.solution[index + 1][1]) != 1:
+                if verbose > 0:
+                    print("Not continuous!")
+                return 100 * self.max_x * self.max_y
+        #Actual fitting value
+        return len(self.solution)
+
+    def mutate(self, patience=10, verbose = 0):
+
+        mutation_point = random.randint(1, len(self.solution) - 3)
+        options = [0, 1, 2, 3]
+
+
+
+        curr_x, curr_y = self.solution[mutation_point]
+        next_x, next_y = self.solution[mutation_point + 1]
+
+        if curr_x < next_x:
+            move = 0
+        elif curr_x > next_x:
+            move = 1
+        elif curr_y < next_y:
+            move = 2
+        else:
+            move = 3
+
+
+        while patience > 0:
+            curr_x, curr_y = self.solution[mutation_point]
+            options.remove(move)
+            moved = False
+            while not moved:
+                move = random.choice(options)
+                options.remove(move)
+                if move == 0 and curr_x + 1 < self.max_x and self.world_map[curr_x + 1][curr_y] != '0':
+                    self.solution[mutation_point + 1] = (curr_x + 1, curr_y)
+                    moved = True
+                elif move == 1 and curr_x - 1 >= 0 and self.world_map[curr_x - 1][curr_y] != '0':
+                    self.solution[mutation_point + 1] = (curr_x - 1, curr_y)
+                    moved = True
+                elif move == 2 and curr_y + 1 < self.max_y and self.world_map[curr_x][curr_y + 1] != '0':
+                    self.solution[mutation_point + 1] = (curr_x, curr_y + 1)
+                    moved = True
+                elif move == 3 and curr_y - 1 >= 0 and self.world_map[curr_x][curr_y - 1] != '0':
+                    self.solution[mutation_point + 1] = (curr_x, curr_y - 1)
+                    moved = True
+                if len(options) == 0 and not moved:
+                    return
+
+            # Szukamy miejsca aby zespolic droge
+            if verbose > 0:
+                print("\tMade one step in a different direction.")
+            # while not done:
+            for indx, step in zip(range(mutation_point + 2, len(self.solution)), self.solution[mutation_point + 2:]):
+                # jesli krok, ktory wykonalismy jest gdzies na liscie
+                if step == self.solution[mutation_point + 1]:
+                    # print("Change")
+                    # print("Old:", self.solution)
+                    new_solution = self.solution[:mutation_point + 1]
+                    new_solution.extend(self.solution[indx:])
+                    if verbose > 0:
+                        print(self.solution[mutation_point:mutation_point + patience])
+                    self.solution = new_solution
+                    if verbose > 0:
+                        print(self.solution[mutation_point:mutation_point+patience])
+                    return
+            if mutation_point < len(self.solution) - 2:
+                mutation_point = mutation_point + 1
+            else:
+                return
+            patience = patience - 1
+            options = [0, 1, 2, 3]
+            move = move + 1 if move % 2 == 0 else move - 1
+
+    def cross_over(self, other, verbose=0):
+        offspring_a = deepcopy(self)
+        offspring_b = deepcopy(other)
+
+        #Select 2 random houses
+
+        house_k_index, house_m_index = random.sample(range(len(self.houses)), 2)
+        if verbose > 0:
+            print(house_k_index, house_m_index)
+
+        a_house_k_index = self.solution.index(self.houses[house_k_index])
+        a_house_m_index = max(loc for loc, val in enumerate(self.solution) if val == self.houses[house_m_index])
+        # a_house_m_index = self.solution.index(self.houses[house_m_index])
+        b_house_k_index = other.solution.index(self.houses[house_k_index])
+        b_house_m_index = max(loc for loc, val in enumerate(other.solution) if val == self.houses[house_m_index])
+        # b_house_m_index = other.solution.index(self.houses[house_m_index])
+
+        # offspring_a
+        new_a_solution = offspring_a.solution[:a_house_k_index]
+        if b_house_k_index < b_house_m_index:
+            new_a_solution.extend(offspring_b.solution[b_house_k_index:b_house_m_index])
+        else:
+            new_a_solution.extend(reversed(offspring_b.solution[b_house_m_index+1:b_house_k_index+1]))
+        new_a_solution.extend(offspring_a.solution[a_house_m_index:])
+        offspring_a.solution = new_a_solution
+
+        # offspring_b
+        new_b_solution = offspring_b.solution[:b_house_k_index]
+        if b_house_k_index < b_house_m_index:
+            new_b_solution.extend(offspring_a.solution[a_house_k_index:a_house_m_index])
+        else:
+            new_b_solution.extend(reversed(offspring_a.solution[a_house_m_index + 1:a_house_k_index + 1]))
+        new_b_solution.extend(offspring_b.solution[b_house_m_index:])
+        offspring_b.solution = new_b_solution
+
+        if verbose > 0:
+            print(a_house_k_index, a_house_m_index)
+            print(b_house_k_index, b_house_m_index)
+
+        return offspring_a, offspring_b
+
+    def __str__(self):
+        return self.solution.__str__()
+
+    def _remove_cycles(self, subsolutions):
+        for ind, subsolution in enumerate(subsolutions[:-1]):
+            # print(subsolutions)
+            shorted = subsolution
+            modified = False
+            beggining_index = 0
+            indices_to_remove_left = []
+            indices_to_remove_right = []
+            #print(len(subsolutions[ind]) == len(set(subsolutions[ind])))
+            while beggining_index < len(subsolution[:-2]):
+
+                for end_index in range(len(subsolution)-1,beggining_index  , -1):
+                    if subsolution[beggining_index] == subsolution[end_index]:
+                        indices_to_remove_left.append(beggining_index)
+                        indices_to_remove_right.append(end_index)
+                        # print("Wycinamy cykl dlugosci", end_index - beggining_index)
+                        modified = True
+                        break
+                if modified:
+                    beggining_index += end_index - beggining_index
+                else:
+                    beggining_index += 1
+
+
+            subsolutions[ind] = reduce(lambda z, y :z + y,[subsolution[indx_1:indx_2] for indx_1,indx_2 in zip([0,*indices_to_remove_right], [*indices_to_remove_left, len(subsolution)])])
+            #Sprawdzenie czy nie ma cykli
+            #print(len(subsolutions[ind]) == len(set(subsolutions[ind])))
+        #print(subsolutions)
+
+        return subsolutions
+
+
+# given an iterable of pairs return the key corresponding to the greatest value
+def argmax(pairs):
