Merge pull request #38 from Vayun-Goel/lin_reg_3d_visualizer

Adding linear regression 3d visualizer

Applications/ML_Algorithms/Linear_Regression/linear_regression_3d_visualizer.py

@@ -0,0 +1,70 @@
+import sys
+
+sys.path.append('/Users/vayungoel/Desktop/Code-Forge/ML_Algorithms')
+
+from Linear_Regression.linear_regression_2d import LinearRegression2D
+
+import numpy as np
+import matplotlib.pyplot as plt
+from mpl_toolkits.mplot3d import Axes3D
+
+n = int(input("Enter 1 for generating equation of a plane without noise\nEnter 2 for generating equation of a plane with gaussian noise\nEnter 3 for generating equation of a non-linear curve\n"))
+
+num_points = 500
+
+x1 = np.random.rand(num_points) * 10  
+x2 = np.random.rand(num_points) * 10  
+
+if(n<=2):
+
+    a = 1.0
+    b = 2.0
+    c = 4.0
+    y = a * x1 + b * x2 + c
+
+if(n==2):
+    noise = np.random.normal(0, 2, num_points)  
+    y = (y + noise)
+
+if(n==3):
+    a = 1.0
+    b = 2.0
+    c = 0.5
+    d = 0.02
+    e = 0.01
+    f = 2.0
+    y = a * x1 + b * x2 + (x1*x2*c) + (x1*x2*x2*d) + (x1**4)*e + f
+
+points = [[0 for i in range(3)] for j in range(500)]
+
+for i in range(500):
+    points[i][0]=x1[i]
+    points[i][1]=x2[i]
+    points[i][2]=y[i]
+
+if(n<=2):
+    a = LinearRegression2D(points,1)
+else:
+    a = LinearRegression2D(points,2)
+
+t = a.visualizer()
+
+points = np.array(points)
+
+x_surf = np.linspace(0, 10, 100)
+y_surf = np.linspace(0, 10, 100)
+X, Y = np.meshgrid(x_surf, y_surf)
+
+print(t)
+
+for i in range(len(t)):
+    fig = plt.figure()
+    ax = fig.add_subplot(111, projection='3d')
+    Z = (float(t[i][0])* X + float(t[i][1]) * Y + float(t[i][2])) 
+    ax.scatter(x1, x2, y, c='b', marker='o')
+    ax.plot_surface(X, Y, Z, alpha=0.5, rstride=100, cstride=100, cmap='viridis')
+    ax.set_xlabel('X')
+    ax.set_ylabel('Y')
+    ax.set_zlabel('Z')
+
+    plt.show()