From 7ea02216304190775c1945cf5cfc7667c22835df Mon Sep 17 00:00:00 2001
From: Namonay <thek7995@gmail.com>
Date: Sun, 4 May 2025 21:43:08 +0200
Subject: [PATCH] add: estimate program

---
 estimate.py | 19 +++++++++++++++++++
 model.py    | 17 +++++++++++++++--
 thetas.csv  |  2 ++
 3 files changed, 36 insertions(+), 2 deletions(-)
 create mode 100644 estimate.py
 create mode 100644 thetas.csv

diff --git a/estimate.py b/estimate.py
new file mode 100644
index 0000000..f9af731
--- /dev/null
+++ b/estimate.py
@@ -0,0 +1,19 @@
+import pandas as pd
+
+class Estimate:
+    def __init__(self, thetas_path="thetas.csv"):
+        self.path = thetas_path
+        self.theta0 = 0
+        self.theta1 = 0
+        self.get_thetas()
+    def get_thetas(self):
+        with open(self.path, 'r') as file:
+            self.data = pd.read_csv(file)
+            self.theta0 = self.data["theta0"].iloc[0]
+            self.theta1 = self.data["theta1"].iloc[0]
+    def estimate_price(self, mileage):
+        return self.theta0 + (self.theta1 * mileage)
+
+estimate = Estimate()
+input =  int(input("Please enter the mileage of your car: "))
+print("The estimated price for a", input, "kilometers car is :", estimate.estimate_price(input))
diff --git a/model.py b/model.py
index a5d9e34..9e44967 100644
--- a/model.py
+++ b/model.py
@@ -1,6 +1,7 @@
 import pandas as pd
 import numpy as np
 import matplotlib.pyplot as plt
+import csv
 
 class Model:
     def __init__(self, data_name="data.csv", learning_rate=0.01, epochs=2000):
@@ -17,6 +18,7 @@ class Model:
             self.cost_history = []
             self.m = len(self.data)
             self.normalize_values()
+
     def __estimate_price(self, mileage):
         return self.theta0 + (self.theta1 * mileage)
 
@@ -46,7 +48,18 @@ class Model:
             tmp_t0, tmp_t1 = self.calculate_thetas()
             self.theta0 -= self.learning_rate * tmp_t0
             self.theta1 -= self.learning_rate * tmp_t1
-        pass
+
+    def export(self):
+        theta1_raw = self.theta1 / (self.km_max - self.km_min)
+        theta0_raw = self.theta0 - self.theta1 * self.km_min / (self.km_max - self.km_min)
+        data = [
+            ["theta0", "theta1"],
+            [theta0_raw, theta1_raw]
+        ]
+        with open("thetas.csv", mode="w", newline="") as file:
+            writer = csv.writer(file)
+
+            writer.writerows(data)
     def visualize(self):
         km_range = np.linspace(min(self.data["km"]), max(self.data["km"]), 100)
         predicted_prices = self.theta0 + (self.theta1 * (km_range - self.km_min) / (self.km_max - self.km_min))
@@ -67,8 +80,8 @@ class Model:
         cost.set_ylabel("Coût")
         cost.grid(True)
         plt.show()
-        pass
 
 first_model = Model()
 first_model.train()
+first_model.export()
 first_model.visualize()
diff --git a/thetas.csv b/thetas.csv
new file mode 100644
index 0000000..51be09d
--- /dev/null
+++ b/thetas.csv
@@ -0,0 +1,2 @@
+theta0,theta1
+8499.599649806703,-0.02144896359049913