+    return max(pairs, key=lambda x: x[1].fitting_function())[0]
+
+def argmin(pairs):
+    return min(pairs, key=lambda x: x[1].fitting_function())[0]
+
+# given an iterable of values return the index of the greatest value
+def argmax_index(values):
+    return argmax(enumerate(values))
+
+def argmin_index(values):
+    return argmin(enumerate(values))
+
+def get_best_parent(candidates):
+    best_index = argmin_index(candidates)
+    return candidates[best_index]
+
+def get_worst_chromosome(population):
+    worst_index = argmax_index(population)
+    return population[worst_index], worst_index
+
+
+
+
+def set_world(path):
+    array = []
+    with open(path) as f:
+        content = f.read().splitlines()
+        for line in content:
+            array.append(line)
+    return array
+
+def k(START):
+
+    world_map = set_world('worldmap.txt')
+    houses = [
+        (1, 5), (2, 11), (6, 6), (6, 12), (9, 3), (9, 7), (9, 17), (12, 6)
+    ]
+
+
+
+    solution = genetic_algoritm(world_map, START, houses, pop_size=20, rounds=200, tournament_size=2, verbose=0)
+
+    print(solution.solution)
+    print(type(solution.solution))
+
+    actions=[]
+    k=0
+    for i in solution.solution:
+
+
+         if (k!=0):
+                 if (i[0]>k[0]):
+                     actions.append("Go down")
+                 if (i[0]<k[0]):
+                     actions.append("Go up")
+                 if (i[1]>k[1]):
+                     actions.append("Go right")
+                 if (i[1]<k[1]):
+                     actions.append("Go left")
+         k=i
+
+    print(solution.fitting_function())
+    print(actions)
+    return actions
\ No newline at end of file
diff --git a/main.py b/main.py
new file mode 100644
index 0000000..d7e3d60
--- /dev/null
+++ b/main.py
@@ -0,0 +1,134 @@
+import time
+import pygame
+import sys
+from random import seed
+from random import random
+from astar import RoutePlanning, astar
+from world import set_world, World, Agent
+from genetic_algorithm import *
+from decision_tree import *
+from neural_network2 import *
+from neural_network_vers2 import *
+start_point = (0, 0)
+position = list(start_point)
+window = pygame.display.set_mode((800, 560))
+cell_size = 40
+final_point = (1, 0)
+world_map_file = set_world('worldmap.txt')
+pygame.init()
+world = World(world_map_file, cell_size)
+sprites = pygame.sprite.Group()
+agent = Agent(world.get_world_cell_size(), world)
+sprites.add(agent)
+window.fill((0, 0, 0))
+roads = []
+houses = [
+    (5,1)],[(11,2)],[(6,6)],[(12,6)],[(3,9)],[(7,9)],[(17,9)],[(6,12)
+]
+
+
+
+for w in range(0, len(world_map_file[0])):
+    for h in range(0, len(world_map_file)):
+        if int(world_map_file[h][w]) != 0:
+            roads.append((w, h))
+
+def move(actions):
+    for x in actions:
+        if x == "Go right":
+            while agent.get_directions()[0] != "E":
+                agent.turn_right()
+            agent.move()
+
+        if x == "Go left":
+            while agent.get_directions()[0] != "W":
+                agent.turn_left()
+            agent.move()
+
+        if x == "Go down":
+            while agent.get_directions()[0] != "S":
+                agent.turn_left()
+            agent.move()
+
+        if x == "Go up":
+            while agent.get_directions()[0] != "N":
+                agent.turn_right()
+            agent.move()
+        world.draw(window)
+        sprites.update()
+        sprites.draw(window)
+        time.sleep(0.2)
+        pygame.display.update()
+
+    while agent.get_directions()[0] != "E":
+        agent.turn_right()
+        world.draw(window)
+        sprites.update()
+        sprites.draw(window)
+        time.sleep(0.1)
+        pygame.display.update()
+    if final_point[0] == 19 and final_point[1] == 8:
+         agent.change_image2()
+    for i in range(0, 8):
+        if (houses[i][0] == (final_point[0],final_point[1])):
+            print("----------drzewo decyzyjne---------------")
+            t = buildTree()
+            seed(1)
+            trash = [[random.random(),random.random(),random.random(),random.random(),random.random(),random.random(),random.random(),random.random()],[random.random(),random.random(),random.random(),random.random(),random.random(),random.random(),random.random(),random.random()]]
+            print(trash)  # prints data array
+            typeOfTrashes = predictTypeOfTrash(t, trash)  # prints recognised trash
+            print(typeOfTrashes)
+            print("-----------sieci neuronowe--------------")
+            b()
+            agent.change_image()
+        world.draw(window)
+        sprites.update()
+        sprites.draw(window)
+        time.sleep(0.1)
+        pygame.display.update()
+
+
+while True:
+    world.draw(window)
+    sprites.update()
+    sprites.draw(window)
+
+    for event in pygame.event.get():
+        if event.type == pygame.QUIT:
+            pygame.quit()
+            sys.exit()
+        if event.type == pygame.KEYDOWN:
+            if event.key == pygame.K_w:
+                agent.move()
+
+            if event.key == pygame.K_a:
+                agent.turn_left()
+
+            if event.key == pygame.K_d:
+                agent.turn_right()
+            elif event.type == pygame.KEYDOWN:
+                if event.key == pygame.K_SPACE or event.key == pygame.K_UP:
+                    actions = k((agent.get_second(), agent.get_first()))
+                    move(actions)
+            if event.key == pygame.K_KP_ENTER:
+                for i in range(0, 8):
+                    if (houses[i][0] == (agent.get_first(), agent.get_second())):
+                        predict()
+
+        elif event.type == pygame.MOUSEBUTTONDOWN:
+            start_point = (agent.get_first(), agent.get_second())
+            pos_x = int(pygame.mouse.get_pos()[1] / cell_size)
+            pos_y = int(pygame.mouse.get_pos()[0] / cell_size)
+            if int(world_map_file[pos_x][pos_y]) != 0:
+                final_point = (pos_y, pos_x)
+                route = RoutePlanning(start_point, final_point, roads)
+                actions = astar(route).reconstruct_path()
+                move(actions)
+
+
+
+
+    pygame.display.update()
+
+
+
diff --git a/model_vers2.py b/model_vers2.py
new file mode 100644
index 0000000..66ed6b5
--- /dev/null
+++ b/model_vers2.py
@@ -0,0 +1,66 @@
+import tensorflow as tf
+import numpy as np
+from tensorflow.keras import layers
+from tensorflow.keras.models import Sequential
+import pathlib
+
+data_dir = pathlib.Path("C:/Users/Natalia/Documents/smieci")
+
+batch_size = 32
+img_height = 180
+img_width = 180
+train_ds = tf.keras.preprocessing.image_dataset_from_directory(
+  data_dir,
+  validation_split=0.2,
+  subset="training",
+  seed=123,
+  image_size=(img_height, img_width),
