Upload files to "ai-wozek"

ai-wozek/wozek.py

@@ -9,6 +9,11 @@ from classes import *
 import numpy as np
 import pandas as pd
 import math
+from sklearn.model_selection import train_test_split
+from sklearn.tree import DecisionTreeClassifier
+import category_encoders
+from sklearn import metrics
+from sklearn import tree
 
 class Node():
     def __init__(self,position,rotation,action,parent,cost):
@@ -335,8 +340,31 @@ def astar(isstate,final):
                 heapq.heappush(fringe,(p,successor))
 
 #drzewko
+cols=['Height', 'Width', 'Depth', 'Weight', 'Damage', 'Label_State', 'Content', 'Value']
 tree_data_base = pd.read_csv('paczki.csv')
-def entropy(data):
+x=tree_data_base.drop(columns='Acceptance')
+y=tree_data_base['Acceptance']
+x_train,x_test,y_train,y_test=train_test_split(x,y,test_size=0.4)
+# Create Decision Tree classifer object
+encoder = category_encoders.OrdinalEncoder(cols=cols)
+
+x_train = encoder.fit_transform(x_train)
+
+x_test = encoder.transform(x_test)
+
+clf = DecisionTreeClassifier(criterion='entropy')
+clf = clf.fit(x_train,y_train)
+
+#Predict the response for test dataset
+y_pred = clf.predict(x_test)
+print("Accuracy:",metrics.accuracy_score(y_test, y_pred))
+
+text_tree=tree.export_text(clf)
+print(text_tree)
+# Train Decision Tree Classifer
+#clf = clf.fit(x_train,y_train)
+
+"""def entropy(data):
     labels = data.iloc[:, -1]  # Ostatnia kolumna zawiera etykiety klas i pomija 1 wiersz bo jest tytulowy
     counts = labels.value_counts() #tu zlicza wszystkie opcje
     probabilities = counts / len(labels)    
@@ -351,10 +379,10 @@ def information_gain(data, attribute):
         subset = data[data[attribute] == value]  # przypisujesz wszystkie wiersze danego value do  subset
         subset_entropy = entropy(subset)
         weighted_entropy += (len(subset) / len(data)) * subset_entropy
-    return (total_entropy - weighted_entropy)
+    return (total_entropy - weighted_entropy)"""
+
 
 
-            
 
 # Main game loop
 def game_loop():