def test_window_slice_windows():
windows = SlidingWindow(size=4, stride=2)
X = signal_embedded_search
X_windows = windows.fit_transform(X)
slice_idx = windows.slice_windows(X)
assert_almost_equal(
np.stack([X[begin:end] for begin, end in slice_idx]), X_windows
)
def test_window_resample():
windows = SlidingWindow(width=3, stride=2)
windows.fit(y)
y_resampled = windows.resample(y)
assert_almost_equal(y_resampled, y[np.arange(3, 20, 2)])
def test_window_transform():
windows = SlidingWindow(width=3, stride=2)
x_windows = windows.fit_transform(signal_embedded_search)
assert (x_windows.shape == (8, 4, 2))
def test_window_params():
windows = SlidingWindow(width=0)
with pytest.raises(ValueError):
windows.fit(signal)
n_timestamps = 10 X, y = np.arange(n_timestamps), np.arange(n_timestamps) - n_timestamps X, y # We can instantiate our sliding window transformer-resampler and run it on the pair ``(X, y)``: # 我们可以实例化滑动窗口转换器-重采样器，然后在（X，y）对上运行它 # In[2]: from gtda.time_series import SlidingWindow window_size = 3 stride = 2 SW = SlidingWindow(size=window_size, stride=stride) X_sw, yr = SW.fit_transform_resample(X, y) X_sw, yr # We note a couple of things: # - ``fit_transform_resample`` returns a pair: the window-transformed ``X`` and the resampled and aligned ``y``. # - ``SlidingWindow`` has made a choice for us on how to resample ``y`` and line it up with the windows from ``X``: a window on ``X`` corresponds to the *last* value in a corresponding window over ``y``. This is common in time series forecasting where, for example, ``y`` could be a shift of ``X`` by one timestamp. # - Some of the initial values of ``X`` may not be found in ``X_sw``. This is because ``SlidingWindow`` only ensures the *last* value is represented in the last window, regardless of the stride. # # 注意以下几点： # fit_transform_resample返回一对：经过窗口转换的X和经过重新采样并对齐的y。 # SlidingWindow已为我们选择了如何对y重新采样并将其与X中的窗口对齐：X上的窗口与y上相应窗口中的最后一个值相对应。这在时间序列预测中很常见，例如，y可能是X偏移一个时间戳。 # X的某些初始值可能在X_sw中找不到。这是因为SlidingWindow仅确保最后一个值在最后一个窗口中表示，而不管步幅如何。 # ## Multivariate time series example: Sliding window + topology ``Pipeline``
def test_window_plot():
windows = SlidingWindow(width=3, stride=2)
X_windows = windows.fit_transform(signal_embedded_search)
windows.plot(X_windows, sample=0)
def test_window_params(size):
windows = SlidingWindow(size=size)
with pytest.raises(ValueError):
windows.fit(signal)
def test_window_transform():
windows = SlidingWindow(size=4, stride=2)
X_windows = windows.fit_transform(signal_embedded_search)
assert (X_windows.shape == (8, 4, 2))