Add linear regression

1 files changed, 69 insertions, 0 deletions

diff --git a/linear_regression/polynomial.py b/linear_regression/polynomial.py
new file mode 100755
index 0000000..a62474a
--- /dev/null
+++ b/linear_regression/polynomial.py
@@ -0,0 +1,69 @@
+#!/usr/bin/env python
+
+import pandas as pd
+import matplotlib.pyplot as plt
+from matplotlib.animation import FuncAnimation
+import numpy as np
+
+# Load the data
+csv="../data/polynomial.csv"
+data=pd.read_csv(csv)
+x=np.array(data["x"])
+y=np.array(data["y"])
+
+# Define the weight
+w1=w2=w3=10
+
+# Define our model
+def h(x):
+    return(w1+w2*x+w3*(x**2))
+
+# Define all partial derivative
+def dh1():
+    return(1/len(x)*np.sum(h(x)-y))
+def dh2():
+    return(1/len(x)*np.sum((h(x)-y)*x))
+def dh3():
+    return(1/len(x)*np.sum((h(x)-y)*(x**2)))
+
+# Perform the gradient decent
+fig, ax = plt.subplots()
+frame=0 # Current frame (plot animation)
+alpha=0.005 # Proportion of the gradient to take into account
+accuracy=0.000001 # Accuracy of the decent
+done=False
+def decent(i):
+    global w1,w2,w3,x,y,frame
+    while True: 
+        w1_old=w1
+        w1_new=w1-alpha*dh1()
+        w2_old=w2
+        w2_new=w2-alpha*dh2()
+        w3_old=w3
+        w3_new=w3-alpha*dh3()
+        w1=w1_new
+        w2=w2_new
+        w3=w3_new
+
+        if abs(w1_new-w1_old) <= accuracy and abs(w2_new-w2_old) <= accuracy and abs(w2_new-w2_old) <= accuracy:
+            done=True
+        frame+=1
+        if frame >=1000:
+            frame=0
+            ax.clear()
+            ax.set_xlim([0, 7])
+            ax.set_ylim([0, 5])
+            ax.plot(x,y,"ro")
+            ax.plot(x,h(x))
+            break
+
+def IsDone():
+    global done
+    i = 0
+    while not done:
+        i += 1
+        yield i
+        
+anim=FuncAnimation(fig,decent,frames=IsDone,repeat=False)
+anim.save('polynomial.gif',dpi=80,writer="imagemagick")
+