def noise_multi(noise=None): w_init = np.array([3, 3]) _t_max = 3000 var = np.random.randint(1, 300, 1)[0] noise = helper.gauss f = model_opt.RosenBrock(noise=noise, var=var) algo = algo_GD.SGD(w_init=w_init, t_max=_t_max, a=0.0007) w_star = f.w_star last_w_store = [] iqr_store = [] for i in range(10000): for i in algo: algo.update(model=f) return algo.wstore iqr_store.append(helper.iqr(algo.noise_store)) last_w_store.append(algo.w)
from tqdm import tqdm import os, sys sys.path.append(os.path.join(os.path.dirname(__file__), "..")) import model_opt import algo_GD import helper import noise if __name__ == "__main__": args = sys.argv t = int(args[1]) w_init = np.array([3, 3]) _t_max = 3000 f = model_opt.RosenBrock() w_star = f.w_star last_w_store = [] iqr_store = [] for i in tqdm(range(t)): a = 5 noise_data = noise.Pareto(dim=2, n=_t_max, a=a).generate() * 300 iqr = helper.iqr(noise_data) algo = algo_GD.SGD(w_init=w_init, t_max=_t_max, a=0.00078) for i in algo: noise_value = noise_data[algo.t - 1] f = model_opt.RosenBrock(noise_value=noise_value) algo.update(model=f) last_w_store.append(algo.w) iqr_store.append(iqr)
import datetime import pandas as pd import sys import tqdm from tqdm.notebook import tqdm as tqdm import model_opt import algo_GD import helper if __name__ == "__main__": args = sys.argv t = int(args[1]) w_init = np.array([3, 3]) _t_max = 3000 f = model_opt.RosenBrock() w_star = f.w_star last_w_store = [] iqr_store = [] for i in tqdm(range(t)): var = np.random.randint(1, 300, 1)[0] noise = helper.gauss f = model_opt.RosenBrock(noise=noise, var=var) algo = algo_GD.SGD(w_init=w_init, t_max=_t_max, a=0.00078) for i in algo: algo.update(model=f) iqr_store.append(helper.iqr(algo.noise_store)) last_w_store.append(algo.w)