cherry.plot¶

cherry.plot.ci95(values)¶

[Source]

Description¶

Computes the 95% confidence interval around the given values.

Arguments¶
• values (list) - List of values for which to compute the 95% confidence interval.
Returns¶
• (float, float) The lower and upper bounds of the confidence interval.
Example¶
from statistics import mean
smoothed = []
for replay in replays:
rewards = replay.rewards.view(-1).tolist()
y_smoothed = ch.plot.smooth(rewards)
smoothed.append(y_smoothed)
means = [mean(r) for r in zip(*smoothed)]
confidences = [ch.plot.ci95(r) for r in zip(*smoothed)]
lower_bound = [conf[0] for conf in confidences]
upper_bound = [conf[1] for conf in confidences]


cherry.plot.exponential_smoothing(x, y = None, temperature = 1.0)¶

[Source]

Decription¶

Two-sided exponential moving average for smoothing a curve.

It performs regular exponential moving average twice from two different sides and then combines the results together.

Arguments¶
• x (ndarray/tensor/list) - x values, in accending order.
• y (ndarray/tensor/list) - y values.
• temperature (float, optional, default=1.0) - The higher, the smoother.
Return¶
• x_smoothed (ndarray) - x values after resampling.
• y_smoothed (ndarray) - y values after smoothing.
Example¶
from cherry.plot import exponential_smoothing
x_smoothed, y_smoothed, _ = exponential_smoothing(x_original,
y_original,
temperature=3.)