+  batch_size=batch_size)
+val_ds = tf.keras.preprocessing.image_dataset_from_directory(
+  data_dir,
+  validation_split=0.2,
+  subset="validation",
+  seed=123,
+  image_size=(img_height, img_width),
+  batch_size=batch_size)
+class_names = train_ds.class_names
+print(class_names)
+
+
+AUTOTUNE = tf.data.AUTOTUNE
+
+train_ds = train_ds.cache().shuffle(1000).prefetch(buffer_size=AUTOTUNE)
+val_ds = val_ds.cache().prefetch(buffer_size=AUTOTUNE)
+normalization_layer = layers.experimental.preprocessing.Rescaling(1./255)
+normalized_ds = train_ds.map(lambda x, y: (normalization_layer(x), y))
+image_batch, labels_batch = next(iter(normalized_ds))
+first_image = image_batch[0]
+# Notice the pixels values are now in `[0,1]`.
+print(np.min(first_image), np.max(first_image))
+num_classes = 4
+
+model = Sequential([
+  layers.experimental.preprocessing.Rescaling(1./255, input_shape=(img_height, img_width, 3)),
+  layers.Conv2D(16, 3, padding='same', activation='relu'),
+  layers.MaxPooling2D(),
+  layers.Conv2D(32, 3, padding='same', activation='relu'),
+  layers.MaxPooling2D(),
+  layers.Conv2D(64, 3, padding='same', activation='relu'),
+  layers.MaxPooling2D(),
+  layers.Flatten(),
+  layers.Dense(128, activation='relu'),
+  layers.Dense(num_classes)
+])
+model.compile(optimizer='adam',
+              loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
+              metrics=['accuracy'])
+epochs=10
+history = model.fit(
+  train_ds,
+  validation_data=val_ds,
+  epochs=epochs
+)
+
+model.save('C:/Users/Natalia/Desktop/lsm04/smieciara/saved_model_vers2')
+
+
diff --git a/neural_network.py b/neural_network.py
new file mode 100644
index 0000000..83f7379
--- /dev/null
+++ b/neural_network.py
@@ -0,0 +1,53 @@
+import numpy as np
+from numpy import loadtxt
+import pandas as pd
+from sklearn.model_selection import train_test_split
+import tensorflow as tf
+from tensorflow.keras.layers import Dense, Dropout
+
+
+products = ['glass', 'mixed', 'metal', 'paper']
+n_products = len(products)
+
+
+
+data = pd.read_csv("data.csv")
+dff = data.loc[:, data.columns != 'Unnamed: 0']
+
+"""#podział danych na treningowe i testowe"""
+
+#jest osiem zmiennych wejściowych i jedna zmienna wyjściowa (ostatnia kolumna).
+
+X = dff.drop(labels=['label'], axis=1)
+y = dff.label
+X_train, X_test, y_train, y_test = train_test_split(X, y, test_size =0.2 )
+
+X_test.to_csv("X_test.csv")
+
+"""## Budowa sieci neuronowej"""
+
+
+
+
+model = tf.keras.models.Sequential([
+                                    Dense(10, input_shape=[8], activation='relu'),
+                                    #Dropout(0.2),
+                                    Dense(n_products, activation='softmax')
+])
+
+model.summary()
+
+
+model.compile(optimizer='adam', loss = 'sparse_categorical_crossentropy', metrics=[ 'accuracy'])
+
+early_stopping = tf.keras.callbacks.EarlyStopping(monitor='val_accuracy', patience=4)
+history = model.fit(x=X_train, y=y_train, validation_split=0.1, epochs=100, batch_size=32, callbacks=[early_stopping])
+# Epoka : jeden przebieg przez wszystkie wiersze w zestawie danych uczących
+# Batch_size: liczba wierszy zestawu danych, które są brane pod uwagę, zanim wagi modelu zostaną zaktualizowane w każdej epoce
+#model.predict(X_test)
+
+#model.evaluate(X_test, y_test)
+
+model.save('C:/Users/Natalia/Desktop/lsm04/smieciara/saved_model')
+
+
diff --git a/neural_network2.py b/neural_network2.py
new file mode 100644
index 0000000..eb80cba
--- /dev/null
+++ b/neural_network2.py
@@ -0,0 +1,27 @@
+import numpy as np
+#from neural_network import *
+from numpy import loadtxt
+import pandas as pd
+from sklearn.model_selection import train_test_split
+import tensorflow as tf
+from tensorflow.keras.layers import Dense, Dropout
+
+products = ['glass', 'mixed', 'metal', 'paper']
+n_products = len(products)
+data = pd.read_csv("X_test.csv")
+dff = data.loc[:, data.columns != 'Unnamed: 0']
+def b():
+    a = dff.sample()
+    pred(a)
+
+model = tf.keras.models.load_model('C:/Users/Natalia/Desktop/lsm04/smieciara/saved_model')
+
+
+def pred(a):
+  print(a)
+  prediction_proba = model.predict(np.array(a))
+  prediction = np.argmax(prediction_proba)
+  text_representation = products[prediction]
+  print(text_representation)
+  print(prediction)
+  return prediction
\ No newline at end of file
diff --git a/neural_network_vers2.py b/neural_network_vers2.py
new file mode 100644
index 0000000..1541381
--- /dev/null
+++ b/neural_network_vers2.py
@@ -0,0 +1,31 @@
+import tensorflow as tf
+import numpy as np
+from tensorflow import keras
+import os
+import random
+from PIL import Image
+
+img_height = 180
+img_width = 180
+class_names=['glass','metal','paper','plastic']
+model = tf.keras.models.load_model('C:/Users/Natalia/Desktop/lsm04/smieciara/saved_model_vers2')
+def predict():
+    path="C:/Users/Natalia/Documents/dane_testowe"
+    files=os.listdir(path)
+    d=random.choice(files)
+    im = Image.open("C:/Users/Natalia/Documents/dane_testowe/" + d)
+    im.show()
+    img = keras.preprocessing.image.load_img(
+        "C:/Users/Natalia/Documents/dane_testowe/" + d, target_size=(img_height, img_width)
+    )
+    img_array = keras.preprocessing.image.img_to_array(img)
+    img_array = tf.expand_dims(img_array, 0) # Create a batch
+    predictions = model.predict(img_array)
+
+    score = tf.nn.softmax(predictions[0])
+
+    print(
+        "This image most likely belongs to {} with a {:.2f} percent confidence."
+        .format(class_names[np.argmax(score)], 100 * np.max(score))
+
+    )
diff --git a/saved_model/keras_metadata.pb b/saved_model/keras_metadata.pb
new file mode 100644
index 0000000..96d7597
--- /dev/null
+++ b/saved_model/keras_metadata.pb
@@ -0,0 +1,6 @@
+
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+�
root.layer_with_weights-0"_tf_keras_layer*�{"name": "dense", "trainable": true, "expects_training_arg": false, "dtype": "float32", "batch_input_shape": {"class_name": "__tuple__", "items": [null, 8]}, "stateful": false, "must_restore_from_config": false, "class_name": "Dense", "config": {"name": "dense", "trainable": true, "batch_input_shape": {"class_name": "__tuple__", "items": [null, 8]}, "dtype": "float32", "units": 10, "activation": "relu", "use_bias": true, "kernel_initializer": {"class_name": "GlorotUniform", "config": {"seed": null}, "shared_object_id": 1}, "bias_initializer": {"class_name": "Zeros", "config": {}, "shared_object_id": 2}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}, "shared_object_id": 3, "input_spec": {"class_name": "InputSpec", "config": {"dtype": null, "shape": null, "ndim": null, "max_ndim": null, "min_ndim": 2, "axes": {"-1": 8}}, "shared_object_id": 8}, "build_input_shape": {"class_name": "TensorShape", "items": [null, 8]}}2


