def test_PAA():
"""Testing 'PAA'."""
# Parameter
X = np.arange(30)
# Test 1
paa = PAA(window_size=2)
arr_actual = paa.fit_transform(X[np.newaxis, :])[0]
arr_desired = np.arange(0.5, 30, 2)
np.testing.assert_allclose(arr_actual, arr_desired, atol=1e-5, rtol=0.)
# Test 2
paa = PAA(window_size=3)
arr_actual = paa.fit_transform(X[np.newaxis, :])[0]
arr_desired = np.arange(1, 30, 3)
np.testing.assert_allclose(arr_actual, arr_desired, atol=1e-5, rtol=0.)
# Test 3
paa = PAA(window_size=5)
arr_actual = paa.fit_transform(X[np.newaxis, :])[0]
arr_desired = np.arange(2, 30, 5)
np.testing.assert_allclose(arr_actual, arr_desired, atol=1e-5, rtol=0.)
# Test 4
paa = PAA(output_size=10)
arr_actual = paa.fit_transform(X[np.newaxis, :])[0]
arr_desired = np.arange(1, 30, 3)
np.testing.assert_allclose(arr_actual, arr_desired, atol=1e-5, rtol=0.)
# Test 5
paa = PAA(window_size=4, overlapping=True)
arr_actual = paa.fit_transform(X[np.newaxis, :])[0]
arr_desired = np.array([1.5, 4.5, 8.5, 12.5, 15.5, 19.5, 23.5, 27.5])
np.testing.assert_allclose(arr_actual, arr_desired, atol=1e-5, rtol=0.)
def PAA(self):
global X_paa
global a
try:
a = int(self.paaline.text())
paa = PAA(window_size=None, output_size=a, overlapping=True)
X_paa = paa.transform(X_standardized)
QMessageBox.information(None, 'Information',
'PAA Applied With Success', QMessageBox.Ok)
except ValueError:
QMessageBox.warning(
None, 'ERROR',
'Standardize Your Data_set or define how many interval do you want!',
QMessageBox.Ok)
def __init__(self,
nu=0.5,
gamma=0.1,
tol=1e-3,
degree=3,
kernel='lcs',
sax_size=4,
quantiles='gaussian',
paa_size=8):
"""
Constructor accepts some args for sklearn.svm.OneClassSVM and SAX inside.
Default params are choosen as the most appropriate for flight-anomaly-detection problem
according the original article.
"""
self.nu = nu
self.gamma = gamma
self.tol = tol
self.degree = degree
self.kernel = kernel
self.stand_scaler = StandardScaler(epsilon=1e-2)
self.paa = PAA(window_size=None,
output_size=paa_size,
overlapping=True)
self.sax = SAX(n_bins=sax_size, quantiles=quantiles)
import sys
sys.path.append("../")
from utils.utils import *
from pyts.transformation import PAA
sensor = 'tangential_strain'
explosive_point = 80
window_sizes = [4]
for window_size in window_sizes:
print "_______________________________________________________________________________"
print "window_size", window_size
paa = PAA(window_size=window_size, overlapping=True)
explosion_expert_reader = pd.read_csv(get_raw_path("training"))
explosion_expert_reader = explosion_expert_reader[explosion_expert_reader.label == 1]
explosive_train = np.array([np.fromstring(e, dtype=float, sep=',')
for e in explosion_expert_reader[sensor]])
print explosive_train.shape
explosive_train = paa.transform(explosive_train)
print explosive_train.shape
explosive_train = np.array([diff(x) for x in explosive_train])
print explosive_train.shape
inflations = []
deflations = []
for each in explosive_train:
inflation = each[:explosive_point/window_size]
deflation = each[explosive_point/window_size:]
import numpy as np
from scipy.stats import norm
from pyts.transformation import StandardScaler
from pyts.visualization import plot_standardscaler
from pyts.transformation import PAA
from pyts.visualization import plot_paa
n_samples = 10
n_features = 48
n_classes = 2
rng = np.random.RandomState(41)
delta = 0.5
dt = 1
X = (norm.rvs(scale=delta ** 2 * dt, size=n_samples * n_features, random_state=rng)
.reshape((n_samples, n_features)))
X[:, 0] = 0
X = np.cumsum(X, axis=1)
y = rng.randint(n_classes, size=n_samples)
standardscaler = StandardScaler(epsilon=1e-2)
X_standardized = standardscaler.transform(X)
plot_standardscaler(X[0])
paa = PAA(window_size=None, output_size=8, overlapping=True)
X_paa = paa.transform(X_standardized)
plot_paa(X_standardized[0], window_size=None, output_size=8, overlapping=True, marker='o')