import rusvm import numpy as np import matplotlib.pyplot as plt # generate sample dataset n = 20 np.random.seed(42) x = np.random.rand(n) y = np.sin(2.0 * np.pi * x) # define parameters for training problem # regularization parameter lmbda = 1.0 # scaling parameter gamma = 10.0 # solve training problem res = rusvm.solve_smo( x=np.sqrt(gamma) * x[:, None], y=y, params_problem=dict( lmbda=lmbda, kind='regression', ), params_smo=dict( time_limit=1.0, ) ) print(res['opt_status']) a = np.array(res['a']) / lmbda a = a[:n] + a[n:] # generate reference points xplot = np.linspace(0.0, 1.0, 100) # evaluate decision function at reference points k = np.exp(-gamma * (xplot[:, None] - x[None, :]) ** 2) yplot = k.dot(a) + res['b'] # plot training points and decision function plt.plot(x, y, 'kx') plt.plot(xplot, yplot, 'r')