From 233f2188995b468bc26024a620c67334eb8c9fcb Mon Sep 17 00:00:00 2001
From: s473554 <you@example.com>
Date: Thu, 25 May 2023 13:21:43 +0200
Subject: [PATCH] added vegetables strorage in tractor and vegetables store

---
 01.csv  | 257 ++++++++++++++++++++++++++++++++++++++++++++++++++++++++
 01.xlsx | Bin 0 -> 17759 bytes
 10.csv  | 257 ++++++++++++++++++++++++++++++++++++++++++++++++++++++++
 IC3.py  | 154 +++++++++++++++++++++++++++++++++
 4 files changed, 668 insertions(+)
 create mode 100644 01.csv
 create mode 100644 01.xlsx
 create mode 100644 10.csv
 create mode 100644 IC3.py

diff --git a/01.csv b/01.csv
new file mode 100644
index 0000000..8ccbdc5
--- /dev/null
+++ b/01.csv
@@ -0,0 +1,257 @@
+can it get to the next point,will it be able to get to the gas station,will it be able to get to the gas station after arriving at the next point,will it be able to take the next vegetable to the tractor storage,will it be able to get to the vegetable warehouse,will it be able to get to the gas station after it arrives at the vegetable warehouse,is the vegetable warehouse closed,is the gas station closed,go to: 1)next veget. 2)gas station 3)warehouse 4)sleep 5)GAME OVER
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diff --git a/01.xlsx b/01.xlsx
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diff --git a/10.csv b/10.csv
new file mode 100644
index 0000000..8ccbdc5
--- /dev/null
+++ b/10.csv
@@ -0,0 +1,257 @@
+can it get to the next point,will it be able to get to the gas station,will it be able to get to the gas station after arriving at the next point,will it be able to take the next vegetable to the tractor storage,will it be able to get to the vegetable warehouse,will it be able to get to the gas station after it arrives at the vegetable warehouse,is the vegetable warehouse closed,is the gas station closed,go to: 1)next veget. 2)gas station 3)warehouse 4)sleep 5)GAME OVER
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diff --git a/IC3.py b/IC3.py
new file mode 100644
index 0000000..bbaa741
--- /dev/null
+++ b/IC3.py
@@ -0,0 +1,154 @@
+import pandas as pd
+import numpy as np
+import json
+
+
+train_data_m = pd.read_csv("01.csv")
+test_data_m = pd.read_csv("10.csv")
+
+def calc_total_entropy(train_data, label, class_list):
+    total_row = train_data.shape[0]
+    total_entr = 0
+
+    for c in class_list:
+        total_class_count = train_data[train_data[label] == c].shape[0]
+        if total_class_count == 0:
+            total_class_entr = 0
+        else:
+            total_class_entr = - (total_class_count / total_row) * np.log2(total_class_count / total_row)
+        total_entr += total_class_entr
+
+    return total_entr
+
+
+def calc_entropy(feature_value_data, label, class_list):
+    class_count = feature_value_data.shape[0]
+    entropy = 0
+
+    for c in class_list:
+        label_class_count = feature_value_data[feature_value_data[label] == c].shape[0]
+
+        entropy_class = 0
+        if label_class_count != 0:
+            probability_class = label_class_count / class_count
+            entropy_class = - probability_class * np.log2(probability_class)
+
+        entropy += entropy_class
+
+    return entropy
+
+
+def calc_info_gain(feature_name, train_data, label, class_list):
+    feature_value_list = train_data[feature_name].unique()
+    total_row = train_data.shape[0]
+    feature_info = 0.0
+
+    for feature_value in feature_value_list:
+        feature_value_data = train_data[train_data[feature_name] == feature_value]
+        feature_value_count = feature_value_data.shape[0]
+        feature_value_entropy = calc_entropy(feature_value_data, label, class_list)
+        feature_value_probability = feature_value_count / total_row
+        feature_info += feature_value_probability * feature_value_entropy
+
+    return calc_total_entropy(train_data, label, class_list) - feature_info
+
+
+def find_most_informative_feature(train_data, label, class_list):
+    feature_list = train_data.columns.drop(label)
+    max_info_gain = -1
+    max_info_feature = None
+
+    for feature in feature_list:
+        feature_info_gain = calc_info_gain(feature, train_data, label, class_list)
+        if max_info_gain < feature_info_gain:
+            max_info_gain = feature_info_gain
+            max_info_feature = feature
+    return max_info_feature
+
+
+def generate_sub_tree(feature_name, train_data, label, class_list):
+    feature_value_count_dict = train_data[feature_name].value_counts(sort=False)
+    tree = {}
+    for feature_value, count in feature_value_count_dict.items():
+        feature_value_data = train_data[train_data[feature_name] == feature_value]
+
+        assigned_to_node = False
+        for c in class_list:
+            class_count = feature_value_data[feature_value_data[label] == c].shape[0]
+            if class_count == count:
+                tree[feature_value] = c
+                train_data = train_data[train_data[feature_name] != feature_value]
+                assigned_to_node = True
+        if not assigned_to_node:
+            tree[feature_value] = "?"
+    return tree, train_data
+
+
+def make_tree(root, prev_feature_value, train_data, label, class_list):
+    if train_data.shape[0] != 0:
+        max_info_feature = find_most_informative_feature(train_data, label, class_list)
+        tree, train_data = generate_sub_tree(max_info_feature, train_data, label, class_list)
+        next_root = None
+        if prev_feature_value != None:
+            root[prev_feature_value] = dict()
+            root[prev_feature_value][max_info_feature] = tree
+            next_root = root[prev_feature_value][max_info_feature]
+        else:
+            root[max_info_feature] = tree
+            next_root = root[max_info_feature]
+
+        for node, branch in list(next_root.items()):
+            if branch == "?":
+                feature_value_data = train_data[train_data[max_info_feature] == node]
+                make_tree(next_root, node, feature_value_data, label, class_list)
+
+
+def id3(train_data_m, label):
+    train_data = train_data_m.copy()
+    tree = {}
+    class_list = train_data[label].unique()
+    make_tree(tree, None, train_data, label, class_list)
+
+    return tree
+
+def predict(tree, instance):
+    if not isinstance(tree, dict):
+        return tree
+    else:
+        root_node = next(iter(tree))
+        feature_value = instance[root_node]
+        if feature_value in tree[root_node]:
+            return predict(tree[root_node][feature_value], instance)
+        else:
+            return None
+
+def evaluate(tree, test_data_m, label):
+    correct_preditct = 0
+    wrong_preditct = 0
+    for index, row in test_data_m.iterrows():
+        result = predict(tree, test_data_m.iloc[index])
+        if result == test_data_m[label].iloc[index]:
+            correct_preditct += 1
+        else:
+            wrong_preditct += 1
+    accuracy = correct_preditct / (correct_preditct + wrong_preditct)
+    return accuracy
+
+class NpEncoder(json.JSONEncoder):
+    def default(self, obj):
+        if isinstance(obj, np.integer):
+            return int(obj)
+        if isinstance(obj, np.floating):
+            return float(obj)
+        if isinstance(obj, np.ndarray):
+            return obj.tolist()
+        return json.JSONEncoder.default(self, obj)
+
+
+tree = id3(train_data_m, 'go to: 1)next veget. 2)gas station 3)warehouse 4)sleep 5)GAME OVER')
+# print(tree)
+json_str = json.dumps(tree, indent=2, cls=NpEncoder)
+print(json_str)
+
+accuracy = evaluate(tree, test_data_m, 'go to: 1)next veget. 2)gas station 3)warehouse 4)sleep 5)GAME OVER') #evaluating the test dataset
+print(accuracy)