+�root.layer_with_weights-1"_tf_keras_layer*�{"name": "dense_1", "trainable": true, "expects_training_arg": false, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "class_name": "Dense", "config": {"name": "dense_1", "trainable": true, "dtype": "float32", "units": 4, "activation": "softmax", "use_bias": true, "kernel_initializer": {"class_name": "GlorotUniform", "config": {"seed": null}, "shared_object_id": 4}, "bias_initializer": {"class_name": "Zeros", "config": {}, "shared_object_id": 5}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}, "shared_object_id": 6, "input_spec": {"class_name": "InputSpec", "config": {"dtype": null, "shape": null, "ndim": null, "max_ndim": null, "min_ndim": 2, "axes": {"-1": 10}}, "shared_object_id": 10}, "build_input_shape": {"class_name": "TensorShape", "items": [null, 10]}}2


+�
)root.keras_api.metrics.0"_tf_keras_metric*�
{"class_name": "Mean", "name": "loss", "dtype": "float32", "config": {"name": "loss", "dtype": "float32"}, "shared_object_id": 11}2


+�
*root.keras_api.metrics.1"_tf_keras_metric*�
{"class_name": "MeanMetricWrapper", "name": "accuracy", "dtype": "float32", "config": {"name": "accuracy", "dtype": "float32", "fn": "sparse_categorical_accuracy"}, "shared_object_id": 9}2


