From 32eeab49cce45962549b04901994976e0516fd85 Mon Sep 17 00:00:00 2001
From: evilsocket <evilsocket@gmail.com>
Date: Thu, 19 Sep 2019 16:33:12 +0200
Subject: [PATCH] misc: small fix or general refactoring i did not bother
commenting
---
.../rootfs/root/pwnagotchi/scripts/test_nn.py | 176 ------------------
1 file changed, 176 deletions(-)
delete mode 100755 sdcard/rootfs/root/pwnagotchi/scripts/test_nn.py
diff --git a/sdcard/rootfs/root/pwnagotchi/scripts/test_nn.py b/sdcard/rootfs/root/pwnagotchi/scripts/test_nn.py
deleted file mode 100755
index e25f5fa..0000000
--- a/sdcard/rootfs/root/pwnagotchi/scripts/test_nn.py
+++ /dev/null
@@ -1,176 +0,0 @@
-#!/usr/bin/python3
-import os
-
-# https://stackoverflow.com/questions/40426502/is-there-a-way-to-suppress-the-messages-tensorflow-prints/40426709
-os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3' # or any {'0', '1', '2'}
-
-import warnings
-
-# https://stackoverflow.com/questions/15777951/how-to-suppress-pandas-future-warning
-warnings.simplefilter(action='ignore', category=FutureWarning)
-
-import time
-import random
-
-print("loading dependencies ...")
-
-start = time.time()
-
-from stable_baselines.common.policies import MlpPolicy
-from stable_baselines.common.vec_env import DummyVecEnv
-from stable_baselines import A2C
-
-quit()
-
-import pwnagotchi.mesh.wifi as wifi
-import pwnagotchi.ai.gym as wrappers
-
-print("deps loaded in %ds" % (time.time() - start))
-print()
-
-
-class EpochMock(object):
- def __init__(self):
- self.epoch = 0
-
- def wait_for_epoch_data(self, timeout=None):
- duration = random.randint(5, 60)
- slept = random.randint(0, duration)
-
- tot_epochs = self.epoch + 1
-
- num_active = random.randint(0, tot_epochs)
- num_inactive = tot_epochs - num_active
-
- tot_interactions = random.randint(0, 100)
- missed = random.randint(0, int(tot_interactions / 10))
- num_deauth = random.randint(0, tot_interactions - missed)
- num_assocs = tot_interactions - num_deauth
-
- # time.sleep(duration)
-
- data = {
- 'aps_histogram': [random.random() for c in range(0, wifi.NumChannels)],
- 'sta_histogram': [random.random() for c in range(0, wifi.NumChannels)],
- 'peers_histogram': [random.random() for c in range(0, wifi.NumChannels)],
-
- 'duration_secs': duration,
- 'slept_for_secs': slept,
- 'blind_for_epochs': random.randint(0, 5),
- 'inactive_for_epochs': num_inactive,
- 'active_for_epochs': num_active,
- 'missed_interactions': missed,
- 'num_hops': random.randint(1, wifi.NumChannels),
- 'num_deauths': num_deauth,
- 'num_associations': num_assocs,
- 'num_handshakes': random.randint(0, tot_interactions),
- 'cpu_load': .5 + random.random(),
- 'mem_usage': .5 + random.random(),
- 'temperature': random.randint(40, 60)
- }
-
- self.epoch += 1
- return data
-
-
-epoch_mock = EpochMock()
-env = wrappers.Environment(epoch_mock)
-env = DummyVecEnv([lambda: env])
-
-print("learning from random data ...")
-
-model = A2C(MlpPolicy, env, verbose=1)
-model.learn(total_timesteps=10)
-
-model.save("test.nn")
-
-print("running ...")
-obs = env.reset()
-for i in range(1000):
- env.render()
- action, _states = model.predict(obs)
- obs, rewards, dones, info = env.step(action)
- env.render()
-
-"""
-memory = Memory()
-
-state = env.reset()
-
-for i in range(0, 10):
- env.render()
- policy = env.action_space.sample()
-
- next_state, reward, done, info = env.step(policy)
-
- if done:
- next_state = np.zeros(state.shape)
- memory.add((state, policy, reward, next_state))
-
- env.reset()
-
- state, reward, done, info = env.step(env.action_space.sample())
- else:
- memory.add((state, policy, reward, next_state))
- state = next_state
-"""
-
-"""
-import numpy as np
-
-import pwnagotchi.ai.nn as nn
-
-
-def on_epoch(epoch, epoch_time, train_accu, valid_accu):
- print("epoch:%d duration:%f t_accu:%f v_accu:%f" % (epoch, epoch_time, train_accu, valid_accu))
- if valid_accu >= 0.98:
- return True
-
-
-x = []
-y = []
-
-with open('nn-data.csv', 'rt') as fp:
- for line in fp:
- line = line.strip()
- if line != "":
- v = np.asarray(list(map(float, line.split(','))))
- x.append(v[1:])
- y.append(v[0])
-
-x = np.asarray(x)
-y = np.asarray(y)
-
-num_inputs = len(x[0])
-num_outputs = 2
-valid_perc = 0.1
-tot_samples = len(x)
-valid_samples = int(tot_samples * valid_perc)
-train_samples = tot_samples - valid_samples
-
-print("loaded %d samples (inputs:%d train:%d validation:%d)" % (tot_samples, num_inputs, train_samples, valid_samples))
-
-x_train = x[:train_samples]
-y_train = y[:train_samples]
-x_val = x[train_samples:]
-y_val = y[train_samples:]
-
-print("training ...")
-
-net = nn.ANN(layers=(
- nn.Dense(num_inputs, 150),
- nn.ReLU(),
- nn.Dense(150, 150),
- nn.ReLU(),
- nn.Dense(150, 150),
- nn.ReLU(),
- nn.Dense(150, num_outputs),
- nn.ReLU(),
-))
-
-net.train(x_train, y_train, x_val, y_val, 1000, epoch_cbs=(on_epoch,))
-
-net.save("test-nn.pkl")
-# def train(self, x_train, y_train, x_val, y_val, epochs, batch_size=32, epoch_cbs=()):
-# if cb(epoch, epoch_time, train_accu, valid_accu) is True:
-"""