7fe99a818f297f195ff6000d4a6ffecc6e7db774,tslearn/docs/examples/metrics/plot_dtw.py,,,#,32

Before Change

numpy.random.seed(0)
n_ts, sz, d = 2, 100, 1
dataset = random_walks(n_ts=n_ts, sz=sz, d=d)
scaler = TimeSeriesScalerMeanVariance(mu=0., std=1.)  // Rescale time series
dataset_scaled = scaler.fit_transform(dataset)

path, sim = metrics.dtw_path(dataset_scaled[0], dataset_scaled[1])

plt.figure(1, figsize=(8, 8))

// definitions for the axes
left, bottom = 0.01, 0.1
w_ts = h_ts = 0.2
left_h = left + w_ts + 0.02
width = height = 0.65
bottom_h = bottom + height + 0.02

rect_s_y = [left, bottom, w_ts, height]
rect_gram = [left_h, bottom, width, height]
rect_s_x = [left_h, bottom_h, width, h_ts]

ax_gram = plt.axes(rect_gram)
ax_s_x = plt.axes(rect_s_x)
ax_s_y = plt.axes(rect_s_y)

mat = cdist(dataset_scaled[0], dataset_scaled[1])

ax_gram.imshow(mat, origin="lower")
ax_gram.axis("off")
ax_gram.autoscale(False)
ax_gram.plot([j for (i, j) in path], [i for (i, j) in path], "w-",
             linewidth=3.)

ax_s_x.plot(numpy.arange(sz), dataset_scaled[1], "b-", linewidth=3.)
ax_s_x.axis("off")
ax_s_x.set_xlim((0, sz - 1))

ax_s_y.plot(- dataset_scaled[0], numpy.arange(sz), "b-", linewidth=3.)
ax_s_y.axis("off")
ax_s_y.set_ylim((0, sz - 1))

After Change

     -0.548, -0.567, -0.592, -0.622, -0.655, -0.690, -0.728, -0.764, -0.795,
     -0.815, -0.823, -0.821])

s_y1 = numpy.concatenate((s_x, s_x)).reshape((-1, 1))
s_y2 = numpy.concatenate((s_x, s_x[::-1])).reshape((-1, 1))
sz = s_y1.shape[0]

path, sim = metrics.dtw_path(s_y1, s_y2)

Instances