\ No newline at end of file
diff --git a/saved_model/saved_model.pb b/saved_model/saved_model.pb
new file mode 100644
index 0000000..305b359
Binary files /dev/null and b/saved_model/saved_model.pb differ
diff --git a/saved_model/variables/variables.data-00000-of-00001 b/saved_model/variables/variables.data-00000-of-00001
new file mode 100644
index 0000000..b79a700
Binary files /dev/null and b/saved_model/variables/variables.data-00000-of-00001 differ
diff --git a/saved_model/variables/variables.index b/saved_model/variables/variables.index
new file mode 100644
index 0000000..75ca5f6
Binary files /dev/null and b/saved_model/variables/variables.index differ
diff --git a/saved_model_vers2/keras_metadata.pb b/saved_model_vers2/keras_metadata.pb
new file mode 100644
index 0000000..3fc1595
--- /dev/null
+++ b/saved_model_vers2/keras_metadata.pb
@@ -0,0 +1,15 @@
+
+�Proot"_tf_keras_sequential*�P{"name": "sequential", "trainable": true, "expects_training_arg": true, "dtype": "float32", "batch_input_shape": null, "must_restore_from_config": false, "class_name": "Sequential", "config": {"name": "sequential", "layers": [{"class_name": "InputLayer", "config": {"batch_input_shape": {"class_name": "__tuple__", "items": [null, 180, 180, 3]}, "dtype": "float32", "sparse": false, "ragged": false, "name": "rescaling_1_input"}}, {"class_name": "Rescaling", "config": {"name": "rescaling_1", "trainable": true, "batch_input_shape": {"class_name": "__tuple__", "items": [null, 180, 180, 3]}, "dtype": "float32", "scale": 0.00392156862745098, "offset": 0.0}}, {"class_name": "Conv2D", "config": {"name": "conv2d", "trainable": true, "dtype": "float32", "filters": 16, "kernel_size": {"class_name": "__tuple__", "items": [3, 3]}, "strides": {"class_name": "__tuple__", "items": [1, 1]}, "padding": "same", "data_format": "channels_last", "dilation_rate": {"class_name": 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+�
root.layer-0"_tf_keras_layer*�{"name": "rescaling_1", "trainable": true, "expects_training_arg": false, "dtype": "float32", "batch_input_shape": {"class_name": "__tuple__", "items": [null, 180, 180, 3]}, "stateful": false, "must_restore_from_config": false, "class_name": "Rescaling", "config": {"name": "rescaling_1", "trainable": true, "batch_input_shape": {"class_name": "__tuple__", "items": [null, 180, 180, 3]}, "dtype": "float32", "scale": 0.00392156862745098, "offset": 0.0}, "shared_object_id": 1}2


+�	root.layer_with_weights-0"_tf_keras_layer*�	{"name": "conv2d", "trainable": true, "expects_training_arg": false, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "class_name": "Conv2D", "config": {"name": "conv2d", "trainable": true, "dtype": "float32", "filters": 16, "kernel_size": {"class_name": "__tuple__", "items": [3, 3]}, "strides": {"class_name": "__tuple__", "items": [1, 1]}, "padding": "same", "data_format": "channels_last", "dilation_rate": {"class_name": "__tuple__", "items": [1, 1]}, "groups": 1, "activation": "relu", "use_bias": true, "kernel_initializer": {"class_name": "GlorotUniform", "config": {"seed": null}, "shared_object_id": 2}, "bias_initializer": {"class_name": "Zeros", "config": {}, "shared_object_id": 3}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}, "shared_object_id": 4, "input_spec": {"class_name": "InputSpec", "config": {"dtype": null, "shape": null, "ndim": null, "max_ndim": null, "min_ndim": 4, "axes": {"-1": 3}}, "shared_object_id": 24}, "build_input_shape": {"class_name": "TensorShape", "items": [null, 180, 180, 3]}}2


+�root.layer-2"_tf_keras_layer*�{"name": "max_pooling2d", "trainable": true, "expects_training_arg": false, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "class_name": "MaxPooling2D", "config": {"name": "max_pooling2d", "trainable": true, "dtype": "float32", "pool_size": {"class_name": "__tuple__", "items": [2, 2]}, "padding": "valid", "strides": {"class_name": "__tuple__", "items": [2, 2]}, "data_format": "channels_last"}, "shared_object_id": 5, "input_spec": {"class_name": "InputSpec", "config": {"dtype": null, "shape": null, "ndim": 4, "max_ndim": null, "min_ndim": null, "axes": {}}, "shared_object_id": 25}}2


