misc: small fix or general refactoring i did not bother commenting
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evilsocket
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@ -1,176 +0,0 @@
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#!/usr/bin/python3
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import os
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# https://stackoverflow.com/questions/40426502/is-there-a-way-to-suppress-the-messages-tensorflow-prints/40426709
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os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3' # or any {'0', '1', '2'}
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import warnings
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# https://stackoverflow.com/questions/15777951/how-to-suppress-pandas-future-warning
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warnings.simplefilter(action='ignore', category=FutureWarning)
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import time
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import random
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print("loading dependencies ...")
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start = time.time()
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from stable_baselines.common.policies import MlpPolicy
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from stable_baselines.common.vec_env import DummyVecEnv
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from stable_baselines import A2C
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quit()
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import pwnagotchi.mesh.wifi as wifi
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import pwnagotchi.ai.gym as wrappers
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print("deps loaded in %ds" % (time.time() - start))
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print()
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class EpochMock(object):
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def __init__(self):
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self.epoch = 0
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def wait_for_epoch_data(self, timeout=None):
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duration = random.randint(5, 60)
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slept = random.randint(0, duration)
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tot_epochs = self.epoch + 1
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num_active = random.randint(0, tot_epochs)
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num_inactive = tot_epochs - num_active
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tot_interactions = random.randint(0, 100)
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missed = random.randint(0, int(tot_interactions / 10))
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num_deauth = random.randint(0, tot_interactions - missed)
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num_assocs = tot_interactions - num_deauth
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# time.sleep(duration)
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data = {
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'aps_histogram': [random.random() for c in range(0, wifi.NumChannels)],
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'sta_histogram': [random.random() for c in range(0, wifi.NumChannels)],
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'peers_histogram': [random.random() for c in range(0, wifi.NumChannels)],
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'duration_secs': duration,
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'slept_for_secs': slept,
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'blind_for_epochs': random.randint(0, 5),
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'inactive_for_epochs': num_inactive,
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'active_for_epochs': num_active,
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'missed_interactions': missed,
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'num_hops': random.randint(1, wifi.NumChannels),
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'num_deauths': num_deauth,
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'num_associations': num_assocs,
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'num_handshakes': random.randint(0, tot_interactions),
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'cpu_load': .5 + random.random(),
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'mem_usage': .5 + random.random(),
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'temperature': random.randint(40, 60)
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}
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self.epoch += 1
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return data
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epoch_mock = EpochMock()
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env = wrappers.Environment(epoch_mock)
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env = DummyVecEnv([lambda: env])
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print("learning from random data ...")
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model = A2C(MlpPolicy, env, verbose=1)
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model.learn(total_timesteps=10)
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model.save("test.nn")
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print("running ...")
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obs = env.reset()
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for i in range(1000):
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env.render()
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action, _states = model.predict(obs)
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obs, rewards, dones, info = env.step(action)
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env.render()
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"""
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memory = Memory()
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state = env.reset()
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for i in range(0, 10):
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env.render()
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policy = env.action_space.sample()
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next_state, reward, done, info = env.step(policy)
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if done:
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next_state = np.zeros(state.shape)
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memory.add((state, policy, reward, next_state))
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env.reset()
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state, reward, done, info = env.step(env.action_space.sample())
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else:
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memory.add((state, policy, reward, next_state))
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state = next_state
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"""
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"""
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import numpy as np
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import pwnagotchi.ai.nn as nn
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def on_epoch(epoch, epoch_time, train_accu, valid_accu):
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print("epoch:%d duration:%f t_accu:%f v_accu:%f" % (epoch, epoch_time, train_accu, valid_accu))
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if valid_accu >= 0.98:
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return True
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x = []
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y = []
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with open('nn-data.csv', 'rt') as fp:
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for line in fp:
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line = line.strip()
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if line != "":
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v = np.asarray(list(map(float, line.split(','))))
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x.append(v[1:])
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y.append(v[0])
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x = np.asarray(x)
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y = np.asarray(y)
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num_inputs = len(x[0])
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num_outputs = 2
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valid_perc = 0.1
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tot_samples = len(x)
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valid_samples = int(tot_samples * valid_perc)
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train_samples = tot_samples - valid_samples
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print("loaded %d samples (inputs:%d train:%d validation:%d)" % (tot_samples, num_inputs, train_samples, valid_samples))
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x_train = x[:train_samples]
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y_train = y[:train_samples]
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x_val = x[train_samples:]
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y_val = y[train_samples:]
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print("training ...")
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net = nn.ANN(layers=(
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nn.Dense(num_inputs, 150),
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nn.ReLU(),
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nn.Dense(150, 150),
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nn.ReLU(),
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nn.Dense(150, 150),
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nn.ReLU(),
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nn.Dense(150, num_outputs),
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nn.ReLU(),
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))
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net.train(x_train, y_train, x_val, y_val, 1000, epoch_cbs=(on_epoch,))
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net.save("test-nn.pkl")
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# def train(self, x_train, y_train, x_val, y_val, epochs, batch_size=32, epoch_cbs=()):
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# if cb(epoch, epoch_time, train_accu, valid_accu) is True:
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"""
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