Import numpy AS NP Import matplotlib.pyplot AS PLT x_data = [338,333,328,207,226,25,179,60,208,606 ] y_data = [640,633,619,393,428,27,193,66,226,1591 ] # generated number from -200 to -100, -100 does not include the # X-axis x = np.arange (-200, -100,1 ) # Y-axis Y = np.arange (-5,5,0.1 ) # store corresponding error the Z = np.zeros ((len (X), len (Y)) ) # X tiled horizontally to X, y tile Vertically to the Y # X-, np.meshgrid the Y = (X, Y) for I in Range (len (X)): for J in Range (len (Y)): b= X [I] W = Y [J] # rows calculating error the Z [J] [I] = 0 # error and for n- in Range (len (x_data)): the Z [J] [I] = the Z [ J] [I] + (y_data [n-] - B - * x_data W [n-]) ** 2 # normalizing the Z [J] [I] = the Z [J] [I] / len (x_data) # Y W * X + B = B = -120 W = -4 LR = 0.0000001 Iteration = 100000 b_history = [B] w_history = [W] for I in Range (Iteration): b_grad= 0.0 w_grad = 0.0 for n in range(len(x_data)): b_grad = b_grad - 2.0*(y_data[n] - b - w*x_data[n])*1.0 w_grad = w_grad - 2.0 * (y_data[n] - b - w * x_data[n]) * x_data[n] b = b - lr * b_grad w = w - lr * w_grad b_history.append(b) w_history.append(w) plt.contourf(x, y, Z, 50, alpha=0.5, cmap=plt.get_cmap('jet')) plt.plot([-188.4],[2.67],'x',ms=12,markeredgewidth=3,color='orange') plt.plot(b_history,w_history,'o-',ms = 3,lw = 1.5,color = 'red') plt.xlim(-200,-100) plt.ylim(-5,5) plt.xlabel(r'$b$',fontsize=16) plt.ylabel(r'$w$',fontsize=16) plt.show()
