def test_dft_mft(use_fallback_dft, norm):
# load training data
X, y = load_gunpoint(split="train", return_X_y=True)
X_tab = from_nested_to_2d_array(X, return_numpy=True)
word_length = 6
alphabet_size = 4
# Single DFT transformation
window_size = np.shape(X_tab)[1]
p = SFA(
word_length=6,
alphabet_size=4,
window_size=window_size,
norm=norm,
use_fallback_dft=use_fallback_dft,
).fit(X, y)
if use_fallback_dft:
dft = p._discrete_fourier_transform(X_tab[0], word_length, norm, 1, True)
else:
dft = p._fast_fourier_transform(X_tab[0])
mft = p._mft(X_tab[0])
assert (mft - dft < 0.0001).all()
# Windowed DFT transformation
window_size = 140
p = SFA(
word_length=word_length,
alphabet_size=alphabet_size,
window_size=window_size,
norm=norm,
use_fallback_dft=use_fallback_dft,
).fit(X, y)
mft = p._mft(X_tab[0])
for i in range(len(X_tab[0]) - window_size + 1):
if use_fallback_dft:
dft = p._discrete_fourier_transform(
X_tab[0, i : window_size + i], word_length, norm, 1, True
)
else:
dft = p._fast_fourier_transform(X_tab[0, i : window_size + i])
assert (mft[i] - dft < 0.001).all()
assert len(mft) == len(X_tab[0]) - window_size + 1
assert len(mft[0]) == word_length
def test_dft_mft():
# load training data
X, Y = load_gunpoint(split="train", return_X_y=True)
X_tab = from_nested_to_2d_array(X, return_numpy=True)
word_length = 6
alphabet_size = 4
# print("Single DFT transformation")
window_size = np.shape(X_tab)[1]
p = SFA(
word_length=word_length,
alphabet_size=alphabet_size,
window_size=window_size,
binning_method="equi-depth",
).fit(X, Y)
dft = p._discrete_fourier_transform(X_tab[0])
mft = p._mft(X_tab[0])
assert (mft - dft < 0.0001).all()
# print("Windowed DFT transformation")
for norm in [True, False]:
for window_size in [140]:
p = SFA(
word_length=word_length,
norm=norm,
alphabet_size=alphabet_size,
window_size=window_size,
binning_method="equi-depth",
).fit(X, Y)
mft = p._mft(X_tab[0])
for i in range(len(X_tab[0]) - window_size + 1):
dft_transformed = p._discrete_fourier_transform(
X_tab[0, i:window_size + i])
assert (mft[i] - dft_transformed < 0.001).all()
assert len(mft) == len(X_tab[0]) - window_size + 1
assert len(mft[0]) == word_length