From 5c72bc22b42fbdfa5494ea53cdfd017c292d3cb0 Mon Sep 17 00:00:00 2001
From: Ceres <ceres@ceressees.dev>
Date: Sun, 15 Feb 2026 13:29:47 +0000
Subject: [PATCH] More Ex 3

---
 Exercise 3/exercise3.py | 65 ++++++++++++++++++++++++++++++++---------
 1 file changed, 51 insertions(+), 14 deletions(-)

diff --git a/Exercise 3/exercise3.py b/Exercise 3/exercise3.py
index 117054c..9da9f66 100644
--- a/Exercise 3/exercise3.py	
+++ b/Exercise 3/exercise3.py	
@@ -2,6 +2,7 @@ import matplotlib.pyplot as plt
 import numpy as np
 from scipy import integrate
 import pandas as pd
+from sklearn.linear_model import LinearRegression
 # from tqdm import tqdm #Import all needed modules
 
 columns = ["Material", "Density", "Radius", "Mass", "Temperature", "Pressure", "Height", "Time"]
@@ -46,6 +47,8 @@ def columnStats(column, units):
 
 df = getData('exercise3data.csv')
 
+####Part 1
+
 # for i in range(len(columns)):
 #     if columns[i] == "Material":
 #         continue
@@ -65,21 +68,55 @@ df = getData('exercise3data.csv')
 #     plt.legend()
 #     plt.show()
 
-dfNoMaterial = df.drop("Material", axis=1)
-corrMatrix = dfNoMaterial.corr(method='pearson')
-print(corrMatrix)
+####Part 2    
 
-fig, ax = plt.subplots()
-im = ax.imshow(corrMatrix, cmap="gnuplot", vmin=-1, vmax=1)
+# dfNoMaterial = df.drop("Material", axis=1)
+# corrMatrix = dfNoMaterial.corr(method='pearson')
+# print(corrMatrix)
 
-ax.set_xticks(range(len(columnsNoMaterial)), labels=columnsNoMaterial)
-ax.set_yticks(range(len(columnsNoMaterial)), labels=columnsNoMaterial)
+# fig, ax = plt.subplots()
+# im = ax.imshow(corrMatrix, cmap="gnuplot", vmin=-1, vmax=1)
 
-for i in range(len(columnsNoMaterial)):
-    for j in range(len(columnsNoMaterial)):
-        text = ax.text(j, i, round(corrMatrix[columnsNoMaterial[i]][columnsNoMaterial[j]], 2),
-                       ha="center", va="center", color="w")
+# ax.set_xticks(range(len(columnsNoMaterial)), labels=columnsNoMaterial)
+# ax.set_yticks(range(len(columnsNoMaterial)), labels=columnsNoMaterial)
 
-fig.colorbar(im)
-fig.tight_layout()
-plt.show()
+# for i in range(len(columnsNoMaterial)):
+#     for j in range(len(columnsNoMaterial)):
+#         text = ax.text(j, i, round(corrMatrix[columnsNoMaterial[i]][columnsNoMaterial[j]], 2),
+#                        ha="center", va="center", color="w")
+
+# fig.colorbar(im)
+# fig.tight_layout()
+# plt.show()
+
+####Part 3
+
+features = df[["Density", "Radius", "Mass", "Temperature", "Pressure", "Height"]]
+targets = df["Time"]
+
+linearReg = LinearRegression()
+linearFit = linearReg.fit(features, targets)
+
+for i in range(len(linearFit.feature_names_in_)):
+    print(f'The coefficient of {linearFit.feature_names_in_[i]} is {linearFit.coef_[i]} {units[i+1]}')
+
+ironDf = df[df["Material"] == "iron"]
+
+def fitByMeans(density, radius, mass, temp, pressure, height):
+    coefs = linearFit.coef_
+    time = linearFit.intercept_+(density*coefs[0])+(radius*coefs[1])+(mass*coefs[2])+(temp*coefs[3])+(pressure*coefs[4])+(height*coefs[5])
+    return time
+
+for radius in radii:
+    radiusDf = ironDf[ironDf["Radius"] == radius]
+    plt.scatter(radiusDf["Height"], radiusDf["Time"],label="Experimental data")
+    radiusFeatures = radiusDf[["Density", "Radius", "Mass", "Temperature", "Pressure", "Height"]]
+    plt.scatter(radiusDf["Height"], linearReg.predict(radiusFeatures),label="Predicted data")
+    heightBounds = [radiusDf["Height"].min(),radiusDf["Height"].max()]
+    linearByMeans = [fitByMeans(radiusDf["Density"].mean(),radiusDf["Radius"].mean(),radiusDf["Mass"].mean(),radiusDf["Temperature"].mean(),radiusDf["Pressure"].mean(),radiusDf["Height"].min()),fitByMeans(radiusDf["Density"].mean(),radiusDf["Radius"].mean(),radiusDf["Mass"].mean(),radiusDf["Temperature"].mean(),radiusDf["Pressure"].mean(),radiusDf["Height"].max())]
+    plt.plot(heightBounds,linearByMeans,label="Fit Using Means")
+    plt.xlabel("Drop Height/m")
+    plt.ylabel("Fall Time/s")
+    plt.legend()
+    plt.title(f'Iron data and predictions for radius of {radius}m')
+    plt.show()