+�	root.layer_with_weights-1"_tf_keras_layer*�	{"name": "conv2d_1", "trainable": true, "expects_training_arg": false, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "class_name": "Conv2D", "config": {"name": "conv2d_1", "trainable": true, "dtype": "float32", "filters": 32, "kernel_size": {"class_name": "__tuple__", "items": [3, 3]}, "strides": {"class_name": "__tuple__", "items": [1, 1]}, "padding": "same", "data_format": "channels_last", "dilation_rate": {"class_name": "__tuple__", "items": [1, 1]}, "groups": 1, "activation": "relu", "use_bias": true, "kernel_initializer": {"class_name": "GlorotUniform", "config": {"seed": null}, "shared_object_id": 6}, "bias_initializer": {"class_name": "Zeros", "config": {}, "shared_object_id": 7}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}, "shared_object_id": 8, "input_spec": {"class_name": "InputSpec", "config": {"dtype": null, "shape": null, "ndim": null, "max_ndim": null, "min_ndim": 4, "axes": {"-1": 16}}, "shared_object_id": 26}, "build_input_shape": {"class_name": "TensorShape", "items": [null, 90, 90, 16]}}2


+�root.layer-4"_tf_keras_layer*�{"name": "max_pooling2d_1", "trainable": true, "expects_training_arg": false, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "class_name": "MaxPooling2D", "config": {"name": "max_pooling2d_1", "trainable": true, "dtype": "float32", "pool_size": {"class_name": "__tuple__", "items": [2, 2]}, "padding": "valid", "strides": {"class_name": "__tuple__", "items": [2, 2]}, "data_format": "channels_last"}, "shared_object_id": 9, "input_spec": {"class_name": "InputSpec", "config": {"dtype": null, "shape": null, "ndim": 4, "max_ndim": null, "min_ndim": null, "axes": {}}, "shared_object_id": 27}}2


+�	root.layer_with_weights-2"_tf_keras_layer*�	{"name": "conv2d_2", "trainable": true, "expects_training_arg": false, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "class_name": "Conv2D", "config": {"name": "conv2d_2", "trainable": true, "dtype": "float32", "filters": 64, "kernel_size": {"class_name": "__tuple__", "items": [3, 3]}, "strides": {"class_name": "__tuple__", "items": [1, 1]}, "padding": "same", "data_format": "channels_last", "dilation_rate": {"class_name": "__tuple__", "items": [1, 1]}, "groups": 1, "activation": "relu", "use_bias": true, "kernel_initializer": {"class_name": "GlorotUniform", "config": {"seed": null}, "shared_object_id": 10}, "bias_initializer": {"class_name": "Zeros", "config": {}, "shared_object_id": 11}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}, "shared_object_id": 12, "input_spec": {"class_name": "InputSpec", "config": {"dtype": null, "shape": null, "ndim": null, "max_ndim": null, "min_ndim": 4, "axes": {"-1": 32}}, "shared_object_id": 28}, "build_input_shape": {"class_name": "TensorShape", "items": [null, 45, 45, 32]}}2


+�root.layer-6"_tf_keras_layer*�{"name": "max_pooling2d_2", "trainable": true, "expects_training_arg": false, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "class_name": "MaxPooling2D", "config": {"name": "max_pooling2d_2", "trainable": true, "dtype": "float32", "pool_size": {"class_name": "__tuple__", "items": [2, 2]}, "padding": "valid", "strides": {"class_name": "__tuple__", "items": [2, 2]}, "data_format": "channels_last"}, "shared_object_id": 13, "input_spec": {"class_name": "InputSpec", "config": {"dtype": null, "shape": null, "ndim": 4, "max_ndim": null, "min_ndim": null, "axes": {}}, "shared_object_id": 29}}2


+�root.layer-7"_tf_keras_layer*�{"name": "flatten", "trainable": true, "expects_training_arg": false, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "class_name": "Flatten", "config": {"name": "flatten", "trainable": true, "dtype": "float32", "data_format": "channels_last"}, "shared_object_id": 14, "input_spec": {"class_name": "InputSpec", "config": {"dtype": null, "shape": null, "ndim": null, "max_ndim": null, "min_ndim": 1, "axes": {}}, "shared_object_id": 30}}2


+�	root.layer_with_weights-3"_tf_keras_layer*�{"name": "dense", "trainable": true, "expects_training_arg": false, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "class_name": "Dense", "config": {"name": "dense", "trainable": true, "dtype": "float32", "units": 128, "activation": "relu", "use_bias": true, "kernel_initializer": {"class_name": "GlorotUniform", "config": {"seed": null}, "shared_object_id": 15}, "bias_initializer": {"class_name": "Zeros", "config": {}, "shared_object_id": 16}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}, "shared_object_id": 17, "input_spec": {"class_name": "InputSpec", "config": {"dtype": null, "shape": null, "ndim": null, "max_ndim": null, "min_ndim": 2, "axes": {"-1": 30976}}, "shared_object_id": 31}, "build_input_shape": {"class_name": "TensorShape", "items": [null, 30976]}}2


