def regression (alpha, epsilon, gamma, secondsToRun, data, ridge): if not ridge: gamma = 0 size = data[0].size - 1 thetas = Cost.randomThetas(0,0, size) ## make a random list of thetas cur = 0 timeout = time.time() + secondsToRun itera = 0 previousCost = 0 while True: va = np.asarray(data[cur]) v = va[0] thetaT = np.zeros(size) dev = Cost.deviation(v, thetas) cost = Cost.costFunction(thetas, data, gamma) if abs(cost - previousCost) < epsilon or timeout < time.time(): break for i in range(size): thetaT[i] = (1 - alpha * gamma) * thetas[i] - alpha * Cost.costDeriv(dev, v[i]) thetas = thetaT if cur < data.shape[0] - 1: # I don't think this is the number you want. You want the number of rows right? cur += 1 else: cur = 0 itera += 1 ##print costFunction(thetas, data[cur], ) ##print cur ##plt.plot(range(len(lst)), lst,'ro') ##plt.axis([0, len(lst), 0, 40]) ##plt.show() ##plot the size of gradient temp = cost previousCost = cost return thetas