+�
+root.layer_with_weights-4"_tf_keras_layer*�{"name": "dense_1", "trainable": true, "expects_training_arg": false, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "class_name": "Dense", "config": {"name": "dense_1", "trainable": true, "dtype": "float32", "units": 4, "activation": "linear", "use_bias": true, "kernel_initializer": {"class_name": "GlorotUniform", "config": {"seed": null}, "shared_object_id": 18}, "bias_initializer": {"class_name": "Zeros", "config": {}, "shared_object_id": 19}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}, "shared_object_id": 20, "input_spec": {"class_name": "InputSpec", "config": {"dtype": null, "shape": null, "ndim": null, "max_ndim": null, "min_ndim": 2, "axes": {"-1": 128}}, "shared_object_id": 32}, "build_input_shape": {"class_name": "TensorShape", "items": [null, 128]}}2


+�
root.keras_api.metrics.0"_tf_keras_metric*�
{"class_name": "Mean", "name": "loss", "dtype": "float32", "config": {"name": "loss", "dtype": "float32"}, "shared_object_id": 33}2


+�
�
root.keras_api.metrics.1"_tf_keras_metric*�
{"class_name": "MeanMetricWrapper", "name": "accuracy", "dtype": "float32", "config": {"name": "accuracy", "dtype": "float32", "fn": "sparse_categorical_accuracy"}, "shared_object_id": 23}2


\ No newline at end of file
diff --git a/saved_model_vers2/saved_model.pb b/saved_model_vers2/saved_model.pb
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diff --git a/saved_model_vers2/variables/variables.index b/saved_model_vers2/variables/variables.index
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diff --git a/sprites/agent.png b/sprites/agent.png
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diff --git a/sprites/agentWithGarbage.png b/sprites/agentWithGarbage.png
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diff --git a/sprites/brick.png b/sprites/brick.png
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index 0000000..96c30a7
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diff --git a/sprites/dzwiek.wav b/sprites/dzwiek.wav
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diff --git a/sprites/grass.png b/sprites/grass.png
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diff --git a/sprites/hole.png b/sprites/hole.png
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index 0000000..535a71b
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diff --git a/sprites/house.png b/sprites/house.png
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index 0000000..e21109e
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diff --git a/sprites/papier.png b/sprites/papier.png
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diff --git a/sprites/plastik.png b/sprites/plastik.png
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diff --git a/sprites/road.png b/sprites/road.png
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diff --git a/sprites/sand.png b/sprites/sand.png
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diff --git a/sprites/speedbump.png b/sprites/speedbump.png
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index 0000000..60fe5d4
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diff --git a/sprites/szklo.png b/sprites/szklo.png
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diff --git a/sprites/tree.png b/sprites/tree.png
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index 0000000..c021781
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diff --git a/sprites/zmieszane.png b/sprites/zmieszane.png
new file mode 100644
index 0000000..af15c82
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diff --git a/world.py b/world.py
new file mode 100644
index 0000000..b5a7e20
--- /dev/null
+++ b/world.py
@@ -0,0 +1,187 @@
+import pygame
+agent = pygame.image.load("sprites/agent.png")
+agentWithGarbage = pygame.image.load("sprites/agentWithGarbage.png")
+grass = pygame.image.load("sprites/grass.png")
+tree = pygame.image.load("sprites/tree.png")  # cost 0
+house = pygame.image.load("sprites/house.png")  # cost 0
+road = pygame.image.load("sprites/road.png")  # cost 1
+brick = pygame.image.load("sprites/brick.png")  # cost 2
+sand = pygame.image.load("sprites/sand.png")  # cost 3
+hole = pygame.image.load("sprites/hole.png")  # cost 9
+speed_bump = pygame.image.load("sprites/speedbump.png")  # cost 8
+glass_bin = pygame.image.load("sprites/szklo.png")
+paper_bin = pygame.image.load("sprites/papier.png")
+plastic_bin = pygame.image.load("sprites/plastik.png")
+other_bin = pygame.image.load("sprites/zmieszane.png")
+a=0
+b=1
+
+def set_world(path):
+    array = []
+    with open(path) as f:
+        content = f.read().splitlines()
+        for line in content:
+            array.append(line)
+    return array
+
+
+class WorldCell(pygame.Rect):
+    def __init__(self, x, y, rect_size, terrain, img):
+        self.rect_size = rect_size
+        self.terrain = terrain
+        self.img = pygame.transform.scale(img, (40, 40))
+        super(WorldCell, self).__init__(x, y, self.rect_size, self.rect_size)
+
+    def draw(self, window):
+        pygame.draw.rect(window, (0, 0, 0), self)
+        window.blit(self.img, self)
+
+    def get_terrain(self) -> int:
+        return self.terrain
+
+
+class World:
+    def __init__(self, world_map, cell_size):
+        self.cell_size = cell_size
+        self.y_size = len(world_map)
+        self.x_size = len(world_map[0])
+        self.world_map = world_map
+        self.world_cells = [[0 for x in range(self.x_size)] for y in range(self.y_size)]
+
+    def draw(self, window):
+        for i in range(self.y_size):
+            for j in range(self.x_size):
+                img = ''
+                terrain = int(self.world_map[i][j])  # zczytywanie z planszy do terrain
+                if terrain == 0:
+                    if ((i * j) + j + i) % 2 == 0:
+                        img = pygame.transform.scale(tree, (40, 40))
+                    else:
+                        img = pygame.transform.scale(house, (40, 40))
+                if terrain == 1:
+                    img = pygame.transform.scale(road, (40, 40))
+                if terrain == 2:
+                    img = pygame.transform.scale(brick, (40, 40))
+                if terrain == 3:
+                    img = pygame.transform.scale(sand, (40, 40))
+                if terrain == 4:
+                    img = pygame.transform.scale(glass_bin, (40, 40))
+                if terrain == 5:
+                    img = pygame.transform.scale(paper_bin, (40, 40))
+                if terrain == 6:
+                    img = pygame.transform.scale(plastic_bin, (40, 40))
+                if terrain == 7:
+                    img = pygame.transform.scale(other_bin, (40, 40))
+                if terrain == 9:
+                    img = pygame.transform.scale(hole, (40, 40))
+                if terrain == 8:
+                    img = pygame.transform.scale(speed_bump, (40, 40))
+
+                self.world_cells[i][j] = WorldCell(j * self.cell_size, i * self.cell_size, self.cell_size, terrain, img)
+                self.world_cells[i][j].draw(window)
+
+    def get_world_cell_size(self):
+        return self.cell_size
+
+
+
+
+class Agent(pygame.sprite.Sprite):
+    def __init__(self, size, world):
+        pygame.sprite.Sprite.__init__(self)
+        self.image = pygame.transform.scale(pygame.image.load("sprites/agent.png"), (size, size))
+        self.rect = self.image.get_rect()
+        self.rect.center = (60, 20)  # agent position on start
+        self.size = size
+        self.directions = ["E", "S", "W", "N"]
+        self.world = world
+        self.a = 1
+        self.b = 0
+
+    def change_image(self):
+        self.image = pygame.transform.scale(pygame.image.load("sprites/agentWithGarbage.png"), (40, 40))
+    def change_image2(self):
+        self.image = pygame.transform.scale(pygame.image.load("sprites/agent.png"), (40, 40))
+    def get_directions(self):
+        return self.directions
+
+    def check_collisions(self, x, y):
+        x_parsed = int((x - 20) / self.world.cell_size)
+        y_parsed = int((y - 20) / self.world.cell_size)
+        if x_parsed < 0:
+            return False
+        if y_parsed < 0:
+            return False
+        if x_parsed >= self.world.x_size:
+            return False
+        if y_parsed >= self.world.y_size:
+            return False
+        if not self.world.world_cells[y_parsed][x_parsed].terrain:
+            return False
+        return True
+
+    def move(self):
+        if self.directions[0] == "N":
+            x = self.rect.center[0]
+            y = self.rect.center[1] - self.size
+            if self.check_collisions(x, y):
+                self.rect.center = [x, y]
+                self.b = self.b - 1
+        if self.directions[0] == "S":
+            x = self.rect.center[0]
+            y = self.rect.center[1] + self.size
+            if self.check_collisions(x, y):
+                self.rect.center = [x, y]
+                self.b = self.b + 1
+        if self.directions[0] == "W":
+            x = self.rect.center[0] - self.size
+            y = self.rect.center[1]
+            if self.check_collisions(x, y):
+                self.rect.center = [x, y]
+                self.a = self.a- 1
+        if self.directions[0] == "E":
+            x = self.rect.center[0] + self.size
+            y = self.rect.center[1]
+            if self.check_collisions(x, y):
+                self.rect.center = [x, y]
+                self.a = self.a + 1
+
+    def get_next_move(self):
+        if self.directions[0] == "N":
+            x = self.rect.center[0]
+            y = self.rect.center[1] - self.size
+            self.a = self.a - 1
+            if self.check_collisions(x, y):
+                return x, y
+        if self.directions[0] == "S":
+            x = self.rect.center[0]
+            y = self.rect.center[1] + self.size
+            if self.check_collisions(x, y):
+                return x, y
+        if self.directions[0] == "W":
+            x = self.rect.center[0] - self.size
+            y = self.rect.center[1]
+            if self.check_collisions(x, y):
+                return x, y
+        if self.directions[0] == "E":
+            x = self.rect.center[0] + self.size
+            y = self.rect.center[1]
+            if self.check_collisions(x, y):
+                return x, y
+
+    def get_first(self):
+        return self.a
+    def get_second(self):
+        return self.b
+
+
+    def turn_right(self):
+        self.image = pygame.transform.rotate(self.image, -90)
+        self.directions = self.directions[1:] + [self.directions[0]]
+
+    def turn_left(self):
+        self.image = pygame.transform.rotate(self.image, 90)
+        self.directions = [self.directions[3]] + self.directions[:3]
+
+
+
diff --git a/worldmap.txt b/worldmap.txt
new file mode 100644
index 0000000..67d61fe
--- /dev/null
+++ b/worldmap.txt
@@ -0,0 +1,14 @@
+23010003030102290811
+23319113332011229991
+91010209011103090311
+33338133322292223311
+21033192039333810311
+23398103328102293212
+23333123932122292212
+21122133398122293212
+33308190393333383017
+22333122132112213911
+21020192030103211111
+91333303311139213311
+21222229221113291111
+12398898133299221221
\ No newline at end of file
diff --git a/wykres_metal.png b/wykres_metal.png
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diff --git a/wykres_papier.png b/wykres_papier.png
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diff --git a/wykres_szklo.png b/wykres_szklo.png
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